| 研究実績の概要 |
This year I continue the work on learner's knowledge assessment (LKA). I have further explored the research of fine-grained assessment and interpretability. Improved on my previous work [BESC’20], I propose a novel model that can not only output the learners’ fine-grained knowledge states but also the item characteristics, enabling the interpretability. Extensive model analyses conducted from six perspectives on five real-world datasets validate its superiority. This work has been published in a top journal [Neurocomputing].
Another work solves the fundamental issues of data sparseness and information loss while improving the model performance. It has explored to incorporate the knowledge structure (KS) into the LKA to potentially resolve the above issues. This work automatically generates the KS from the learning logs and proposes a novel graph model with the attention mechanism. Extensive experiments show the effectiveness. This work has been published in a top journal [IJIS].
The above work stimulates a new idea of multimodal learning analysis. I have published a review paper about the empirical evidence on the usage of multimodal analysis to provide insights for smarter education. I also participated in a work published in [ICCE’21], in which a graph-based method is proposed for LKA.
I also finished my doctoral thesis, in which I summarize my PhD works. Overall, it proposes a general framework for dynamic LKA by integrating both learner and domain modeling. Based on this framework, it proposes three approaches, each addressing one specific issue in existing studies.
|
