Improving early prediction of academic failure using sentiment analysis on self‐evaluated comments

• Published in: Journal of computer assisted learning Vol. 34; no. 4; pp. 358 - 365
• Main Authors: Yu, L.C., Lee, C.W., Pan, H.I., Chou, C.Y., Chao, P.Y., Chen, Z.H., Tseng, S.F., Chan, C.L., Lai, K.R.
• Format: Journal Article
• Language: English
• Published: Oxford Wiley-Blackwell 01-08-2018; Wiley Subscription Services, Inc
• Subjects: Academic Failure; Accuracy; Attendance; Data Analysis; Data mining; early prediction; Failure analysis; Homework; Identification; learning analytics; Learning Processes; Prediction; Predictions; Scientific visualization; Scores; Self Evaluation (Individuals); Sentiment analysis; Students; Teaching Methods; Unstructured data
• ISSN: 0266-4909; 1365-2729
• DOI: 10.1111/jcal.12247

This study presents a model for the early identification of students who are likely to fail in an academic course. To enhance predictive accuracy, sentiment analysis is used to identify affective information from text‐based self‐evaluated comments written by students. Experimental results demonstrated that adding extracted sentiment information from student self‐evaluations yields a significant improvement in early‐stage prediction quality. The results also indicate the limited early‐stage predictive value of structured data, such as homework completion, attendance, and exam grades, due to data sparseness at the beginning of the course. Thus, applying sentiment analysis to unstructured data (e.g., self‐evaluation comments) can play an important role in improving the accuracy of early‐stage predictions. The findings present educators with an opportunity to provide students with real‐time feedback and support to help students become self‐regulated learners. Using the exploring results for improvement in teaching and learning initiatives is important to maintain students' performances and the effectiveness of the learning process.
Lay Description
What is already known about this topic:
The impact of emotional state on academic performance
Early intervention is a key factor in preventing academic failure by at‐risk students.
What this paper adds:
Apply sentiment analysis to enhance predictive accuracy in early stage.
Built a Chinese affective resource with valence ratings for each affective word, and use it to automatically extract emotions from self‐evaluation comments.
Implications for practice and/or policy:
Point out the limited early‐stage predictive ability of structured data and using unstructured data to bridge the gap.
Using information visualization to build self‐regulated system by structured and unstructured data. For educators, easy to recognize students' emotion during the class; for students, easy to understand how well they are performing in a class in a timely manner.