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Enhancing Student Performance Prediction via Educational Data Mining on Academic Data

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Title: Enhancing Student Performance Prediction via Educational Data Mining on Academic Data
Language: English
Authors: Zareen Alamgir, Habiba Akram, Saira Karim, Aamir Wali
Source: Informatics in Education. 2024 23(1):1-24.
Availability: Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://infedu.vu.lt/journal/INFEDU
Peer Reviewed: Y
Page Count: 24
Publication Date: 2024
Document Type: Journal Articles
Information Analyses
Education Level: Higher Education
Postsecondary Education
Descriptors: Data Analysis, Information Retrieval, Content Analysis, Information Technology, Pattern Recognition, Undergraduate Students, Academic Achievement, Grade Prediction, At Risk Students, Core Curriculum, Computer Science Education, Learning Analytics
ISSN: 1648-5831
2335-8971
Abstract: Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modelling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students' performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performances and identify the critical areas that need significant improvement.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1428731
Database: ERIC
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  Data: Enhancing Student Performance Prediction via Educational Data Mining on Academic Data
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  Data: <searchLink fieldCode="AR" term="%22Zareen+Alamgir%22">Zareen Alamgir</searchLink><br /><searchLink fieldCode="AR" term="%22Habiba+Akram%22">Habiba Akram</searchLink><br /><searchLink fieldCode="AR" term="%22Saira+Karim%22">Saira Karim</searchLink><br /><searchLink fieldCode="AR" term="%22Aamir+Wali%22">Aamir Wali</searchLink>
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  Data: Vilnius University Institute of Mathematics and Informatics, Lithuanian Academy of Sciences. Akademjos str. 4, Vilnius LT 08663 Lithuania. Tel: +37-5-21-09300; Fax: +37-5-27-29209; e-mail: info@mii.vu.lt; Web site: https://infedu.vu.lt/journal/INFEDU
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  Data: Educational data mining is widely deployed to extract valuable information and patterns from academic data. This research explores new features that can help predict the future performance of undergraduate students and identify at-risk students early on. It answers some crucial and intuitive questions that are not addressed by previous studies. Most of the existing research is conducted on data from 2-3 years in an absolute grading scheme. We examined the effects of historical academic data of 15 years on predictive modelling. Additionally, we explore the performance of undergraduate students in a relative grading scheme and examine the effects of grades in core courses and initial semesters on future performances. As a pilot study, we analyzed the academic performance of Computer Science university students. Many exciting discoveries were made; the duration and size of the historical data play a significant role in predicting future performance, mainly due to changes in curriculum, faculty, society, and evolving trends. Furthermore, predicting grades in advanced courses based on initial pre-requisite courses is challenging in a relative grading scheme, as students' performance depends not only on their efforts but also on their peers. In short, educational data mining can come to the rescue by uncovering valuable insights from academic data to predict future performances and identify the critical areas that need significant improvement.
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      – Text: English
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      – SubjectFull: Data Analysis
        Type: general
      – SubjectFull: Information Retrieval
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      – SubjectFull: Content Analysis
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      – SubjectFull: Information Technology
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      – SubjectFull: Pattern Recognition
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      – SubjectFull: Undergraduate Students
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      – SubjectFull: Academic Achievement
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      – SubjectFull: Grade Prediction
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      – SubjectFull: At Risk Students
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      – SubjectFull: Core Curriculum
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      – SubjectFull: Computer Science Education
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      – SubjectFull: Learning Analytics
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      – TitleFull: Enhancing Student Performance Prediction via Educational Data Mining on Academic Data
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