Datasets:
The dataset viewer is not available for this dataset.
Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
📚 Indo-Bloom BSE RAW Corpus
This dataset serves as the raw material corpus for the Indo-Bloom research project at Universitas Negeri Malang (UM).
Current State:
Extracted & Cleaned Context from BSE TextbooksNext Stage:
QA Pair Generation (Stage 1) → Silver Corpus
This version is permanently frozen to ensure reproducibility. This corpus will be used as input for QA generation pipeline.
📄 Associated Publication
Preprint Paper:
Ibrahim, F., Prasetya, D. D., & Widiyaningtyas, T. (2026). Towards Indo-Bloom: A Preliminary Study on Controllable Bloom's Taxonomy-aligned Question Generation in Indonesian. TechRxiv.
DOI: 10.36227/techrxiv.177220024.48567886/v1
Publication Date: February 27, 2026
Status: 📝 Preprint (Not Peer-Reviewed)
Read the Full Paper: TechRxiv Preprint
🎓 Dissertation Details
- Project Title: A Unified Framework for Controllable Indonesian Automatic Question Generation Aligned with Bloom's Taxonomy
- Principal Investigator: Firmansyah Ibrahim (Ph.D. Candidate)
- Student ID (NIM): 250534903885
- Institution: Doctoral Program in Electrical and Informatics Engineering, Universitas Negeri Malang (UM)
- Supervision Team:
- Promotor: Dr. Eng. Didik Dwi Prasetya, S.T., M.T.
- Co-Promotor: Dr. Ir. Triyanna Widiyaningtyas, M.T.
📝 Dataset Description
Indo-Bloom BSE RAW Corpus is a curated collection of educational text chunks extracted from Indonesian high school textbooks (Buku Sekolah Elektronik - BSE) published by Kemdikbudristek. This corpus serves as the foundational raw material for generating Bloom's Taxonomy-aligned question-answer pairs in the Indo-Bloom research project.
Purpose in Research Pipeline
This dataset represents Stage 0 in the Indo-Bloom methodology:
Stage 0 (This Dataset) → Stage 1 (QA Generation) → Stage 2 (Expert Annotation) → Stage 3 (Gold Standard)
Role:
- Provides clean, domain-specific context passages for QA generation
- Ensures educational content quality and curriculum alignment
- Enables controlled generation of pedagogically valid questions
📊 Dataset Specifications
1. Corpus Statistics
| Metric | Value |
|---|---|
| Total Chunks | 1,825 |
| Total Words | ~292,000 |
| Avg Chunk Length | 160 words |
| Min Chunk Length | 50 words |
| Max Chunk Length | 450 words |
2. Subject Distribution
| Mata Pelajaran | Chunks | Percentage | Description |
|---|---|---|---|
| Sejarah | ~450 | 24.7% | Indonesian History |
| Geografi | ~460 | 25.2% | Geography |
| Biologi | ~455 | 24.9% | Biology |
| Sosiologi | ~460 | 25.2% | Sociology |
| TOTAL | 1,825 | 100% | — |
3. Grade Level Distribution
| Jenjang | Kelas | Chunks | Percentage |
|---|---|---|---|
| SMA/MA | X (10) | ~610 | 33.4% |
| SMA/MA | XI (11) | ~607 | 33.3% |
| SMA/MA | XII (12) | ~608 | 33.3% |
| TOTAL | — | 1,825 | 100% |
📂 Data Structure
Each row in the dataset contains the following fields:
| Field | Type | Description | Example |
|---|---|---|---|
chunk_id |
string |
Unique identifier | chunk_0001 |
mata_pelajaran |
string |
Subject name | Sejarah |
jenjang |
string |
Grade level | SMA/MA |
kelas |
string |
Specific grade | X, XI, XII |
context |
string |
Text passage (50-450 words) | "Indonesia memiliki sejarah panjang..." |
word_count |
integer |
Number of words in context | 160 |
noise_score |
integer |
Quality metric (0 = clean) | 0 |
source_file |
string |
Original BSE filename | Sejarah_X_2024.pdf |
page_range |
string |
Source page numbers | 15-20 |
💻 How to Use
Loading the Dataset (Python)
from datasets import load_dataset
# Load the BSE RAW corpus
dataset = load_dataset("Firmansyah-Ibrahim/indo-bloom-bse-raw")
# Print first example
print(dataset['train'][0])
# Output structure:
# {
# 'chunk_id': 'chunk_0001',
# 'mata_pelajaran': 'Sejarah',
# 'jenjang': 'SMA/MA',
# 'kelas': 'X',
# 'context': '...',
# 'word_count': 160,
# 'noise_score': 0,
# 'source_file': 'Sejarah_X_2024.pdf',
# 'page_range': '15-20'
# }
Filtering by Subject
import pandas as pd
# Load as pandas DataFrame
df = pd.DataFrame(dataset['train'])
# Filter by subject
sejarah_chunks = df[df['mata_pelajaran'] == 'Sejarah']
geografi_chunks = df[df['mata_pelajaran'] == 'Geografi']
biologi_chunks = df[df['mata_pelajaran'] == 'Biologi']
sosiologi_chunks = df[df['mata_pelajaran'] == 'Sosiologi']
print(f"Sejarah: {len(sejarah_chunks)} chunks")
print(f"Geografi: {len(geografi_chunks)} chunks")
Filtering by Grade Level
# Filter by grade
kelas_10 = df[df['kelas'] == 'X']
kelas_11 = df[df['kelas'] == 'XI']
kelas_12 = df[df['kelas'] == 'XII']
print(f"Kelas X: {len(kelas_10)} chunks")
print(f"Kelas XI: {len(kelas_11)} chunks")
print(f"Kelas XII: {len(kelas_12)} chunks")
Quality Filtering
# Get only high-quality chunks (noise_score = 0)
clean_chunks = df[df['noise_score'] == 0]
# Filter by word count range
medium_chunks = df[(df['word_count'] >= 100) & (df['word_count'] <= 200)]
print(f"Clean chunks: {len(clean_chunks)}")
print(f"Medium-length chunks: {len(medium_chunks)}")
🔧 Data Processing Pipeline
Extraction Process (IBEX v3.0)
graph TD
A[BSE PDF Files] -->|IBEX Extractor| B[Raw Text Extraction]
B -->|Noise Filter L1| C[Remove Instructional Content]
C -->|Noise Filter L2| D[Remove Exercise/Quiz Patterns]
D -->|Sentence Cleaning| E[Remove Pilgan Patterns]
E -->|Chunking Algorithm| F[Fixed-size Chunks 150±50 words]
F -->|Quality Check| G[Noise Score = 0]
G -->|Metadata Tagging| H[Final BSE RAW Corpus]
Technical Specifications
Extraction Tool
- Tool: IBEX v3.0 (Indo-Bloom Context Extractor)
- PDF Library: PyMuPDF (fitz)
- Processing: Python 3.8+
Cleaning Filters
Level 1 (Noise Patterns):
- Instructional phrases: "Simak gambar", "Jawablah pertanyaan"
- Learning objectives: "Tujuan pembelajaran", "Kata kunci"
- Metadata: ISBN, author names, page numbers
Level 2 (Sentence Cleaning):
- Multiple-choice patterns:
a. ... b. ... c. ... - Question prompts: "Diskusikan dengan teman"
- Activity instructions: "Buatlah laporan"
Chunking Algorithm
# Chunking strategy
CHUNK_SIZE = 150 # words
OVERLAP = 37 # 25% overlap
MIN_LENGTH = 50 # minimum viable chunk
MAX_LENGTH = 450 # maximum to prevent context overflow
# Quality threshold
NOISE_THRESHOLD = 0 # Only chunks with score 0 included
🔄 Dataset Relationship in Research Pipeline
Stage 0: BSE RAW Corpus (This Dataset)
Input: PDF textbooks from Kemdikbudristek
Process: IBEX v3.0 extraction & cleaning
Output: 1,825 clean educational text chunks
Status: ✅ Completed & Frozen
Stage 1: QA Generation
Input: BSE RAW Corpus (this dataset)
Process: Qwen2.5-3B-Instruct with Kemdikbud prompts
Output: Indo-Bloom Silver Corpus (2,768 QA pairs)
Status: ✅ Completed
Stage 2: Expert Annotation
Input: Indo-Bloom Silver Corpus
Process: Human expert validation (3 annotators)
Output: Indo-Bloom Annotated Corpus
Status: 🔵 In Progress
Stage 3: Gold Standard
Input: Indo-Bloom Annotated Corpus
Process: IAA validation (Kappa ≥ 0.70)
Output: Indo-Bloom Gold Corpus v1.0
Status: 🟡 Planned
📋 Extraction Scripts Documentation
Script: IBEX v3.0 (Indo-Bloom Context Extractor)
Input: BSE PDF files
Output: CSV with cleaned text chunks
Key Features:
- Margin removal (8% top/bottom)
- Hyphenation correction
- Noise pattern detection (30+ patterns)
- Chunk size balancing with overlap
- Domain metadata preservation
Extraction Statistics:
| Metric | Value |
|---|---|
| Raw pages processed | ~2,500 |
| Raw chunks extracted | ~3,200 |
| After noise filtering | 1,825 (57% retention) |
| Avg processing time | ~2.5 min/book |
🚀 Research Roadmap
- Stage 0 (Completed): BSE text extraction & cleaning (This Dataset)
- Stage 1 (Completed): QA generation with LLM (Silver Corpus)
- Stage 2 (In Progress): Expert annotation
- Stage 3 (Planned): IAA validation & Gold Standard release
- Stage 4 (Future): Model training & evaluation
⚖️ License & Attribution
Dataset License
License: CC BY 4.0
You are free to:
- ✅ Share — copy and redistribute the material
- ✅ Adapt — remix, transform, and build upon the material
Under the following terms:
- Attribution — You must give appropriate credit
Source Material
Original Content: Buku Sekolah Elektronik (BSE)
Publisher: Kementerian Pendidikan, Kebudayaan, Riset, dan Teknologi (Kemdikbudristek) Indonesia
Original License: CC BY 4.0 (as per BSE repository)
Books Included:
- Sejarah Indonesia (Kelas X, XI, XII)
- Geografi Indonesia (Kelas X, XI, XII)
- Biologi (Kelas X, XI, XII)
- Sosiologi (Kelas X, XI, XII)
📜 Citation
If you use this dataset in your research, please cite:
Primary Citation (Dataset Repository)
@misc{ibrahim2026bseraw,
author = {Ibrahim, Firmansyah},
title = {Indo-Bloom BSE RAW Corpus: Educational Context Extraction from Indonesian Textbooks},
year = {2026},
publisher = {Hugging Face},
howpublished = {\url{https://huggingface.co/datasets/Firmansyah-Ibrahim/indo-bloom-bse-raw}},
note = {Raw corpus for Indo-Bloom AQG research. Extracted from BSE Kemdikbudristek.}
}
Secondary Citation (Associated Research Paper)
@article{ibrahim2026indobloom_paper,
author = {Ibrahim, Firmansyah and Prasetya, Didik Dwi and Widiyaningtyas, Triyanna},
title = {Towards Indo-Bloom: A Preliminary Study on Controllable Bloom's
Taxonomy-aligned Question Generation in Indonesian},
journal = {TechRxiv},
year = {2026},
month = {February},
day = {27},
doi = {10.36227/techrxiv.177220024.48567886/v1},
url = {https://doi.org/10.36227/techrxiv.177220024.48567886/v1},
note = {Preprint — not peer-reviewed}
}
Combined Citation (For Academic Papers)
Text Format:
Ibrahim, F. (2026). Indo-Bloom BSE RAW Corpus: Educational Context Extraction from Indonesian Textbooks. Hugging Face. https://huggingface.co/datasets/Firmansyah-Ibrahim/indo-bloom-bse-raw
Associated paper: Ibrahim, F., Prasetya, D. D., & Widiyaningtyas, T. (2026). Towards Indo-Bloom. TechRxiv. https://doi.org/10.36227/techrxiv.177220024.48567886/v1
📞 Contact & Support
Principal Investigator:
Firmansyah Ibrahim (Ph.D. Candidate)
📧 Email: firmansyah.ibrahim.2505349@students.um.ac.id
🏛️ Institution: Universitas Negeri Malang
🔗 Dataset: indo-bloom-bse-raw
📄 Paper: TechRxiv Preprint
Supervision Team:
- Dr. Eng. Didik Dwi Prasetya, S.T., M.T. — Promotor
- Dr. Ir. Triyanna Widiyaningtyas, M.T. — Co-Promotor
🙏 Acknowledgments
We gratefully acknowledge:
- Kemdikbudristek Indonesia for publishing BSE textbooks under CC BY 4.0
- Universitas Negeri Malang for institutional support
- Hugging Face for open-source hosting infrastructure
- Indonesian NLP Community for domain expertise
- TechRxiv (IEEE) for preprint hosting
⚠️ Disclaimer
This dataset is extracted from educational materials published by Kemdikbudristek Indonesia. While we have performed careful cleaning and quality control, users should:
- ✅ Verify extracted content against original sources for critical applications
- ✅ Respect the original CC BY 4.0 license terms
- ✅ Acknowledge both this corpus and the original BSE sources
Educational Use: This corpus is intended for research and educational purposes, particularly for developing AI-assisted educational tools aligned with the Indonesian national curriculum.
🔗 Related Datasets
| Dataset | Stage | Status | Link |
|---|---|---|---|
| BSE RAW Corpus | Stage 0 | ✅ Released | [This dataset] |
| Indo-Bloom Silver | Stage 1 | ✅ Released | indo-bloom-corpus |
| Indo-Bloom Annotated | Stage 2 | 🔵 In Progress | Coming Soon |
| Indo-Bloom Gold | Stage 3 | 🟡 Planned | TBA |
Last Updated: February 28, 2026
Version: Raw v1.0 — 🔒 FROZEN
Status: ✅ Production Ready — Stage 0 Complete
Paper DOI: 10.36227/techrxiv.177220024.48567886/v1
© 2026 Firmansyah Ibrahim | Universitas Negeri Malang
- Downloads last month
- 13