Predicting Academic Outcomes: A Survey from 2007 Till 2018
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Predicting Academic Outcomes: A Survey from 2007 Till 2018 Sarah Alturki1 · Ioana Hulpuș1 · Heiner Stuckenschmidt1 Accepted: 19 September 2020 © The Author(s) 2020
Abstract The tremendous growth of educational institutions’ electronic data provides the opportu‑ nity 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 perfor‑ mance 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 high‑ light 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. Keywords Prediction · Higher education · Educational data mining · Academic achievement
* Sarah Alturki [email protected]‑mannheim.de Ioana Hulpuș [email protected]‑mannheim.de Heiner Stuckenschmidt [email protected]‑mannheim.de 1
Data and Web Science Group, Faculty of Business Informatics and Mathematics, University of Mannheim, Mannheim, Germany
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1 Introduction 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. In order to increase students’ success rate, it is vital to understand and define academic success. The definition of academic success is rather complex and wide-ranging; there‑ fore, it is frequently misused within educational research. However, 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 als
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