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Cited by 14Online Learning and AnalyticsData Mining and Machine Learning ApplicationsArtificial Intelligence in Healthcare

Application of Machine Learning on Student Data for the Appraisal of Academic Performance

Alloghani, Mohamed, Al-Jumeily, Dhiya, Hussain, Abir, Aljaaf, Ahmed J., Mustafina, Jamila, Petrov, Egor·2018·2018 11th International Conference on Developments in eSystems Engineering (DeSE)
DOI 10.1109/dese.2018.00038
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Abstract

Education With the inclusion and integration of internet and digital learning Education 2. 0 brought tools in the different context of education. The use of social networking concepts such as chat rooms and the ever-growing student data have placed education on the brink of becoming one of the craters and users of Big Data. As such, this paper explores educational data mining techniques alongside some of the emerging learning analytics with the objective of gaining insight into some of the common learning behaviors among students. The task at hand embraces predictive analytics and it employs decision trees, neural networks, and Naïve Bayes algorithms to classify and cluster student learning patterns that can explain academic performance. Predictive analytics has emerged as one of the tools furthering adaptive learning among other lifechanging novelties. Nonetheless, integration of big data in academia is in its infancy although the western hemisphere is making progress towards the integration. Such progress will increase the relevance of data mining in education and this paper envisages to be among the first ones to address the applicability of machine learning in improving education. Hence, the objective of this paper is to develop predictive models based on the decision tree, neural network, and Naïve Bayes algorithms.

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Crossref · 10.1109/dese.2018.00038

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20 of 20 references verified0 citation errors · 0 not checkable

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  • In [29], predictive analytics using decision trees, neural networks, and Naïve Bayes was used to gain insights into student learning behaviors, cluster learning patterns, and explain academic performance.

    “The type of ML method being employed here is predictive analytics, which focuses on making predictions about future outcomes based on historical data.The objective of using predictive analytics, as mentioned, is to gain insights into student learning behaviors, cluster student learning patterns, and explain academic performance.”

    From Adaptive Learning Using Artificial Intelligence in e-Learning: A Literature Review (2023) · cites this paper as [29]

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Authors
Alloghani, M., Al-Jumeily, D., Hussain, A., Aljaaf, A. J., Mustafina, J., & Petrov, E.
Affiliations
  • Liverpool John Moores University (GB)
  • University of Anbar (IQ)
  • Kazan Federal University (RU)
ORCID
Venue
2018 11th International Conference on Developments in eSystems Engineering (DeSE)
Publisher
IEEE
Published
2018-09
Pages
157-162
DOI
10.1109/dese.2018.00038
OpenAlex
W2916725386
Cited by
14
References
20
Citations by year
2025: 2 · 2024: 2 · 2023: 2 · 2022: 5 · 2021: 2 · 2019: 1
FWCI
0.85
Type
conference paper
Language
en
Open access
closed
Locations
International Conference on Developments in eSystems Engineering (DeSE) (publishedVersion)
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, student data, educational data mining, learning analytics, big data in education, student learning patterns, adaptive learning, decision tree, artificial neural networks, naive Bayes, predictive analytics

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