Atomic Learning Objectives Labeling: A High-Resolution Approach for Physics Education

• URL: http://arxiv.org/abs/2412.09914v1
• Date: Fri, 13 Dec 2024 07:05:31 GMT
• Title: Atomic Learning Objectives Labeling: A High-Resolution Approach for Physics Education
• Authors: Naiming Liu, Shashank Sonkar, Debshila Basu Mallick, Richard Baraniuk, Zhongzhou Chen,
• Abstract summary: This paper introduces a novel approach to create a high-resolution "map" for physics learning. We use Large Language Models for automated labeling of physics questions. Our work contributes to the field of learning analytics by proposing a more granular approach to mapping learning objectives with questions.
• Score: 2.6771924284364452
• License:
• Abstract: This paper introduces a novel approach to create a high-resolution "map" for physics learning: an "atomic" learning objectives (LOs) system designed to capture detailed cognitive processes and concepts required for problem solving in a college-level introductory physics course. Our method leverages Large Language Models (LLMs) for automated labeling of physics questions and introduces a comprehensive set of metrics to evaluate the quality of the labeling outcomes. The atomic LO system, covering nine chapters of an introductory physics course, uses a "subject-verb-object'' structure to represent specific cognitive processes. We apply this system to 131 questions from expert-curated question banks and the OpenStax University Physics textbook. Each question is labeled with 1-8 atomic LOs across three chapters. Through extensive experiments using various prompting strategies and LLMs, we compare automated LOs labeling results against human expert labeling. Our analysis reveals both the strengths and limitations of LLMs, providing insight into LLMs reasoning processes for labeling LOs and identifying areas for improvement in LOs system design. Our work contributes to the field of learning analytics by proposing a more granular approach to mapping learning objectives with questions. Our findings have significant implications for the development of intelligent tutoring systems and personalized learning pathways in STEM education, paving the way for more effective "learning GPS'' systems.

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