提高高等教育机构的课程评估流程:模块化系统方法
İlker Kocaoğlu1, Erinç Karataş2
1Management Information Systems, Baskent University, Ankara, Turkey.
PeerJ. Computer science
|September 24, 2025
概括
本研究引入了一个模块化系统,通过检测数字和文本反之间的不一致性来改进课程和教师评估 (CIE). 该系统通过可靠的数据洞察提高了教育质量评估.
科学领域:
- 教育技术的教育技术
- 教育中的人工智能
- 高等教育中的数据科学
背景情况:
- 传统的课程和教师评估 (CIE) 面临着结构化响应和开放式反之间的不一致数据的挑战.
- 现有的方法往往无法将数字分数与文本评论相协调,导致不可靠的洞察力和增加的行政负担.
- 提高CIE数据的可靠性对于高等教育的有效决策至关重要.
研究的目的:
- 设计和评估一种新的模块化系统,以提高课程和教师评估的准确性和可靠性.
- 通过整合情绪分析和不一致性检测来解决CIE数据中的不一致性.
- 为高等教育机构提供可扩展和适应的数据驱动决策解决方案.
主要方法:
- 利用设计科学研究方法 (DSRM) 开发了一个五模块系统架构.
- 采用机器学习算法,包括GPT-4 Turbo Preview,分析了13651个匿名的土耳其CIE记录.
- 对比情绪分析的结果是开放式反与结构化响应以识别数据不一致.
主要成果:
- GPT-4 Turbo Preview 模型在情绪分析和不一致性检测方面表现出卓越的性能.
- 一个原型系统在CIE数据的一个子集中发现了37%的不一致率.
- 排除不一致的数据产生了可靠的报告,对课程和教师表现有可操作的见解.
结论:
- 拟议的模块化系统有效地提高了课程和教师评估的准确性和可靠性.
- 该系统为寻求改善教育质量评估的高等教育机构提供了可扩展和适应的解决方案.
- 集成先进的机器学习技术代表了利用技术提高教育水平的重大进步.
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