Dynamic Bayesian Networks for Student Modeling
Dynamic Bayesian Networks for Student Modeling
复制标题
用于学生建模的动态贝叶斯网络
DOI：
10.1109/tlt.2017.2689017
复制
发表时间：
2017-10
影响因子：
3.7
通讯作者：
Tanja Käser;Severin Klingler;A. Schwing;Markus H. Gross
中科院分区：
文献类型：
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作者：
Tanja Käser;Severin Klingler;A. Schwing;Markus H. Gross
智能辅导系统根据学生个体的需求调整课程。因此，对学生知识的准确表示和预测至关重要。贝叶斯知识追踪 (BKT) 是一种流行的学生建模方法。然而，BKT 模型的结构使其无法表示学习领域的不同技能之间的层次结构和关系。另一方面，动态贝叶斯网络（DBN）能够在一个模型中联合表示多种技能。在这项工作中，我们建议使用 DBN 进行学生建模。我们引入了一种用于此类模型参数学习的约束优化算法。我们在数学、拼写学习和物理等不同学习领域的五个大型数据集上广泛评估和解释了我们的方法的预测准确性。我们还提供了与以前的学生建模方法的比较，并分析了不同学生建模技术对教学政策的影响。我们证明，我们的方法在所有学习领域的未见数据的预测准确性方面优于以前的技术，并产生有意义的教学策略。
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore, an accurate representation and prediction of student knowledge is essential. Bayesian Knowledge Tracing (BKT) is a popular approach for student modeling. The structure of BKT models, however, makes it impossible to represent the hierarchy and relationships between the different skills of a learning domain. Dynamic Bayesian networks (DBN) on the other hand are able to represent multiple skills jointly within one model. In this work, we suggest the use of DBNs for student modeling. We introduce a constrained optimization algorithm for parameter learning of such models. We extensively evaluate and interpret the prediction accuracy of our approach on five large-scale data sets of different learning domains such as mathematics, spelling learning, and physics. We furthermore provide comparisons to previous student modeling approaches and analyze the influence of the different student modeling techniques on instructional policies. We demonstrate that our approach outperforms previous techniques in prediction accuracy on unseen data across all learning domains and yields meaningful instructional policies.