使用人工智能分析课程模式复杂性以提高按时毕业率
Analyzing Curricular Pattern Complexity Using AI to Improve On-Time Graduation Rates
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中文总结 AI 辅助
该研究针对软件工程本科课程模式，利用人工智能技术尤其是大语言模型，分析课程模式并给出修订建议，以减少课程变更时间，减少瓶颈和毕业延迟，满足学生需求，提高按时毕业率。
中文摘要 AI 辅助
人工智能的兴起使大量数据的自动分析成为可能。以前耗时费力的任务借助人工智能能更高效完成。本研究运用人工智能技术分析和修订软件工程本科学位的课程模式。课程常存在长序列，序列内课程不及格可能危及四年内完成学位。大学教师手动分析和修订课程既冗长又费力，导致变化极少且无法跟上学生不断变化的需求。本研究利用大语言模型分析课程模式并提出修订建议，减少课程变更时间，减少瓶颈和毕业延迟。
英文摘要
The rise of Artificial Intelligence (AI) enables automatic analysis of large amounts of data. Previously time-consuming and labor-intensive tasks can be completed much more efficiently with the use of AI. This work uses AI techniques to analyze and revise curricular patterns in an undergraduate degree for Software Engineering. Curricula often have long sequences where failure to pass a class within the sequence may jeopardize completion of the degree within four years. Manual analysis and revision of curricula by university faculty is a lengthy and labor-intensive process, causing changes to occur rarely and making it impossible to keep up with the changing needs of students. This work reduces the time-to-change for curricula and reduces bottlenecks and graduation delays by using Large Language Models (LLMs) to analyze curricular patterns and suggest revisions.
发表机构
- Embry-Riddle Aeronautical University（安柏瑞德航空大学）
机构由 AI 辅助整理，请以论文原文为准。
