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LLMs tested for automated assessment of student programming explanations

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the use of Large Language Models (LLMs) for automatically assessing student self-explanations in programming education. The study compares LLM-based scoring methods against traditional semantic similarity techniques, aiming to determine the most effective approach for evaluating student-generated content. This research addresses the challenge of accurately judging the correctness of student explanations, a crucial component for enhancing learning through worked examples. AI

IMPACT This research could lead to more efficient and scalable methods for evaluating student understanding in programming courses.

RANK_REASON The cluster contains an academic paper detailing a comparison of LLMs against existing methods for a specific educational task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs tested for automated assessment of student programming explanations

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The cluster contains an academic paper detailing a comparison of LLMs against existing methods for a specific educational task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Arun-Balajiee Lekshmi-Narayanan, Mohammad Hassany, Peter Brusilovsky ·

    Exploring the Effectiveness of Using LLMs for Automated Assessment of Student Self Explanations in Programming Education

    arXiv:2605.21614v1 Announce Type: cross Abstract: Worked examples are step-by-step solutions to problems in a specific domain, offered to students to acquire domain-specific problem-solving skills. The effectiveness of worked examples could be enhanced by combining them with self…

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