Leveraging Human-Large Language Model Collaboration to Generate High-Quality Math Multiple Choice Questions

Core Concepts

Crafting high-quality math multiple choice questions (MCQs) is a labor-intensive process that requires educators to formulate precise stems and plausible distractors. This paper introduces a prototype tool that facilitates collaboration between large language models (LLMs) and educators to streamline the math MCQ generation process.

Abstract

The paper introduces a prototype tool called the Human Enhanced Distractor Generation Engine (HEDGE) that leverages the expertise of educators to generate math MCQs through a two-step process:

Generation of the question stem, key, and explanation:

The LLM (GPT-4) generates the initial stem, key, and explanation, which the educators then evaluate and edit to ensure mathematical accuracy and relevance to the intended knowledge component (KC).

Generation of distractors, misconceptions, and feedback:

The LLM generates a set of possible errors/misconceptions and the corresponding distractors and feedback, which the educators then evaluate and edit to ensure they correspond to valid distractors for the generated question stem.

The pilot study involving four math educators reveals that while 70% of the generated stems, keys, and explanations were considered valid, only 37% of the generated misconceptions, distractors, and feedback were deemed valid. This observation underscores the necessity of involving human experts in the process of generating math MCQs to leverage their knowledge of common student errors and misconceptions.
The paper also discusses potential improvements to the tool, such as using multiple in-context examples, providing a bank of distractors, and allowing educators to customize the content to make the questions more engaging and relevant for students.

Stats

70% of the generated stems, keys, and explanations were considered valid by the participants.
Only 37% of the generated misconceptions, distractors, and feedback were deemed valid by the participants.

Quotes

"The emergence of large language models (LLMs) has raised hopes for making MCQ creation more scalable by automating the process."
"Nevertheless, a human-AI collaboration has the potential to enhance the efficiency and effectiveness of MCQ generation."

Key Insights Distilled From

Math Multiple Choice Question Generation via Human-Large Language Model Collaboration

by Jaewook Lee,... at arxiv.org 05-03-2024

https://arxiv.org/pdf/2405.00864.pdf

Deeper Inquiries

How can the tool be further enhanced to better capture common student errors and misconceptions in the generated distractors?

To better capture common student errors and misconceptions in the generated distractors, the tool can be enhanced in several ways:

Error Bank Integration: Implementing an error bank within the tool where educators can contribute and select from a pool of common student errors and misconceptions. This would allow for a more targeted selection of distractors that align closely with actual student misconceptions.

Multiple In-Context Examples: Providing multiple in-context examples for each question stem to prompt LLMs to generate a wider range of potential misconceptions. This would expose the model to a variety of scenarios, increasing the likelihood of capturing common errors.

Ranking Misconceptions: Allowing educators to rank generated misconceptions based on their relevance and likelihood of student occurrence. This ranking system can guide the selection of distractors that best reflect common student errors.

Feedback Loop: Implementing a feedback loop where educators can provide feedback on the quality and relevance of generated distractors. This iterative process can help refine the distractors to better align with anticipated student errors.

Diverse Question Difficulty Levels: Incorporating questions with varying levels of difficulty and complexity to prompt the generation of distractors that cater to different skill levels. This would ensure that distractors are not only aligned with misconceptions but also with the cognitive abilities of students.

How can the tool be designed to facilitate the sharing and incorporation of educator-provided misconceptions to continuously improve the quality of the generated MCQs?

To facilitate the sharing and incorporation of educator-provided misconceptions for continuous improvement of the generated MCQs, the tool can be designed with the following features:

Collaborative Platform: Create a collaborative platform where educators can contribute, share, and discuss common student errors and misconceptions. This platform can serve as a repository for educators to collectively build a comprehensive database of misconceptions.

Misconception Library: Integrate a misconception library within the tool that houses a curated collection of educator-provided misconceptions. Educators can easily access and select relevant misconceptions to incorporate into the MCQ generation process.

Tagging System: Implement a tagging system that allows educators to tag misconceptions based on subject areas, difficulty levels, and specific topics. This tagging system would streamline the process of searching for and incorporating relevant misconceptions into MCQs.

Version Control: Incorporate version control features to track changes made to misconceptions and distractors over time. This would enable educators to monitor the evolution of the generated MCQs and assess the impact of incorporating different misconceptions.

Feedback Mechanism: Include a feedback mechanism where educators can provide input on the effectiveness of incorporated misconceptions in improving student engagement and learning outcomes. This feedback loop would inform future iterations of MCQ generation and refinement.

What other domains beyond math MCQ generation could benefit from a human-LLM collaboration approach?

Several domains beyond math MCQ generation could benefit from a human-LLM collaboration approach, including:

Language and Literature: Generating diverse and engaging reading comprehension questions, analyzing literary texts, and crafting language-based assessments.

Science and Biology: Creating interactive science quizzes, formulating biology-related multiple-choice questions, and developing assessments for scientific concepts.

History and Social Studies: Designing historical timeline quizzes, generating questions on significant events, and assessing knowledge of social studies topics.

Computer Science: Developing coding challenges, creating programming-related MCQs, and evaluating understanding of algorithms and data structures.

Medical Education: Crafting medical terminology quizzes, generating anatomy and physiology questions, and assessing clinical knowledge through multiple-choice assessments.

Psychology and Behavioral Sciences: Formulating psychology-related MCQs, analyzing case studies, and evaluating understanding of behavioral theories and concepts.

By leveraging the collaborative capabilities of human educators and LLMs in these domains, it is possible to enhance the quality, diversity, and effectiveness of assessment materials and educational resources.