文章基本信息

标题：Predicting Performance of Students in a Flipped Classroom Using Machine Learning: Towards Automated Data-Driven Formative Feedback
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作者：Jalal Nouri ; Mohammed Saqr ; Uno Fors 等
期刊名称：Journal of Systemics, Cybernetics and Informatics
印刷版ISSN：1690-4532
电子版ISSN：1690-4524
出版年度：2019
卷号：17
期号：2
页码：17-21
出版社：International Institute of Informatics and Cybernetics
摘要：
Education in schools and universities suffers from different problems like the lack of interaction between the lecturer and the students as well as the fear of asking irrelevant questions or providing wrong answers in front of a large audience. A lot of systems exist that try to solve these issues by means of technical tools; e.g., audience response systems. Each of these individual systems supports different functional scopes with different didactic purposes in order to support specific use cases. For the lecturer, it is very hard to choose an appropriate system. Besides the functional scope, there are a lot of predefined limitations, such as a given room with technical restrictions or a favorite operating system and presentation software to present the slides. This paper gives an overview of fifty existing systems (with varying degree of detail) and proposes a filter mechanism based on the index card metaphor to select appropriate systems depending on their individual limitations. In order to simplify this selection process for the lecturer, the filter mechanism is implemented in a web-based selection tool.