Predicting student dropout: A machine learning approach

Kemper, Lorenz; Vorhoff, Gerrit; Wigger, Berthold U.

doi:10.1080/21568235.2020.1718520

Cited by 108 publications

(66 citation statements)

References 19 publications

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“…The success rate of the previous semester, which was usually measured by GPA, has also been used often (Nghe et al 2007;Kabakchieva 2013;Alemu Yehuala 2015;Abu Saa 2016;Asif et al 2017;Al luhaybi et al 2018;Kemper 2018) in the studies we have reviewed. That is due to the fact that students' success is highly dependent on previously acquired knowledge or skills.…”

Section: Results In Previous Semestermentioning

confidence: 99%

Predicting Academic Outcomes: A Survey from 2007 Till 2018

2020

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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.

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Section: Results In Previous Semestermentioning

confidence: 99%

Predicting Academic Outcomes: A Survey from 2007 Till 2018

2020

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“…In the second paper of this section, Kemper, Vorhoff, and Wigger (2020) perform two approaches of machine learning, logistic regressions and decision trees, to predict student dropout at the Karlsruhe Institute of Technology (KIT). The models are computed on the basis of examination data, i.e.…”

Section: Overview Of the Papers Included In The Special Issuementioning

confidence: 99%

The role of higher education for the development of entrepreneurial ecosystems

Lehmann

Meoli

Paleari

et al. 2020

European Journal of Higher Education

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“…The most popular data mining methods include decision trees, neural networks, logistic regression and cluster analysis [9]. The traditional classification cannot directly be utilized for estimating probability events [10]. The neural network can make predictions, but the process of the algorithm cannot be interpreted [11].…”

Section: Introductionmentioning

confidence: 99%

Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout

Csalódi

Abonyi

2021

Mathematics

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A data-driven method to identify frequent sets of course failures that students should avoid in order to minimize the likelihood of their dropping out from their university training is proposed. The overall probability distribution of the dropout is determined by survival analysis. This result can only describe the mean dropout rate of the undergraduates. However, due to the failure of different courses, the chances of dropout can be highly varied, so the traditional survival model should be extended with event analysis. The study paths of students are represented as events in relation to the lack of completing the required subjects for every semester. Frequent patterns of backlogs are discovered by the mining of frequent sets of these events. The prediction of dropout is personalised by classifying the success of the transitions between the semesters. Based on the explored frequent item sets and classifiers, association rules are formed providing the estimates of the success of the continuation of the studies in the form of confidence metrics. The results can be used to identify critical study paths and courses. Furthermore, based on the patterns of individual uncompleted subjects, it is suitable to predict the chance of continuation in every semester. The analysis of the critical study paths can be used to design personalised actions minimizing the risk of dropout, or to redesign the curriculum aiming the reduction in the dropout rate. The applicability of the method is demonstrated based on the analysis of the progress of chemical engineering students at the University of Pannonia in Hungary. The method is suitable for the examination of more general problems assuming the occurrence of a set of events whose combinations may trigger a set of critical events.

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Predicting student dropout: A machine learning approach

Cited by 108 publications

References 19 publications

Predicting Academic Outcomes: A Survey from 2007 Till 2018

Predicting Academic Outcomes: A Survey from 2007 Till 2018

The role of higher education for the development of entrepreneurial ecosystems

Integrated Survival Analysis and Frequent Pattern Mining for Course Failure-Based Prediction of Student Dropout

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