MACHINE PERCEPTION AND ROBOTICS GROUP

機械知覚&ロボティクスグループ
中部大学

With the digitalization of the educational environment, educational support is anticipated by predicting student performance from the operation log data of digital teaching materials. However, these methods require the construction of large-scale systems and have to collect extensive long-term log data. Therefore, we focus on the response sentences from lecture questionnaires, which have a simple recording system. We collected the response sentences from lectures given at Japanese universities, we will classify students’ grades using a Transformer Encoder. In particular, utilizing Term Frequency-Inverse Document Frequency (TF-IDF) to analyze written responses, we identify words indicative of each student’s grade. Then, to emphasize the identified words during the inference phase of the Transformer Encoder model for grade prediction, we aim to improve the accuracy of the predictions. In the evaluation experiment using the proposed method, the accuracy of grade prediction improved by 2.5 pt and the f1-score improved by 1.2 pt, compared to the baseline.