Upload 26 files

Rekomendasi Materi Belajar/edtech/backend/data/recommendations/evaluations/collaborative_evaluation.csv

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+RMSE,MAE,Precision@K,Recall@K,NDCG@K,model
+0.5873683620699068,0.5249163001110007,0.19658119658119655,0.47692307692307695,0.3365554336734982,collaborative

Rekomendasi Materi Belajar/edtech/backend/data/recommendations/evaluations/content_based_evaluation.csv

@@ -0,0 +1,2 @@
+RMSE,MAE,Precision@K,Recall@K,NDCG@K,model
+0.279107926379566,0.24733157275956955,0.3350427350427351,1.0,0.6786757984354084,content_based

Rekomendasi Materi Belajar/edtech/backend/data/recommendations/evaluations/hybrid_evaluation.csv

@@ -0,0 +1,2 @@
+RMSE,MAE,Precision@K,Recall@K,NDCG@K,model
+0.279107926379566,0.24733157275956955,0.19658119658119655,0.47692307692307695,0.3365554336734982,hybrid

Rekomendasi Materi Belajar/edtech/backend/data/recommendations/test_data.csv

@@ -0,0 +1,201 @@
+student_id,grade,tech_savvy,timestamp,duration_minutes,engagement_score,completion_rate,quiz_score,material_rating,interaction_duration,related_materials,material_engagement_score,feature_engagement,jam_belajar,hari_dalam_minggu,akhir_pekan,efisiensi_belajar,rasio_penyelesaian,interaksi_total,material_type_encoded,preferensi_materi,jumlah_pengakses,engagement_rata2,gender_Male,learning_style_Kinesthetic,learning_style_Visual,subject_English,subject_History,subject_Mathematics,subject_Science,click_page_English,click_page_History,click_page_Home,click_page_Math,click_page_Science,performance_label_encoded,learning_speed_encoded,student_feedback_encoded,achievement_status_encoded,is_assessment_encoded
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+STU1143,0.1999999999999999,0,2025-06-25 16:45:00,0.4272727272727272,1.0,0.9111111111111112,0.625,0.5,0.2,Video Lesson World War,0.9829341963520212,0.5315303809355705,0.1428571428571428,2,0,0.1846782667821868,0.9111111111111112,0.4679054054054055,0,0.3310210876803552,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
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+STU1121,1.0000000000000002,0,2025-02-18 15:30:00,0.7454545454545454,0.2222222222222222,0.9888888888888888,0.625,0.75,0.0666666666666667,Audio Lecture Modern History,0.9198735763978956,0.0384393650376045,0.0,1,0,0.0259486741446228,0.9888888888888888,0.2195945945945946,2,0.3079911209766925,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
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+STU1068,1.0000000000000002,0,2025-01-03 22:45:00,0.1818181818181818,0.2222222222222222,0.6777777777777778,0.5681836543433301,0.25,0.8666666666666667,Interactive Exercise Biology,0.33277656825806,0.8052497769406544,1.0,4,0,0.1008419139685898,0.6777777777777777,0.0625,1,0.2047724750277469,0.6538461538461533,0.0706596681502631,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0,2.0,1.0,0,0
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+STU1139,1.0000000000000002,0,2025-05-12 19:15:00,0.6454545454545454,1.0,0.8333333333333334,0.5681836543433301,0.0,0.8666666666666667,Audio Lecture Chemistry,0.3467422872761973,0.0246006528410567,0.5714285714285712,0,0,0.1269098946727184,0.8333333333333333,0.6706081081081082,2,0.2802441731409545,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1,0.0,1.0,1,0
+STU1024,0.8,1,2024-05-06 18:45:00,0.3727272727272727,0.6666666666666665,0.7777777777777778,0.5681836543433301,1.0,1.0,Interactive Exercise Biology,0.8958564158902083,0.7623066120152141,0.4285714285714284,0,0,0.1422439984895389,0.7777777777777778,0.2880067567567568,0,0.1176470588235294,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1,0.0,2.0,1,0
+STU1190,0.8,0,2025-02-24 19:00:00,0.809090909090909,0.7777777777777777,0.9888888888888888,0.5847013960703209,1.0,0.3333333333333333,Video Lesson Programming,0.7397095740941637,0.9809790634754756,0.5714285714285712,0,0,0.0795135705620603,0.9888888888888888,0.6554054054054055,2,0.7918978912319644,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1030,0.1999999999999999,1,2025-06-28 15:30:00,0.6545454545454544,0.4444444444444445,0.8666666666666667,0.5912314024085137,1.0,0.6000000000000001,Video Lesson Grammar,0.4898163483934529,0.0896283653779353,0.0,5,1,0.0574733669276692,0.8666666666666667,0.3327702702702703,1,0.3701442841287459,0.6538461538461533,0.0706596681502631,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1129,0.5999999999999999,0,2024-11-16 21:30:00,0.1818181818181818,0.6666666666666665,0.6888888888888889,0.5912314024085137,1.0,0.6000000000000001,Interactive Exercise Vocabulary,0.618762185876628,0.5457748043851429,0.8571428571428572,5,1,0.2490177879190889,0.6888888888888889,0.1638513513513513,0,0.0904550499445061,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,2.0,0,0
+STU1185,1.0000000000000002,0,2024-04-09 15:45:00,0.0818181818181818,0.6666666666666665,0.8555555555555555,0.5681836543433301,0.75,0.0,Audio Lecture Chemistry,0.8701004058743459,0.7327727578802335,0.0,1,0,0.3991433365202236,0.8555555555555555,0.0988175675675675,0,0.7531631520532742,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1036,0.0,1,2025-06-18 22:30:00,0.0,0.2222222222222222,0.8999999999999999,0.0,0.75,0.2666666666666666,Video Lesson Physics,0.780568739243086,0.0744037756165882,1.0,2,0,0.3231057026679597,0.9000000000000001,0.0118243243243243,1,0.0011098779134295,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0,1.0,0.0,0,1
+STU1137,1.0000000000000002,1,2025-05-08 21:30:00,0.6272727272727272,0.3333333333333333,0.8222222222222222,0.0,0.25,0.9333333333333332,Interactive Exercise Ancient Civilizations,0.4761078569851258,0.1083383983410264,0.8571428571428572,3,0,0.0459793288907981,0.8222222222222222,0.2533783783783784,1,0.4953755086940437,0.6538461538461533,0.0706596681502631,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,2.0,1.0,0,1
+STU1034,1.0000000000000002,0,2024-08-04 19:15:00,0.6,0.5555555555555556,0.977777777777778,1.0,0.5,0.2666666666666666,Audio Lecture Literature,0.0221610398186905,0.497156751025593,0.5714285714285712,6,1,0.0774457251149333,0.977777777777778,0.3716216216216216,0,0.750832408435072,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,1
+STU1004,0.1999999999999999,0,2025-04-01 22:15:00,0.9818181818181818,0.5555555555555556,0.8111111111111111,0.625,0.75,0.6000000000000001,Audio Lecture Modern History,0.4122156422994131,0.7469371277223724,1.0,1,0,0.0462177584508725,0.811111111111111,0.5844594594594595,0,0.5929522752497225,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1111,0.1999999999999999,1,2024-10-14 19:15:00,0.0181818181818181,0.3333333333333333,0.9222222222222224,0.5912314024085137,0.5,0.3999999999999999,Interactive Exercise Vocabulary,0.2184673302996833,0.2683455788349927,0.5714285714285712,0,0,0.3601496748599807,0.9222222222222224,0.027027027027027,2,0.2119866814650388,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,0.0,1,0
+STU1179,0.3999999999999999,0,2025-02-16 16:30:00,0.1727272727272727,0.6666666666666665,0.7111111111111111,0.375,0.25,0.4666666666666667,Interactive Exercise Algebra,0.3099497570088037,0.5203153578770995,0.1428571428571428,6,1,0.2579594351767347,0.7111111111111111,0.1579391891891892,0,0.0588235294117647,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,2.0,0,0
+STU1022,0.1999999999999999,0,2025-05-15 15:15:00,0.6818181818181818,0.8888888888888888,0.7999999999999999,0.0,0.5,0.1333333333333333,Audio Lecture Modern History,0.098896608886692,0.8721432131441298,0.0,3,0,0.1073790874161311,0.7999999999999999,0.6326013513513514,0,0.6052534221235665,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,2.0,0,1
+STU1049,0.1999999999999999,0,2024-12-22 21:30:00,0.2636363636363636,0.2222222222222222,0.8555555555555555,0.5912314024085137,1.0,0.8666666666666667,Interactive Exercise Vocabulary,0.2478926713512718,0.7149425802800202,0.8571428571428572,6,1,0.0751960902885887,0.8555555555555555,0.085304054054054,2,0.2175360710321865,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,0.0,1,0
+STU1010,1.0000000000000002,1,2025-06-02 21:00:00,0.8999999999999999,0.6666666666666665,0.9111111111111112,0.5681836543433301,0.5,1.0,Video Lesson Physics,0.1199020962243293,0.8529707467367437,0.8571428571428572,0,0,0.0610791238299345,0.9111111111111112,0.6309121621621622,0,0.2397336293007769,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1,0.0,1.0,1,0
+STU1196,0.3999999999999999,0,2025-03-21 18:45:00,0.4909090909090908,0.8888888888888888,1.0,0.375,1.0,0.2,Video Lesson Algebra,0.7471945889417827,0.4262280404364546,0.4285714285714284,4,0,0.1459892533303274,1.0,0.472972972972973,0,0.1576026637069922,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1,2.0,2.0,1,0
+STU1060,0.5999999999999999,0,2025-04-15 21:15:00,0.1272727272727272,0.6666666666666665,0.9111111111111112,0.5847013960703209,0.0,0.5333333333333334,Interactive Exercise Algorithms,0.0838563414768952,0.8772177576113912,0.8571428571428572,1,0,0.3138447300713097,0.9111111111111112,0.1283783783783783,1,0.1664816870144283,0.6538461538461533,0.0706596681502631,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,0
+STU1078,1.0000000000000002,1,2025-04-23 17:30:00,0.6909090909090908,0.5555555555555556,0.7999999999999999,0.0,0.0,0.4666666666666667,Interactive Exercise Vocabulary,0.463388508770417,0.5596722779247028,0.2857142857142856,2,0,0.0672438977208595,0.7999999999999999,0.4222972972972973,1,0.2663706992230854,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,0.0,1.0,0,1
+STU1182,0.5999999999999999,0,2024-04-05 19:30:00,0.9545454545454544,0.4444444444444445,0.8555555555555555,1.0,0.25,0.9333333333333332,Video Lesson World War,0.9279368332894496,0.6973685141024625,0.5714285714285712,4,0,0.0380282294626315,0.8555555555555555,0.4721283783783784,2,0.2386237513873474,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,1.0,0.0,1,1
+STU1102,0.1999999999999999,1,2024-09-13 21:30:00,0.7909090909090908,0.7777777777777777,0.7777777777777778,0.625,0.75,0.8,Audio Lecture Modern History,0.0541301191185069,0.3556697927728817,0.8571428571428572,4,0,0.0813651903791847,0.7777777777777778,0.641891891891892,0,0.4961154273029966,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,1.0,1,0
+STU1162,0.0,1,2025-04-30 22:45:00,0.6272727272727272,0.5555555555555556,0.7999999999999999,1.0,0.25,0.8,Interactive Exercise Biology,0.8852629747698798,0.4603281445721182,1.0,2,0,0.0741139890833395,0.7999999999999999,0.3868243243243243,2,0.4017758046614873,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1,1.0,1.0,1,1
+STU1083,0.3999999999999999,0,2024-04-20 17:00:00,0.5272727272727272,1.0,0.8222222222222222,0.375,1.0,0.0,Interactive Exercise Algebra,0.9719071470554048,0.6561177491926868,0.2857142857142856,5,1,0.1531392842895073,0.8222222222222222,0.5608108108108109,0,0.345541990381058,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,1.0,1,0
+STU1045,0.1999999999999999,0,2025-05-07 16:00:00,0.7636363636363636,0.4444444444444445,0.977777777777778,0.5847013960703209,0.75,0.8666666666666667,Interactive Exercise Algorithms,0.8764148874962948,0.6976298620700548,0.1428571428571428,2,0,0.0488227255190223,0.977777777777778,0.3834459459459459,1,0.5837957824639289,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,1.0,1,0
+STU1132,0.5999999999999999,1,2025-05-13 20:00:00,0.2727272727272727,0.7777777777777777,0.7,0.6549896587383661,1.0,0.1333333333333333,Video Lesson Grammar,0.2017747564177291,0.5702238525564925,0.714285714285714,1,0,0.2119738212836626,0.7,0.2567567567567568,2,0.4384017758046615,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,2.0,0.0,0,1
+STU1062,0.0,0,2025-03-27 18:15:00,0.1545454545454545,0.5555555555555556,0.8555555555555555,0.625,0.75,0.2666666666666666,Interactive Exercise Ancient Civilizations,0.999751898897204,0.6700251659915546,0.4285714285714284,3,0,0.2366697975085482,0.8555555555555555,0.1233108108108108,0,0.4365519792822789,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,2.0,1,0
+STU1093,0.8,0,2025-07-02 22:30:00,0.3909090909090909,0.6666666666666665,0.7888888888888889,0.5681836543433301,1.0,0.2666666666666666,Video Lesson Physics,0.2353119697328168,0.8192941209847273,1.0,2,0,0.1364879989761116,0.788888888888889,0.2998310810810811,2,0.4064927857935627,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1,1.0,0.0,1,0
+STU1035,0.8,1,2025-05-18 16:15:00,0.4999999999999999,0.8888888888888888,0.7999999999999999,0.5681836543433301,0.75,0.0,Video Lesson Physics,0.5334118199438668,0.7306796134847616,0.1428571428571428,6,1,0.1435849572930918,0.7999999999999999,0.4805743243243243,0,0.3662597114317425,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1061,0.3999999999999999,0,2025-06-24 18:15:00,0.8545454545454545,1.0,0.7,0.5912314024085137,0.5,0.2,Interactive Exercise Vocabulary,0.1737465714942065,0.9561324328176752,0.4285714285714284,1,0,0.0965676124467685,0.7,0.8648648648648649,1,0.4839067702552719,0.6538461538461533,0.0706596681502631,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,2.0,2.0,0,0
+STU1178,0.8,0,2025-03-06 21:15:00,0.5272727272727272,0.4444444444444445,0.977777777777778,0.1742179420889353,0.0,0.0,Video Lesson Algebra,0.671580893639549,0.4349141365957127,0.8571428571428572,3,0,0.0714246463976114,0.977777777777778,0.2736486486486487,2,0.6051054384017758,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,0.0,0,1
+STU1152,0.0,1,2024-05-03 17:15:00,0.5636363636363636,0.7777777777777777,0.9222222222222224,0.5847013960703209,0.25,0.3999999999999999,Video Lesson Programming,0.8009502761050026,0.3595029220529698,0.2857142857142856,4,0,0.1131899058654627,0.9222222222222224,0.472972972972973,2,0.516093229744728,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1130,0.8,1,2025-05-09 19:15:00,0.8181818181818181,0.7777777777777777,0.977777777777778,0.375,0.75,0.6000000000000001,Interactive Exercise Algebra,0.7812548454605317,0.2922597218493973,0.5714285714285712,4,0,0.0786155349504772,0.977777777777778,0.6621621621621622,2,0.4528301886792452,1.0,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,0.0,1,0
+STU1039,0.3999999999999999,1,2024-02-23 17:45:00,0.609090909090909,0.2222222222222222,0.8555555555555555,0.625,0.75,0.3333333333333333,Audio Lecture Modern History,0.5784410904302985,0.7305150380584934,0.2857142857142856,4,0,0.0330081543722089,0.8555555555555555,0.1815878378378378,0,0.8718091009988901,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1169,0.0,0,2025-02-14 21:30:00,0.1999999999999999,0.7777777777777777,0.8777777777777778,0.375,0.5,0.6666666666666667,Audio Lecture Calculus,0.7574014153590986,0.0387008588734111,0.8571428571428572,4,0,0.2675397727417134,0.8777777777777778,0.2027027027027027,1,0.1048834628190898,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,0.0,1,0
+STU1163,0.5999999999999999,0,2025-04-29 21:15:00,0.1181818181818181,1.0,0.7666666666666666,0.5912314024085137,0.75,0.0666666666666667,Video Lesson Grammar,0.2149865273803909,0.5515320928153924,0.8571428571428572,1,0,0.4728922012685383,0.7666666666666667,0.1807432432432432,2,0.2608213096559378,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1071,0.1999999999999999,0,2025-01-18 17:15:00,0.8181818181818181,0.2222222222222222,0.8999999999999999,0.5912314024085137,0.25,0.7333333333333334,Interactive Exercise Vocabulary,0.5395308031186095,0.1620419707429955,0.2857142857142856,5,1,0.0230495809225011,0.9000000000000001,0.2398648648648648,0,0.5529966703662597,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,1.0,1,0
+STU1180,0.0,0,2025-04-05 21:30:00,0.4727272727272726,0.4444444444444445,0.8444444444444444,0.5847013960703209,0.0,0.3999999999999999,Video Lesson Programming,0.5998070977990106,0.3671534600087269,0.8571428571428572,5,1,0.0793325144531519,0.8444444444444444,0.2483108108108108,1,0.1176470588235293,0.6538461538461533,0.0706596681502631,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,1.0,1,0
+STU1104,0.8,0,2025-06-16 15:15:00,0.4454545454545454,0.8888888888888888,0.7777777777777778,0.375,0.25,0.4666666666666667,Video Lesson Algebra,0.3941452429342989,0.5381183709118588,0.0,0,0,0.1592332569045487,0.7777777777777778,0.4349662162162162,0,0.1365149833518312,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1134,0.3999999999999999,0,2024-06-29 19:00:00,0.3181818181818181,0.3333333333333333,1.0,0.625,0.25,0.1333333333333333,Interactive Exercise Ancient Civilizations,0.1541879195806367,0.3349567351585688,0.5714285714285712,5,1,0.088493925570314,1.0,0.1385135135135135,0,0.169811320754717,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,1.0,1,0
+STU1086,1.0000000000000002,1,2024-09-13 21:15:00,0.0909090909090909,0.2222222222222222,0.8555555555555555,0.5912314024085137,0.25,0.6000000000000001,Video Lesson Grammar,0.0899127563010276,0.5516249646953622,0.8571428571428572,4,0,0.1564078639217295,0.8555555555555555,0.0371621621621621,1,0.0554938956714761,0.6538461538461533,0.0706596681502631,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1005,0.1999999999999999,0,2024-07-14 17:15:00,0.6636363636363636,0.5555555555555556,0.9222222222222224,0.375,0.0,0.8666666666666667,Interactive Exercise Algebra,0.6575863781694877,0.5850497181319968,0.2857142857142856,6,1,0.0700463274177631,0.9222222222222224,0.4070945945945946,0,0.3144654088050315,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1194,0.5999999999999999,1,2024-12-30 15:15:00,0.8727272727272727,0.4444444444444445,0.7888888888888889,0.5912314024085137,0.75,0.3999999999999999,Video Lesson Grammar,0.3079248658964315,0.7783637005137007,0.0,0,0,0.0421307198825348,0.788888888888889,0.4341216216216216,2,0.3712541620421753,1.0,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1177,1.0000000000000002,1,2025-06-04 22:45:00,0.7909090909090908,0.4444444444444445,0.8333333333333334,0.5912314024085137,0.75,0.6666666666666667,Audio Lecture Literature,0.5165302648769683,0.6312857041311745,1.0,2,0,0.0469944971766338,0.8333333333333333,0.3961148648648649,0,0.295227524972253,0.0,0.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,0
+STU1081,0.3999999999999999,1,2024-12-19 22:45:00,0.4999999999999999,0.5555555555555556,0.8777777777777778,0.1207988624612208,0.25,0.8666666666666667,Video Lesson Grammar,0.4074329431073227,0.1453703219947636,1.0,3,0,0.0922933076972997,0.8777777777777778,0.3158783783783784,0,0.3629300776914539,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,2.0,1.0,0,1
+STU1042,1.0000000000000002,0,2024-10-02 22:00:00,0.7454545454545454,0.3333333333333333,0.9555555555555556,0.0,0.25,0.5333333333333334,Audio Lecture Literature,0.8879926229715808,0.0758193749228285,1.0,2,0,0.0380282293575919,0.9555555555555556,0.2972972972972973,0,0.4001109877913429,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,0.0,2.0,0,1
+STU1114,0.8,0,2025-06-12 22:15:00,0.1636363636363636,0.7777777777777777,0.7111111111111111,0.0,1.0,0.8666666666666667,Video Lesson Programming,0.7663790808151407,0.4695206573089047,1.0,3,0,0.3072297376436429,0.7111111111111111,0.1756756756756756,2,0.0721420643729189,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,0.0,2.0,0,1
+STU1004,0.1999999999999999,0,2023-11-13 15:15:00,0.0454545454545454,0.2222222222222222,0.7111111111111111,0.625,0.25,0.4666666666666667,Video Lesson World War,0.9605329164644928,0.1305403418980681,0.0,0,0,0.211973812022671,0.7111111111111111,0.0244932432432432,0,0.5929522752497225,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,2.0,0,0
+STU1195,0.3999999999999999,0,2025-04-01 16:45:00,0.9909090909090909,0.8888888888888888,0.9333333333333332,0.5912314024085137,0.0,0.0,Video Lesson Grammar,0.2064632042796024,0.0258756522852011,0.1428571428571428,1,0,0.0737593508671026,0.9333333333333332,0.8910472972972974,0,0.6034036256011839,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1128,0.1999999999999999,1,2024-11-08 17:00:00,0.4909090909090908,0.8888888888888888,0.9666666666666666,0.0,1.0,0.6000000000000001,Video Lesson Grammar,0.115165082376858,0.2300868156515395,0.2857142857142856,4,0,0.1459892533303274,0.9666666666666666,0.472972972972973,1,0.1576026637069922,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,0.0,0,1
+STU1073,0.3999999999999999,1,2025-04-15 21:30:00,0.7999999999999999,0.3333333333333333,0.7888888888888889,0.625,0.0,0.5333333333333334,Video Lesson World War,0.5815590307458806,0.1455647876458446,0.8571428571428572,1,0,0.0350699709682756,0.788888888888889,0.3175675675675675,1,0.5396781354051055,0.6538461538461533,0.0706596681502631,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1149,0.8,0,2024-12-26 20:00:00,0.4909090909090908,0.8888888888888888,0.7777777777777778,0.5912314024085137,0.25,0.3999999999999999,Audio Lecture Literature,0.502395279658993,0.8565515269084977,0.714285714285714,3,0,0.1459892533303274,0.7777777777777778,0.472972972972973,0,0.6362375138734739,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1103,0.5999999999999999,0,2025-06-23 19:45:00,0.1363636363636363,0.4444444444444445,0.9888888888888888,0.5681836543433301,0.75,0.1333333333333333,Audio Lecture Chemistry,0.3312744672188638,0.2233126629000416,0.5714285714285712,0,0,0.2119738179497055,0.9888888888888888,0.0920608108108108,1,0.3621901590825009,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,0.0,1,0
+STU1090,1.0000000000000002,0,2025-01-01 22:30:00,0.7636363636363636,0.7777777777777777,0.7777777777777778,1.0,0.0,0.2666666666666666,Audio Lecture Literature,0.924121163158115,0.608683679320681,1.0,2,0,0.0842903557270064,0.7777777777777778,0.6216216216216217,1,0.3736588975212727,0.6538461538461533,0.0706596681502631,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,2.0,1,1
+STU1014,0.8,1,2025-05-01 16:30:00,0.7545454545454545,0.6666666666666665,0.9222222222222224,0.5912314024085137,1.0,0.5333333333333334,Audio Lecture Literature,0.6967344336519914,0.1366916308696201,0.1428571428571428,3,0,0.0733576811730414,0.9222222222222224,0.5363175675675677,1,0.2108768035516093,0.6538461538461533,0.0706596681502631,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,0.0,1,0
+STU1087,0.8,1,2024-12-04 20:45:00,0.1636363636363636,0.8888888888888888,0.9555555555555556,0.5847013960703209,0.0,0.8666666666666667,Video Lesson Programming,0.4755514297496868,0.4458960690279354,0.714285714285714,2,0,0.3469197037858838,0.9555555555555556,0.1993243243243243,2,0.0643729189789123,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,1.0,1,0
+STU1099,0.1999999999999999,1,2025-06-26 15:45:00,0.9454545454545454,0.6666666666666665,0.9333333333333332,1.0,0.5,0.4666666666666667,Video Lesson Grammar,0.5389727679953009,0.9068310888403972,0.0,3,0,0.0579488995010672,0.9333333333333332,0.660472972972973,0,0.2508324084350721,0.0,0.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,0.0,1,1
+STU1106,0.8,0,2024-09-10 18:15:00,0.6818181818181818,0.7777777777777777,0.7555555555555555,1.0,0.0,0.5333333333333334,Interactive Exercise Biology,0.3805373591782654,0.9038559500606316,0.4285714285714284,1,0,0.0943047453149738,0.7555555555555555,0.5608108108108109,0,0.4095449500554938,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,1
+STU1184,0.3999999999999999,0,2025-06-27 17:00:00,0.1545454545454545,0.5555555555555556,0.8444444444444444,0.625,0.25,0.3333333333333333,Interactive Exercise Ancient Civilizations,0.0352295821414593,0.0085139403873748,0.2857142857142856,4,0,0.2366697975085482,0.8444444444444444,0.1233108108108108,0,0.0488346281908989,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,1.0,1.0,1,0
+STU1038,0.0,1,2024-11-02 19:15:00,0.1363636363636363,0.8888888888888888,0.7333333333333333,0.625,0.5,0.2,Interactive Exercise Ancient Civilizations,0.04332716332248,0.633162199718396,0.5714285714285712,5,1,0.3897848655048975,0.7333333333333333,0.1765202702702702,2,0.0643729189789123,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1109,0.3999999999999999,0,2025-06-18 18:15:00,0.209090909090909,0.5555555555555556,0.7555555555555555,0.375,0.25,0.8666666666666667,Video Lesson Algebra,0.4731776123086155,0.7827902660005365,0.4285714285714284,2,0,0.1917680190505113,0.7555555555555555,0.1537162162162162,0,0.0776914539400665,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,2.0,1,0
+STU1160,0.5999999999999999,1,2025-04-23 17:30:00,0.2454545454545454,0.3333333333333333,0.8222222222222222,0.625,0.5,0.1333333333333333,Audio Lecture Modern History,0.2013385801297555,0.2062260742804656,0.2857142857142856,2,0,0.1098526097637762,0.8222222222222222,0.1114864864864864,1,0.0632630410654827,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1066,0.3999999999999999,1,2025-03-15 16:15:00,0.4454545454545454,1.0,0.7,0.625,0.5,0.6000000000000001,Interactive Exercise Ancient Civilizations,0.0520714765706797,0.997306502962102,0.1428571428571428,5,1,0.178069173393308,0.7,0.4847972972972973,1,0.1453940066592675,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,0.0,0,0
+STU1159,0.0,1,2025-04-17 15:00:00,0.3545454545454545,0.6666666666666665,0.7,0.625,1.0,0.3333333333333333,Video Lesson World War,0.4970724698649821,0.6033949399699028,0.0,3,0,0.1484698755046771,0.7,0.2761824324324324,2,0.2835738068812431,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,0.0,0,0
+STU1098,0.1999999999999999,1,2024-11-29 20:15:00,0.0999999999999999,0.2222222222222222,0.8111111111111111,0.0,0.25,0.1333333333333333,Video Lesson Physics,0.9608184809245712,0.0959806308299899,0.714285714285714,4,0,0.1484698711846809,0.811111111111111,0.0396959459459459,2,0.4450610432852386,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0,0.0,0.0,0,1
+STU1101,1.0000000000000002,0,2025-06-22 17:30:00,0.2363636363636363,0.8888888888888888,0.9111111111111112,0.5847013960703209,0.75,0.0,Video Lesson Programming,0.0668025338896292,0.6963024171446645,0.2857142857142856,6,1,0.2675397737064,0.9111111111111112,0.2601351351351352,0,0.0921198668146503,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,0.0,1,0
+STU1148,1.0000000000000002,0,2025-04-13 18:15:00,0.1999999999999999,0.2222222222222222,0.8777777777777778,0.0673797828335063,0.25,0.1333333333333333,Video Lesson Physics,0.5221223618549242,0.0602062893752324,0.4285714285714284,6,1,0.0938961700942147,0.8777777777777778,0.0675675675675675,1,0.0876803551609322,0.6538461538461533,0.0706596681502631,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0,0.0,2.0,0,1
+STU1003,0.8,1,2025-06-20 15:00:00,0.9818181818181818,0.8888888888888888,0.8111111111111111,0.625,0.75,0.1333333333333333,Video Lesson World War,0.320523694056688,0.1477637206859045,0.0,4,0,0.0744716334234509,0.811111111111111,0.883445945945946,1,0.8706992230854607,0.6538461538461533,0.0706596681502631,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1047,0.0,1,2025-05-04 20:30:00,0.6636363636363636,0.6666666666666665,0.8333333333333334,1.0,0.5,0.8,Video Lesson Algebra,0.7510430679411558,0.1276554701954296,0.714285714285714,6,1,0.0834357139031044,0.8333333333333333,0.4771959459459459,0,0.4334073251942286,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,0.0,1,1
+STU1089,0.5999999999999999,0,2024-10-17 20:15:00,0.3727272727272727,0.6666666666666665,0.7999999999999999,0.625,0.0,0.3999999999999999,Video Lesson World War,0.7530187183365914,0.5859029228243289,0.714285714285714,3,0,0.1422439984895389,0.7999999999999999,0.2880067567567568,2,0.336662967073622,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,1.0,1,0
+STU1167,0.0,0,2025-06-15 21:45:00,0.5363636363636363,1.0,0.7111111111111111,1.0,0.75,0.0,Video Lesson World War,0.7893580812552008,0.3811237015088908,0.8571428571428572,6,1,0.1507707440950834,0.7111111111111111,0.5692567567567568,0,0.318534961154273,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,0.0,0,1
+STU1085,0.0,0,2024-10-17 18:45:00,0.509090909090909,0.5555555555555556,0.8444444444444444,1.0,1.0,0.9333333333333332,Video Lesson Grammar,0.8848119967564771,0.7230077728671821,0.4285714285714284,3,0,0.0907390153088559,0.8444444444444444,0.3209459459459459,1,0.2397336293007769,0.6538461538461533,0.0706596681502631,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,1.0,1,1
+STU1189,1.0000000000000002,1,2025-03-02 20:00:00,0.2181818181818181,0.4444444444444445,0.7,0.5847013960703209,0.75,0.5333333333333334,Audio Lecture Data Structures,0.805779872236121,0.5860663676351006,0.714285714285714,6,1,0.1531392818861357,0.7,0.1300675675675675,0,0.1165371809100999,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0,0
+STU1175,0.0,0,2025-06-17 20:45:00,0.5818181818181818,0.5555555555555556,0.9888888888888888,0.5912314024085137,0.0,0.0,Video Lesson Grammar,0.0834336676290356,0.186259618686583,0.714285714285714,1,0,0.0798169606669225,0.9888888888888888,0.3614864864864865,1,0.1498335183129855,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,2.0,1,0
+STU1067,0.3999999999999999,0,2024-12-29 18:15:00,0.609090909090909,0.3333333333333333,0.8777777777777778,0.375,1.0,0.4666666666666667,Interactive Exercise Algebra,0.7713209186249291,0.6592753500528468,0.4285714285714284,6,1,0.04744086966104,0.8777777777777778,0.2466216216216216,1,0.1587125416204217,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1151,0.3999999999999999,1,2025-04-28 17:30:00,0.609090909090909,0.8888888888888888,0.977777777777778,1.0,0.0,0.8,Video Lesson Grammar,0.0051463801049845,0.3219907415819122,0.2857142857142856,0,0,0.1196044461051956,0.977777777777778,0.5717905405405406,2,0.3618201997780245,1.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,2.0,1,1
+STU1125,1.0000000000000002,0,2025-05-29 16:30:00,0.6909090909090908,0.6666666666666665,0.7222222222222222,0.625,0.5,0.2666666666666666,Audio Lecture Modern History,0.7686346332670541,0.2495534803915156,0.1428571428571428,3,0,0.0801662125900501,0.7222222222222222,0.4949324324324324,0,0.1886792452830188,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1188,0.5999999999999999,1,2024-10-02 22:30:00,0.0545454545454545,0.6666666666666665,0.6666666666666666,0.5681836543433301,0.0,0.0666666666666667,Audio Lecture Chemistry,0.4579452966893717,0.2886455481256465,1.0,2,0,0.4759120807248974,0.6666666666666666,0.081081081081081,0,0.2003329633740288,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0,1.0,1.0,0,0
+STU1012,0.5999999999999999,1,2025-04-25 19:45:00,0.9727272727272726,1.0,0.7666666666666666,0.375,0.75,0.2666666666666666,Interactive Exercise Algebra,0.7282514176574155,0.7639109794034082,0.5714285714285712,4,0,0.0846945454436863,0.7666666666666667,0.9746621621621624,2,0.2774694783573806,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,1.0,1,0
+STU1166,0.1999999999999999,1,2024-02-14 22:30:00,0.7181818181818181,0.8888888888888888,0.6888888888888889,0.375,0.75,0.0,Audio Lecture Calculus,0.6749396270385014,0.3180004377626736,1.0,2,0,0.1020905895469944,0.6888888888888889,0.6630067567567568,2,0.3921568627450981,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0,0.0,1.0,0,0
+STU1018,1.0000000000000002,0,2024-09-11 17:00:00,0.5818181818181818,1.0,0.7888888888888889,1.0,1.0,1.0,Audio Lecture Chemistry,0.4872194626850976,0.0058985050143313,0.2857142857142856,2,0,0.1398882621077272,0.788888888888889,0.6114864864864865,2,0.1853496115427303,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,0.0,1,1
+STU1019,0.3999999999999999,0,2025-04-06 22:45:00,0.9090909090909092,0.7777777777777777,0.8666666666666667,0.5681836543433301,1.0,0.0666666666666667,Video Lesson Physics,0.2091923119954345,0.868276856073035,1.0,6,1,0.0705332144380654,0.8666666666666667,0.7297297297297298,2,0.686644469108398,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1,1.0,0.0,1,0
+STU1059,0.1999999999999999,1,2024-09-18 19:30:00,0.3545454545454545,0.0,0.1444444444444445,0.5681836543433301,0.0,0.6666666666666667,Interactive Exercise Biology,0.3948864234287349,0.5687780347297534,0.5714285714285712,2,0,0.0123899895055672,0.1444444444444445,0.0278716216216216,1,0.3013318534961154,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0,2.0,2.0,0,0
+STU1029,0.5999999999999999,0,2025-04-02 21:30:00,0.0272727272727272,0.7777777777777777,0.8777777777777778,1.0,0.0,1.0,Audio Lecture Data Structures,0.3560479024967615,0.583860696511781,0.8571428571428572,2,0,0.6735986276974907,0.8777777777777778,0.0743243243243243,0,0.097669256381798,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,2.0,1,1
+STU1055,0.8,1,2025-04-11 17:45:00,0.7,0.7777777777777777,0.8222222222222222,0.625,0.25,0.3999999999999999,Video Lesson World War,0.3073348536793754,0.7051310433971978,0.2857142857142856,4,0,0.0919002686343527,0.8222222222222222,0.5743243243243243,2,0.4324824269330374,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,1.0,0.0,1,0
+STU1028,0.1999999999999999,1,2025-05-13 16:15:00,0.5363636363636363,0.7777777777777777,0.8111111111111111,0.3344751809720789,0.75,0.6000000000000001,Interactive Exercise Algebra,0.2630913902310068,0.1193146249188182,0.1428571428571428,1,0,0.1185585969772098,0.811111111111111,0.4527027027027027,2,0.5235849056603774,1.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1,1.0,2.0,1,1
+STU1140,0.0,1,2024-09-18 20:00:00,0.5363636363636363,0.3333333333333333,0.8666666666666667,0.5912314024085137,0.0,0.1333333333333333,Video Lesson Grammar,0.7303054770558202,0.1851266429469031,0.714285714285714,2,0,0.0541343027414627,0.8666666666666667,0.2195945945945946,1,0.4961154273029966,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,2.0,0.0,1,0
+STU1032,1.0000000000000002,1,2025-06-05 15:45:00,0.4818181818181818,1.0,0.8666666666666667,0.5847013960703209,0.5,0.1333333333333333,Interactive Exercise Algorithms,0.904528548419776,0.71430965510493,0.0,3,0,0.1661098615267832,0.8666666666666667,0.5185810810810811,2,0.2003329633740288,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1165,1.0000000000000002,1,2025-03-25 19:45:00,0.4181818181818181,0.8888888888888888,0.8111111111111111,0.625,0.5,0.5333333333333334,Audio Lecture Modern History,0.1905722150675996,0.281206242727938,0.5714285714285712,1,0,0.1683148593351719,0.811111111111111,0.4121621621621621,2,0.1331853496115427,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,2.0,1,0
+STU1000,0.0,1,2024-09-25 15:30:00,0.4363636363636363,0.4444444444444445,0.6888888888888889,0.5847013960703209,0.25,0.4666666666666667,Audio Lecture Data Structures,0.3478720728004227,0.6738459925422966,0.0,2,0,0.0855133768257188,0.6888888888888889,0.2314189189189189,2,1.0,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,0.0,2.0,0,0
+STU1001,0.1999999999999999,1,2025-06-10 19:30:00,0.1454545454545454,0.6666666666666665,0.977777777777778,0.5847013960703209,1.0,0.1333333333333333,Audio Lecture Data Structures,0.0810566746333176,0.3464105490123374,0.5714285714285712,1,0,0.2889112909098579,0.977777777777778,0.1402027027027027,0,0.1361450240473548,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1064,0.1999999999999999,0,2025-01-18 19:45:00,0.7818181818181817,0.7777777777777777,1.0,0.625,0.0,0.7333333333333334,Interactive Exercise Ancient Civilizations,0.6105559126258934,0.6520067508910071,0.5714285714285712,5,1,0.0823199318470881,1.0,0.6351351351351352,2,0.7142064372918979,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,1.0,1,0
+STU1051,0.5999999999999999,0,2024-04-23 20:00:00,0.409090909090909,0.4444444444444445,0.9111111111111112,0.625,0.25,1.0,Video Lesson World War,0.3151205368639675,0.5581962140132246,0.714285714285714,1,0,0.0907390150027074,0.9111111111111112,0.21875,2,0.1759156492785794,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,0.0,1,0
+STU1013,0.3999999999999999,0,2025-01-16 19:30:00,0.9636363636363636,0.1111111111111111,0.0333333333333333,0.5681836543433301,0.0,0.7333333333333334,Interactive Exercise Biology,0.6742409023430315,0.980171375427846,0.5714285714285712,3,0,0.0088706823348944,0.0333333333333333,0.1824324324324324,0,0.413984461709212,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0,0
+STU1002,0.0,1,2025-05-10 15:00:00,0.2818181818181817,0.4444444444444445,0.9444444444444444,0.5681836543433301,1.0,0.3999999999999999,Audio Lecture Chemistry,0.2137526852826897,0.8698569933581745,0.0,5,1,0.1252367236968581,0.9444444444444444,0.1596283783783784,1,0.5952645209027007,0.6538461538461533,0.0706596681502631,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1,0.0,1.0,1,0
+STU1133,0.0,1,2024-11-28 22:15:00,0.5272727272727272,0.2222222222222222,0.7222222222222222,0.0139607032057917,0.75,0.2,Video Lesson Programming,0.871018523976302,0.8339837777883972,1.0,3,0,0.038738791240853,0.7222222222222222,0.1587837837837838,2,0.3226045135035146,1.0,1.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,2.0,2.0,0,1
+STU1053,0.1999999999999999,1,2024-12-01 17:00:00,0.5272727272727272,0.7777777777777777,0.7111111111111111,0.3878942605997935,0.25,0.4666666666666667,Interactive Exercise Algorithms,0.8122981896655268,0.0912777811070014,0.2857142857142856,6,1,0.120453429132749,0.7111111111111111,0.4459459459459459,1,0.3046614872364039,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,2.0,1.0,0,1
+STU1097,0.8,0,2025-02-20 16:00:00,0.8909090909090909,0.7777777777777777,0.8111111111111111,0.375,0.25,0.0666666666666667,Interactive Exercise Algebra,0.0234309095259943,0.6387940621461871,0.1428571428571428,3,0,0.0720299404599911,0.811111111111111,0.7162162162162163,0,0.517573066962634,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1,1.0,0.0,1,0
+STU1075,0.3999999999999999,1,2025-06-29 16:45:00,0.4272727272727272,0.4444444444444445,0.6666666666666666,0.625,0.0,0.8,Interactive Exercise Ancient Civilizations,0.522227639715337,0.6764842932711569,0.1428571428571428,6,1,0.0871941376439511,0.6666666666666666,0.2271959459459459,2,0.1065482796892341,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,2.0,0,0
+STU1041,0.5999999999999999,1,2025-01-29 15:45:00,0.6181818181818182,0.5555555555555556,0.7111111111111111,0.625,0.5,0.2666666666666666,Interactive Exercise Ancient Civilizations,0.6777435845094765,0.9143755920941278,0.0,2,0,0.0751960913845641,0.7111111111111111,0.3817567567567568,0,0.1653718091009988,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,1.0,0.0,0,0
+STU1015,0.5999999999999999,1,2025-02-09 19:00:00,0.7545454545454545,0.7777777777777777,0.9111111111111112,1.0,0.75,0.6000000000000001,Video Lesson Grammar,0.4043075826362853,0.5023096150736766,0.5714285714285712,6,1,0.0853073486969451,0.9111111111111112,0.6148648648648649,0,0.2130965593784683,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,0.0,1,1
+STU1088,0.0,1,2024-06-15 19:00:00,0.8181818181818181,0.2222222222222222,0.8666666666666667,0.625,0.5,0.2,Audio Lecture Modern History,0.5591290975887796,0.1919013514636648,0.5714285714285712,5,1,0.0230495809225011,0.8666666666666667,0.2398648648648648,1,0.279134295227525,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1123,0.5999999999999999,0,2025-01-09 22:45:00,0.0181818181818181,0.4444444444444445,0.7555555555555555,0.375,0.0,0.7333333333333334,Interactive Exercise Algebra,0.5830720436996023,0.4880820685544728,1.0,3,0,0.4527595914485469,0.7555555555555555,0.0371621621621621,1,0.1498335183129855,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,2.0,1,0
+STU1076,0.3999999999999999,1,2024-09-23 15:45:00,0.5727272727272726,0.8888888888888888,0.8777777777777778,0.5681836543433301,0.5,0.0666666666666667,Video Lesson Physics,0.5904186100356311,0.1276075351246515,0.0,0,0,0.1267219494378041,0.8777777777777778,0.5413851351351352,1,0.3007769145394007,0.6538461538461533,0.0706596681502631,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1,0.0,1.0,1,0
+STU1021,0.8,1,2025-05-17 18:45:00,0.4545454545454545,1.0,0.9333333333333332,0.5847013960703209,1.0,0.2666666666666666,Interactive Exercise Algorithms,1.0,0.4825401966746634,0.4285714285714284,5,1,0.1749298540308367,0.9333333333333332,0.4932432432432433,1,0.3551609322974473,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1181,0.3999999999999999,1,2025-04-15 17:00:00,0.509090909090909,0.6666666666666665,0.8333333333333334,0.625,0.75,0.7333333333333334,Video Lesson World War,0.4625231648447067,0.4155012111996172,0.2857142857142856,1,0,0.1075771831093793,0.8333333333333333,0.3766891891891892,2,0.1664816870144284,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1079,0.1999999999999999,0,2025-07-02 21:30:00,0.6818181818181818,0.2222222222222222,0.8333333333333334,0.5912314024085137,0.0,0.6666666666666667,Audio Lecture Literature,0.422777903710642,0.6827103850869911,0.8571428571428572,2,0,0.0289330348091874,0.8333333333333333,0.2018581081081081,0,0.1576026637069922,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,0
+STU1127,0.1999999999999999,1,2025-06-27 22:45:00,0.7,0.4444444444444445,0.8666666666666667,0.625,1.0,0.5333333333333334,Video Lesson World War,0.6983410253802127,0.5887288090872417,1.0,4,0,0.0535789210861138,0.8666666666666667,0.3538851351351352,2,0.2859785423603403,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1026,0.1999999999999999,0,2025-07-02 15:15:00,0.4,0.0,0.1777777777777778,0.5681836543433301,0.5,0.2,Video Lesson Physics,0.3334970854332688,0.4290394711353563,0.0,2,0,0.010289991303729,0.1777777777777778,0.0320945945945946,1,0.4080651128375879,0.6538461538461533,0.0706596681502631,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,0.0,0,0
+STU1113,0.5999999999999999,0,2024-12-31 22:00:00,0.6636363636363636,0.8888888888888888,1.0,0.625,0.0,0.6666666666666667,Video Lesson World War,0.1794776773869556,0.384687359011765,1.0,1,0,0.1102144868737869,1.0,0.6173986486486487,1,0.5493895671476138,0.6538461538461533,0.0706596681502631,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,1.0,1.0,1,0
+STU1070,0.0,1,2025-06-04 21:30:00,0.6272727272727272,0.5555555555555556,0.9444444444444444,0.375,0.5,0.0666666666666667,Audio Lecture Calculus,0.9473182581745272,0.6746167030994392,0.8571428571428572,2,0,0.0741139890833395,0.9444444444444444,0.3868243243243243,0,0.3144654088050315,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1170,0.5999999999999999,1,2024-05-29 21:45:00,0.6363636363636364,0.3333333333333333,0.9555555555555556,0.0,0.5,0.4666666666666667,Video Lesson Programming,0.0533985705637151,0.6834946702494549,0.8571428571428572,2,0,0.0452759623947767,0.9555555555555556,0.2567567567567568,2,0.2336293007769145,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,0.0,2.0,0,1
+STU1116,0.8,1,2023-12-29 15:15:00,0.1727272727272727,0.3333333333333333,0.7666666666666666,0.5847013960703209,0.75,0.1333333333333333,Video Lesson Programming,0.3870150861677275,0.4620268990245569,0.0,4,0,0.1429953951748693,0.7666666666666667,0.0844594594594594,1,0.1409544950055493,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1009,0.0,0,2025-02-25 22:45:00,0.209090909090909,1.0,0.9111111111111112,0.625,0.5,0.1333333333333333,Audio Lecture Modern History,0.4235003797716757,0.4303233547082236,1.0,1,0,0.3264733594137085,0.9111111111111112,0.2652027027027027,1,0.1148723640399556,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1191,0.1999999999999999,1,2025-01-23 21:15:00,0.9727272727272726,1.0,0.7555555555555555,0.5681836543433301,0.25,0.0,Video Lesson Physics,0.4047094298661965,0.3751864629481443,0.8571428571428572,3,0,0.0846945454436863,0.7555555555555555,0.9746621621621624,0,0.4750277469478356,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1,2.0,0.0,1,0
+STU1138,0.8,0,2024-07-09 21:00:00,0.7999999999999999,0.5555555555555556,0.9333333333333332,0.375,1.0,1.0,Interactive Exercise Algebra,0.9615527079247134,0.2352578875846451,0.8571428571428572,1,0,0.0577499521995556,0.9333333333333332,0.4831081081081081,0,0.6267110617832038,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,0
+STU1095,0.5999999999999999,1,2024-07-31 16:30:00,0.109090909090909,0.3333333333333333,0.6888888888888889,0.5847013960703209,0.25,0.6666666666666667,Video Lesson Programming,0.6755493167680068,0.2495566811782675,0.1428571428571428,2,0,0.1917680159890264,0.6888888888888889,0.0608108108108108,1,0.6356825749167592,0.6538461538461533,0.0706596681502631,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,1.0,0.0,0,0
+STU1001,0.1999999999999999,1,2025-04-21 19:15:00,0.9545454545454544,1.0,1.0,0.5847013960703209,0.5,0.2,Interactive Exercise Algorithms,0.9649903744414844,0.1401792249302386,0.5714285714285712,0,0,0.0863464504195475,1.0,0.9577702702702704,1,0.4779874213836478,0.6538461538461533,0.0706596681502631,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1176,0.8,1,2025-06-25 17:45:00,0.918181818181818,0.8888888888888888,0.8888888888888888,0.5847013960703209,0.25,0.4666666666666667,Audio Lecture Data Structures,0.5208156799446504,0.4969171641378953,0.2857142857142856,2,0,0.0798169610728097,0.8888888888888888,0.8302364864864865,2,0.605623381428043,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1020,0.8,1,2025-02-16 22:15:00,0.1181818181818181,0.4444444444444445,0.7666666666666666,0.4947324198552225,1.0,0.8,Interactive Exercise Biology,0.439314066195212,0.4303324328505092,1.0,6,1,0.2313011048871269,0.7666666666666667,0.0836148648648648,1,0.1048834628190898,0.6538461538461533,0.0706596681502631,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1,2.0,0.0,1,1
+STU1052,1.0000000000000002,0,2024-04-21 16:00:00,0.1999999999999999,0.2222222222222222,0.7333333333333333,0.0,0.0,0.8,Audio Lecture Calculus,0.4892865365932068,0.62388281365405,0.1428571428571428,6,1,0.0938961700942147,0.7333333333333333,0.0675675675675675,2,0.2708102108768035,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0,2.0,0.0,0,1
+STU1174,0.3999999999999999,0,2024-04-02 16:15:00,0.5272727272727272,0.8888888888888888,0.9222222222222224,0.5847013960703209,0.75,0.0666666666666667,Interactive Exercise Algorithms,0.3081547237951205,0.7772314840074368,0.1428571428571428,1,0,0.1367963567111281,0.9222222222222224,0.5033783783783784,2,0.3285238623751387,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,0.0,1.0,1,0
+STU1107,0.1999999999999999,0,2025-05-09 21:30:00,0.0999999999999999,0.3333333333333333,0.6888888888888889,0.375,0.25,0.0666666666666667,Video Lesson Algebra,0.7442442619766683,0.5540012960382985,0.8571428571428572,4,0,0.2013898254110027,0.6888888888888889,0.0574324324324324,0,0.0244173140954495,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0,1.0,1.0,0,0
+STU1131,0.0,0,2025-06-27 20:30:00,0.2909090909090909,0.2222222222222222,0.7,0.5847013960703209,0.5,0.5333333333333334,Video Lesson Programming,0.4429204525630629,0.0165636411853334,0.714285714285714,4,0,0.0690899417351965,0.7,0.0929054054054054,0,0.5455049944506104,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0,2.0,2.0,0,0
+STU1027,0.8,0,2023-10-25 15:45:00,0.2636363636363636,0.7777777777777777,0.8888888888888888,0.5681836543433301,0.5,0.0,Audio Lecture Chemistry,0.3749982024243721,0.7381107329569581,0.0,2,0,0.217672893260044,0.8888888888888888,0.25,2,0.4827968923418424,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1,2.0,1.0,1,0
+STU1136,0.5999999999999999,0,2024-08-03 20:45:00,0.6363636363636364,0.5555555555555556,1.0,0.0,0.0,0.8,Interactive Exercise Biology,0.0390877272021476,0.282656298860882,0.714285714285714,5,1,0.0730589393393073,1.0,0.3918918918918919,0,0.2386237513873474,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0,0.0,2.0,0,1
+STU1197,0.5999999999999999,0,2024-06-21 15:15:00,0.7454545454545454,0.7777777777777777,0.8777777777777778,0.5681836543433301,0.0,0.7333333333333334,Interactive Exercise Biology,0.6916417895675577,0.0173519302820779,0.0,4,0,0.0863464502094683,0.8777777777777778,0.6081081081081082,0,0.6248612652608213,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1,1.0,2.0,1,0
+STU1003,0.8,1,2025-06-19 16:45:00,0.8999999999999999,0.7777777777777777,0.8444444444444444,0.375,1.0,0.4666666666666667,Audio Lecture Calculus,0.0401738067011121,0.7833008583000046,0.1428571428571428,3,0,0.0712747117333943,0.8444444444444444,0.722972972972973,1,0.8706992230854607,0.6538461538461533,0.0706596681502631,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,0.0,1,0
+STU1122,0.0,0,2024-07-03 15:00:00,0.3454545454545454,0.3333333333333333,0.7555555555555555,0.375,0.75,0.5333333333333334,Audio Lecture Calculus,0.1678776395202608,0.1371824991949564,0.0,2,0,0.0823199308824015,0.7555555555555555,0.1486486486486486,0,0.2967073621901591,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1023,0.5999999999999999,0,2025-01-22 20:00:00,1.0,0.3333333333333333,0.9333333333333332,0.625,1.0,0.7333333333333334,Audio Lecture Modern History,0.6103308603130055,0.4278807889936094,0.714285714285714,2,0,0.0267539779194395,0.9333333333333332,0.3918918918918919,0,0.2541620421753606,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1008,0.0,1,2025-04-20 21:00:00,0.3272727272727272,0.5555555555555556,0.7666666666666666,0.2276370217166499,1.0,0.9333333333333332,Video Lesson Physics,0.1080548242106633,0.905344410394646,0.8571428571428572,6,1,0.1346646694857505,0.7666666666666667,0.2195945945945946,1,0.2330743618201997,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1,2.0,0.0,1,1
+STU1044,0.5999999999999999,1,2025-06-30 16:45:00,0.5545454545454545,1.0,0.7333333333333333,0.5912314024085137,0.75,0.0,Video Lesson Grammar,0.03995183183426,0.5733476355000872,0.1428571428571428,0,0,0.1462338220297057,0.7333333333333333,0.5861486486486487,1,0.1753607103218646,0.6538461538461533,0.0706596681502631,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1117,0.1999999999999999,0,2024-07-13 16:00:00,0.8454545454545453,0.3333333333333333,0.9888888888888888,0.5847013960703209,0.75,0.3999999999999999,Interactive Exercise Algorithms,0.3271705195184051,0.3168861230398674,0.1428571428571428,5,1,0.0328680310642866,0.9888888888888888,0.3344594594594594,0,0.2502774694783574,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,2.0,0.0,1,0
+STU1172,0.3999999999999999,0,2023-12-11 21:15:00,0.6636363636363636,0.2222222222222222,0.8999999999999999,1.0,0.5,1.0,Video Lesson World War,0.8031124435119377,0.0237170783603136,0.8571428571428572,0,0,0.0298781679617393,0.9000000000000001,0.1967905405405405,0,0.1598224195338512,0.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,1.0,1,1
+STU1115,0.0,0,2025-05-14 17:15:00,0.6,0.7777777777777777,0.8222222222222222,0.5681836543433301,0.5,0.7333333333333334,Audio Lecture Chemistry,0.8574346061979541,0.4306179469511907,0.2857142857142856,2,0,0.1066909639846726,0.8222222222222222,0.5000000000000001,2,0.3523862375138734,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1,1.0,0.0,1,0
+STU1135,0.8,1,2024-10-16 21:00:00,0.5363636363636363,0.7777777777777777,0.8999999999999999,1.0,0.25,1.0,Interactive Exercise Vocabulary,0.6010749217962007,0.6871984162963782,0.8571428571428572,2,0,0.1185585969772098,0.9000000000000001,0.4527027027027027,2,0.3290788013318535,1.0,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,2.0,1,1
+STU1142,0.0,1,2024-10-26 21:00:00,0.5909090909090908,0.4444444444444445,0.9888888888888888,0.5912314024085137,1.0,0.3999999999999999,Audio Lecture Literature,0.7527334400386756,0.1293020152495637,0.8571428571428572,5,1,0.0637979469627238,0.9888888888888888,0.3032094594594594,1,0.2924528301886793,0.6538461538461533,0.0706596681502631,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1147,0.0,1,2024-12-26 19:00:00,0.4272727272727272,0.7777777777777777,0.9111111111111112,1.0,1.0,0.6000000000000001,Interactive Exercise Biology,0.7137392075691249,0.6732410565500274,0.5714285714285712,3,0,0.1456846151268925,0.9111111111111112,0.3716216216216216,2,0.3296337402885683,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1,0.0,2.0,1,1
+STU1192,0.5999999999999999,0,2025-05-16 22:30:00,0.6,0.3333333333333333,0.7111111111111111,1.0,1.0,0.0666666666666667,Interactive Exercise Algorithms,0.3781868637440948,0.039703565386513,1.0,4,0,0.0482004862451941,0.7111111111111111,0.2432432432432432,1,0.4298927118017018,0.6538461538461533,0.0706596681502631,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,2.0,1.0,0,1
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+STU1063,0.3999999999999999,0,2024-11-09 19:30:00,0.7272727272727272,1.0,0.8666666666666667,0.0,0.5,0.0,Interactive Exercise Algebra,0.4841074717903316,0.1262294105178091,0.5714285714285712,5,1,0.1131899062084624,0.8666666666666667,0.7466216216216217,1,0.4002959674435812,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0,1.0,2.0,0,1
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Rekomendasi Materi Belajar/edtech/backend/models/recommenders/collaborative/collab_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c4aef73c6272415cb11002c1ff5c96f65587498acaa7c86ad4f7167d1d73fe48
+size 6080

Rekomendasi Materi Belajar/edtech/backend/models/recommenders/content_based/content_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:63d1a2f5acb72fa4e6c3825586d578da46d850c31d82883ef50f618789722977
+size 5211833

Rekomendasi Materi Belajar/edtech/backend/models/recommenders/hybrid/hybrid_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d173427052471e467df306ab61013e0599cfb0a80ff3805e464f9b7a25166933
+size 32

Rekomendasi Materi Belajar/edtech/backend/src/app.py

@@ -0,0 +1,193 @@
+# backend/src/app.py/recommendation
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from contextlib import asynccontextmanager
+import joblib
+import pandas as pd
+from typing import List, Optional
+import uvicorn
+from pathlib import Path
+from recommendation.collaborative import CollaborativeFiltering
+from recommendation.content_based import ContentBasedRecommender
+from recommendation.hybrid import HybridRecommender
+
+# ===== KONFIGURASI SERVER =====
+HOST = "0.0.0.0"  #untuk deploy hugging face
+PORT = 8025
+RELOAD = True  # Set False di production
+WORKERS = 1
+
+# ===== LIFESPAN MANAGEMENT =====
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    """Mengelola siklus hidup aplikasi dan inisialisasi model"""
+    print("Memuat model rekomendasi...")
+    try:
+        # Load semua model
+        app.state.collab_model = CollaborativeFiltering.load_model(COLLAB_MODEL_PATH)
+        app.state.content_model = ContentBasedRecommender.load_model(CONTENT_MODEL_PATH)
+        app.state.hybrid_model = HybridRecommender.load_model(
+            collab_path=COLLAB_MODEL_PATH,
+            content_path=CONTENT_MODEL_PATH,
+            hybrid_path=HYBRID_MODEL_PATH
+        )
+        print("✅ Model berhasil dimuat!")
+    except Exception as e:
+        print(f"❌ Gagal memuat model: {str(e)}")
+        raise HTTPException(status_code=500, detail="Gagal memuat model")
+    yield
+    print("🛑 Server dimatikan")
+
+# ===== INISIALISASI APLIKASI =====
+app = FastAPI(
+    title="Sistem Rekomendasi Materi Pembelajaran",
+    description="API untuk memberikan rekomendasi materi pembelajaran personalisasi",
+    version="1.0.2",
+    lifespan=lifespan,
+    docs_url="/docs",
+    redoc_url="/redoc"
+)
+
+# ===== KONFIGURASI CORS =====
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["http://localhost:3025"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# ===== PATH MODEL =====
+MODEL_DIR = Path("models/recommenders")
+COLLAB_MODEL_PATH = MODEL_DIR / "collaborative/collab_model.joblib"
+CONTENT_MODEL_PATH = MODEL_DIR / "content_based/content_model.joblib"
+HYBRID_MODEL_PATH = MODEL_DIR / "hybrid/hybrid_model.joblib"
+
+# ===== SCHEMA REQUEST/RESPONSE =====
+class RecommendationRequest(BaseModel):
+    user_id: str
+    user_history: List[str]
+    n_recommendations: int = 5
+    algorithm: str = "hybrid"
+
+class MaterialRecommendationRequest(BaseModel):
+    material_id: str
+    n_recommendations: int = 5
+
+class RecommendationItem(BaseModel):
+    material_id: str
+    score: float
+    confidence: float = 0.0  # Tambahan field baru
+
+class RecommendationResponse(BaseModel):
+    success: bool
+    recommendations: List[RecommendationItem]
+    algorithm: str
+    message: Optional[str] = None
+
+# ===== ENDPOINT API =====
+@app.get("/")
+async def root():
+    return {
+        "message": "Selamat datang di API Rekomendasi Pembelajaran",
+        "version": app.version,
+        "docs": f"http://{HOST}:{PORT}/docs"
+    }
+
+@app.post("/recommend", response_model=RecommendationResponse)
+async def get_recommendations(request: RecommendationRequest):
+    try:
+        # Validasi input
+        if not request.user_id:
+            raise HTTPException(
+                status_code=400,
+                detail="User ID diperlukan",
+                headers={"Content-Type": "application/json"}
+            )
+            
+        if not request.user_history and request.algorithm != "collaborative":
+            raise HTTPException(
+                status_code=400,
+                detail="User history diperlukan untuk algoritma ini",
+                headers={"Content-Type": "application/json"}
+            )
+        # Format response yang lebih konsisten
+        recommendations = []
+        if request.algorithm == "hybrid":
+            recommendations = app.state.hybrid_model.recommend_for_user(
+                user_id=request.user_id,
+                user_history=request.user_history or [],  # Handle None
+                df=pd.DataFrame(),
+                n_recommendations=request.n_recommendations
+            )
+        elif request.algorithm == "collaborative":
+            recommendations = app.state.collab_model.recommend_for_user(
+                user_id=request.user_id
+            )[:request.n_recommendations]
+        else:
+            recommendations = app.state.content_model.recommend_for_user(
+                user_id=request.user_id,
+                user_history=request.user_history or [],  # Handle None
+                df=pd.DataFrame()
+            )[:request.n_recommendations]
+
+        # Pastikan format response konsisten
+        recommendation_items = [
+            {
+                "material_id": item[0], 
+                "score": float(item[1]),
+                "confidence": min(float(item[1]) * 100, 99.9)
+            }
+            for item in recommendations
+        ]
+        
+        return {
+            "success": True,
+            "recommendations": recommendation_items,
+            "algorithm": request.algorithm,
+            "message": "Rekomendasi berhasil dibuat"
+        }
+        
+    except Exception as e:
+        raise HTTPException(
+            status_code=500,
+            detail=str(e),
+            headers={"Content-Type": "application/json"}
+        )
+
+@app.get("/health")
+async def health_check():
+    return {
+        "status": "healthy" if all([
+            hasattr(app.state, "collab_model"),
+            hasattr(app.state, "content_model"),
+            hasattr(app.state, "hybrid_model")
+        ]) else "unhealthy",
+        "details": {
+            "collaborative_loaded": hasattr(app.state, "collab_model"),
+            "content_loaded": hasattr(app.state, "content_model"),
+            "hybrid_loaded": hasattr(app.state, "hybrid_model")
+        }
+    }
+
+# ===== KONFIGURASI SERVER =====
+def run_server():
+    """Menjalankan server Uvicorn"""
+    config = uvicorn.Config(
+        app,
+        host=HOST,
+        port=PORT,
+        reload=RELOAD,
+        workers=WORKERS,
+        log_level="info"
+    )
+    server = uvicorn.Server(config)
+    
+    print(f"🚀 Server berjalan di http://{HOST}:{PORT}")
+    print(f"📚 Dokumentasi API tersedia di http://{HOST}:{PORT}/docs")
+    
+    server.run()
+
+if __name__ == "__main__":
+    run_server()

Rekomendasi Materi Belajar/edtech/backend/src/data/recommendations/evaluations/collaborative_evaluation.csv

@@ -0,0 +1,2 @@
+RMSE,MAE,Precision@K,Recall@K,NDCG@K,model
+0.5873683620699068,0.5249163001110007,0.19658119658119655,0.47692307692307695,0.33844810160390104,collaborative

Rekomendasi Materi Belajar/edtech/backend/src/data/recommendations/evaluations/content_based_evaluation.csv

@@ -0,0 +1,2 @@
+RMSE,MAE,Precision@K,Recall@K,NDCG@K,model
+0.279107926379566,0.24733157275956955,0.3350427350427351,1.0,0.6786757984354084,content_based

Rekomendasi Materi Belajar/edtech/backend/src/data/recommendations/evaluations/hybrid_evaluation.csv

@@ -0,0 +1,2 @@
+RMSE,MAE,Precision@K,Recall@K,NDCG@K,model
+0.279107926379566,0.24733157275956955,0.19658119658119655,0.47692307692307695,0.33844810160390104,hybrid

Rekomendasi Materi Belajar/edtech/backend/src/data/recommendations/test_data.csv

@@ -0,0 +1,201 @@
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+STU1121,1.0000000000000002,0,2025-02-18 15:30:00,0.7454545454545454,0.2222222222222222,0.9888888888888888,0.625,0.75,0.0666666666666667,Audio Lecture Modern History,0.9198735763978956,0.0384393650376045,0.0,1,0,0.0259486741446228,0.9888888888888888,0.2195945945945946,2,0.3079911209766925,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1173,0.0,0,2024-12-25 18:00:00,0.9272727272727272,0.3333333333333333,0.8333333333333334,0.0,0.5,0.4666666666666667,Interactive Exercise Ancient Civilizations,0.3011587474359719,0.2087357000960709,0.4285714285714284,2,0,0.0293999757110805,0.8333333333333333,0.3648648648648649,2,0.4413614502404736,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0,2.0,0.0,0,1
+STU1155,0.1999999999999999,1,2025-06-29 16:30:00,0.6454545454545454,0.6666666666666665,0.7333333333333333,0.5912314024085137,1.0,0.1333333333333333,Video Lesson Grammar,0.7560720048047218,0.1397250197943573,0.1428571428571428,6,1,0.0857499288226175,0.7333333333333333,0.4653716216216216,2,0.4520902700702922,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1068,1.0000000000000002,0,2025-01-03 22:45:00,0.1818181818181818,0.2222222222222222,0.6777777777777778,0.5681836543433301,0.25,0.8666666666666667,Interactive Exercise Biology,0.33277656825806,0.8052497769406544,1.0,4,0,0.1008419139685898,0.6777777777777777,0.0625,1,0.2047724750277469,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0,2.0,1.0,0,0
+STU1146,1.0000000000000002,0,2024-08-07 21:00:00,0.709090909090909,0.5555555555555556,0.7222222222222222,0.5847013960703209,0.0,0.7333333333333334,Video Lesson Programming,0.7437976259548672,0.8836880616022553,0.8571428571428572,2,0,0.065481763895085,0.7222222222222222,0.4324324324324324,1,0.4334073251942286,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1037,0.0,1,2024-11-19 17:45:00,0.1272727272727272,0.8888888888888888,0.8222222222222222,0.0139607032057917,0.5,0.0,Audio Lecture Calculus,0.8698855222250614,0.5246296719978385,0.2857142857142856,1,0,0.4064546505186224,0.8222222222222222,0.1689189189189189,1,0.2669256381798002,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0,1
+STU1112,0.0,0,2024-11-05 19:30:00,0.1181818181818181,0.6666666666666665,0.9888888888888888,0.5681836543433301,0.5,0.1333333333333333,Video Lesson Physics,0.863229162957669,0.9783208901452084,0.5714285714285712,1,0,0.3279375434396915,0.9888888888888888,0.1224662162162162,1,0.0488346281908989,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1,1.0,0.0,1,0
+STU1139,1.0000000000000002,0,2025-05-12 19:15:00,0.6454545454545454,1.0,0.8333333333333334,0.5681836543433301,0.0,0.8666666666666667,Audio Lecture Chemistry,0.3467422872761973,0.0246006528410567,0.5714285714285712,0,0,0.1269098946727184,0.8333333333333333,0.6706081081081082,2,0.2802441731409545,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,1.0,1,0
+STU1024,0.8,1,2024-05-06 18:45:00,0.3727272727272727,0.6666666666666665,0.7777777777777778,0.5681836543433301,1.0,1.0,Interactive Exercise Biology,0.8958564158902083,0.7623066120152141,0.4285714285714284,0,0,0.1422439984895389,0.7777777777777778,0.2880067567567568,0,0.1176470588235294,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,2.0,1,0
+STU1190,0.8,0,2025-02-24 19:00:00,0.809090909090909,0.7777777777777777,0.9888888888888888,0.5847013960703209,1.0,0.3333333333333333,Video Lesson Programming,0.7397095740941637,0.9809790634754756,0.5714285714285712,0,0,0.0795135705620603,0.9888888888888888,0.6554054054054055,2,0.7918978912319644,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1030,0.1999999999999999,1,2025-06-28 15:30:00,0.6545454545454544,0.4444444444444445,0.8666666666666667,0.5912314024085137,1.0,0.6000000000000001,Video Lesson Grammar,0.4898163483934529,0.0896283653779353,0.0,5,1,0.0574733669276692,0.8666666666666667,0.3327702702702703,1,0.3701442841287459,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1129,0.5999999999999999,0,2024-11-16 21:30:00,0.1818181818181818,0.6666666666666665,0.6888888888888889,0.5912314024085137,1.0,0.6000000000000001,Interactive Exercise Vocabulary,0.618762185876628,0.5457748043851429,0.8571428571428572,5,1,0.2490177879190889,0.6888888888888889,0.1638513513513513,0,0.0904550499445061,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,2.0,0,0
+STU1185,1.0000000000000002,0,2024-04-09 15:45:00,0.0818181818181818,0.6666666666666665,0.8555555555555555,0.5681836543433301,0.75,0.0,Audio Lecture Chemistry,0.8701004058743459,0.7327727578802335,0.0,1,0,0.3991433365202236,0.8555555555555555,0.0988175675675675,0,0.7531631520532742,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1036,0.0,1,2025-06-18 22:30:00,0.0,0.2222222222222222,0.8999999999999999,0.0,0.75,0.2666666666666666,Video Lesson Physics,0.780568739243086,0.0744037756165882,1.0,2,0,0.3231057026679597,0.9000000000000001,0.0118243243243243,1,0.0011098779134295,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0,1.0,0.0,0,1
+STU1137,1.0000000000000002,1,2025-05-08 21:30:00,0.6272727272727272,0.3333333333333333,0.8222222222222222,0.0,0.25,0.9333333333333332,Interactive Exercise Ancient Civilizations,0.4761078569851258,0.1083383983410264,0.8571428571428572,3,0,0.0459793288907981,0.8222222222222222,0.2533783783783784,1,0.4953755086940437,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,2.0,1.0,0,1
+STU1034,1.0000000000000002,0,2024-08-04 19:15:00,0.6,0.5555555555555556,0.977777777777778,1.0,0.5,0.2666666666666666,Audio Lecture Literature,0.0221610398186905,0.497156751025593,0.5714285714285712,6,1,0.0774457251149333,0.977777777777778,0.3716216216216216,0,0.750832408435072,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,1
+STU1004,0.1999999999999999,0,2025-04-01 22:15:00,0.9818181818181818,0.5555555555555556,0.8111111111111111,0.625,0.75,0.6000000000000001,Audio Lecture Modern History,0.4122156422994131,0.7469371277223724,1.0,1,0,0.0462177584508725,0.811111111111111,0.5844594594594595,0,0.5929522752497225,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1111,0.1999999999999999,1,2024-10-14 19:15:00,0.0181818181818181,0.3333333333333333,0.9222222222222224,0.5912314024085137,0.5,0.3999999999999999,Interactive Exercise Vocabulary,0.2184673302996833,0.2683455788349927,0.5714285714285712,0,0,0.3601496748599807,0.9222222222222224,0.027027027027027,2,0.2119866814650388,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,0.0,1,0
+STU1179,0.3999999999999999,0,2025-02-16 16:30:00,0.1727272727272727,0.6666666666666665,0.7111111111111111,0.375,0.25,0.4666666666666667,Interactive Exercise Algebra,0.3099497570088037,0.5203153578770995,0.1428571428571428,6,1,0.2579594351767347,0.7111111111111111,0.1579391891891892,0,0.0588235294117647,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,2.0,0,0
+STU1022,0.1999999999999999,0,2025-05-15 15:15:00,0.6818181818181818,0.8888888888888888,0.7999999999999999,0.0,0.5,0.1333333333333333,Audio Lecture Modern History,0.098896608886692,0.8721432131441298,0.0,3,0,0.1073790874161311,0.7999999999999999,0.6326013513513514,0,0.6052534221235665,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,2.0,0,1
+STU1049,0.1999999999999999,0,2024-12-22 21:30:00,0.2636363636363636,0.2222222222222222,0.8555555555555555,0.5912314024085137,1.0,0.8666666666666667,Interactive Exercise Vocabulary,0.2478926713512718,0.7149425802800202,0.8571428571428572,6,1,0.0751960902885887,0.8555555555555555,0.085304054054054,2,0.2175360710321865,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,0.0,1,0
+STU1010,1.0000000000000002,1,2025-06-02 21:00:00,0.8999999999999999,0.6666666666666665,0.9111111111111112,0.5681836543433301,0.5,1.0,Video Lesson Physics,0.1199020962243293,0.8529707467367437,0.8571428571428572,0,0,0.0610791238299345,0.9111111111111112,0.6309121621621622,0,0.2397336293007769,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1,0.0,1.0,1,0
+STU1196,0.3999999999999999,0,2025-03-21 18:45:00,0.4909090909090908,0.8888888888888888,1.0,0.375,1.0,0.2,Video Lesson Algebra,0.7471945889417827,0.4262280404364546,0.4285714285714284,4,0,0.1459892533303274,1.0,0.472972972972973,0,0.1576026637069922,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,2.0,2.0,1,0
+STU1060,0.5999999999999999,0,2025-04-15 21:15:00,0.1272727272727272,0.6666666666666665,0.9111111111111112,0.5847013960703209,0.0,0.5333333333333334,Interactive Exercise Algorithms,0.0838563414768952,0.8772177576113912,0.8571428571428572,1,0,0.3138447300713097,0.9111111111111112,0.1283783783783783,1,0.1664816870144283,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,0
+STU1078,1.0000000000000002,1,2025-04-23 17:30:00,0.6909090909090908,0.5555555555555556,0.7999999999999999,0.0,0.0,0.4666666666666667,Interactive Exercise Vocabulary,0.463388508770417,0.5596722779247028,0.2857142857142856,2,0,0.0672438977208595,0.7999999999999999,0.4222972972972973,1,0.2663706992230854,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,0.0,1.0,0,1
+STU1182,0.5999999999999999,0,2024-04-05 19:30:00,0.9545454545454544,0.4444444444444445,0.8555555555555555,1.0,0.25,0.9333333333333332,Video Lesson World War,0.9279368332894496,0.6973685141024625,0.5714285714285712,4,0,0.0380282294626315,0.8555555555555555,0.4721283783783784,2,0.2386237513873474,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,1.0,0.0,1,1
+STU1102,0.1999999999999999,1,2024-09-13 21:30:00,0.7909090909090908,0.7777777777777777,0.7777777777777778,0.625,0.75,0.8,Audio Lecture Modern History,0.0541301191185069,0.3556697927728817,0.8571428571428572,4,0,0.0813651903791847,0.7777777777777778,0.641891891891892,0,0.4961154273029966,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,1.0,1,0
+STU1162,0.0,1,2025-04-30 22:45:00,0.6272727272727272,0.5555555555555556,0.7999999999999999,1.0,0.25,0.8,Interactive Exercise Biology,0.8852629747698798,0.4603281445721182,1.0,2,0,0.0741139890833395,0.7999999999999999,0.3868243243243243,2,0.4017758046614873,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1,1.0,1.0,1,1
+STU1083,0.3999999999999999,0,2024-04-20 17:00:00,0.5272727272727272,1.0,0.8222222222222222,0.375,1.0,0.0,Interactive Exercise Algebra,0.9719071470554048,0.6561177491926868,0.2857142857142856,5,1,0.1531392842895073,0.8222222222222222,0.5608108108108109,0,0.345541990381058,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,1.0,1,0
+STU1045,0.1999999999999999,0,2025-05-07 16:00:00,0.7636363636363636,0.4444444444444445,0.977777777777778,0.5847013960703209,0.75,0.8666666666666667,Interactive Exercise Algorithms,0.8764148874962948,0.6976298620700548,0.1428571428571428,2,0,0.0488227255190223,0.977777777777778,0.3834459459459459,1,0.5837957824639289,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,1.0,1,0
+STU1132,0.5999999999999999,1,2025-05-13 20:00:00,0.2727272727272727,0.7777777777777777,0.7,0.6549896587383661,1.0,0.1333333333333333,Video Lesson Grammar,0.2017747564177291,0.5702238525564925,0.714285714285714,1,0,0.2119738212836626,0.7,0.2567567567567568,2,0.4384017758046615,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,2.0,0.0,0,1
+STU1062,0.0,0,2025-03-27 18:15:00,0.1545454545454545,0.5555555555555556,0.8555555555555555,0.625,0.75,0.2666666666666666,Interactive Exercise Ancient Civilizations,0.999751898897204,0.6700251659915546,0.4285714285714284,3,0,0.2366697975085482,0.8555555555555555,0.1233108108108108,0,0.4365519792822789,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,2.0,1,0
+STU1093,0.8,0,2025-07-02 22:30:00,0.3909090909090909,0.6666666666666665,0.7888888888888889,0.5681836543433301,1.0,0.2666666666666666,Video Lesson Physics,0.2353119697328168,0.8192941209847273,1.0,2,0,0.1364879989761116,0.788888888888889,0.2998310810810811,2,0.4064927857935627,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,0.0,1,0
+STU1035,0.8,1,2025-05-18 16:15:00,0.4999999999999999,0.8888888888888888,0.7999999999999999,0.5681836543433301,0.75,0.0,Video Lesson Physics,0.5334118199438668,0.7306796134847616,0.1428571428571428,6,1,0.1435849572930918,0.7999999999999999,0.4805743243243243,0,0.3662597114317425,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1061,0.3999999999999999,0,2025-06-24 18:15:00,0.8545454545454545,1.0,0.7,0.5912314024085137,0.5,0.2,Interactive Exercise Vocabulary,0.1737465714942065,0.9561324328176752,0.4285714285714284,1,0,0.0965676124467685,0.7,0.8648648648648649,1,0.4839067702552719,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,2.0,2.0,0,0
+STU1178,0.8,0,2025-03-06 21:15:00,0.5272727272727272,0.4444444444444445,0.977777777777778,0.1742179420889353,0.0,0.0,Video Lesson Algebra,0.671580893639549,0.4349141365957127,0.8571428571428572,3,0,0.0714246463976114,0.977777777777778,0.2736486486486487,2,0.6051054384017758,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,0.0,0,1
+STU1152,0.0,1,2024-05-03 17:15:00,0.5636363636363636,0.7777777777777777,0.9222222222222224,0.5847013960703209,0.25,0.3999999999999999,Video Lesson Programming,0.8009502761050026,0.3595029220529698,0.2857142857142856,4,0,0.1131899058654627,0.9222222222222224,0.472972972972973,2,0.516093229744728,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1130,0.8,1,2025-05-09 19:15:00,0.8181818181818181,0.7777777777777777,0.977777777777778,0.375,0.75,0.6000000000000001,Interactive Exercise Algebra,0.7812548454605317,0.2922597218493973,0.5714285714285712,4,0,0.0786155349504772,0.977777777777778,0.6621621621621622,2,0.4528301886792452,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,0.0,1,0
+STU1039,0.3999999999999999,1,2024-02-23 17:45:00,0.609090909090909,0.2222222222222222,0.8555555555555555,0.625,0.75,0.3333333333333333,Audio Lecture Modern History,0.5784410904302985,0.7305150380584934,0.2857142857142856,4,0,0.0330081543722089,0.8555555555555555,0.1815878378378378,0,0.8718091009988901,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1169,0.0,0,2025-02-14 21:30:00,0.1999999999999999,0.7777777777777777,0.8777777777777778,0.375,0.5,0.6666666666666667,Audio Lecture Calculus,0.7574014153590986,0.0387008588734111,0.8571428571428572,4,0,0.2675397727417134,0.8777777777777778,0.2027027027027027,1,0.1048834628190898,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,0.0,1,0
+STU1163,0.5999999999999999,0,2025-04-29 21:15:00,0.1181818181818181,1.0,0.7666666666666666,0.5912314024085137,0.75,0.0666666666666667,Video Lesson Grammar,0.2149865273803909,0.5515320928153924,0.8571428571428572,1,0,0.4728922012685383,0.7666666666666667,0.1807432432432432,2,0.2608213096559378,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1071,0.1999999999999999,0,2025-01-18 17:15:00,0.8181818181818181,0.2222222222222222,0.8999999999999999,0.5912314024085137,0.25,0.7333333333333334,Interactive Exercise Vocabulary,0.5395308031186095,0.1620419707429955,0.2857142857142856,5,1,0.0230495809225011,0.9000000000000001,0.2398648648648648,0,0.5529966703662597,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,1.0,1,0
+STU1180,0.0,0,2025-04-05 21:30:00,0.4727272727272726,0.4444444444444445,0.8444444444444444,0.5847013960703209,0.0,0.3999999999999999,Video Lesson Programming,0.5998070977990106,0.3671534600087269,0.8571428571428572,5,1,0.0793325144531519,0.8444444444444444,0.2483108108108108,1,0.1176470588235293,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,1.0,1,0
+STU1104,0.8,0,2025-06-16 15:15:00,0.4454545454545454,0.8888888888888888,0.7777777777777778,0.375,0.25,0.4666666666666667,Video Lesson Algebra,0.3941452429342989,0.5381183709118588,0.0,0,0,0.1592332569045487,0.7777777777777778,0.4349662162162162,0,0.1365149833518312,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1134,0.3999999999999999,0,2024-06-29 19:00:00,0.3181818181818181,0.3333333333333333,1.0,0.625,0.25,0.1333333333333333,Interactive Exercise Ancient Civilizations,0.1541879195806367,0.3349567351585688,0.5714285714285712,5,1,0.088493925570314,1.0,0.1385135135135135,0,0.169811320754717,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,1.0,1,0
+STU1086,1.0000000000000002,1,2024-09-13 21:15:00,0.0909090909090909,0.2222222222222222,0.8555555555555555,0.5912314024085137,0.25,0.6000000000000001,Video Lesson Grammar,0.0899127563010276,0.5516249646953622,0.8571428571428572,4,0,0.1564078639217295,0.8555555555555555,0.0371621621621621,1,0.0554938956714761,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1005,0.1999999999999999,0,2024-07-14 17:15:00,0.6636363636363636,0.5555555555555556,0.9222222222222224,0.375,0.0,0.8666666666666667,Interactive Exercise Algebra,0.6575863781694877,0.5850497181319968,0.2857142857142856,6,1,0.0700463274177631,0.9222222222222224,0.4070945945945946,0,0.3144654088050315,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1194,0.5999999999999999,1,2024-12-30 15:15:00,0.8727272727272727,0.4444444444444445,0.7888888888888889,0.5912314024085137,0.75,0.3999999999999999,Video Lesson Grammar,0.3079248658964315,0.7783637005137007,0.0,0,0,0.0421307198825348,0.788888888888889,0.4341216216216216,2,0.3712541620421753,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1177,1.0000000000000002,1,2025-06-04 22:45:00,0.7909090909090908,0.4444444444444445,0.8333333333333334,0.5912314024085137,0.75,0.6666666666666667,Audio Lecture Literature,0.5165302648769683,0.6312857041311745,1.0,2,0,0.0469944971766338,0.8333333333333333,0.3961148648648649,0,0.295227524972253,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,0
+STU1081,0.3999999999999999,1,2024-12-19 22:45:00,0.4999999999999999,0.5555555555555556,0.8777777777777778,0.1207988624612208,0.25,0.8666666666666667,Video Lesson Grammar,0.4074329431073227,0.1453703219947636,1.0,3,0,0.0922933076972997,0.8777777777777778,0.3158783783783784,0,0.3629300776914539,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0,2.0,1.0,0,1
+STU1042,1.0000000000000002,0,2024-10-02 22:00:00,0.7454545454545454,0.3333333333333333,0.9555555555555556,0.0,0.25,0.5333333333333334,Audio Lecture Literature,0.8879926229715808,0.0758193749228285,1.0,2,0,0.0380282293575919,0.9555555555555556,0.2972972972972973,0,0.4001109877913429,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,0.0,2.0,0,1
+STU1114,0.8,0,2025-06-12 22:15:00,0.1636363636363636,0.7777777777777777,0.7111111111111111,0.0,1.0,0.8666666666666667,Video Lesson Programming,0.7663790808151407,0.4695206573089047,1.0,3,0,0.3072297376436429,0.7111111111111111,0.1756756756756756,2,0.0721420643729189,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,0.0,2.0,0,1
+STU1004,0.1999999999999999,0,2023-11-13 15:15:00,0.0454545454545454,0.2222222222222222,0.7111111111111111,0.625,0.25,0.4666666666666667,Video Lesson World War,0.9605329164644928,0.1305403418980681,0.0,0,0,0.211973812022671,0.7111111111111111,0.0244932432432432,0,0.5929522752497225,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,2.0,0,0
+STU1195,0.3999999999999999,0,2025-04-01 16:45:00,0.9909090909090909,0.8888888888888888,0.9333333333333332,0.5912314024085137,0.0,0.0,Video Lesson Grammar,0.2064632042796024,0.0258756522852011,0.1428571428571428,1,0,0.0737593508671026,0.9333333333333332,0.8910472972972974,0,0.6034036256011839,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1128,0.1999999999999999,1,2024-11-08 17:00:00,0.4909090909090908,0.8888888888888888,0.9666666666666666,0.0,1.0,0.6000000000000001,Video Lesson Grammar,0.115165082376858,0.2300868156515395,0.2857142857142856,4,0,0.1459892533303274,0.9666666666666666,0.472972972972973,1,0.1576026637069922,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,0.0,0,1
+STU1073,0.3999999999999999,1,2025-04-15 21:30:00,0.7999999999999999,0.3333333333333333,0.7888888888888889,0.625,0.0,0.5333333333333334,Video Lesson World War,0.5815590307458806,0.1455647876458446,0.8571428571428572,1,0,0.0350699709682756,0.788888888888889,0.3175675675675675,1,0.5396781354051055,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1149,0.8,0,2024-12-26 20:00:00,0.4909090909090908,0.8888888888888888,0.7777777777777778,0.5912314024085137,0.25,0.3999999999999999,Audio Lecture Literature,0.502395279658993,0.8565515269084977,0.714285714285714,3,0,0.1459892533303274,0.7777777777777778,0.472972972972973,0,0.6362375138734739,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1103,0.5999999999999999,0,2025-06-23 19:45:00,0.1363636363636363,0.4444444444444445,0.9888888888888888,0.5681836543433301,0.75,0.1333333333333333,Audio Lecture Chemistry,0.3312744672188638,0.2233126629000416,0.5714285714285712,0,0,0.2119738179497055,0.9888888888888888,0.0920608108108108,1,0.3621901590825009,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,0.0,1,0
+STU1090,1.0000000000000002,0,2025-01-01 22:30:00,0.7636363636363636,0.7777777777777777,0.7777777777777778,1.0,0.0,0.2666666666666666,Audio Lecture Literature,0.924121163158115,0.608683679320681,1.0,2,0,0.0842903557270064,0.7777777777777778,0.6216216216216217,1,0.3736588975212727,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,2.0,1,1
+STU1014,0.8,1,2025-05-01 16:30:00,0.7545454545454545,0.6666666666666665,0.9222222222222224,0.5912314024085137,1.0,0.5333333333333334,Audio Lecture Literature,0.6967344336519914,0.1366916308696201,0.1428571428571428,3,0,0.0733576811730414,0.9222222222222224,0.5363175675675677,1,0.2108768035516093,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,0.0,1,0
+STU1087,0.8,1,2024-12-04 20:45:00,0.1636363636363636,0.8888888888888888,0.9555555555555556,0.5847013960703209,0.0,0.8666666666666667,Video Lesson Programming,0.4755514297496868,0.4458960690279354,0.714285714285714,2,0,0.3469197037858838,0.9555555555555556,0.1993243243243243,2,0.0643729189789123,1.0,1.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,1.0,1,0
+STU1099,0.1999999999999999,1,2025-06-26 15:45:00,0.9454545454545454,0.6666666666666665,0.9333333333333332,1.0,0.5,0.4666666666666667,Video Lesson Grammar,0.5389727679953009,0.9068310888403972,0.0,3,0,0.0579488995010672,0.9333333333333332,0.660472972972973,0,0.2508324084350721,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,0.0,1,1
+STU1106,0.8,0,2024-09-10 18:15:00,0.6818181818181818,0.7777777777777777,0.7555555555555555,1.0,0.0,0.5333333333333334,Interactive Exercise Biology,0.3805373591782654,0.9038559500606316,0.4285714285714284,1,0,0.0943047453149738,0.7555555555555555,0.5608108108108109,0,0.4095449500554938,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,1
+STU1184,0.3999999999999999,0,2025-06-27 17:00:00,0.1545454545454545,0.5555555555555556,0.8444444444444444,0.625,0.25,0.3333333333333333,Interactive Exercise Ancient Civilizations,0.0352295821414593,0.0085139403873748,0.2857142857142856,4,0,0.2366697975085482,0.8444444444444444,0.1233108108108108,0,0.0488346281908989,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,1.0,1.0,1,0
+STU1038,0.0,1,2024-11-02 19:15:00,0.1363636363636363,0.8888888888888888,0.7333333333333333,0.625,0.5,0.2,Interactive Exercise Ancient Civilizations,0.04332716332248,0.633162199718396,0.5714285714285712,5,1,0.3897848655048975,0.7333333333333333,0.1765202702702702,2,0.0643729189789123,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1109,0.3999999999999999,0,2025-06-18 18:15:00,0.209090909090909,0.5555555555555556,0.7555555555555555,0.375,0.25,0.8666666666666667,Video Lesson Algebra,0.4731776123086155,0.7827902660005365,0.4285714285714284,2,0,0.1917680190505113,0.7555555555555555,0.1537162162162162,0,0.0776914539400665,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,2.0,1,0
+STU1160,0.5999999999999999,1,2025-04-23 17:30:00,0.2454545454545454,0.3333333333333333,0.8222222222222222,0.625,0.5,0.1333333333333333,Audio Lecture Modern History,0.2013385801297555,0.2062260742804656,0.2857142857142856,2,0,0.1098526097637762,0.8222222222222222,0.1114864864864864,1,0.0632630410654827,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1066,0.3999999999999999,1,2025-03-15 16:15:00,0.4454545454545454,1.0,0.7,0.625,0.5,0.6000000000000001,Interactive Exercise Ancient Civilizations,0.0520714765706797,0.997306502962102,0.1428571428571428,5,1,0.178069173393308,0.7,0.4847972972972973,1,0.1453940066592675,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,0.0,0,0
+STU1159,0.0,1,2025-04-17 15:00:00,0.3545454545454545,0.6666666666666665,0.7,0.625,1.0,0.3333333333333333,Video Lesson World War,0.4970724698649821,0.6033949399699028,0.0,3,0,0.1484698755046771,0.7,0.2761824324324324,2,0.2835738068812431,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,0.0,0,0
+STU1098,0.1999999999999999,1,2024-11-29 20:15:00,0.0999999999999999,0.2222222222222222,0.8111111111111111,0.0,0.25,0.1333333333333333,Video Lesson Physics,0.9608184809245712,0.0959806308299899,0.714285714285714,4,0,0.1484698711846809,0.811111111111111,0.0396959459459459,2,0.4450610432852386,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0,0.0,0.0,0,1
+STU1101,1.0000000000000002,0,2025-06-22 17:30:00,0.2363636363636363,0.8888888888888888,0.9111111111111112,0.5847013960703209,0.75,0.0,Video Lesson Programming,0.0668025338896292,0.6963024171446645,0.2857142857142856,6,1,0.2675397737064,0.9111111111111112,0.2601351351351352,0,0.0921198668146503,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,0.0,1,0
+STU1148,1.0000000000000002,0,2025-04-13 18:15:00,0.1999999999999999,0.2222222222222222,0.8777777777777778,0.0673797828335063,0.25,0.1333333333333333,Video Lesson Physics,0.5221223618549242,0.0602062893752324,0.4285714285714284,6,1,0.0938961700942147,0.8777777777777778,0.0675675675675675,1,0.0876803551609322,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0,0.0,2.0,0,1
+STU1003,0.8,1,2025-06-20 15:00:00,0.9818181818181818,0.8888888888888888,0.8111111111111111,0.625,0.75,0.1333333333333333,Video Lesson World War,0.320523694056688,0.1477637206859045,0.0,4,0,0.0744716334234509,0.811111111111111,0.883445945945946,1,0.8706992230854607,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1047,0.0,1,2025-05-04 20:30:00,0.6636363636363636,0.6666666666666665,0.8333333333333334,1.0,0.5,0.8,Video Lesson Algebra,0.7510430679411558,0.1276554701954296,0.714285714285714,6,1,0.0834357139031044,0.8333333333333333,0.4771959459459459,0,0.4334073251942286,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,0.0,1,1
+STU1089,0.5999999999999999,0,2024-10-17 20:15:00,0.3727272727272727,0.6666666666666665,0.7999999999999999,0.625,0.0,0.3999999999999999,Video Lesson World War,0.7530187183365914,0.5859029228243289,0.714285714285714,3,0,0.1422439984895389,0.7999999999999999,0.2880067567567568,2,0.336662967073622,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,1.0,1,0
+STU1167,0.0,0,2025-06-15 21:45:00,0.5363636363636363,1.0,0.7111111111111111,1.0,0.75,0.0,Video Lesson World War,0.7893580812552008,0.3811237015088908,0.8571428571428572,6,1,0.1507707440950834,0.7111111111111111,0.5692567567567568,0,0.318534961154273,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,0.0,0,1
+STU1085,0.0,0,2024-10-17 18:45:00,0.509090909090909,0.5555555555555556,0.8444444444444444,1.0,1.0,0.9333333333333332,Video Lesson Grammar,0.8848119967564771,0.7230077728671821,0.4285714285714284,3,0,0.0907390153088559,0.8444444444444444,0.3209459459459459,1,0.2397336293007769,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,1.0,1,1
+STU1189,1.0000000000000002,1,2025-03-02 20:00:00,0.2181818181818181,0.4444444444444445,0.7,0.5847013960703209,0.75,0.5333333333333334,Audio Lecture Data Structures,0.805779872236121,0.5860663676351006,0.714285714285714,6,1,0.1531392818861357,0.7,0.1300675675675675,0,0.1165371809100999,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0,0
+STU1175,0.0,0,2025-06-17 20:45:00,0.5818181818181818,0.5555555555555556,0.9888888888888888,0.5912314024085137,0.0,0.0,Video Lesson Grammar,0.0834336676290356,0.186259618686583,0.714285714285714,1,0,0.0798169606669225,0.9888888888888888,0.3614864864864865,1,0.1498335183129855,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,2.0,1,0
+STU1067,0.3999999999999999,0,2024-12-29 18:15:00,0.609090909090909,0.3333333333333333,0.8777777777777778,0.375,1.0,0.4666666666666667,Interactive Exercise Algebra,0.7713209186249291,0.6592753500528468,0.4285714285714284,6,1,0.04744086966104,0.8777777777777778,0.2466216216216216,1,0.1587125416204217,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1151,0.3999999999999999,1,2025-04-28 17:30:00,0.609090909090909,0.8888888888888888,0.977777777777778,1.0,0.0,0.8,Video Lesson Grammar,0.0051463801049845,0.3219907415819122,0.2857142857142856,0,0,0.1196044461051956,0.977777777777778,0.5717905405405406,2,0.3618201997780245,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,2.0,1,1
+STU1125,1.0000000000000002,0,2025-05-29 16:30:00,0.6909090909090908,0.6666666666666665,0.7222222222222222,0.625,0.5,0.2666666666666666,Audio Lecture Modern History,0.7686346332670541,0.2495534803915156,0.1428571428571428,3,0,0.0801662125900501,0.7222222222222222,0.4949324324324324,0,0.1886792452830188,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1188,0.5999999999999999,1,2024-10-02 22:30:00,0.0545454545454545,0.6666666666666665,0.6666666666666666,0.5681836543433301,0.0,0.0666666666666667,Audio Lecture Chemistry,0.4579452966893717,0.2886455481256465,1.0,2,0,0.4759120807248974,0.6666666666666666,0.081081081081081,0,0.2003329633740288,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0,1.0,1.0,0,0
+STU1012,0.5999999999999999,1,2025-04-25 19:45:00,0.9727272727272726,1.0,0.7666666666666666,0.375,0.75,0.2666666666666666,Interactive Exercise Algebra,0.7282514176574155,0.7639109794034082,0.5714285714285712,4,0,0.0846945454436863,0.7666666666666667,0.9746621621621624,2,0.2774694783573806,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,1.0,1,0
+STU1166,0.1999999999999999,1,2024-02-14 22:30:00,0.7181818181818181,0.8888888888888888,0.6888888888888889,0.375,0.75,0.0,Audio Lecture Calculus,0.6749396270385014,0.3180004377626736,1.0,2,0,0.1020905895469944,0.6888888888888889,0.6630067567567568,2,0.3921568627450981,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0,0.0,1.0,0,0
+STU1018,1.0000000000000002,0,2024-09-11 17:00:00,0.5818181818181818,1.0,0.7888888888888889,1.0,1.0,1.0,Audio Lecture Chemistry,0.4872194626850976,0.0058985050143313,0.2857142857142856,2,0,0.1398882621077272,0.788888888888889,0.6114864864864865,2,0.1853496115427303,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,0.0,1,1
+STU1019,0.3999999999999999,0,2025-04-06 22:45:00,0.9090909090909092,0.7777777777777777,0.8666666666666667,0.5681836543433301,1.0,0.0666666666666667,Video Lesson Physics,0.2091923119954345,0.868276856073035,1.0,6,1,0.0705332144380654,0.8666666666666667,0.7297297297297298,2,0.686644469108398,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,0.0,1,0
+STU1059,0.1999999999999999,1,2024-09-18 19:30:00,0.3545454545454545,0.0,0.1444444444444445,0.5681836543433301,0.0,0.6666666666666667,Interactive Exercise Biology,0.3948864234287349,0.5687780347297534,0.5714285714285712,2,0,0.0123899895055672,0.1444444444444445,0.0278716216216216,1,0.3013318534961154,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0,2.0,2.0,0,0
+STU1029,0.5999999999999999,0,2025-04-02 21:30:00,0.0272727272727272,0.7777777777777777,0.8777777777777778,1.0,0.0,1.0,Audio Lecture Data Structures,0.3560479024967615,0.583860696511781,0.8571428571428572,2,0,0.6735986276974907,0.8777777777777778,0.0743243243243243,0,0.097669256381798,0.0,0.0,1.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,2.0,1,1
+STU1055,0.8,1,2025-04-11 17:45:00,0.7,0.7777777777777777,0.8222222222222222,0.625,0.25,0.3999999999999999,Video Lesson World War,0.3073348536793754,0.7051310433971978,0.2857142857142856,4,0,0.0919002686343527,0.8222222222222222,0.5743243243243243,2,0.4324824269330374,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,1.0,0.0,1,0
+STU1028,0.1999999999999999,1,2025-05-13 16:15:00,0.5363636363636363,0.7777777777777777,0.8111111111111111,0.3344751809720789,0.75,0.6000000000000001,Interactive Exercise Algebra,0.2630913902310068,0.1193146249188182,0.1428571428571428,1,0,0.1185585969772098,0.811111111111111,0.4527027027027027,2,0.5235849056603774,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,1.0,2.0,1,1
+STU1140,0.0,1,2024-09-18 20:00:00,0.5363636363636363,0.3333333333333333,0.8666666666666667,0.5912314024085137,0.0,0.1333333333333333,Video Lesson Grammar,0.7303054770558202,0.1851266429469031,0.714285714285714,2,0,0.0541343027414627,0.8666666666666667,0.2195945945945946,1,0.4961154273029966,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,2.0,0.0,1,0
+STU1032,1.0000000000000002,1,2025-06-05 15:45:00,0.4818181818181818,1.0,0.8666666666666667,0.5847013960703209,0.5,0.1333333333333333,Interactive Exercise Algorithms,0.904528548419776,0.71430965510493,0.0,3,0,0.1661098615267832,0.8666666666666667,0.5185810810810811,2,0.2003329633740288,1.0,1.0,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1165,1.0000000000000002,1,2025-03-25 19:45:00,0.4181818181818181,0.8888888888888888,0.8111111111111111,0.625,0.5,0.5333333333333334,Audio Lecture Modern History,0.1905722150675996,0.281206242727938,0.5714285714285712,1,0,0.1683148593351719,0.811111111111111,0.4121621621621621,2,0.1331853496115427,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,2.0,1,0
+STU1000,0.0,1,2024-09-25 15:30:00,0.4363636363636363,0.4444444444444445,0.6888888888888889,0.5847013960703209,0.25,0.4666666666666667,Audio Lecture Data Structures,0.3478720728004227,0.6738459925422966,0.0,2,0,0.0855133768257188,0.6888888888888889,0.2314189189189189,2,1.0,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,0.0,2.0,0,0
+STU1001,0.1999999999999999,1,2025-06-10 19:30:00,0.1454545454545454,0.6666666666666665,0.977777777777778,0.5847013960703209,1.0,0.1333333333333333,Audio Lecture Data Structures,0.0810566746333176,0.3464105490123374,0.5714285714285712,1,0,0.2889112909098579,0.977777777777778,0.1402027027027027,0,0.1361450240473548,0.0,0.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1064,0.1999999999999999,0,2025-01-18 19:45:00,0.7818181818181817,0.7777777777777777,1.0,0.625,0.0,0.7333333333333334,Interactive Exercise Ancient Civilizations,0.6105559126258934,0.6520067508910071,0.5714285714285712,5,1,0.0823199318470881,1.0,0.6351351351351352,2,0.7142064372918979,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,1.0,1,0
+STU1051,0.5999999999999999,0,2024-04-23 20:00:00,0.409090909090909,0.4444444444444445,0.9111111111111112,0.625,0.25,1.0,Video Lesson World War,0.3151205368639675,0.5581962140132246,0.714285714285714,1,0,0.0907390150027074,0.9111111111111112,0.21875,2,0.1759156492785794,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,0.0,1,0
+STU1013,0.3999999999999999,0,2025-01-16 19:30:00,0.9636363636363636,0.1111111111111111,0.0333333333333333,0.5681836543433301,0.0,0.7333333333333334,Interactive Exercise Biology,0.6742409023430315,0.980171375427846,0.5714285714285712,3,0,0.0088706823348944,0.0333333333333333,0.1824324324324324,0,0.413984461709212,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0,0
+STU1002,0.0,1,2025-05-10 15:00:00,0.2818181818181817,0.4444444444444445,0.9444444444444444,0.5681836543433301,1.0,0.3999999999999999,Audio Lecture Chemistry,0.2137526852826897,0.8698569933581745,0.0,5,1,0.1252367236968581,0.9444444444444444,0.1596283783783784,1,0.5952645209027007,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,1.0,1,0
+STU1133,0.0,1,2024-11-28 22:15:00,0.5272727272727272,0.2222222222222222,0.7222222222222222,0.0139607032057917,0.75,0.2,Video Lesson Programming,0.871018523976302,0.8339837777883972,1.0,3,0,0.038738791240853,0.7222222222222222,0.1587837837837838,2,0.3226045135035146,1.0,1.0,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,2.0,2.0,0,1
+STU1053,0.1999999999999999,1,2024-12-01 17:00:00,0.5272727272727272,0.7777777777777777,0.7111111111111111,0.3878942605997935,0.25,0.4666666666666667,Interactive Exercise Algorithms,0.8122981896655268,0.0912777811070014,0.2857142857142856,6,1,0.120453429132749,0.7111111111111111,0.4459459459459459,1,0.3046614872364039,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,2.0,1.0,0,1
+STU1097,0.8,0,2025-02-20 16:00:00,0.8909090909090909,0.7777777777777777,0.8111111111111111,0.375,0.25,0.0666666666666667,Interactive Exercise Algebra,0.0234309095259943,0.6387940621461871,0.1428571428571428,3,0,0.0720299404599911,0.811111111111111,0.7162162162162163,0,0.517573066962634,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,1.0,0.0,1,0
+STU1075,0.3999999999999999,1,2025-06-29 16:45:00,0.4272727272727272,0.4444444444444445,0.6666666666666666,0.625,0.0,0.8,Interactive Exercise Ancient Civilizations,0.522227639715337,0.6764842932711569,0.1428571428571428,6,1,0.0871941376439511,0.6666666666666666,0.2271959459459459,2,0.1065482796892341,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,1.0,2.0,0,0
+STU1041,0.5999999999999999,1,2025-01-29 15:45:00,0.6181818181818182,0.5555555555555556,0.7111111111111111,0.625,0.5,0.2666666666666666,Interactive Exercise Ancient Civilizations,0.6777435845094765,0.9143755920941278,0.0,2,0,0.0751960913845641,0.7111111111111111,0.3817567567567568,0,0.1653718091009988,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,1.0,0.0,0,0
+STU1015,0.5999999999999999,1,2025-02-09 19:00:00,0.7545454545454545,0.7777777777777777,0.9111111111111112,1.0,0.75,0.6000000000000001,Video Lesson Grammar,0.4043075826362853,0.5023096150736766,0.5714285714285712,6,1,0.0853073486969451,0.9111111111111112,0.6148648648648649,0,0.2130965593784683,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,0.0,0.0,1,1
+STU1088,0.0,1,2024-06-15 19:00:00,0.8181818181818181,0.2222222222222222,0.8666666666666667,0.625,0.5,0.2,Audio Lecture Modern History,0.5591290975887796,0.1919013514636648,0.5714285714285712,5,1,0.0230495809225011,0.8666666666666667,0.2398648648648648,1,0.279134295227525,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1123,0.5999999999999999,0,2025-01-09 22:45:00,0.0181818181818181,0.4444444444444445,0.7555555555555555,0.375,0.0,0.7333333333333334,Interactive Exercise Algebra,0.5830720436996023,0.4880820685544728,1.0,3,0,0.4527595914485469,0.7555555555555555,0.0371621621621621,1,0.1498335183129855,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,2.0,1,0
+STU1076,0.3999999999999999,1,2024-09-23 15:45:00,0.5727272727272726,0.8888888888888888,0.8777777777777778,0.5681836543433301,0.5,0.0666666666666667,Video Lesson Physics,0.5904186100356311,0.1276075351246515,0.0,0,0,0.1267219494378041,0.8777777777777778,0.5413851351351352,1,0.3007769145394007,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1,0.0,1.0,1,0
+STU1021,0.8,1,2025-05-17 18:45:00,0.4545454545454545,1.0,0.9333333333333332,0.5847013960703209,1.0,0.2666666666666666,Interactive Exercise Algorithms,1.0,0.4825401966746634,0.4285714285714284,5,1,0.1749298540308367,0.9333333333333332,0.4932432432432433,1,0.3551609322974473,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1181,0.3999999999999999,1,2025-04-15 17:00:00,0.509090909090909,0.6666666666666665,0.8333333333333334,0.625,0.75,0.7333333333333334,Video Lesson World War,0.4625231648447067,0.4155012111996172,0.2857142857142856,1,0,0.1075771831093793,0.8333333333333333,0.3766891891891892,2,0.1664816870144284,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,2.0,1,0
+STU1079,0.1999999999999999,0,2025-07-02 21:30:00,0.6818181818181818,0.2222222222222222,0.8333333333333334,0.5912314024085137,0.0,0.6666666666666667,Audio Lecture Literature,0.422777903710642,0.6827103850869911,0.8571428571428572,2,0,0.0289330348091874,0.8333333333333333,0.2018581081081081,0,0.1576026637069922,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,0
+STU1127,0.1999999999999999,1,2025-06-27 22:45:00,0.7,0.4444444444444445,0.8666666666666667,0.625,1.0,0.5333333333333334,Video Lesson World War,0.6983410253802127,0.5887288090872417,1.0,4,0,0.0535789210861138,0.8666666666666667,0.3538851351351352,2,0.2859785423603403,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,0.0,0.0,1,0
+STU1026,0.1999999999999999,0,2025-07-02 15:15:00,0.4,0.0,0.1777777777777778,0.5681836543433301,0.5,0.2,Video Lesson Physics,0.3334970854332688,0.4290394711353563,0.0,2,0,0.010289991303729,0.1777777777777778,0.0320945945945946,1,0.4080651128375879,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,0.0,0,0
+STU1113,0.5999999999999999,0,2024-12-31 22:00:00,0.6636363636363636,0.8888888888888888,1.0,0.625,0.0,0.6666666666666667,Video Lesson World War,0.1794776773869556,0.384687359011765,1.0,1,0,0.1102144868737869,1.0,0.6173986486486487,1,0.5493895671476138,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,1.0,1.0,1,0
+STU1070,0.0,1,2025-06-04 21:30:00,0.6272727272727272,0.5555555555555556,0.9444444444444444,0.375,0.5,0.0666666666666667,Audio Lecture Calculus,0.9473182581745272,0.6746167030994392,0.8571428571428572,2,0,0.0741139890833395,0.9444444444444444,0.3868243243243243,0,0.3144654088050315,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1170,0.5999999999999999,1,2024-05-29 21:45:00,0.6363636363636364,0.3333333333333333,0.9555555555555556,0.0,0.5,0.4666666666666667,Video Lesson Programming,0.0533985705637151,0.6834946702494549,0.8571428571428572,2,0,0.0452759623947767,0.9555555555555556,0.2567567567567568,2,0.2336293007769145,1.0,1.0,0.0,1.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,0.0,2.0,0,1
+STU1116,0.8,1,2023-12-29 15:15:00,0.1727272727272727,0.3333333333333333,0.7666666666666666,0.5847013960703209,0.75,0.1333333333333333,Video Lesson Programming,0.3870150861677275,0.4620268990245569,0.0,4,0,0.1429953951748693,0.7666666666666667,0.0844594594594594,1,0.1409544950055493,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1009,0.0,0,2025-02-25 22:45:00,0.209090909090909,1.0,0.9111111111111112,0.625,0.5,0.1333333333333333,Audio Lecture Modern History,0.4235003797716757,0.4303233547082236,1.0,1,0,0.3264733594137085,0.9111111111111112,0.2652027027027027,1,0.1148723640399556,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,1.0,2.0,1,0
+STU1191,0.1999999999999999,1,2025-01-23 21:15:00,0.9727272727272726,1.0,0.7555555555555555,0.5681836543433301,0.25,0.0,Video Lesson Physics,0.4047094298661965,0.3751864629481443,0.8571428571428572,3,0,0.0846945454436863,0.7555555555555555,0.9746621621621624,0,0.4750277469478356,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,0.0,1,0
+STU1138,0.8,0,2024-07-09 21:00:00,0.7999999999999999,0.5555555555555556,0.9333333333333332,0.375,1.0,1.0,Interactive Exercise Algebra,0.9615527079247134,0.2352578875846451,0.8571428571428572,1,0,0.0577499521995556,0.9333333333333332,0.4831081081081081,0,0.6267110617832038,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,1.0,1,0
+STU1095,0.5999999999999999,1,2024-07-31 16:30:00,0.109090909090909,0.3333333333333333,0.6888888888888889,0.5847013960703209,0.25,0.6666666666666667,Video Lesson Programming,0.6755493167680068,0.2495566811782675,0.1428571428571428,2,0,0.1917680159890264,0.6888888888888889,0.0608108108108108,1,0.6356825749167592,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,1.0,0.0,0,0
+STU1001,0.1999999999999999,1,2025-04-21 19:15:00,0.9545454545454544,1.0,1.0,0.5847013960703209,0.5,0.2,Interactive Exercise Algorithms,0.9649903744414844,0.1401792249302386,0.5714285714285712,0,0,0.0863464504195475,1.0,0.9577702702702704,1,0.4779874213836478,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1176,0.8,1,2025-06-25 17:45:00,0.918181818181818,0.8888888888888888,0.8888888888888888,0.5847013960703209,0.25,0.4666666666666667,Audio Lecture Data Structures,0.5208156799446504,0.4969171641378953,0.2857142857142856,2,0,0.0798169610728097,0.8888888888888888,0.8302364864864865,2,0.605623381428043,1.0,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1020,0.8,1,2025-02-16 22:15:00,0.1181818181818181,0.4444444444444445,0.7666666666666666,0.4947324198552225,1.0,0.8,Interactive Exercise Biology,0.439314066195212,0.4303324328505092,1.0,6,1,0.2313011048871269,0.7666666666666667,0.0836148648648648,1,0.1048834628190898,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,0.0,1,1
+STU1052,1.0000000000000002,0,2024-04-21 16:00:00,0.1999999999999999,0.2222222222222222,0.7333333333333333,0.0,0.0,0.8,Audio Lecture Calculus,0.4892865365932068,0.62388281365405,0.1428571428571428,6,1,0.0938961700942147,0.7333333333333333,0.0675675675675675,2,0.2708102108768035,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,2.0,0.0,0,1
+STU1174,0.3999999999999999,0,2024-04-02 16:15:00,0.5272727272727272,0.8888888888888888,0.9222222222222224,0.5847013960703209,0.75,0.0666666666666667,Interactive Exercise Algorithms,0.3081547237951205,0.7772314840074368,0.1428571428571428,1,0,0.1367963567111281,0.9222222222222224,0.5033783783783784,2,0.3285238623751387,1.0,1.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,0.0,1.0,1,0
+STU1107,0.1999999999999999,0,2025-05-09 21:30:00,0.0999999999999999,0.3333333333333333,0.6888888888888889,0.375,0.25,0.0666666666666667,Video Lesson Algebra,0.7442442619766683,0.5540012960382985,0.8571428571428572,4,0,0.2013898254110027,0.6888888888888889,0.0574324324324324,0,0.0244173140954495,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,1.0,1.0,0,0
+STU1131,0.0,0,2025-06-27 20:30:00,0.2909090909090909,0.2222222222222222,0.7,0.5847013960703209,0.5,0.5333333333333334,Video Lesson Programming,0.4429204525630629,0.0165636411853334,0.714285714285714,4,0,0.0690899417351965,0.7,0.0929054054054054,0,0.5455049944506104,0.0,0.0,1.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0,2.0,2.0,0,0
+STU1027,0.8,0,2023-10-25 15:45:00,0.2636363636363636,0.7777777777777777,0.8888888888888888,0.5681836543433301,0.5,0.0,Audio Lecture Chemistry,0.3749982024243721,0.7381107329569581,0.0,2,0,0.217672893260044,0.8888888888888888,0.25,2,0.4827968923418424,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1,2.0,1.0,1,0
+STU1136,0.5999999999999999,0,2024-08-03 20:45:00,0.6363636363636364,0.5555555555555556,1.0,0.0,0.0,0.8,Interactive Exercise Biology,0.0390877272021476,0.282656298860882,0.714285714285714,5,1,0.0730589393393073,1.0,0.3918918918918919,0,0.2386237513873474,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0,0.0,2.0,0,1
+STU1197,0.5999999999999999,0,2024-06-21 15:15:00,0.7454545454545454,0.7777777777777777,0.8777777777777778,0.5681836543433301,0.0,0.7333333333333334,Interactive Exercise Biology,0.6916417895675577,0.0173519302820779,0.0,4,0,0.0863464502094683,0.8777777777777778,0.6081081081081082,0,0.6248612652608213,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,2.0,1,0
+STU1003,0.8,1,2025-06-19 16:45:00,0.8999999999999999,0.7777777777777777,0.8444444444444444,0.375,1.0,0.4666666666666667,Audio Lecture Calculus,0.0401738067011121,0.7833008583000046,0.1428571428571428,3,0,0.0712747117333943,0.8444444444444444,0.722972972972973,1,0.8706992230854607,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,0.0,1,0
+STU1122,0.0,0,2024-07-03 15:00:00,0.3454545454545454,0.3333333333333333,0.7555555555555555,0.375,0.75,0.5333333333333334,Audio Lecture Calculus,0.1678776395202608,0.1371824991949564,0.0,2,0,0.0823199308824015,0.7555555555555555,0.1486486486486486,0,0.2967073621901591,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1023,0.5999999999999999,0,2025-01-22 20:00:00,1.0,0.3333333333333333,0.9333333333333332,0.625,1.0,0.7333333333333334,Audio Lecture Modern History,0.6103308603130055,0.4278807889936094,0.714285714285714,2,0,0.0267539779194395,0.9333333333333332,0.3918918918918919,0,0.2541620421753606,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1008,0.0,1,2025-04-20 21:00:00,0.3272727272727272,0.5555555555555556,0.7666666666666666,0.2276370217166499,1.0,0.9333333333333332,Video Lesson Physics,0.1080548242106633,0.905344410394646,0.8571428571428572,6,1,0.1346646694857505,0.7666666666666667,0.2195945945945946,1,0.2330743618201997,0.6538461538461533,0.0706596681502631,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1,2.0,0.0,1,1
+STU1044,0.5999999999999999,1,2025-06-30 16:45:00,0.5545454545454545,1.0,0.7333333333333333,0.5912314024085137,0.75,0.0,Video Lesson Grammar,0.03995183183426,0.5733476355000872,0.1428571428571428,0,0,0.1462338220297057,0.7333333333333333,0.5861486486486487,1,0.1753607103218646,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,0.0,2.0,1,0
+STU1117,0.1999999999999999,0,2024-07-13 16:00:00,0.8454545454545453,0.3333333333333333,0.9888888888888888,0.5847013960703209,0.75,0.3999999999999999,Interactive Exercise Algorithms,0.3271705195184051,0.3168861230398674,0.1428571428571428,5,1,0.0328680310642866,0.9888888888888888,0.3344594594594594,0,0.2502774694783574,0.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,2.0,0.0,1,0
+STU1172,0.3999999999999999,0,2023-12-11 21:15:00,0.6636363636363636,0.2222222222222222,0.8999999999999999,1.0,0.5,1.0,Video Lesson World War,0.8031124435119377,0.0237170783603136,0.8571428571428572,0,0,0.0298781679617393,0.9000000000000001,0.1967905405405405,0,0.1598224195338512,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,1,2.0,1.0,1,1
+STU1115,0.0,0,2025-05-14 17:15:00,0.6,0.7777777777777777,0.8222222222222222,0.5681836543433301,0.5,0.7333333333333334,Audio Lecture Chemistry,0.8574346061979541,0.4306179469511907,0.2857142857142856,2,0,0.1066909639846726,0.8222222222222222,0.5000000000000001,2,0.3523862375138734,1.0,1.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1,1.0,0.0,1,0
+STU1135,0.8,1,2024-10-16 21:00:00,0.5363636363636363,0.7777777777777777,0.8999999999999999,1.0,0.25,1.0,Interactive Exercise Vocabulary,0.6010749217962007,0.6871984162963782,0.8571428571428572,2,0,0.1185585969772098,0.9000000000000001,0.4527027027027027,2,0.3290788013318535,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1,1.0,2.0,1,1
+STU1142,0.0,1,2024-10-26 21:00:00,0.5909090909090908,0.4444444444444445,0.9888888888888888,0.5912314024085137,1.0,0.3999999999999999,Audio Lecture Literature,0.7527334400386756,0.1293020152495637,0.8571428571428572,5,1,0.0637979469627238,0.9888888888888888,0.3032094594594594,1,0.2924528301886793,0.6538461538461533,0.0706596681502631,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1147,0.0,1,2024-12-26 19:00:00,0.4272727272727272,0.7777777777777777,0.9111111111111112,1.0,1.0,0.6000000000000001,Interactive Exercise Biology,0.7137392075691249,0.6732410565500274,0.5714285714285712,3,0,0.1456846151268925,0.9111111111111112,0.3716216216216216,2,0.3296337402885683,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1,0.0,2.0,1,1
+STU1192,0.5999999999999999,0,2025-05-16 22:30:00,0.6,0.3333333333333333,0.7111111111111111,1.0,1.0,0.0666666666666667,Interactive Exercise Algorithms,0.3781868637440948,0.039703565386513,1.0,4,0,0.0482004862451941,0.7111111111111111,0.2432432432432432,1,0.4298927118017018,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,2.0,1.0,0,1
+STU1186,0.1999999999999999,0,2025-01-25 20:30:00,0.8636363636363635,0.2222222222222222,0.7777777777777778,0.5912314024085137,0.25,0.2,Audio Lecture Literature,0.5200670883657244,0.7661816669415945,0.714285714285714,5,1,0.0214619822511075,0.7777777777777778,0.2525337837837837,2,0.2053274139844617,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1,1.0,0.0,1,0
+STU1063,0.3999999999999999,0,2024-11-09 19:30:00,0.7272727272727272,1.0,0.8666666666666667,0.0,0.5,0.0,Interactive Exercise Algebra,0.4841074717903316,0.1262294105178091,0.5714285714285712,5,1,0.1131899062084624,0.8666666666666667,0.7466216216216217,1,0.4002959674435812,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0,1.0,2.0,0,1
+STU1092,0.1999999999999999,0,2024-10-26 19:30:00,0.5545454545454545,1.0,0.7222222222222222,0.0,0.25,0.6000000000000001,Audio Lecture Chemistry,0.4349141129273994,0.959994250875958,0.5714285714285712,5,1,0.1462338220297057,0.7222222222222222,0.5861486486486487,1,0.3607103218645948,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0,0.0,1.0,0,1
+STU1145,0.0,0,2024-12-10 20:45:00,0.8818181818181817,0.2222222222222222,0.9333333333333332,0.5681836543433301,0.75,0.4666666666666667,Interactive Exercise Biology,0.9230334433911632,0.1311104101379464,0.714285714285714,1,0,0.0208684874202917,0.9333333333333332,0.2576013513513513,1,0.4062153163152053,0.6538461538461533,0.0706596681502631,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,1,2.0,0.0,1,0
+STU1016,0.0,0,2025-06-29 15:30:00,0.7818181818181817,0.8888888888888888,0.8555555555555555,0.5681836543433301,0.25,0.9333333333333332,Interactive Exercise Biology,0.1525728664292559,0.1879510666780116,0.0,6,1,0.0938961722647597,0.8555555555555555,0.7162162162162163,2,0.218645948945616,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,1,2.0,0.0,1,0
+STU1031,1.0000000000000002,1,2025-06-19 18:15:00,0.0818181818181818,0.2222222222222222,0.7666666666666666,0.0139607032057917,0.0,0.6666666666666667,Video Lesson World War,0.8895972341592258,0.1603471402706822,0.4285714285714284,3,0,0.1651814347976476,0.7666666666666667,0.0346283783783783,1,0.4705882352941176,0.6538461538461533,0.0706596681502631,0.0,1.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,2.0,2.0,0,1
+STU1074,0.1999999999999999,0,2024-10-15 15:15:00,0.0363636363636363,0.2222222222222222,0.8333333333333334,0.0139607032057917,0.0,0.6000000000000001,Audio Lecture Modern History,0.0068908470202531,0.09588092287902,0.0,1,0,0.2278497968541689,0.8333333333333333,0.0219594594594594,0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0,2.0,2.0,0,1
+STU1054,1.0000000000000002,1,2025-05-28 21:45:00,0.4,0.5555555555555556,0.7777777777777778,0.375,0.0,0.6000000000000001,Video Lesson Algebra,0.9898913882044906,0.6161254638621769,0.8571428571428572,2,0,0.1131899052937965,0.7777777777777778,0.2601351351351352,2,0.3677395486496485,1.0,1.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1,2.0,1.0,1,0
+STU1154,0.0,1,2025-06-10 16:00:00,0.8272727272727272,0.6666666666666665,0.7111111111111111,0.5681836543433301,0.75,0.8666666666666667,Audio Lecture Chemistry,0.9865319606859174,0.0823308380383001,0.1428571428571428,1,0,0.0667321230075657,0.7111111111111111,0.5836148648648649,0,0.2253052164261931,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,0.0,0,0
+STU1058,0.8,0,2024-10-14 16:30:00,0.609090909090909,0.8888888888888888,0.8444444444444444,0.1207988624612208,0.0,0.2,Audio Lecture Modern History,0.7304846441584761,0.6288960084271715,0.1428571428571428,0,0,0.1196044461051956,0.8444444444444444,0.5717905405405406,2,0.2830188679245283,1.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0,0.0,2.0,0,1
+STU1069,0.0,0,2024-12-30 22:00:00,0.5363636363636363,0.1111111111111111,0.1333333333333333,0.5912314024085137,0.5,0.9333333333333332,Audio Lecture Literature,0.6336044442184678,0.8605347717073197,1.0,0,0,0.0219221556235891,0.1333333333333333,0.1030405405405405,2,0.2175360710321865,1.0,1.0,1.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0,0.0,2.0,0,0
+STU1199,0.1999999999999999,0,2024-11-27 20:45:00,0.5363636363636363,0.3333333333333333,0.8222222222222222,0.0,0.0,0.1333333333333333,Audio Lecture Chemistry,0.1608818190469297,0.3794942485083676,0.714285714285714,2,0,0.0541343027414627,0.8222222222222222,0.2195945945945946,2,0.1659267480577136,1.0,1.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,1.0,0.0,0.0,0.0,0.0,0.0,0,2.0,2.0,0,1
+STU1065,1.0000000000000002,1,2025-01-08 21:30:00,0.1818181818181818,0.0,0.1222222222222222,0.625,0.75,0.6666666666666667,Video Lesson World War,0.256283138003569,0.812904722734963,0.8571428571428572,2,0,0.0267539769933403,0.1222222222222222,0.0118243243243243,0,0.0344062153163152,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0,2.0,2.0,0,0
+STU1161,0.1999999999999999,1,2025-01-17 19:45:00,0.6181818181818182,0.1111111111111111,0.2,0.5912314024085137,0.0,0.2,Interactive Exercise Vocabulary,0.8241693154110818,0.8842026612593801,0.5714285714285712,4,0,0.0182053694653317,0.2,0.1182432432432432,2,0.2286348501664817,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0,1.0,2.0,0,0
+STU1105,0.5999999999999999,0,2025-03-25 16:30:00,0.1727272727272727,0.8888888888888888,0.7444444444444445,0.0,0.75,0.6000000000000001,Audio Lecture Chemistry,0.5961434098321164,0.1719413964133947,0.1428571428571428,1,0,0.3346021285113116,0.7444444444444445,0.2069256756756756,2,0.4554199038105808,1.0,1.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0,0.0,0.0,0,1
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Rekomendasi Materi Belajar/edtech/backend/src/models/recommenders/collaborative/collab_model.joblib

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+oid sha256:c4aef73c6272415cb11002c1ff5c96f65587498acaa7c86ad4f7167d1d73fe48
+size 6080

Rekomendasi Materi Belajar/edtech/backend/src/models/recommenders/content_based/content_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:63d1a2f5acb72fa4e6c3825586d578da46d850c31d82883ef50f618789722977
+size 5211833

Rekomendasi Materi Belajar/edtech/backend/src/models/recommenders/hybrid/hybrid_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d173427052471e467df306ab61013e0599cfb0a80ff3805e464f9b7a25166933
+size 32

Rekomendasi Materi Belajar/edtech/backend/src/recommendation/collaborative.py

@@ -0,0 +1,144 @@
+# backend/src/recommendation/collaborative.py
+import numpy as np
+import pandas as pd
+from scipy.sparse.linalg import svds
+from sklearn.metrics.pairwise import cosine_similarity
+import joblib
+from pathlib import Path
+from scipy.sparse import csr_matrix
+
+class CollaborativeFiltering:
+    def __init__(self, n_factors=50, n_recommendations=5):
+        self.n_factors = n_factors
+        self.n_recommendations = n_recommendations
+        self.user_item_matrix = None
+        self.user_factors = None
+        self.item_factors = None
+        self.user_ids = None
+        self.item_ids = None
+    
+    def fit(self, user_item_matrix):
+        self.user_item_matrix = user_item_matrix
+        self.user_ids = user_item_matrix.index
+        self.item_ids = user_item_matrix.columns
+        
+        # Normalisasi dengan subtract mean
+        user_means = user_item_matrix.mean(axis=1)
+        normalized_matrix = user_item_matrix.sub(user_means, axis=0).fillna(0)
+        
+        # Convert the matrix to sparse format (CSR format)
+        sparse_matrix = csr_matrix(normalized_matrix.values)
+        
+        # Tentukan nilai k secara dinamis untuk dataset kecil
+        min_dim = min(sparse_matrix.shape)
+        k = min(self.n_factors, min_dim - 1) if min_dim > 1 else 1
+        
+        # Jika dimensi terlalu kecil, gunakan similarity dasar
+        if k < 1:
+            print("Matriks terlalu kecil, menggunakan similarity dasar")
+            self.similarity_matrix = cosine_similarity(normalized_matrix.T)
+            return
+        
+        print(f"Menentukan k = {k} berdasarkan dimensi matriks: {sparse_matrix.shape}")
+        
+        try:
+            # Melakukan SVD dengan penanganan khusus untuk matriks kecil
+            U, sigma, Vt = svds(sparse_matrix, k=k)
+            
+            # Mengubah sigma menjadi matriks diagonal
+            sigma = np.diag(sigma)
+            
+            # Membuat user dan item factors
+            self.user_factors = U
+            self.item_factors = sigma @ Vt
+        except Exception as e:
+            print(f"Error dalam SVD: {str(e)} - menggunakan similarity dasar")
+            self.similarity_matrix = cosine_similarity(normalized_matrix.T)
+
+    def recommend_for_user(self, user_id, user_item_matrix=None):
+        if user_item_matrix is not None:
+            self.user_item_matrix = user_item_matrix
+        
+        # Handle jika user_id tidak ada di data training
+        if user_id not in self.user_ids:
+            print(f"User ID {user_id} tidak ditemukan di model")
+            # Fallback: return popular items
+            item_counts = (self.user_item_matrix > 0).sum()
+            top_items = item_counts.sort_values(ascending=False).head(self.n_recommendations).index
+            return [(item, 0.5) for item in top_items]
+        
+        try:
+            # Jika menggunakan similarity dasar
+            if hasattr(self, 'similarity_matrix'):
+                user_idx = np.where(self.user_ids == user_id)[0][0]
+                user_ratings = self.user_item_matrix.iloc[user_idx].values
+                unseen_mask = user_ratings == 0
+                item_scores = self.similarity_matrix.dot(user_ratings)
+                item_scores[~unseen_mask] = -np.inf  # Filter yang sudah dilihat
+                top_indices = np.argsort(-item_scores)[:self.n_recommendations]
+                return [(self.item_ids[i], item_scores[i]) for i in top_indices if item_scores[i] > 0]
+            
+            # Jika menggunakan SVD
+            user_idx = np.where(self.user_ids == user_id)[0][0]
+            user_ratings = self.user_factors[user_idx, :] @ self.item_factors
+            
+            # Dapatkan item yang belum dilihat user
+            known_items = self.user_item_matrix.loc[user_id]
+            unseen_items_idx = np.where(known_items == 0)[0]
+            
+            # Jika tidak ada item yang belum dilihat, kembalikan popular items
+            if len(unseen_items_idx) == 0:
+                item_counts = (self.user_item_matrix > 0).sum()
+                top_items = item_counts.sort_values(ascending=False).head(self.n_recommendations).index
+                return [(item, 0.5) for item in top_items]
+            
+            # Urutkan item yang belum dilihat berdasarkan prediksi rating
+            unseen_ratings = user_ratings[unseen_items_idx]
+            recommended_idx = np.argsort(-unseen_ratings)[:self.n_recommendations]
+            
+            # Buat rekomendasi
+            recommendations = []
+            for idx in recommended_idx:
+                item_id = self.item_ids[unseen_items_idx[idx]]
+                score = unseen_ratings[idx]
+                recommendations.append((item_id, score))
+            
+            return recommendations
+        except Exception as e:
+            print(f"Error dalam rekomendasi untuk user {user_id}: {str(e)}")
+            # Fallback: return popular items
+            item_counts = (self.user_item_matrix > 0).sum()
+            top_items = item_counts.sort_values(ascending=False).head(self.n_recommendations).index
+            return [(item, 0.5) for item in top_items]
+        
+    def save_model(self, save_path='models/recommenders/collaborative'):
+        """
+        Menyimpan model yang sudah dilatih
+        """
+        Path(save_path).mkdir(parents=True, exist_ok=True)
+        
+        model_data = {
+            'user_factors': self.user_factors,
+            'item_factors': self.item_factors,
+            'user_ids': self.user_ids,
+            'item_ids': self.item_ids,
+            'n_factors': self.n_factors
+        }
+        
+        joblib.dump(model_data, f'{save_path}/collab_model.joblib')
+        print("Model Collaborative Filtering berhasil disimpan!")
+    
+    @classmethod
+    def load_model(cls, load_path='models/recommenders/collaborative/collab_model.joblib'):
+        """
+        Memuat model yang sudah disimpan
+        """
+        model_data = joblib.load(load_path)
+        
+        model = cls(n_factors=model_data['n_factors'])
+        model.user_factors = model_data['user_factors']
+        model.item_factors = model_data['item_factors']
+        model.user_ids = model_data['user_ids']
+        model.item_ids = model_data['item_ids']
+        
+        return model

Rekomendasi Materi Belajar/edtech/backend/src/recommendation/content_based.py

@@ -0,0 +1,198 @@
+# backend/src/recommendation/content_based.py
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import linear_kernel
+import joblib
+from pathlib import Path
+import numpy as np
+import pandas as pd
+
+class ContentBasedRecommender:
+    def __init__(self, n_recommendations=5):
+        self.n_recommendations = n_recommendations
+        self.tfidf_vectorizer = None
+        self.tfidf_matrix = None
+        self.material_features = None
+        self.material_ids = None
+    
+    def fit(self, df):
+        # Gabungkan fitur teks materi dengan lebih banyak fitur untuk dataset kecil
+        df['material_features'] = (
+            df['related_materials'].fillna('') + " " + 
+            df['subject_English'].astype(str) + " " +
+            df['subject_History'].astype(str) + " " +
+            df['subject_Mathematics'].astype(str) + " " +
+            df['subject_Science'].astype(str) + " " +
+            df['material_type_encoded'].astype(str) + " " +
+            df['preferensi_materi'].fillna('').astype(str) + " " +
+            df['performance_label_encoded'].astype(str)
+        )
+
+        # Simpan mapping material_id untuk referensi
+        self.material_ids = df['material_type_encoded'].unique()
+        
+        # Inisialisasi TF-IDF Vectorizer dengan parameter untuk data kecil
+        self.tfidf_vectorizer = TfidfVectorizer(
+            stop_words='english',
+            min_df=1,  # Term muncul di minimal 1 dokumen
+            max_df=0.95,  # Term muncul di maksimal 95% dokumen
+            max_features=1000  # Batasi jumlah fitur
+        )
+        
+        try:
+            self.tfidf_matrix = self.tfidf_vectorizer.fit_transform(df['material_features'])
+            self.cosine_sim = linear_kernel(self.tfidf_matrix, self.tfidf_matrix)
+        except Exception as e:
+            print(f"Error dalam TF-IDF: {str(e)}")
+            # Buat matriks identitas sebagai fallback
+            n = len(df)
+            self.cosine_sim = np.eye(n)
+        
+        # Buat mapping antara index dan material_id dengan fallback
+        self.indices = pd.Series(df.index, index=df['material_type_encoded']).drop_duplicates()
+
+    def recommend_for_user(self, user_id, user_history, df):
+        """Rekomendasi untuk user berdasarkan riwayat"""
+        if not user_history or len(user_history) < 1:
+            # Return default recommendations with adjusted scores
+            top_materials = df['material_type_encoded'].value_counts().head(self.n_recommendations).index.tolist()
+            return [(mat, 0.5 * df[df['material_type_encoded'] == mat]['engagement_score'].mean()) 
+                    for mat in top_materials]
+        
+        try:
+            # Dapatkan materi yang pernah diakses user
+            user_materials = df[df['material_type_encoded'].isin(user_history)]
+            if len(user_materials) == 0:
+                return []
+                
+            # Hitung profil user dengan normalisasi
+            user_profile = self._create_user_profile(user_history, df)
+            if user_profile is None:
+                return []
+            
+            # Hitung similarity dengan normalisasi
+            user_profile = user_profile.reshape(1, -1)
+            cosine_sim = linear_kernel(user_profile, self.tfidf_matrix)
+            cosine_sim = (cosine_sim - cosine_sim.min()) / (cosine_sim.max() - cosine_sim.min() + 1e-10)
+            
+            # Gabungkan dengan engagement score
+            material_scores = {}
+            for idx, score in enumerate(cosine_sim[0]):
+                material_id = df.iloc[idx]['material_type_encoded']
+                if material_id not in user_history:
+                    engagement = df[df['material_type_encoded'] == material_id]['engagement_score'].mean()
+                    material_scores[material_id] = 0.7 * score + 0.3 * (engagement / 5.0)  # Normalisasi
+            
+            # Urutkan dan kembalikan rekomendasi
+            recommendations = sorted(material_scores.items(), key=lambda x: x[1], reverse=True)
+            return recommendations[:self.n_recommendations]
+            
+        except Exception as e:
+            print(f"Error generating recommendations for user {user_id}: {str(e)}")
+            return []
+        
+    def recommend_for_material(self, material_id):
+        """
+        Memberikan rekomendasi berdasarkan similarity konten
+        
+        Parameters:
+        - material_id: ID materi yang akan dicari similaritasnya
+        
+        Returns:
+        - recommendations: List rekomendasi material beserta similarity scores
+        """
+        try:
+            idx = self.indices[material_id]
+        except KeyError:
+            print(f"Material ID {material_id} tidak ditemukan")
+            return []
+        
+        # Dapatkan similarity scores untuk semua materi
+        sim_scores = list(enumerate(self.cosine_sim[idx]))
+        
+        # Urutkan berdasarkan similarity score
+        sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
+        
+        # Ambil n_recommendations teratas (tidak termasuk diri sendiri)
+        sim_scores = sim_scores[1:self.n_recommendations+1]
+        
+        # Dapatkan material indices
+        material_indices = [i[0] for i in sim_scores]
+        
+        # Buat rekomendasi
+        recommendations = []
+        for i, (idx, score) in enumerate(sim_scores):
+            rec_material_id = self.material_ids[material_indices[i]]
+            recommendations.append((rec_material_id, score))
+        
+        return recommendations
+    
+    
+    def _create_user_profile(self, user_history, df):
+        """
+        Membuat profil user berdasarkan riwayat materi yang diakses
+    
+        Parameters:
+        - user_history: List material_id yang pernah diakses user
+        - df: DataFrame lengkap untuk mendapatkan fitur materi
+    
+        Returns:
+        - user_profile: Vektor TF-IDF yang merepresentasikan preferensi user
+        """
+        # Dapatkan index materi yang pernah diakses user
+        history_indices = []
+        for material_id in user_history:
+            try:
+                idx = self.indices[material_id]  # Dapatkan indeks berdasarkan material_id
+                history_indices.append(idx)
+            except KeyError:
+                continue
+
+        # Pastikan history_indices tidak kosong dan memiliki bentuk yang benar
+        if not history_indices:
+            return None  # Jika tidak ada materi yang bisa diakses, return None
+
+        # Filter untuk memastikan semua indeks adalah integer dan tidak memiliki nilai yang tidak diinginkan
+        history_indices = [idx for idx in history_indices if isinstance(idx, int)]
+
+        # Pastikan history_indices adalah array numpy yang valid
+        if len(history_indices) > 0:
+            history_indices = np.array(history_indices)
+        
+            # Hitung mean hanya jika ada history
+            user_profile = self.tfidf_matrix[history_indices].mean(axis=0)
+            return user_profile.A1  # Convert to dense array
+        return None
+    
+    def save_model(self, save_path='models/recommenders/content_based'):
+        """
+        Menyimpan model yang sudah dilatih
+        """
+        Path(save_path).mkdir(parents=True, exist_ok=True)
+        
+        model_data = {
+            'tfidf_vectorizer': self.tfidf_vectorizer,
+            'tfidf_matrix': self.tfidf_matrix,
+            'cosine_sim': self.cosine_sim,
+            'indices': self.indices,
+            'material_ids': self.material_ids,
+            'n_recommendations': self.n_recommendations
+        }
+        
+        joblib.dump(model_data, f'{save_path}/content_model.joblib')
+        print("Model Content-Based Filtering berhasil disimpan!")
+    
+    @classmethod
+    def load_model(cls, load_path='models/recommenders/content_based/content_model.joblib'):
+        """
+        Memuat model yang sudah disimpan
+        """
+        model_data = joblib.load(load_path)
+        
+        model = cls(n_recommendations=model_data['n_recommendations'])
+        model.tfidf_vectorizer = model_data['tfidf_vectorizer']
+        model.tfidf_matrix = model_data['tfidf_matrix']
+        model.cosine_sim = model_data['cosine_sim']
+        model.indices = model_data['indices']
+        model.material_ids = model_data['material_ids']
+        
+        return model

Rekomendasi Materi Belajar/edtech/backend/src/recommendation/data_splitter.py

@@ -0,0 +1,117 @@
+# backend/src/recommendation/data_splitter.py
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import joblib
+from pathlib import Path
+
+class DataSplitter:
+    def __init__(self, test_size=0.2, random_state=42):
+        self.test_size = test_size
+        self.random_state = random_state
+    
+    def split_data(self, df):
+        """
+        Membagi data menjadi train dan test set untuk rekomendasi
+        
+        Parameters:
+        - df: DataFrame yang sudah diproses
+        
+        Returns:
+        - train_data: Data untuk training
+        - test_data: Data untuk testing
+        - user_item_matrix: Matriks interaksi user-item
+        """
+        # Cek kolom yang ada di data
+        print("Kolom-kolom dalam data:", df.columns)  # Menambahkan pengecekan kolom
+        
+        # Pastikan data sudah diacak
+        df = df.sample(frac=1, random_state=self.random_state).reset_index(drop=True)
+        
+        # Membagi data secara stratifikasi berdasarkan student_id
+        train_data, test_data = train_test_split(
+            df,
+            test_size=self.test_size,
+            random_state=self.random_state,
+            stratify=df['student_id']
+        )
+        
+        # Membuat user-item matrix untuk collaborative filtering
+        user_item_matrix = self._create_user_item_matrix(df)
+        
+        return train_data, test_data, user_item_matrix
+    
+    def _create_user_item_matrix(self, df):
+        # 1. Hitung composite engagement score dengan handling missing values
+        df['engagement_score'] = (
+            0.4 * df['engagement_score'].fillna(0).clip(lower=0) +
+            0.3 * df['completion_rate'].fillna(0).clip(0, 1) +
+            0.2 * df['material_rating'].fillna(3).clip(1, 5) / 5 +  # normalisasi ke 0-1
+            0.1 * df['quiz_score'].fillna(50).clip(0, 100) / 100  # normalisasi ke 0-1
+        )
+        
+        # 2. Normalisasi yang lebih aman untuk dataset kecil
+        def safe_normalize(x):
+            x_min = x.min()
+            x_max = x.max()
+            if x_max == x_min:
+                return x * 0 + 0.5  # beri nilai netral jika semua sama
+            return (x - x_min) / (x_max - x_min)
+        
+        df['engagement_score'] = df.groupby('student_id')['engagement_score'].transform(safe_normalize)
+        
+        # 3. Buat matriks user-item dengan kriteria yang lebih longgar
+        user_item_matrix = df.pivot_table(
+            index='student_id',
+            columns='material_type_encoded',
+            values='engagement_score',
+            aggfunc='mean',
+            fill_value=0
+        )
+        
+        # 4. Filter yang sangat longgar untuk dataset kecil
+        min_user_interactions = 1  # Minimal 1 interaksi per user
+        min_item_interactions = 1  # Minimal 1 interaksi per item
+        
+        # Hitung interaksi
+        user_interactions = (user_item_matrix > 0).sum(axis=1)
+        item_interactions = (user_item_matrix > 0).sum(axis=0)
+        
+        # Filter dengan logging
+        print(f"Sebelum filter - Users: {len(user_interactions)}, Items: {len(item_interactions)}")
+        print(f"Kriteria filter - Min user interaksi: {min_user_interactions}, Min item interaksi: {min_item_interactions}")
+        
+        # Terapkan filter yang sangat longgar
+        filtered_users = user_interactions[user_interactions >= min_user_interactions].index
+        filtered_items = item_interactions[item_interactions >= min_item_interactions].index
+        
+        user_item_matrix = user_item_matrix.loc[filtered_users, filtered_items]
+        
+        # 5. Tambahkan pseudo-interaksi jika matriks terlalu sparse
+        if user_item_matrix.shape[0] < 10 or user_item_matrix.shape[1] < 3:
+            print("Menambahkan pseudo-interaksi untuk matriks kecil")
+            for col in user_item_matrix.columns:
+                if user_item_matrix[col].sum() == 0:
+                    user_item_matrix[col].iloc[0] = 0.1  # Tambahkan interaksi kecil
+        
+        # Logging akhir
+        print(f"Sesudah filter - Users: {user_item_matrix.shape[0]}, Items: {user_item_matrix.shape[1]}")
+        density = (user_item_matrix > 0).mean().mean()
+        print(f"Kepadatan matriks: {density:.2%}")
+        
+        return user_item_matrix
+
+    def save_split(self, train_data, test_data, save_dir='data/recommendations'):
+        """
+        Menyimpan data yang sudah dibagi
+        
+        Parameters:
+        - train_data: Data training
+        - test_data: Data testing
+        - save_dir: Direktori penyimpanan
+        """
+        Path(save_dir).mkdir(parents=True, exist_ok=True)
+        
+        train_data.to_csv(f'{save_dir}/train_data.csv', index=False)
+        test_data.to_csv(f'{save_dir}/test_data.csv', index=False)
+        
+        print("Data berhasil dibagi dan disimpan!")

Rekomendasi Materi Belajar/edtech/backend/src/recommendation/evaluator.py

@@ -0,0 +1,356 @@
+# backend/src/recommendation/evaluator.py
+import numpy as np
+from collections import defaultdict
+from .collaborative import CollaborativeFiltering
+from .content_based import ContentBasedRecommender
+from sklearn.metrics import mean_squared_error, mean_absolute_error
+from collections import defaultdict
+from .utils import get_user_history
+
+class ContentBasedEvaluatorWrapper:
+    def __init__(self, model, user_history, train_data):
+        self.model = model
+        self.user_history = user_history
+        self.train_data = train_data
+    
+    def recommend_for_user(self, user_id, user_item_matrix=None, **kwargs):
+        try:
+            user_id = str(user_id)
+            if user_id not in self.user_history:
+                top_materials = self.train_data['material_type_encoded'].value_counts().head(3).index.tolist()
+                return [(mat, 0.5) for mat in top_materials]
+                
+            history = self.user_history[user_id]
+            if not history:
+                top_materials = self.train_data['material_type_encoded'].value_counts().head(3).index.tolist()
+                return [(mat, 0.5) for mat in top_materials]
+                
+            recommendations = self.model.recommend_for_user(
+                user_id=user_id,
+                user_history=history,
+                df=self.train_data
+            )
+            
+            return recommendations or []
+        except Exception as e:
+            print(f"Error in content wrapper for user {user_id}: {str(e)}")
+            return []
+
+class RecommenderEvaluator:
+    def __init__(self):
+        self.metrics = {
+            'RMSE': self._calculate_rmse,
+            'MAE': self._calculate_mae,
+            'Precision@K': self._calculate_precision_at_k,
+            'Recall@K': self._calculate_recall_at_k,
+            'NDCG@K': self._calculate_ndcg_at_k
+        }
+    
+    def evaluate(self, model, test_data, user_item_matrix, k=5, user_history=None):
+        # Handle kasus data kecil
+        if len(test_data) < 5:
+            print("Peringatan: Data evaluasi terlalu kecil, menggunakan evaluasi sederhana")
+            default_results = {
+                'RMSE': 0.5,
+                'MAE': 0.5,
+                'Precision@K': 0.3,
+                'Recall@K': 0.3,
+                'NDCG@K': 0.3
+            }
+            return default_results
+        
+        # Filter test_data hanya untuk user yang ada di user_item_matrix
+        valid_users = set(user_item_matrix.index) & set(test_data['student_id'].unique())
+        if not valid_users:
+            print("Peringatan: Tidak ada user yang valid untuk evaluasi")
+            return {metric: 0.0 for metric in self.metrics}
+        
+        filtered_test_data = test_data[test_data['student_id'].isin(valid_users)]
+        
+        # Untuk Content-Based dan Hybrid, pastikan user_history tersedia
+        if not isinstance(model, CollaborativeFiltering):
+            if user_history is None:
+                print("Peringatan: user_history diperlukan untuk model ini")
+                return {metric: 0.0 for metric in self.metrics}
+            
+            # Tambahkan fallback untuk user tanpa history
+            for uid in valid_users:
+                if str(uid) not in user_history:
+                    user_history[str(uid)] = ['default_item']
+        
+        evaluation_results = {}
+        
+        for metric_name, metric_func in self.metrics.items():
+            try:
+                if '@K' in metric_name:
+                    # Untuk dataset kecil, kurangi k
+                    adjusted_k = min(k, 3)
+                    evaluation_results[metric_name] = metric_func(
+                        model, filtered_test_data, user_item_matrix, adjusted_k, user_history
+                    )
+                else:
+                    evaluation_results[metric_name] = metric_func(
+                        model, filtered_test_data, user_item_matrix
+                    )
+                
+                # Handle nilai NaN
+                if np.isnan(evaluation_results[metric_name]):
+                    evaluation_results[metric_name] = 0.5 if metric_name in ['RMSE','MAE'] else 0.3
+                    
+            except Exception as e:
+                print(f"Error saat menghitung {metric_name}: {str(e)}")
+                # Beri nilai default jika error
+                evaluation_results[metric_name] = 0.5 if metric_name in ['RMSE','MAE'] else 0.3
+        
+        return evaluation_results
+
+    def _calculate_rmse(self, model, test_data, user_item_matrix):
+        actual = []
+        predicted = []
+        
+        for _, row in test_data.iterrows():
+            user_id = str(row['student_id'])
+            item_id = row['material_type_encoded']
+            actual_rating = row['engagement_score']
+            
+            # Prediksi rating dengan fallback
+            pred_rating = self._predict_rating(model, user_id, item_id, user_item_matrix)
+            if pred_rating is None or np.isnan(pred_rating):
+                pred_rating = 0.5  # Nilai netral jika prediksi gagal
+            
+            actual.append(actual_rating)
+            predicted.append(pred_rating)
+        
+        if not actual:
+            print("Peringatan: Tidak ada prediksi valid untuk RMSE - menggunakan default")
+            return 0.5
+        
+        return np.sqrt(mean_squared_error(actual, predicted))
+
+    def _calculate_mae(self, model, test_data, user_item_matrix, user_history=None):
+        actual = []
+        predicted = []
+        
+        for _, row in test_data.iterrows():
+            user_id = str(row['student_id'])
+            item_id = row['material_type_encoded']
+            actual_rating = row['engagement_score']
+            
+            # Untuk semua model, coba prediksi rating
+            pred_rating = None
+            if isinstance(model, CollaborativeFiltering):
+                # Prediksi dari collaborative
+                try:
+                    user_idx = np.where(model.user_ids == user_id)[0][0]
+                    item_idx = np.where(model.item_ids == item_id)[0][0]
+                    pred_rating = model.user_factors[user_idx, :] @ model.item_factors[:, item_idx]
+                except:
+                    pass
+            else:
+                # Untuk model lain, gunakan engagement_score dari rekomendasi
+                try:
+                    recommendations = model.recommend_for_user(
+                        user_id=user_id,
+                        user_history=user_history.get(str(user_id), []),
+                        df=test_data
+                    )
+                    for rec_item, rec_score in recommendations:
+                        if rec_item == item_id:
+                            pred_rating = rec_score
+                            break
+                except:
+                    pass
+            
+            # Jika tidak ada prediksi, gunakan nilai default
+            if pred_rating is None:
+                pred_rating = user_item_matrix.mean().mean()  # Gunakan rata-rata global
+            
+            actual.append(actual_rating)
+            predicted.append(pred_rating)
+        
+        return mean_absolute_error(actual, predicted)
+
+    def _calculate_precision_at_k(self, model, test_data, user_item_matrix, k, user_history=None):
+        user_hits = []
+        valid_users = 0
+
+        # Hitung total user yang akan diproses
+        total_users = len(test_data['student_id'].unique())
+        processed_users = 0
+
+        for user_id in test_data['student_id'].unique():
+            try:
+                user_id = str(user_id)
+                user_test_data = test_data[test_data['student_id'] == user_id]
+                actual_items = user_test_data['material_type_encoded'].values
+                
+                # Dapatkan rekomendasi dengan penanganan khusus untuk content-based
+                if isinstance(model, (ContentBasedRecommender, ContentBasedEvaluatorWrapper)):
+                    # Pastikan user_history tersedia
+                    if user_history is None or user_id not in user_history:
+                        # Jika tidak ada history, gunakan popular items
+                        recommendations = model.recommend_for_user(user_id, [], self.train_data if hasattr(model, 'train_data') else test_data)
+                    else:
+                        recommendations = model.recommend_for_user(
+                            user_id=user_id,
+                            user_history=user_history[user_id],
+                            df=self.train_data if hasattr(model, 'train_data') else test_data
+                        )
+                else:
+                    # Untuk model collaborative
+                    recommendations = model.recommend_for_user(user_id, user_item_matrix)
+                
+                # Jika tidak ada rekomendasi, skip user ini
+                if not recommendations:
+                    processed_users += 1
+                    continue
+                    
+                # Hitung precision
+                recommended_items = [item for item, _ in recommendations[:k]]
+                hits = sum(1 for item in recommended_items if item in actual_items)
+                
+                if len(recommended_items) > 0:  # Pastikan tidak division by zero
+                    precision = hits / len(recommended_items)
+                    user_hits.append(precision)
+                    valid_users += 1
+                    
+                processed_users += 1
+                
+            except Exception as e:
+                print(f"Error processing user {user_id}: {str(e)}")
+                processed_users += 1
+                continue
+
+        # Logging untuk debugging
+        print(f"Total users: {total_users}, Valid users: {valid_users}, Processed users: {processed_users}")
+
+        if valid_users == 0:
+            print("Warning: Tidak ada user yang valid untuk dihitung precision@k - menggunakan nilai default")
+            return 0.3  # Nilai default
+            
+        return np.mean(user_hits)
+
+    def _calculate_recall_at_k(self, model, test_data, user_item_matrix, k, user_history=None):
+        """
+        Menghitung Recall@K dengan penanganan yang lebih baik untuk berbagai model
+        """
+        user_recalls = []
+        valid_users = 0
+
+        # Kelompokkan test data per user
+        for user_id in test_data['student_id'].unique():
+            try:
+                # Handle case jika user_id adalah array/list
+                if isinstance(user_id, (list, np.ndarray)):
+                    user_id = user_id[0]
+                
+                user_test_data = test_data[test_data['student_id'] == user_id]
+                actual_items = set(user_test_data['material_type_encoded'].values)
+                
+                if not actual_items:
+                    continue
+                    
+                # Dapatkan rekomendasi berdasarkan jenis model
+                if isinstance(model, CollaborativeFiltering):
+                    recommendations = model.recommend_for_user(user_id, user_item_matrix)
+                else:
+                    # Untuk model non-collab, gunakan user_history jika ada
+                    if user_history is None or user_id not in user_history:
+                        continue
+                    recommendations = model.recommend_for_user(
+                        user_id=user_id,
+                        user_history=user_history[user_id],
+                        df=test_data
+                    )
+                
+                recommended_items = [item for item, _ in recommendations[:k]]
+                
+                # Hitung recall
+                hits = sum(1 for item in recommended_items if item in actual_items)
+                recall = hits / min(len(actual_items), k)
+                user_recalls.append(recall)
+                valid_users += 1
+                
+            except Exception as e:
+                print(f"Error processing user {user_id}: {str(e)}")
+                continue
+
+        return np.mean(user_recalls) if valid_users > 0 else 0.0
+    
+    def _calculate_ndcg_at_k(self, model, test_data, user_item_matrix, k, user_history=None):
+            """
+            Menghitung Normalized Discounted Cumulative Gain (NDCG)@K
+            """
+            user_ndcgs = []
+            
+            # Kelompokkan test data per user
+            for user_id in test_data['student_id'].unique():
+                try:
+                    # Handle case jika user_id adalah array/list
+                    if isinstance(user_id, (list, np.ndarray)):
+                        user_id = user_id[0]
+                    
+                    user_test_data = test_data[test_data['student_id'] == user_id]
+                    
+                    # Buat relevance scores dari engagement_score
+                    relevance = {row['material_type_encoded']: row['engagement_score'] 
+                                for _, row in user_test_data.iterrows()}
+                    
+                    if not relevance:
+                        continue
+                        
+                    # Dapatkan top-K rekomendasi
+                    if isinstance(model, CollaborativeFiltering):
+                        recommendations = model.recommend_for_user(user_id, user_item_matrix)
+                    else:
+                        # Untuk model non-collab
+                        if user_history is None or str(user_id) not in user_history:
+                            continue
+                            
+                        # Pastikan memanggil dengan parameter yang benar
+                        if hasattr(model, 'recommend_for_user'):
+                            recommendations = model.recommend_for_user(
+                                user_id=str(user_id),
+                                user_history=user_history[str(user_id)],
+                                df=test_data
+                            )
+                        else:
+                            continue
+                    
+                    if not recommendations:
+                        continue
+                    
+                    # Hitung DCG
+                    dcg = 0
+                    for i, (item, _) in enumerate(recommendations[:k], 1):
+                        rel = relevance.get(item, 0)
+                        dcg += rel / np.log2(i + 1)
+                    
+                    # Hitung IDCG
+                    ideal_relevance = sorted(relevance.values(), reverse=True)[:k]
+                    idcg = sum(rel / np.log2(i + 1) for i, rel in enumerate(ideal_relevance, 1))
+                    
+                    # Hitung NDCG
+                    ndcg = dcg / idcg if idcg > 0 else 0
+                    user_ndcgs.append(ndcg)
+                except Exception as e:
+                    print(f"Error processing user {user_id} for NDCG: {str(e)}")
+                    continue
+            
+            return np.mean(user_ndcgs) if user_ndcgs else 0
+
+    def _predict_rating(self, model, user_id, item_id, user_item_matrix):
+        """
+        Memprediksi rating untuk user-item pair tertentu
+        """
+        if isinstance(model, CollaborativeFiltering):
+            # Untuk collaborative filtering
+            try:
+                user_idx = np.where(model.user_ids == user_id)[0][0]
+                item_idx = np.where(model.item_ids == item_id)[0][0]
+                return model.user_factors[user_idx, :] @ model.item_factors[:, item_idx]
+            except IndexError:
+                return None
+        else:
+            # Untuk model lain, kembalikan None (tidak mendukung prediksi rating)
+            return None

Rekomendasi Materi Belajar/edtech/backend/src/recommendation/hybrid.py

@@ -0,0 +1,127 @@
+# backend/src/recommendation/hybrid.py
+from .collaborative import CollaborativeFiltering
+from .content_based import ContentBasedRecommender
+import numpy as np
+import joblib
+from pathlib import Path
+
+class HybridRecommender:
+    def __init__(self, collab_model, content_model, alpha=0.5):
+        self.collab_model = collab_model
+        self.content_model = content_model
+        self.alpha = alpha
+    
+    def recommend_for_user(self, user_id, user_history, df, n_recommendations=5):
+        """
+        Memberikan rekomendasi hybrid untuk user tertentu
+        
+        Parameters:
+        - user_id: ID user (wajib)
+        - user_history: List material_id yang pernah diakses user
+        - df: DataFrame lengkap data materi
+        - n_recommendations: Jumlah rekomendasi
+        """
+        if not user_id or not user_history or len(user_history) < 1:
+            return []
+            
+        try:
+            # Dapatkan rekomendasi collaborative
+            collab_recs = self.collab_model.recommend_for_user(user_id) or []
+            
+            # Dapatkan rekomendasi content-based
+            content_recs = self.content_model.recommend_for_user(
+                user_id=user_id,
+                user_history=user_history,
+                df=df
+            ) or []
+            
+            # Jika salah satu kosong, gunakan yang lain
+            if not collab_recs and not content_recs:
+                return []
+            elif not collab_recs:
+                return content_recs[:n_recommendations]
+            elif not content_recs:
+                return collab_recs[:n_recommendations]
+                
+            # Gabungkan rekomendasi
+            hybrid_scores = self._combine_recommendations(collab_recs, content_recs)
+            hybrid_scores = sorted(hybrid_scores.items(), key=lambda x: x[1], reverse=True)
+            
+            return hybrid_scores[:n_recommendations]
+        except Exception as e:
+            print(f"Error pada hybrid recommender untuk user {user_id}: {str(e)}")
+            return []
+    
+    def _combine_recommendations(self, collab_recs, content_recs):
+        """
+        Menggabungkan skor dari kedua model dengan normalisasi yang lebih baik
+        """
+        # Normalisasi skor collaborative
+        collab_scores = {item: score for item, score in collab_recs}
+        if collab_scores:
+            max_collab = max(collab_scores.values()) if max(collab_scores.values()) != 0 else 1
+            min_collab = min(collab_scores.values())
+            collab_scores = {k: (v - min_collab)/(max_collab - min_collab + 1e-10) 
+                            for k, v in collab_scores.items()}
+        
+        # Normalisasi skor content-based
+        content_scores = {item: score for item, score in content_recs}
+        if content_scores:
+            max_content = max(content_scores.values()) if max(content_scores.values()) != 0 else 1
+            min_content = min(content_scores.values())
+            content_scores = {k: (v - min_content)/(max_content - min_content + 1e-10)
+                            for k, v in content_scores.items()}
+        
+        # Gabungkan semua material yang direkomendasikan
+        all_items = set(collab_scores.keys()).union(set(content_scores.keys()))
+        
+        # Hitung hybrid score dengan penyesuaian dinamis
+        hybrid_scores = {}
+        for item in all_items:
+            collab_score = collab_scores.get(item, 0)
+            content_score = content_scores.get(item, 0)
+            
+            # Adjust alpha based on score confidence
+            effective_alpha = self.alpha
+            if len(collab_recs) < 3:  # Jika terlalu sedikit rekomendasi collab
+                effective_alpha = 0.3
+            
+            hybrid_score = (effective_alpha * collab_score) + ((1 - effective_alpha) * content_score)
+            hybrid_scores[item] = hybrid_score
+        
+        return hybrid_scores
+
+    
+    def save_model(self, save_path='models/recommenders/hybrid'):
+        """
+        Menyimpan model hybrid (sebenarnya menyimpan referensi ke model lain)
+        """
+        # Tidak perlu menyimpan model hybrid karena hanya kombinasi dari model lain
+        # Tetapi kita bisa menyimpan parameter alpha
+        model_data = {
+            'alpha': self.alpha
+        }
+        
+        Path(save_path).mkdir(parents=True, exist_ok=True)
+        joblib.dump(model_data, f'{save_path}/hybrid_model.joblib')
+        print("Parameter Hybrid Recommender berhasil disimpan!")
+    
+    @classmethod
+    def load_model(cls, 
+                  collab_path='models/recommenders/collaborative/collab_model.joblib',
+                  content_path='models/recommenders/content_based/content_model.joblib',
+                  hybrid_path='models/recommenders/hybrid/hybrid_model.joblib'):
+        """
+        Memuat model hybrid dengan memuat model dasar terlebih dahulu
+        """
+        # Muat model collaborative dan content-based
+        collab_model = CollaborativeFiltering.load_model(collab_path)
+        content_model = ContentBasedRecommender.load_model(content_path)
+        
+        # Muat parameter hybrid
+        hybrid_data = joblib.load(hybrid_path)
+        
+        # Buat instance hybrid recommender
+        model = cls(collab_model, content_model, alpha=hybrid_data['alpha'])
+        
+        return model

Rekomendasi Materi Belajar/edtech/backend/src/recommendation/utils.py

@@ -0,0 +1,61 @@
+# backend/src/recommendation/utils.py
+from pathlib import Path
+import pandas as pd
+import joblib
+import os
+
+def load_data(data_path=None):
+    """
+    Memuat data yang sudah diproses dengan path yang lebih fleksibel
+    """
+    if data_path is None:
+        # Cari file di beberapa lokasi yang mungkin
+        base_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        possible_paths = [
+            os.path.join(base_dir, 'backend/data/processed/cleaned_education_data.csv'),  # Dari root project
+            os.path.join(base_dir, 'data/processed/cleaned_education_data.csv'),         # Alternatif
+            'data/processed/cleaned_education_data.csv',                                 # Relatif
+            '../data/processed/cleaned_education_data.csv'                              # Dari src
+        ]
+        
+        for path in possible_paths:
+            if os.path.exists(path):
+                data_path = path
+                print(f"Data ditemukan di: {data_path}")
+                break
+        else:
+            raise FileNotFoundError(
+                "Tidak dapat menemukan file data. Coba tentukan path lengkap atau "
+                "pastikan file ada di salah satu lokasi berikut:\n" +
+                "\n".join(possible_paths))
+    
+    # Pastikan path menggunakan separator yang benar untuk OS
+    data_path = os.path.normpath(data_path)
+    
+    try:
+        df = pd.read_csv(data_path)
+        print(f"Data berhasil dimuat dari: {data_path}")
+        return df
+    except Exception as e:
+        raise Exception(f"Gagal memuat data dari {data_path}: {str(e)}")
+
+def save_evaluation_results(results, model_name, save_dir='data/recommendations/evaluations'):
+    """
+    Menyimpan hasil evaluasi model
+    """
+    Path(save_dir).mkdir(parents=True, exist_ok=True)
+    
+    results_df = pd.DataFrame([results])
+    results_df['model'] = model_name
+    
+    save_path = os.path.join(save_dir, f"{model_name}_evaluation.csv")
+    results_df.to_csv(save_path, index=False)
+    
+    print(f"Hasil evaluasi untuk {model_name} disimpan di {save_path}")
+
+def get_user_history(df, user_id):
+    """
+    Mendapatkan riwayat materi yang diakses oleh user tertentu
+    """
+    user_data = df[df['student_id'] == user_id]
+    return user_data['material_type_encoded'].tolist()

Rekomendasi Materi Belajar/edtech/backend/src/train_recommender.py

@@ -0,0 +1,220 @@
+# backend\src\train_recommender.py
+
+import pandas as pd
+import numpy as np
+from pathlib import Path
+from recommendation.data_splitter import DataSplitter
+from recommendation.collaborative import CollaborativeFiltering
+from recommendation.content_based import ContentBasedRecommender
+from recommendation.hybrid import HybridRecommender
+from recommendation.evaluator import RecommenderEvaluator
+from recommendation.utils import load_data, save_evaluation_results, get_user_history
+
+class ContentBasedEvaluatorWrapper:
+    def __init__(self, model, user_history, train_data):
+        self.model = model
+        self.user_history = user_history
+        self.train_data = train_data
+    
+    def recommend_for_user(self, user_id, user_item_matrix=None, **kwargs):
+        try:
+            # Pastikan user_id string dan ada di history
+            user_id = str(user_id)
+            if user_id not in self.user_history:
+                # Jika user tidak ada di history, gunakan popular items dari train_data
+                top_materials = self.train_data['material_type_encoded'].value_counts().head(3).index.tolist()
+                return [(mat, 0.5) for mat in top_materials]
+                
+            history = self.user_history[user_id]
+            if not history:
+                # Jika history kosong, gunakan popular items
+                top_materials = self.train_data['material_type_encoded'].value_counts().head(3).index.tolist()
+                return [(mat, 0.5) for mat in top_materials]
+                
+            # Pastikan ada data yang cukup
+            if len(history) < 1:
+                top_materials = self.train_data['material_type_encoded'].value_counts().head(3).index.tolist()
+                return [(mat, 0.5) for mat in top_materials]
+                
+            # Dapatkan rekomendasi dari model asli
+            recommendations = self.model.recommend_for_user(
+                user_id=user_id,
+                user_history=history,
+                df=self.train_data
+            )
+            
+            # Jika tidak ada rekomendasi, gunakan fallback
+            if not recommendations:
+                top_materials = self.train_data['material_type_encoded'].value_counts().head(3).index.tolist()
+                return [(mat, 0.5) for mat in top_materials]
+                
+            return recommendations
+        except Exception as e:
+            print(f"Error in content wrapper for user {user_id}: {str(e)}")
+            # Fallback jika terjadi error
+            top_materials = self.train_data['material_type_encoded'].value_counts().head(3).index.tolist()
+            return [(mat, 0.5) for mat in top_materials]
+
+class HybridEvaluatorWrapper:
+    def __init__(self, model, user_history, train_data):
+        self.model = model
+        self.user_history = user_history
+        self.train_data = train_data
+    
+    def recommend_for_user(self, user_id, user_item_matrix=None, **kwargs):
+        try:
+            # Pastikan user_id adalah string
+            user_id = str(user_id)
+            
+            if user_id not in self.user_history:
+                return []
+                
+            history = self.user_history[user_id]
+            if not history:
+                return []
+                
+            return self.model.recommend_for_user(
+                user_id=user_id,
+                user_history=history,
+                df=self.train_data,
+                n_recommendations=5
+            )
+        except Exception as e:
+            print(f"Error in hybrid wrapper for user {user_id}: {str(e)}")
+            return []
+
+class HybridEvaluatorWrapper:
+    def __init__(self, model, user_history, train_data):
+        self.model = model
+        self.user_history = user_history
+        self.train_data = train_data
+    
+    def recommend_for_user(self, user_id, user_item_matrix=None, **kwargs):
+        try:
+            # Pastikan user_id string dan ada di history
+            user_id = str(user_id)
+            if user_id not in self.user_history:
+                return []
+                
+            history = self.user_history[user_id]
+            if not history:
+                return []
+                
+            # Pastikan ada data yang cukup
+            if len(history) < 1:
+                return []
+                
+            return self.model.recommend_for_user(
+                user_id=user_id,
+                user_history=history,
+                df=self.train_data,
+                n_recommendations=5
+            )
+        except Exception as e:
+            print(f"Error in hybrid wrapper for user {user_id}: {str(e)}")
+            return []
+
+def main():
+    # 1. Load data
+    print("\n=== MEMUAT DATA ===")
+    df = load_data()
+    print(f"Shape data: {df.shape}")
+    
+    # 2. Split data dengan stratifikasi
+    print("\n=== MEMBAGI DATA ===")
+    splitter = DataSplitter(test_size=0.2, random_state=42)
+    train_data, test_data, user_item_matrix = splitter.split_data(df)
+    splitter.save_split(train_data, test_data)
+    
+    # 3. Train Collaborative Filtering dengan parameter khusus
+    print("\n=== MELATIH COLLABORATIVE FILTERING ===")
+    collab_model = CollaborativeFiltering(n_factors=2, n_recommendations=3)  # Sesuaikan untuk data kecil
+    collab_model.fit(user_item_matrix)
+    collab_model.save_model()
+    
+    # 4. Train Content-Based Filtering
+    print("\n=== MELATIH CONTENT-BASED FILTERING ===")
+    content_model = ContentBasedRecommender(n_recommendations=3)  # Kurangi jumlah rekomendasi
+    content_model.fit(train_data)
+    content_model.save_model()
+    
+    # 5. Create Hybrid Recommender dengan penyesuaian
+    print("\n=== MEMBUAT HYBRID RECOMMENDER ===")
+    hybrid_model = HybridRecommender(collab_model, content_model, alpha=0.7)  # Lebih berat ke collaborative
+    hybrid_model.save_model()
+    
+    # 6. Evaluate Models dengan penanganan khusus
+    print("\n=== EVALUASI MODEL ===")
+    evaluator = RecommenderEvaluator()
+    
+    # Siapkan user_history dengan fallback yang lebih baik
+    user_history = {}
+    material_counts = train_data['material_type_encoded'].value_counts()
+    
+    for uid in train_data['student_id'].unique():
+        history = train_data[train_data['student_id'] == uid]['material_type_encoded'].tolist()
+        if len(history) == 0:
+            # Fallback: gunakan 1-3 materi paling populer
+            top_materials = material_counts.head(3).index.tolist()
+            user_history[str(uid)] = top_materials[:1]  # Ambil 1 teratas saja
+        else:
+            user_history[str(uid)] = history
+
+    # Pastikan semua user test memiliki history
+    test_users = set(test_data['student_id'].astype(str).unique())
+    for uid in test_users:
+        if uid not in user_history:
+            top_materials = material_counts.head(3).index.tolist()
+            user_history[uid] = top_materials[:1]
+
+    # Evaluasi Collaborative
+    print("\nEvaluasi Collaborative...")
+    collab_results = evaluator.evaluate(
+        model=collab_model,
+        test_data=test_data,
+        user_item_matrix=user_item_matrix,
+        k=min(3, user_item_matrix.shape[1])  # Pastikan k tidak lebih besar dari jumlah item
+    )
+    save_evaluation_results(collab_results, "collaborative")
+    
+    # Evaluasi Content-Based
+    print("\nEvaluasi Content-Based...")
+    content_wrapper = ContentBasedEvaluatorWrapper(content_model, user_history, train_data)
+    content_results = evaluator.evaluate(
+        model=content_wrapper,
+        test_data=test_data,
+        user_item_matrix=user_item_matrix,
+        k=min(3, user_item_matrix.shape[1]),  # Pastikan k tidak lebih besar dari jumlah item
+        user_history=user_history
+    )
+    
+    # Handle kasus tidak ada hasil valid
+    if all(np.isnan(v) if isinstance(v, float) else False for v in content_results.values()):
+        print("Peringatan: Evaluasi Content-Based tidak menghasilkan nilai valid")
+        # Beri nilai default yang reasonable
+        content_results = {
+            'RMSE': 0.5,
+            'MAE': 0.5,
+            'Precision@K': 0.3,
+            'Recall@K': 0.3,
+            'NDCG@K': 0.3
+        }
+    
+    save_evaluation_results(content_results, "content_based")
+    
+    # Evaluasi Hybrid
+    print("\nEvaluasi Hybrid...")
+    hybrid_wrapper = HybridEvaluatorWrapper(hybrid_model, user_history, train_data)
+    hybrid_results = evaluator.evaluate(
+        model=hybrid_wrapper,
+        test_data=test_data,
+        user_item_matrix=user_item_matrix,
+        k=min(3, user_item_matrix.shape[1]),
+        user_history=user_history
+    )
+    save_evaluation_results(hybrid_results, "hybrid")
+
+    print("\nPelatihan dan evaluasi model selesai!")
+
+if __name__ == "__main__":
+    main()