generating-practice-questions

--- name: generating-practice-questions description: Generate educational practice questions from lecture notes to test student understanding. Use when users request practice questions, exam preparation materials, study guides, or assessment items based on lecture content. --- # Practice Question Generator Generate comprehensive practice questions from lecture notes to test student understanding of learning objectives and key concepts. ## Input **Supported formats**: LaTeX (.tex), PDF, Markdown (.md), plain text (.txt) - **PDF**: Use `pdfplumber` for text extraction - **LaTeX**: Read as text, strip preamble (everything before `\begin{document}`), preserve math environments (`$...$`, `\[...\]`, `\begin{equation}`, etc.) - **Markdown/Text** **Content to extract**: 1. **Learning objectives** - Usually at beginning: "After this lecture, you should be able to..." or may be in section: "Learning Outcomes","Objectives", "Goals". If absent, derive from main topics. 2. **Main topics** - Section headings, bold terms, definitions, algorithms. 3. **Examples** - Use for realistic scenarios in questions. ## Question Structure Generate questions in this order: 1. **True/False** (one per learning objective, or 3-5 if no objectives) 2. **Explanatory Questions** (3-5 covering main topics) 3. **Coding Question** (1 algorithm implementation or concept simulation) 4. **Use Case** (1 realistic application) For each question type, follow guidelines below, and never include answer key. ## Question Guidelines ### Type 1: True/False Test factual understanding and common misconceptions. **Coverage**: - One per learning objective, or 3-5 covering main topics if no objectives **Difficulty progression**: - Start with 1-2 simple definitional questions - Include 2-3 reasoning-based questions requiring concept application or testing relationships between concepts **Quality criteria**: - Unambiguous with one correct interpretation - Clear language without complex nested clauses - Answer directly found in lecture notes - Wrong answer reveals common misconception **Examples**: - *Easy*: "In supervised learning, the training data includes both input features and their corresponding labels." - *Medium*: "A model with high training accuracy but low test accuracy is likely underfitting the data." ### Type 2: Explanatory Questions Test deeper understanding by requiring students to articulate concepts, compare approaches, and explain reasoning. **Topic selection**: - Choose 3-5 main topics (key algorithms and their implementations, Advantages/limitations of approaches, Relationships between concepts) - Avoid repetition: If topic appears in T/F, ask about a *different aspect* **Question formulations**: - "Explain..." - requires description in student's words - "Compare and contrast..." - tests understanding of differences - "Why does..." - tests causal reasoning - "What are the advantages/disadvantages..." - tests critical analysis - "Describe the steps..." - tests procedural knowledge **Quality criteria**: - Open-ended but focused - Cannot be answered with simple yes/no - Requires 3-5 sentences to answer well **Examples**: - "Explain the bias-variance tradeoff. How does increasing model complexity affect bias and variance?" - "Compare K-Nearest Neighbors and Decision Trees in terms of decision boundaries, training time, and prediction time." ### Type 3: Coding Question Test practical implementation through code. **Scope**: - Implementation of an algorithm discussed in lecture - Simulation of a concept or process - Must be achievable with lecture knowledge only - Should take 15-30 minutes for prepared student **Required structure**: 1. Clear objective 2. Step-by-step instructions (3-5 steps) 3. Function signature (if applicable) 4. Expected behavior with input/output examples 5. Hints (optional but helpful) **Language**: - Python (default) with standard library. - If using external libraries: NumPy, pandas, matplotlib, scikit-learn. - Should not require advanced Python features ### Type 4: Use Case Question Test ability to apply concepts or algorithms explained in lecture notes to realistic scenarios. **Components**: 1. **Context** - Realistic scenario description 2. **Data description** - What data is available (provide generation code if needed) 3. **Task** - What needs accomplishment 4. **Constraints** (optional) - Time, space, accuracy requirements 5. **Hints** (2-3) - Guidance without giving solution 6. **Libraries** - Can use scikit-learn, pandas, NumPy **Data generation**: If needed, provide simple, clear code to generate appropriate data. ## Output Format Guidelines Output format depends on user request (LaTeX, PDF, Markdown, plain text). **General structure for all formats**: 1. Title with document name 2. Instructions section 3. Part 1: True/False Questions (numbered sequentially) 4. Part 2: Explanatory Questions (numbered sequentially) 5. Part 3: Coding Question (with steps, signature, examples, hints) 6. Part 4: Use Case Application (with scenario, data, task, requirements, hints) **For specific formats**: For LaTeX and Markdown document structures, use the following templates (in `assets/` folder): - `questions_template.tex` - Complete LaTeX document structure with formatting - `markdown_template.md` - Complete Markdown document structure ## Supporting Resources **References** (in `references/` folder): - `examples_by_topic.md` - Domain-specific question examples for ML topics (algorithms, preprocessing, evaluation, etc.)