Explaining educational recommendations through a concept-level knowledge visualization
Explaining educational recommendations through a concept-level knowledge visualization
复制标题
通过概念级知识可视化解释教育建议
DOI：
10.1145/3308557.3308690
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
Brusilovsky, Peter
中科院分区：
文献类型：
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作者：
Barria-Pineda, Jordan;Brusilovsky, Peter
在这篇演示文章中，我们提出了一种可视化的方法来解释个性化实践系统掌握网格中的学习内容推荐。所提出的方法使用Java编程中学生知识的概念级可视化来演示为什么系统推荐特定的练习内容。可视化的学生知识通过贝叶斯知识跟踪方法进行估计，该方法跟踪学生的问题解决表现。直观的解释部分，既显示了细粒度的知识水平，又显示了聚合的知识水平，并与文本解释一起呈现给学生。这种方法的目的是根据学生当前的知识和目标，即他们正在学习的当前主题，展示每个推荐项目的适宜性。
In this demo paper, we present a visual approach for explaining learning content recommendation in the personalized practice system Mastery Grids. The proposed approach uses a concept-level visualization of student knowledge in Java programming to demonstrate why specific practice content is recommended by the system. The visualized student knowledge is estimated by a Bayesian Knowledge Tracing approach, which traces student problem-solving performance. The visual explanatory components, which show both a fine-grained and aggregated knowledge level, are presented to the students along with textual explanations. The goal of this approach is to display the suitability of each recommended item in the context of a student's current knowledge and goal, i.e., the current topic they are studying.
编程入门课程的细粒度开放学习者模型
DOI：
10.1145/3209219.3209242
发表时间：
2018
期刊：
Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization
影响因子：
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作者：
Jordan Barria;Julio Daniel Guerra Hollstein;Peter Brusilovsky
通讯作者：
Peter Brusilovsky
2012 年推荐系统挑战赛
DOI：
--
发表时间：
2012
期刊：
ACM Conference on Recommender Systems
影响因子：
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作者：
N. Manouselis;A. Said;D. Tikk;J. Hermanns;B. Kille;H. Drachsler;K. Verbert;Kris Jack
通讯作者：
Kris Jack