Pass or Fail? Prediction of Students’ Exam Outcomes from Self-reported Measures and Study Activities

Bianca Clavio Christensen, Brian Bemman, Hendrik Knoche, Rikke Gade

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3 Citationer (Scopus)
145 Downloads (Pure)

Abstrakt

Technical educations often exhibit poor student performance and consequently high rates of attrition. Providing students with early feedback on their learning progress can assist them 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 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 and analyzed how well the obtained data predicted the final exam scores. Best-subset-regression and lasso regressions yielded several significant predictors. Apart from relevant predictors known from the literature on exam scores and drop-out factors such as midterm exam results and student retention factors, data from self-assessment quizzes, peer reviewing activities, and interactive online exercises helped predict exam performance and identified struggling students.
OriginalsprogEngelsk
TidsskriftInteraction Design and Architecture(s)
Vol/bind39
Sider (fra-til)44-60
Antal sider17
ISSN1826-9745
StatusUdgivet - 2019

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  • Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities

    Christensen, B. C., Bemman, B., Knoche, H. & Gade, R., 2018, The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development. Knoche, H., Popescu, E. & Cartelli, A. (red.). Springer, s. 167-176 10 s. (Smart Innovation, Systems and Technologies, Bind 95).

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