What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling
What Should I Do Next? Adaptive Sequencing in the Context of Open Social Student Modeling
复制标题
接下来我应该做什么？
DOI：
10.1007/978-3-319-24258-3_12
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
Peter Brusilovsky
中科院分区：
文献类型：
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作者：
R. Hosseini;I;Julio Guerra;Peter Brusilovsky
智能教育系统的最初目标之一是引导每个学生获得最合适的教育内容。在之前的研究中，我们探索了基于知识的指导方法和社会指导方法，并了解到每种方法都有其弱点。在目前的工作中，我们探索了将社会引导与更传统的基于知识的引导系统相结合的想法，以支持更优化的内容导航。我们提出了一种贪婪排序方法，旨在最大化每个学生的知识水平，并在开放的社会学生建模界面的背景下实现它。我们进行了一项课堂研究，以检验这种组合指导方法的影响。我们的课堂研究结果表明，贪婪的指导方法对学生的导航产生了积极的影响，提高了优秀学生的学习速度，并提高了学生的整体表现，无论是在系统内还是通过课后评估。
One of the original goals of intelligent educational systems was to guide each student to the most appropriate educational content. In previous studies, we explored both knowledge-based and social guidance approaches and learned that each has a weak side. In the present work, we have explored the idea of combining social guidance with more traditional knowledge-based guidance systems in hopes of supporting more optimal content navigation. We propose a greedy sequencing approach aimed at maximizing each student’s level of knowledge and implemented it in the context of an open social student modeling interface. We performed a classroom study to examine the impact of this combined guidance approach. The results of our classroom study show that a greedy guidance approach positively affected students’ navigation, increased the speed of learning for strong students, and improved the overall performance of students, both within the system and through end-of-course assessments.