Mining Educational Data to Predict Students Performance: A Comparative Study of Data Mining Techniques

Autor/inn/en	Nahar, Khaledun; Shova, Boishakhe Islam; Ria, Tahmina; Rashid, Humayara Binte; Islam, A. H. M. Saiful
Titel	Mining Educational Data to Predict Students Performance: A Comparative Study of Data Mining Techniques
Quelle	In: Education and Information Technologies, 26 (2021) 5, S.6051-6067 (17 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-021-10575-3
Schlagwörter	Learning Analytics; College Students; Engineering Education; Data Collection; Prediction; Academic Achievement; Educational Counseling; Classification; Artificial Intelligence; Courses; Grades (Scholastic); Tests; Accuracy; Bayesian Statistics; Models; Decision Making; Comparative Analysis + Suchen Sie Ihr Suchwort? Collegestudent; Ingenieurausbildung; Data capture; Datensammlung; Vorhersage; Schulleistung; Educational counselling; Educational guidance; Bildungsberatung; Erziehungsberatung; Classification system; Klassifikation; Klassifikationssystem; Künstliche Intelligenz; Kursangebot; Notenspiegel; Examination; Prüfung; Examen; Analogiemodell; Decision-making; Entscheidungsfindung
Abstract	Information is everywhere in a hidden and scattered way. It becomes useful when we apply Data mining to extracts the hidden, meaningful, and potentially useful patterns from these vast data resources. Educational data mining ensures a quality education by analyzing educational data based on various aspects. In this paper, we have analyzed the academic results and behavior of some engineering students. For this study, we collect data from 80 students from the CSE department. We gather data from mark sheets and other relevant factors that accelerate the results, collected through a survey. Our main goal is to predict the students' performance. According to this prediction, the counseling department will guide them in advance so that those who are likely to have bad results can do better. The classification can be based on various aspects, as many factors improve the educational system. We have created two datasets focusing on two different angles. Our first dataset classifies and predicts the category of a student (good, bad, medium) on a specific course based on their prerequisite course performance. We have implemented this in the artificial intelligence course. Our second dataset also classifies and predicts the final grade (A, B, C) of any random subject, here we organize our data such a way where it will only focus on how their performance was till the midterm exam. We analyze and compare six classification algorithms. We have focused on all aspects of an algorithm, not only the accuracy level but also the complexity and cost. We have built two final models for two of our datasets based on a decision tree and the naive Bayes algorithms accordingly. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01