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

TL;DR

This paper introduces a semantic method to quantify prior cognition from educational data, aiding early prediction of student performance by establishing its impact through association mining on large datasets.

Contribution

It proposes a novel semantic approach to measure domain knowledge from data, enhancing early student performance prediction models.

Findings

Prior cognition significantly influences student performance.

Association mining confirms the impact of prior knowledge.

The approach enables early performance prediction using domain knowledge.

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.