backgound

reference/intake_graph

@@ -0,0 +1,73 @@
+graph TD
+    A[Student Enrollment] --> B[Learner Profile Assessment]
+    
+    B --> C[Technical Background]
+    B --> D[Mathematical Foundation]
+    B --> E[Domain Knowledge]
+    B --> F[Learning Preferences]
+    B --> G[Prior Knowledge]
+    
+    C --> H[Profile Classification]
+    D --> H
+    E --> H
+    F --> H
+    G --> H
+    
+    H --> I{Learner Archetype}
+    
+    I -->|High Tech + High Knowledge| J[Advanced Technical Path]
+    I -->|High Tech + Low Knowledge| K[Accelerated Technical Path]
+    I -->|Low Tech + High Domain| L[Applied Research Path]
+    I -->|Low Tech + Low Knowledge| M[Foundational Path]
+    
+    J --> N[Bloom's Taxonomy Outcomes]
+    K --> N
+    L --> N
+    M --> N
+    
+    N --> O[Remember Level]
+    N --> P[Understand Level]
+    N --> Q[Apply Level]
+    N --> R[Analyze Level]
+    N --> S[Evaluate Level]
+    N --> T[Create Level]
+    
+    O --> U[Adaptive Content Selection]
+    P --> U
+    Q --> U
+    R --> U
+    S --> U
+    T --> U
+    
+    U --> V[Personalized Learning Activities]
+    
+    V --> W[Personalized Clustering Curriculum]
+    
+    subgraph "Content Library"
+        EE[Key Concepts & Explanations]
+        FF[Worked Examples]
+        GG[Visual Analogies]
+        HH[Code Examples]
+        II[Research Application Cases]
+        JJ[Interactive Exercises]
+        KK[Practice Questions]
+    end
+    
+    U -.-> EE
+    U -.-> FF
+    U -.-> GG
+    U -.-> HH
+    U -.-> II
+    U -.-> JJ
+    U -.-> KK
+
+    
+    style A fill:#e1f5fe
+    style I fill:#fff3e0
+    style J fill:#f3e5f5
+    style K fill:#f3e5f5
+    style L fill:#f3e5f5
+    style M fill:#f3e5f5
+    style N fill:#e8f5e8
+    style U fill:#fff8e1
+    style W fill:#e8f5e8

src/modules/module3.py

@@ -8,6 +8,13 @@ from sklearn.cluster import KMeans
 from sklearn.preprocessing import StandardScaler
 from sklearn.datasets import make_blobs
 
+# Set page config
+st.set_page_config(
+    page_title="Machine Learning Visualizations",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
 def generate_regression_data(n_samples=100, noise=10):
     """Generate data for linear regression visualization."""
     np.random.seed(42)
@@ -28,7 +35,8 @@ def generate_clustering_data(n_samples=300):
     X, _ = make_blobs(n_samples=n_samples, centers=3, cluster_std=1.5)
     return X
 
-def show():
+def show_student_view():
+    """Display the student view of the module."""
     st.title("Interactive Machine Learning Visualizations")
 
     # Introduction
@@ -55,10 +63,11 @@ def show():
         # Generate and plot data
         X, y = generate_regression_data(n_samples, noise)
         
-        # Create scatter plot
+        # Create scatter plot with dark theme
         fig = px.scatter(x=X.flatten(), y=y, 
                         title="Linear Regression Visualization",
-                        labels={'x': 'Feature (X)', 'y': 'Target (y)'})
+                        labels={'x': 'Feature (X)', 'y': 'Target (y)'},
+                        template="plotly_dark")
         
         # Add regression line
         model = LinearRegression()
@@ -70,6 +79,12 @@ def show():
                                 name='Regression Line',
                                 line=dict(color='red')))
         
+        fig.update_layout(
+            plot_bgcolor='#1E1E1E',
+            paper_bgcolor='#1E1E1E',
+            font=dict(color='white')
+        )
+        
         st.plotly_chart(fig, use_container_width=True)
         
         # Display model information
@@ -86,11 +101,12 @@ def show():
         # Generate data
         X, y = generate_classification_data()
         
-        # Create scatter plot
+        # Create scatter plot with dark theme
         fig = px.scatter(x=X[:, 0], y=X[:, 1], 
                         color=y.astype(str),
                         title="Logistic Regression Visualization",
-                        labels={'x': 'Feature 1', 'y': 'Feature 2'})
+                        labels={'x': 'Feature 1', 'y': 'Feature 2'},
+                        template="plotly_dark")
         
         # Add decision boundary
         model = LogisticRegression()
@@ -110,6 +126,12 @@ def show():
                                 opacity=0.3,
                                 colorscale='RdBu'))
         
+        fig.update_layout(
+            plot_bgcolor='#1E1E1E',
+            paper_bgcolor='#1E1E1E',
+            font=dict(color='white')
+        )
+        
         st.plotly_chart(fig, use_container_width=True)
         
         # Display model information
@@ -133,19 +155,26 @@ def show():
         kmeans = KMeans(n_clusters=n_clusters, random_state=42)
         clusters = kmeans.fit_predict(X)
         
-        # Create scatter plot
+        # Create scatter plot with dark theme
         fig = px.scatter(x=X[:, 0], y=X[:, 1], 
                         color=clusters.astype(str),
                         title="K-Means Clustering Visualization",
-                        labels={'x': 'Feature 1', 'y': 'Feature 2'})
+                        labels={'x': 'Feature 1', 'y': 'Feature 2'},
+                        template="plotly_dark")
         
         # Add cluster centers
         fig.add_trace(go.Scatter(x=kmeans.cluster_centers_[:, 0],
                                 y=kmeans.cluster_centers_[:, 1],
                                 mode='markers',
-                                marker=dict(size=12, symbol='star', color='black'),
+                                marker=dict(size=12, symbol='star', color='white'),
                                 name='Cluster Centers'))
         
+        fig.update_layout(
+            plot_bgcolor='#1E1E1E',
+            paper_bgcolor='#1E1E1E',
+            font=dict(color='white')
+        )
+        
         st.plotly_chart(fig, use_container_width=True)
         
         # Display clustering information
@@ -161,4 +190,82 @@ def show():
         - Linear Regression: Fits a line to predict continuous values
         - Logistic Regression: Creates a decision boundary for classification
         - K-Means Clustering: Groups similar data points into clusters
-    """) 
+    """)
+
+def show_instructor_view():
+    """Display the instructor view of the module."""
+    st.title("Instructor View: Machine Learning Module")
+
+    # Overview section
+    st.info("""
+        **Module Overview**
+        This module covers three fundamental machine learning concepts with interactive visualizations.
+        Students can experiment with different parameters and see real-time results.
+    """)
+
+    # Learning objectives
+    st.subheader("Learning Objectives")
+    st.markdown("""
+        1. Understand the basic principles of linear regression
+        2. Comprehend binary classification using logistic regression
+        3. Learn about unsupervised learning through k-means clustering
+        4. Develop intuition for model parameters and their effects
+    """)
+
+    # Student progress tracking
+    st.subheader("Student Progress Tracking")
+    progress_data = pd.DataFrame({
+        'Student': ['Student 1', 'Student 2', 'Student 3'],
+        'Linear Regression': [85, 90, 75],
+        'Logistic Regression': [80, 85, 70],
+        'Clustering': [90, 80, 85]
+    })
+    st.dataframe(progress_data)
+
+    # Common misconceptions
+    st.subheader("Common Misconceptions")
+    st.markdown("""
+        - Linear regression can only model linear relationships
+        - Logistic regression is only for binary classification
+        - K-means clustering always finds the optimal number of clusters
+    """)
+
+    # Teaching tips
+    st.subheader("Teaching Tips")
+    st.markdown("""
+        1. Start with simple examples and gradually increase complexity
+        2. Encourage students to experiment with different parameters
+        3. Use the visualizations to explain key concepts
+        4. Relate the concepts to real-world applications
+    """)
+
+    # Assessment guidelines
+    st.subheader("Assessment Guidelines")
+    st.markdown("""
+        - Evaluate understanding through interactive exercises
+        - Check comprehension of model parameters
+        - Assess ability to interpret results
+        - Review practical applications of each concept
+    """)
+
+def show():
+    """Main function to display the appropriate view based on user role."""
+    # Check if user role is set in session state
+    if 'user_role' not in st.session_state:
+        # If not set, show role selection
+        st.title("Welcome to the Machine Learning Module")
+        role = st.radio("Select your role:", ["Student", "Instructor"])
+        if st.button("Continue"):
+            st.session_state.user_role = role
+            st.experimental_rerun()
+    else:
+        # Display appropriate view based on role
+        if st.session_state.user_role == "Student":
+            show_student_view()
+        else:
+            show_instructor_view()
+        
+        # Add option to switch roles
+        if st.sidebar.button("Switch Role"):
+            del st.session_state.user_role
+            st.experimental_rerun()