Classification of Dropouts to Improve Student Re-Engagement: The Case of Chilean Secondary Opportunity Centers

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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“…. P(e n k k (j))) (12) This probability defines the hazard function h c (t k ) for the e c k competing risk of the survival analysis:…”

Section: Integrated Analysis Of the Association Rulesmentioning

confidence: 99%

“…This method is also not suitable to determine frequent itemsets. Cluster analysis is suitable for partitioning the students, but it cannot provide a prediction model [12]. We note that there are often only a few samples to which to apply a complex parametric model.…”

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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Classification of Dropouts to Improve Student Re-Engagement: The Case of Chilean Secondary Opportunity Centers

Cited by 1 publication

References 65 publications

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

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

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