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.
翻译:数据开采越来越受欢迎,促使研究人员探索各种令人振奋的教育方面。早期预测学生成绩是其中的一个新兴领域。研究人员在业绩建模研究中使用了各种预测器。虽然先前的认知会影响学生的表现,但建立他们的关系仍然是一项公开的研究挑战。从现成的数据中量化知识是这里的主要挑战。我们为此提出了一种语义学方法。协会在近35万次观测中发现,先前的认知会影响学生的表现。拟议的域知识计量方法有助于早期绩效建模研究将其用作预测器。