Generating Practice Questions

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. Do not include an answer key — students need to arrive at answers independently, and providing one on the same document defeats the purpose of self-testing.

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:

• If the user requests LaTeX output, read assets/questions_template.tex before generating — use it as the document skeleton.
• If the user requests Markdown output, read assets/markdown_template.md before generating — use it as the document skeleton.
• For plain text, no template is needed.

Supporting Resources

References (in references/ folder):

• examples_by_topic.md — Domain-specific question examples for ML topics (algorithms, preprocessing, evaluation, linear algebra, etc.). Read this file if the lecture covers any ML topic to get calibrated examples before generating questions. Note: this file covers ML only — skip it for non-ML lectures (history, biology, economics, etc.).