Predictive Modelling and Analytics of Students' Grades Using Machine Learning Algorithms

Autor/inn/en	Badal, Yudish Teshal; Sungkur, Roopesh Kevin
Titel	Predictive Modelling and Analytics of Students' Grades Using Machine Learning Algorithms
Quelle	In: Education and Information Technologies, 28 (2023) 3, S.3027-3057 (31 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Sungkur, Roopesh Kevin)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-022-11299-8
Schlagwörter	Prediction; Models; Learning Analytics; Grades (Scholastic); Artificial Intelligence; Algorithms; Electronic Learning; Academic Achievement; Learner Engagement + Suchen Sie Ihr Suchwort? Vorhersage; Analogiemodell; Notenspiegel; Künstliche Intelligenz; Algorithm; Algorithmus; Schulleistung
Abstract	The outbreak of COVID-19 has caused significant disruption in all sectors and industries around the world. To tackle the spread of the novel coronavirus, the learning process and the modes of delivery had to be altered. Most courses are delivered traditionally with face-to-face or a blended approach through online learning platforms. In addition, researchers and educational specialists around the globe always had a keen interest in predicting a student's performance based on the student's information such as previous exam results obtained and experiences. With the upsurge in using online learning platforms, predicting the student's performance by including their interactions such as discussion forums could be integrated to create a predictive model. The aims of the research are to provide a predictive model to forecast students' performance (grade/engagement) and to analyse the effect of online learning platform's features. The model created in this study made use of machine learning techniques to predict the final grade and engagement level of a learner. The quantitative approach for student's data analysis and processing proved that the Random Forest classifier outperformed the others. An accuracy of 85% and 83% were recorded for grade and engagement prediction respectively with attributes related to student profile and interaction on a learning platform. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01