.png){kind=icon}
.png){kind=icon}
Return
Can We Predict Student Performance Based on Tabular and Textual Data?
.png){kind=icon}
PRE
AI
DOI：10.1109/ACCESS.2022.3198682.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
Predictive models
Behavioral sciences
Data mining
Data integration
Sentiment analysis
Soft sensors
Transformers
Deep learning
Electronic education
Distance learning
Performance evaluation
Random forests
Educational data mining
deep learning
multimodal
data fusion
random forest
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W