Abstract
Curricular analytics is the area of learning analytics that looks for insights and evidence on the relationship between curricular elements and the degree of achievement of curricular outcomes. For higher education institutions, curricular analytics can be useful for identifying the strengths and weaknesses of the curricula and for justifying changes in learning pathways for students. This work presents the study of curricular trajectories as processes (i.e., sequence of events) using process mining techniques. Specifically, the Backpack Process Model (BPPM) is defined as a novel model to unveil student trajectories, not by the courses that they take, but according to the courses that they have failed and have yet to pass. The usefulness of the proposed model is validated through the analysis of the curricular trajectories of N = 4466 engineering students considering the first courses in their program. We found differences between backpack trajectories that resulted in retention or in dropout; specific courses in the backpack and a larger initial backpack sizes were associated with a higher proportion of dropout. BPPM can contribute to understanding how students handle failed courses they must retake, providing information that could contribute to designing and implementing timely interventions in higher education institutions.
Highlights
In the last decade, different techniques have progressively emerged for the analysis of data recorded by information systems, with the purpose of supporting informed decisionmaking in Higher Education Institutions (HEIs) [1]
To broaden the current understanding of how curricula can be improved based on evidence from data, this paper presents a model for systematizing the analysis of curricular trajectories as processes, based on the backpack concept and making use of formal Process Mining (PM) techniques
As mentioned above, our Backpack Process Model (BPPM) model proposes a different perspective on curricular data, where each event in a student’s trajectory is the backpack he or she has at the end of each academic term
Summary
Different techniques have progressively emerged for the analysis of data recorded by information systems, with the purpose of supporting informed decisionmaking in Higher Education Institutions (HEIs) [1]. Some tools have been developed that integrate different techniques for the analysis of curricular trajectories (e.g., data mining techniques such as clustering and classification) [10,11], with the purpose of obtaining a “snapshot of the student” at a specific moment in time with respect to their progress Some of these tools are, e.g., academic advising systems, which allow to see the progress status of a student according to their curriculum [4,7,12] or early dropout prediction systems in MOOC courses [13].
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