| 現在までの達成度 (区分) |
現在までの達成度 (区分)
1: 当初の計画以上に進展している
理由
This research is generally going well beyond expected. It proposes two models for tracing learner knowledge states dynamically based on the learners’ exercising logs on the online learning systems. These two models model leaners’ performance and overcome issues of existing models from different perspectives.
One explores the factors that influence the knowledge acquisition and makes use of rich information during learners’ learning interactions to achieve more precise prediction of learner knowledge. The other explores to combine the knowledge tracing and cognitive diagnosis assessment to fulfill the requirements of both large-scale assessment and interpretability to explain the diagnosed results. Both models are evaluated on the real-world datasets and demonstrate the superiority over existing models. The research results are published in one international journal and an intentional conference, making good contributions to the research field.
The above work implements the idea in the research plan. This is a big improvement to the existing framework, and will also be a starting point to some future works. I also strengthen myself a lot and grow up as a researcher smoothly through this work.
|
| 今後の研究の推進方策 |
I will continue my research plan, and contribute more to the intelligent tutoring community. Firstly, I will further explore the current research of fine-grained assessment and interpretability. We have verified its effectiveness on learner performance modeling, however, the deep model analysis to investigate its ability and the effectiveness of its modules has not been fully conducted. I will further test the ability of knowledge proficiency inference in a multi-granularity manner and the interpretability of the learner performances in terms of the knowledge states and item characteristics.
Secondly, I will keep on the research in learner knowledge assessment, especially paying attention to some fundamental issues of the existing work. Especially, the performances of existing models greatly suffer from the sparseness of the input data and the information loss when modeling the learning process. How to alleviate the sparseness and the information loss while improving the model performance is another future work. We will develop new techniques to cope with this issue.
Thirdly, the work of learner knowledge assessment can be further improved by incorporating multimodal data collected during the learning process. This multimodal learning analysis will build more reliable model for assessing the learners.
|
