ON THE EFFECTIVENESS OF AN AI-DRIVEN EDUCATIONAL RESOURCE RECOMMENDATION SYSTEM FOR HIGHER EDUCATION

Johannes Schrumpf
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Abstract

Digital resources offer a vast assortment of educational opportunities for students in higher education. From 2018 to 2022, a digital study assistant (DSA), named SIDDATA, was developed at three German universities and consequently field-tested. One of the DSA’s features is an AI-driven natural language interface for educational resource recommendation. This paper performs an analysis of the effectiveness of recommendations, by analyzing data generated over the course of two years of DSA usage. We find that although initial user interest is high, only a small percentage of users engage with the recommendation feature. Furthermore, we find that quality of recommendations was perceived as mixed to negative.
人工智能驱动的高等教育教育资源推荐系统有效性研究
数字资源为高等教育的学生提供了各种各样的教育机会。从2018年到2022年,德国三所大学开发了名为SIDDATA的数字学习助理(DSA),并进行了实地测试。DSA的功能之一是人工智能驱动的自然语言界面,用于教育资源推荐。本文通过分析两年DSA使用过程中生成的数据,对推荐的有效性进行了分析。我们发现,虽然最初的用户兴趣很高,但只有一小部分用户参与推荐功能。此外,我们发现推荐的质量被认为是好坏参半的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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