Proximity-Based Educational Recommendations: A Multi-Objective Framework
Proximity-Based Educational Recommendations: A Multi-Objective Framework
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发表时间：
2022
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通讯作者：
Chunpai Wang;Shaghayegh Sherry Sahebi;Peter Brusilovsky
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作者：
Chunpai Wang;Shaghayegh Sherry Sahebi;Peter Brusilovsky
个性化学习和教育推荐系统是现代在线教育系统的组成部分。在这种背景下，向学生推荐最佳学习材料的问题是顺序多目标推荐的一个很好的例子。教材编写者在学生学习过程的每一步都需要在适应学生能力、调整教材难度、增加学生知识、服务学生兴趣等多个目标之间进行优化和平衡。然而，这些目标的模糊性和不兼容性给学习材料的编写者带来了额外的挑战。为了解决这些挑战，我们提出了基于邻近度的教育推荐（PEAR），一个推荐框架，通过近似和平衡问题难度和学生能力来建议问题的排名列表。为了实现这些目标的精确近似，PEAR可以与任何最先进的学生和领域知识模型集成。作为这种学生和领域知识模型的一个例子，我们引入了基于深度Q矩阵的知识跟踪模型（DQKT），并将PEAR与之集成。该框架通过跟踪学生的知识水平，而不是静态推荐，在每一步动态地建议新的问题。我们使用一个离线评估框架，鲁棒评估矩阵（REM），比较PEAR与各种基线推荐政策在三个不同的学生模拟器，并证明我们提出的模型的有效性。我们用不同的学生轨迹长度进行了实验，结果表明，虽然PEAR可以用更少的数据比基线策略表现得更好，但它在更长的序列长度下也很稳健。
Personalized learning and educational recommender systems are integral parts of modern online education systems. In this context, the problem of recommending the best learning material to students is a perfect example of sequential multi-objective recommendation. Learning material recommenders need to optimize for and balance between multiple goals, such as adapting to student ability, adjusting the learning material difficulty, increasing student knowledge, and serving student interest, at every step of the student learning sequence. However, the obscurity and incompatibility of these objectives pose additional challenges for learning material recommenders. To address these challenges, we propose Proximity-based Educational Recommendation (PEAR), a recommendation framework that suggests a ranked list of problems by approximating and balancing between problem difficulty and student ability. To achieve an accurate approximation of these objectives, PEAR can integrate with any state-of-the-art student and domain knowledge model. As an example of such student and domain knowledge model, we introduce Deep Q-matrix based Knowledge Tracing model (DQKT), and integrate PEAR with it. Rather than static recommendations, this framework dynamically suggests new problems at each step by tracking student knowledge level over time. We use an offline evaluation framework, Robust Evaluation Matrix (REM), to compare PEAR with various baseline recommendation policies under three different student simulators and demonstrate the effectiveness of our proposed model. We experiment with different student trajectory lengths and show that while PEAR can perform better than the baseline policies with fewer data, it is also robust with longer sequence lengths.