MACHINE PERCEPTION AND ROBOTICS GROUP

機械知覚&ロボティクスグループ
中部大学

Many studies applying neural networks to the field of education have focused on student performance prediction and explainability of their decisions.
While those studies introduced neural networks into educational settings, such networks cannot directly support student learnings in place of teachers.
Therefore, we present a method that uses a general Transformer encoder to recommend appropriate learning actions for improving student performance.
By considering the attention weight of a low-performing student to be close to that of a high-performing student, our method recommends the learning materials and actions for learning the materials.
To evaluate the effectiveness of our method, we trained a deep neural network (DNN) on a private dataset of student operations (e.g., NEXT, PREV, OPEN) on digital learning materials obtained from a Japanese university.
The number of operations divided by each learning material and by type of operation are input to the DNN, and the DNN outputs the student’s grade on 5-point scale.
We applied our method with this trained DNN to samples that successfully predicted grades, and the number of operations increased on the basis of the recommended learning materials and actions.
By re-inputting modified sample into the DNN, we then observe how the student performance changes.
The results of this simple experiment indicate that more students improved their performance with both the material-based and operation-based recommendations than with random recommendations. The percentage of students whose grades improved tended to be larger for those with low grades.
Specifically, the improvement ratio for students with the two lowest grades was over 90% by operation-based recommendation.
This is consistent with our intuition that low-performing students are more likely to improve.