Predicting Student Performance in Higher Education Institutions Using Decision Tree Analysis
Open accessAbstract
The overall success of educational institutions can be measured by the success of its students. Providing factors that increase success rate and reduce the failure of students is profoundly helpful to educational organizations. Data mining is the best solution to finding hidden patterns and giving suggestions that enhance the performance of students. This paper presents a model based on decision tree algorithms and suggests the best algorithm based on performance. Three built classifiers (J48, Random Tree and REPTree) were used in this model with the questionnaires filled in by students. The survey consists of 60 questions that cover the fields, such as health, social activity, relationships, and academic performance, most related to and affect the performance of students. A total of 161 questionnaires were collected. The Weka 3.8 tool was used to construct this model. Finally, the J48 algorithm was considered as the best algorithm based on its performance compared with the Random Tree and RepTree algorithms.
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AI governance functions as an invisible educational infrastructure that guides the formation of critical skills and shapes not only behaviours but also values.
“AI governance functions, in this sense, as an invisible educational infrastructure that guides the formation of critical skills. It shapes not only behaviours but also values, providing students with clear guidelines in an ever-changing technological universe [33,47].”
From Artificial Intelligence Governance in Higher Education: The Role of Knowledge-Based Strategies in Fostering Legal Awareness and Ethical Artificial Intelligence Literacy (2025) · cites this paper as [47]
The Abstract and Conclusion describe decision-tree predictions that may give students and staff a roadmap for improving academic performance. Searches for AI governance, educational infrastructure, critical skills, behaviours, values, and governance guidelines found no passage addressing the attributed claim; the roadmap concerns a predictive model, not AI governance. (LLM, conf=0.76)
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- Authors
- Hamoud, A. K., Hashim, A. S., & Awadh, W. A.
- Affiliations
- University of Basrah (IQ)
- ORCID
- Hamoud, Alaa Khalaf
- Venue
- International Journal of Interactive Multimedia and Artificial Intelligence
- Published
- 2018-09-01
- Volume
- 5
- Issue
- 2
- Pages
- 26-31
- ISSN
- 1989-1660 (ISSN-L)
- OpenAlex
- W2789635809
- Cited by
- 184
- References
- 0
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 38 · 2023: 28 · 2022: 38 · 2021: 34 · 2020: 16 · 2019: 8 · 2018: 3 · 2017: 1
- FWCI
- 19.29
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal of Interactive Multimedia and Artificial Intelligence (publishedVersion, cc-by)
- SSRN Electronic Journal (submittedVersion)
- Dialnet (Universidad de la Rioja) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Re-Unir (International University of La Rioja) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- student performance prediction, school dropout, higher education, decision tree classification, J48 decision tree, random trees, REPTree, student survey data, student health, student social activity, student relationships, academic performance
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