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Motivating Students in Collaborative Activities With Game-Theoretic Group Recommendations

机译：激励学生在游戏 - 理论集团的建议中进行协作活动

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摘要

Recommending educational resources to groups of students is a common task in collaborative learning contexts. However, differences in within-group motivational factors might lead to conflicts in students' intention to use the resources. Previous methods fail to achieve high goodness of recommendation for the majority of students in heterogeneous groups. This study demonstrates a game-theoretic solution for recommending educational resources to homogeneous and heterogeneous groups. The group members are the players, the resources comprise the set of possible actions, and selecting those items that will maximize all students' motivation in the collaborative activity is a problem of finding the Nash Equilibrium (NE). In case the NE is Pareto efficient, none of the players can get more payoff (motivation) without decreasing the payoff of any other player, indicating an optimal benefit for the group as a whole. The suggested approach was empirically evaluated in a controlled experiment with a real dataset. The relevance of each delivered item to its corresponding students was explored both from the perspective of the group and its the individual students. The accuracy of the predicted group/individual motivation, the goodness of the ranked list of recommendations, and the problem-solving performance for the treatment group were significantly higher compared to the control groups. Limitations of the approach, as well as future work plans conclude the paper.
机译：推荐给学生群体的教育资源是协同学习环境中的共同任务。然而，集团内部动机因素的差异可能导致学生使用资源的意图发生冲突。以前的方法无法实现异构群体中大多数学生的高位建议。本研究表明，用于将教育资源推荐给同类和异质组的游戏理论解决方案。该组成员是玩家，资源包括这一组可能的行动，并选择将最大化协作活动中所有学生动机的项目是找到纳什均衡（NE）的问题。如果NE是帕累托有效的情况下，没有一个玩家可以获得更多的回报（动机）而不降低任何其他玩家的回报，表明整个组的最佳益处。建议的方法在具有真实数据集的受控实验中进行了经验评估。从本集团及其个人学生的角度来看，每个交付物品与其相应学生的相关性。与对照组相比，预测群体/个人动机的准确性，建议的良好，以及治疗组的问题溶解性的性能明显高得多。该方法的局限性以及未来的工作计划得出结论本文。

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