Open AccessCan We Predict Student Performance Based on Tabular and Textual Data?
Author(s) -
Yubin Qu,
Fang Li,
Long Li,
Xianzhen Dou,
Hongmei Wang
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3198682
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
With the emergence of more new teaching systems, such as Massive Open Online Courses (MOOCs), massive amounts of data are constantly being collected. There is a huge value in these massive teaching data. However, the data, including both student behavior data and student comment data about the course, is not processed to discover models and paradigms which can be useful for school management. There is no multimodal dataset with tabular and textual data for educational data mining yet. We first collect a dataset that included student behavior data and course comments textual data. Then we fuse the student behavior data with course comments textual data to predict student performance, using a Transformer-based framework with a uniform vector representation. The empirical results of the collected dataset show the effectiveness of our proposed method. In terms of F1 and AUC the performance of our method improves by up to 3.33% and 4.37% respectively. We find that the uniform feature vector representation learned by our proposed method can indeed improve the classifier’s performance, compared with existing works. Further, we validate our approach on an open dataset. The results of the empirical study show that our proposed method has a strong generalization capability. Moreover, we perform interpretability analysis using the SHapley Additive exPlanation (SHAP) method and find that text features have a more important influence on the classification model. This further illustrates that fusing text features can improve the performance of classification models.