Abstract
Late dropout is one of the most pressing challenges currently facing higher education, and the process that each student follows to arrive at that decision usually involves several academic periods. This work presents a curricular analytics approach at the program level, to analyze how educational trajectories of undergraduate students in high-failure rate courses help to describe the process that leads to late dropout. Educational trajectories (n = 10,969) of high-failure rate courses are created using Process Mining techniques, and the results are discussed based on established theoretical frameworks. Late dropout was more frequent among students who took a stopout while having high-failure rate courses they must retake. Furthermore, students who ended in late dropout with high-failure rate courses they must retake had educational trajectories that were on average shorter and less satisfactory. On the other hand, the educational trajectories of students who ended in late dropout without high-failure rate courses they must retake were more similar to those of students who graduated late. Moreover, some differences found among ISCED fields are also described. The proposed approach can be replicated in any other university to understand the educational trajectories of late dropout students from a longitudinal perspective, generating new knowledge about the dynamic behavior of the students. This knowledge can trigger improvements to the curriculum and in the follow-up mechanisms used to increase student retention.
Highlights
We focus on analyzing late dropout using Process Mining [17], which aims to extract knowledge from event logs obtained from information systems, in order to discover process models, verify conformance, and suggest improvements [17]
The use of Process Mining techniques to model educational trajectories based on academic records allowed us to analyze them with a comprehensive process-oriented approach
We proposed a novel curricular analytics approach to modeling the educational trajectories in high-failure rate courses, called RETAKE-STOPOUT model
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations In recent years, both researchers and practitioners have been applying learning analytics to support curriculum understanding and improvement [1,2]. CA uses data both at course-level and program-level, to analyze the interactions between students and the curriculum along time [1] This understanding can help to identify points in the program structure where interventions are more relevant to improve student progression [15]. The advantage of the process-centric approach lies in that it supports a more holistic comprehension about the process (in this case, the educational trajectory), using graphical and analytical tools [17] These kinds of models facilitate visual cognition and make understanding of sequences of events more efficient [19].
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