Mining educational data to predict students performance

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...

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Bibliographic Details
Published in:Education and information technologies 2021-09, Vol.26 (5), p.6051-6067
Main Authors: Nahar, Khaledun, Shova, Boishakhe Islam, Ria, Tahmina, Rashid, Humayara Binte, Islam, A. H. M. Saiful
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
College students
Data mining
Datasets
Educational Quality
Engineering Education
Equipment and supplies
Prerequisites
Students
Surveys
Teaching
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Summary: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.
ISSN:1360-2357
1573-7608
DOI:10.1007/s10639-021-10575-3