Measuring Domain Knowledge for Early Prediction of Student Performance: A Semantic Approach

Abstract

The growing popularity of data mining catalyses the researchers to explore various exciting aspects of education. Early prediction of student performance is an emerging area among them. The researchers have used various predictors in performance modelling studies. Although prior cognition can affect student performance, establishing their relationship is still an open research challenge. Quantifying the knowledge from readily available data is the major challenge here. We have proposed a semantic approach for this purpose. Association mining on nearly 0.35 million observations establishes that prior cognition impacts the student performance. The proposed approach of measuring domain knowledge can help the early performance modelling studies to use it as a predictor.

Published in 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). 8 pages, 5 figures (includes 16 plots). Accessible at https://ieeexplore.ieee.org/document/9368439