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Thrilled by Your Progress! Large Language Models (GPT-4) No Longer Struggle to Pass Assessments in Higher Education Programming courses 23

Thrilled by Your Progress! Large Language Models (GPT-4) No ...

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19th Annual ACM Conference on International Computing Education Research V.1 (ICER)

作者： Savelka, Jaromir Agarwal, Arav An, Marshall Bogart, Chris Sakr, Majd Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450399760

This paper studies recent developments in large language models' (LLM) abilities to pass assessments in introductory and intermediate python programming courses at the postsecondary level. The emergence of ChatGPT resulted in heated debates of its potential uses (e.g., exercise generation, code explanation) as well as misuses in programming classes (e.g., cheating). Recent studies show that while the technology performs surprisingly well on diverse sets of assessment instruments employed in typical programming classes the performance is usually not sufficient to pass the courses. The release of GPT-4 largely emphasized notable improvements in the capabilities related to handling assessments originally designed for human test-takers. This study is the necessary analysis in the context of this ongoing transition towards mature generative AI systems. Specifically, we report the performance of GPT-4, comparing it to the previous generations of GPT models, on three python courses with assessments ranging from simple multiple-choice questions (no code involved) to complex programming projects with code bases distributed into multiple files (599 exercises overall). Additionally, we analyze the assessments that were not handled well by GPT-4 to understand the current limitations of the model, as well as its capabilities to leverage feedback provided by an auto-grader. We found that the GPT models evolved from completely failing the typical programming class' assessments (the original GPT-3) to confidently passing the courses with no human involvement (GPT-4). While we identified certain limitations in GPT-4's handling of MCQs and coding exercises, the rate of improvement across the recent generations of GPT models strongly suggests their potential to handle almost any type of assessment widely used in higher education programming courses. These findings could be leveraged by educators and institutions to adapt the design of programming assessments as well as to fuel the nec

关键词： AI code generation introductory and intermediate programming Multiple-choice question answering MCQ coding exercises generative pre-trained transformers GPT python course programming knowledge assessment ChatGPT Codex GitHub Copilot Alpha-Code

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Can Generative Pre-trained Transformers (GPT) Pass Assessments in Higher Education Programming courses? 2023

Can Generative Pre-trained Transformers (GPT) Pass Assessmen...

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28th Annual Conference on Innovation and Technology in Computer Science Education (ITiCSE)

作者： Savelka, Jaromir Agarwal, Arav Bogart, Christopher Song, Yifan Sakr, Majd Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9798400701382

We evaluated the capability of generative pre-trained transformers (GPT), to pass assessments in introductory and intermediate python programming courses at the postsecondary level. Discussions of potential uses (e.g., exercise generation, code explanation) and misuses (e.g., cheating) of this emerging technology in programming education have intensified, but to date there has not been a rigorous analysis of the models' capabilities in the realistic context of a full-fledged programming course with diverse set of assessment instruments. We evaluated GPT on three python courses that employ assessments ranging from simple multiple-choice questions (no code involved) to complex programming projects with code bases distributed into multiple files (599 exercises overall). Further, we studied if and how successfully GPT models leverage feedback provided by an auto-grader. We found that the current models are not capable of passing the full spectrum of assessments typically involved in a python programming course (<70% on even entry-level modules). Yet, it is clear that a straightforward application of these easily accessible models could enable a learner to obtain a non-trivial portion of the overall available score (>55%) in introductory and intermediate courses alike. While the models exhibit remarkable capabilities, including correcting solutions based on auto-grader's feedback, some limitations exist (e.g., poor handling of exercises requiring complex chains of reasoning steps). These findings can be leveraged by instructors wishing to adapt their assessments so that GPT becomes a valuable assistant for a learner as opposed to an end-to-end solution.

关键词： AI code generation python course programming knowledge assessment introductory and intermediate programming GitHub copilot generative pre-trained transformers GPT codex alphacode

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