Back to Search Start Over

Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics.

Authors :
Salazar-Fernandez, Juan Pablo
Munoz-Gama, Jorge
Maldonado-Mahauad, Jorge
Bustamante, Diego
SepĂșlveda, Marcos
Nieminen, Pentti
Source :
Applied Sciences (2076-3417); May2021, Vol. 11 Issue 9, p4265, 18p
Publication Year :
2021

Abstract

Featured Application: In this work, Process Mining techniques are used with a curricular analytics approach, to model the educational trajectories of engineering students during their first courses. 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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
11
Issue :
9
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
150375116
Full Text :
https://doi.org/10.3390/app11094265