Enhancing Student Learning with LLM-Generated Retrieval Practice Questions: An Empirical Study in Data Science Courses

• URL: http://arxiv.org/abs/2507.05629v2
• Date: Tue, 29 Jul 2025 14:11:25 GMT
• Title: Enhancing Student Learning with LLM-Generated Retrieval Practice Questions: An Empirical Study in Data Science Courses
• Authors: Yuan An, John Liu, Niyam Acharya, Ruhma Hashmi,
• Abstract summary: Large Language Models (LLMs) can generate retrieval practice questions in response to prompts.<n>Students exposed to LLM-generated retrieval practice achieved significantly higher knowledge retention, with an average accuracy of 89%.<n>These findings suggest that LLM-generated retrieval questions can effectively support student learning and may provide a scalable solution for integrating retrieval practice into real-time teaching.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval practice is a well-established pedagogical technique known to significantly enhance student learning and knowledge retention. However, generating high-quality retrieval practice questions is often time-consuming and labor intensive for instructors, especially in rapidly evolving technical subjects. Large Language Models (LLMs) offer the potential to automate this process by generating questions in response to prompts, yet the effectiveness of LLM-generated retrieval practice on student learning remains to be established. In this study, we conducted an empirical study involving two college-level data science courses, with approximately 60 students. We compared learning outcomes during one week in which students received LLM-generated multiple-choice retrieval practice questions to those from a week in which no such questions were provided. Results indicate that students exposed to LLM-generated retrieval practice achieved significantly higher knowledge retention, with an average accuracy of 89%, compared to 73% in the week without such practice. These findings suggest that LLM-generated retrieval questions can effectively support student learning and may provide a scalable solution for integrating retrieval practice into real-time teaching. However, despite these encouraging outcomes and the potential time-saving benefits, cautions must be taken, as the quality of LLM-generated questions can vary. Instructors must still manually verify and revise the generated questions before releasing them to students.

Related papers

• NLP Methods May Actually Be Better Than Professors at Estimating Question Difficulty [15.12489035385276]
  We compare various Large Language Model-based methods with three professors in their ability to estimate what percentage of students will give correct answers on True/False exam questions.<n>We obtained even better results using uncertainties of the LLMs solving the questions in a supervised learning setting, using only 42 training samples.
  arXiv  Detail & Related papers  (2025-08-05T10:12:38Z)
• On the effectiveness of LLMs for automatic grading of open-ended questions in Spanish [0.8224695424591679]
  This paper explores the performance of different LLMs and prompting techniques in automatically grading short-text answers to open-ended questions.<n>Results are notably sensitive to prompt styles, suggesting biases toward certain words or content in the prompt.
  arXiv  Detail & Related papers  (2025-03-23T13:43:27Z)
• Automated Assignment Grading with Large Language Models: Insights From a Bioinformatics Course [0.0]
  Natural language processing and large language models (LLMs) offer a promising solution by enabling the efficient delivery of personalized feedback.<n>Recent advances in natural language processing and large language models (LLMs) offer a promising solution by enabling the efficient delivery of personalized feedback.<n>Our results show that with well-designed prompts, LLMs can achieve grading accuracy and feedback quality comparable to human graders.
  arXiv  Detail & Related papers  (2025-01-24T13:59:14Z)
• Embracing AI in Education: Understanding the Surge in Large Language Model Use by Secondary Students [53.20318273452059]
  Large language models (LLMs) like OpenAI's ChatGPT have opened up new avenues in education.<n>Despite school restrictions, our survey of over 300 middle and high school students revealed that a remarkable 70% of students have utilized LLMs.<n>We propose a few ideas to address such issues, including subject-specific models, personalized learning, and AI classrooms.
  arXiv  Detail & Related papers  (2024-11-27T19:19:34Z)
• Exploring Knowledge Boundaries in Large Language Models for Retrieval Judgment [56.87031484108484]
  Large Language Models (LLMs) are increasingly recognized for their practical applications. Retrieval-Augmented Generation (RAG) tackles this challenge and has shown a significant impact on LLMs. By minimizing retrieval requests that yield neutral or harmful results, we can effectively reduce both time and computational costs.
  arXiv  Detail & Related papers  (2024-11-09T15:12:28Z)
• Exploring Knowledge Tracing in Tutor-Student Dialogues using LLMs [49.18567856499736]
  We investigate whether large language models (LLMs) can be supportive of open-ended dialogue tutoring.<n>We apply a range of knowledge tracing (KT) methods on the resulting labeled data to track student knowledge levels over an entire dialogue.<n>We conduct experiments on two tutoring dialogue datasets, and show that a novel yet simple LLM-based method, LLMKT, significantly outperforms existing KT methods in predicting student response correctness in dialogues.
  arXiv  Detail & Related papers  (2024-09-24T22:31:39Z)
• Supporting Self-Reflection at Scale with Large Language Models: Insights from Randomized Field Experiments in Classrooms [7.550701021850185]
  We investigate the potential of Large Language Models (LLMs) to help students engage in post-lesson reflection. We conducted two randomized field experiments in undergraduate computer science courses.
  arXiv  Detail & Related papers  (2024-06-01T02:41:59Z)
• Explainable Few-shot Knowledge Tracing [48.877979333221326]
  We propose a cognition-guided framework that can track the student knowledge from a few student records while providing natural language explanations. Experimental results from three widely used datasets show that LLMs can perform comparable or superior to competitive deep knowledge tracing methods.
  arXiv  Detail & Related papers  (2024-05-23T10:07:21Z)
• BIDER: Bridging Knowledge Inconsistency for Efficient Retrieval-Augmented LLMs via Key Supporting Evidence [23.55601157586831]
  This paper introduces BIDER, an approach that refines retrieval documents into Key Supporting Evidence. We train BIDER by learning from crafting KSE, while maximizing its output to align with LLM's information acquisition preferences. Evaluations show BIDER boosts LLMs' answer quality by 7% while reducing input content length in retrieval documents by 80%, outperforming existing methods.
  arXiv  Detail & Related papers  (2024-02-19T14:28:31Z)
• Self-Knowledge Guided Retrieval Augmentation for Large Language Models [59.771098292611846]
  Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. Self-Knowledge guided Retrieval augmentation (SKR) is a simple yet effective method which can let LLMs refer to the questions they have previously encountered.
  arXiv  Detail & Related papers  (2023-10-08T04:22:33Z)
• Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation [109.8527403904657]
  We show that large language models (LLMs) possess unwavering confidence in their knowledge and cannot handle the conflict between internal and external knowledge well. Retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries. We propose a simple method to dynamically utilize supporting documents with our judgement strategy.
  arXiv  Detail & Related papers  (2023-07-20T16:46:10Z)
• Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey [53.73359052511171]
  Reinforcement learning (RL) is a popular paradigm for addressing sequential decision tasks in which the agent has only limited environmental feedback. We present a framework for curriculum learning (CL) in RL, and use it to survey and classify existing CL methods in terms of their assumptions, capabilities, and goals.
  arXiv  Detail & Related papers  (2020-03-10T20:41:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.