This research paper utilizes qualitative methods that connect with a learning analytics’ application in order to understand successful learning strategies of first-year students in risk of academic dismissal in a selective engineering school. The Engineering School developed a model that predicts students consecutive failing of at least one first semester course. This is important because failing a course twice results in School’s dismissal. The first application of the model, which did not considered intervention at all, worked remarkably well since it had a recall of 86%, having almost no cases of false negative (error type II). However, there was a significant number of students that were false positive (error type I). These students were individuals with a high probability of failing at least one course twice but ended up passing in the second semester all the courses that they failed in the first semester. In this study, we sought to learn more about “false positive” students and their strategies for achieving academic success. We conducted semi-structured interviews with “false positive” students. These interviews were enhanced with a journey-map exercise (Meyer & Marx, 2014) about the students’ experiences in first year. The interviews were analyzed with a semi-inductive process, which considered the substantive knowledge about students’ academic performance in first-year engineering, including the association between personal and institutional factors. In total, we conducted 10 interviews. As preliminary results, we identifying the following themes: the positive role of family support, the importance of “being” in the School (i.e., assistance to classes, participation in extracurricular activities, and using study rooms), and the absence of a sense of achievement.
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