Generating Practice Questions Agent Skill

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:

• 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.
• Main topics - Section headings, bold terms, definitions, algorithms.
• Examples - Use for realistic scenarios in questions.

Question Structure

Generate questions in this order:

• True/False (one per learning objective, or 3-5 if no objectives)
• Explanatory Questions (3-5 covering main topics)
• Coding Question (1 algorithm implementation or concept simulation)
• 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:

• Clear objective
• Step-by-step instructions (3-5 steps)
• Function signature (if applicable)
• Expected behavior with input/output examples
• 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:

• Context - Realistic scenario description
• Data description - What data is available (provide generation code if needed)
• Task - What needs accomplishment
• Constraints (optional) - Time, space, accuracy requirements
• Hints (2-3) - Guidance without giving solution
• 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:

• Title with document name
• Instructions section
• Part 1: True/False Questions (numbered sequentially)
• Part 2: Explanatory Questions (numbered sequentially)
• Part 3: Coding Question (with steps, signature, examples, hints)
• 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.)