Open AccessStudents Performance Prediction by Mining Behavioural Pattern
Author(s) -
Hongbin Sun,
Shunping He,
Olaoluwa Esho
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1881/4/042051
Subject(s) - construct (python library) , cognition , frame (networking) , computer science , correlation , academic achievement , regression analysis , variable (mathematics) , educational data mining , psychology , regression , artificial intelligence , machine learning , mathematics education , applied psychology , mathematics , telecommunications , mathematical analysis , geometry , neuroscience , psychoanalysis , programming language
Analysing and monitoring students’ progress and performance is an active research area in educational data mining. Some research work uses direct construct relating to academic achievements such as GPA, SAT in predicting performance while others make use of psychometric construct in measuring skill and knowledge, ability and educational achievement from data gotten from questionnaires. In this paper, we propose the use of psychometric construct as a method of extracting non-cognitive features from campus check-ins data in predicting academic performance of college students. A P-FRAME framework was developed and non-cognitive attributes extracted through psychological theories were used as the predicting variables. The extracted variable was applied to various penalized regression algorithm and the results were compared. The result of our experiments showed a high correlation between the actual score and the predicted score which is within 2:0 of reported score.