Browsing by Author "Salazar Fernandez, Juan Pablo"

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    Backpack Process Model (BPPM): A Process Mining Approach for Curricular Analytics
    (MDPI, 2021) Salazar Fernandez, Juan Pablo; Muñoz Gama, Jorge; Maldonado Mahauad, Jorge; Bustamante, Diego; Sepúlveda, Marcos
    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.
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    Influence of Student Diversity on Educational Trajectories in Engineering High-Failure Rate Courses that Lead to Late Dropout
    (IEEE, 2019) Salazar Fernandez, Juan Pablo; Sepúlveda Cárdenas, Marcos Daniel; Munoz Gama, Jorge
    Global growth in participation in higher education has helped to increase diversity of students, and traditionally underrepresented minorities on gender, income and math skills have expanded their presence in engineering education. Nevertheless, late dropout has increased and the number of engineering graduates remains low in western world. The analysis of educational trajectories using process mining techniques can help to explain the relationship between a sequence of academic results and late dropout. This case study seeks to answer how gender, income and entry math skills may explain differences on educational trajectories of engineering students in high-failure rate courses that lead to late dropout. Academic records for 794 engineering students at Universidad Austral de Chile that belongs to cohorts 2007 to 2009, were extracted and analyzed using process mining discovery techniques. Models of educational trajectories on high-failure rate courses were created and then analyzed using the Investment Model as a reference framework. Findings reveal the following: late dropout is related to the number of consecutive semesters that a student maintain pending failed courses; low-income students and those with low entry math skills tend to be more persistent, even if they have unsatisfactory trajectories; female students tend to be more risk-averse when they have unsatisfactory results. Understanding the educational trajectories of students who end in late dropout can help managers and policy makers to improve the curriculum, entry conditions and programs that support disadvantaged students.