Abstract
Technical educations often experience poor student performance and consequently high rates of attrition. Providing students with early feedback on their learning progress can assist students in self-study activities or in their decision-making process regarding a change in educational direction. In this paper, we present a set of instruments designed to identify at-risk undergraduate students in a Problem-based Learning (PBL) university, using an introductory programming course between two campus locations as a case study. Collectively, these instruments form the basis of a proposed learning ecosystem designed to identify struggling students by predicting their final exam grades in this course. We implemented this ecosystem at one of the two campus locations and analyzed how well the obtained data predicted the final exam grades compared to the other campus, where midterm exam grades alone were used in the prediction model. Results of a multiple linear regression model found several significant assessment predictors related to how often students attempted self-guided course assignments and their self-reported programming experience, among others.
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Christensen, B.C., Bemman, B., Knoche, H., Gade, R. (2019). Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities. In: Knoche, H., Popescu, E., Cartelli, A. (eds) The Interplay of Data, Technology, Place and People for Smart Learning. SLERD 2018 2018. Smart Innovation, Systems and Technologies, vol 95. Springer, Cham. https://doi.org/10.1007/978-3-319-92022-1_15
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