Abstract
The tremendous growth of educational institutions’ electronic data provides the opportunity to extract information that can be used to predict students’ overall success, predict students’ dropout rate, evaluate the performance of teachers and instructors, improve the learning material according to students’ needs, and much more. This paper aims to review the latest trends in predicting students’ performance in higher education. We provide a comprehensive background for understanding Educational Data Mining (EDM). We also explain the measures of determining academic success and highlight the strengths and weaknesses of the most common data mining (DM) tools and methods used nowadays. Moreover, we provide a rich literature review of the EDM work that has been published during the past 12 years (2007–2018) with focus on the prediction of academic performance in higher education. We analyze the most commonly used features and methods in predicting academic achievement, and highlight the benefits of the mostly used DM tools in EDM. The results of this paper could assist researchers and educational planners who are attempting to carry out EDM solutions in the domain of higher education as we highlight the type of features that the previous researches found to have significant impact on the prediction, as well as the benefits and drawbacks of the DM methods and tools used for predicting academic outcomes.
Highlights
Since higher education plays an essential role in the development of a society (Pinheiro et al 2015), increasing student success is a long-term goal for academic institutions
The study of York et al (2015) suggests a theoretically grounded definition of academic success that is made up of six components: (1) academic achievement, which is nearly entirely meas‐ ured with course grades and grade point average (GPA), (2) satisfaction, which is often captured either by course evaluation or institutional surveys, (3) persistence, which is measured by retention between particular years of college and degree attainment rates, (4) acquisition of skills and competencies, which can be measured by assignments and course evaluations, (5) attainment of learning objectives, which can be measured by assignments and course evaluations, and (6) career success, which can be determined by job attainment rates, promotion histories, career satisfaction and profes‐ sional goal attainment
This paper provides a rich literature review on the prediction of aca‐ demic achievement in higher education for the past 12 years with the final aim of providing researchers and educational planners with information to assist them when attempting to carry out an Educational Data Mining (EDM) solution
Summary
Since higher education plays an essential role in the development of a society (Pinheiro et al 2015), increasing student success is a long-term goal for academic institutions. Awareness of students’ success factors could assist in achieving the highest level of quality education (Yassein et al 2017) It can potentially help in providing a clear and strong description of the types of knowl‐ edge and behaviour that are associated with adequate performance. EDM refers to techniques and tools designed for automatically extracting useful information and patterns from huge data repositories related to people learning activities in an educational environment (Nithya et al 2016). Those tools employ machine learning algorithms, database systems, statistical analysis, and artificial intelligence. The DM techniques include regression, clustering, clas‐ sification, association, and prediction
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