Predicting academic success in higher education: literature review and best practices
By Eyman A. Alyahyan, Dilek Düştegör. International Journal of Educational Technology in Higher Education, 2020
520 citations recorded by the source corpus.
Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance. Early detection of students at risk, along with preventive measures, can drastically improve their success. Lately, machine learning techniques have been extensively used for prediction purpose. While there is a plethora of success stories in the literature, these techniques are mainly accessible to “computer science”, or more precisely, “artificial intelligence” literate educators. Indeed, the effective and efficient application of data mining methods entail many decisions, ranging from how to define student’s success , through which student attributes to focus on , up to which machine learning method is more appropriate to the given problem . This study aims to provide a step-by-step set of guidelines for educators willing to apply data mining techniques to predict student success. For this, the literature has been reviewed, and the state-of-the-art has been compiled into a systematic process, where possible decisions and parameters are comprehensively covered and explained along with arguments. This study will provide to educators an easier access to data mining techniques, enabling all the potential of their application to the field of education.
Subfields: Artificial Intelligence, Computer Science Applications
View Paper: https://www.semanticscholar.org/paper/28ee2bc6d5b1a072dd0c82521be0e325544dea10
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In Artificial Intelligence
In Computer Science Applications
Citations in the corpus
- Human-centered artificial intelligence in education: Seeing the invisible through the visible (2021, background): “Learning analytics and assessments must also be considered (Alyahyan and Düs¸teg € or, 2020).”
- Artificial intelligence in online higher education: A systematic review of empirical research from 2011 to 2020 (2022, background): “For instance, existing review work has summarized the trends of AIEd (Tang et al., 2021; Xie et al., 2019; Zhai et al., 2021), applications (Alyahyan & Düştegör, 2020; Hooshyar et al., 2016; Liz-Domínguez et al., 2019; Shahiri et al., 2015), theoretical paradigms (Ouyang & Jiao, 2021) and AI…”
- A Review of Artificial Intelligence (AI) in Education from 2010 to 2020 (2021, methodology): “…teachers Song and Wang [84] Analyzing analysis of worldwide educational artificial intelligence research development in recent twenty years Ulum [ [106] Constructing guidelines to apply data mining techniques to predict student success Renz and Hilbig [107] Analyzing the drivers and barriers that…”
- A Systematic Literature Review of Student’ Performance Prediction Using Machine Learning Techniques (2021, background): “Determine the remedial plans for the observed cases in the first three objectives There are some previous attempts to survey the literature on academic performance [9,10]; however, most of them are general literature reviews and targeted towards the generic students performance prediction.”