Resumé
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Interaction Design and Architecture(s) |
| Vol/bind | 39 |
| Sider (fra-til) | 44-60 |
| Antal sider | 17 |
| ISSN | 1826-9745 |
| Status | Udgivet - 2019 |
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Pass or Fail? Prediction of Students’ Exam Outcomes from Self-reported Measures and Study Activities. / Christensen, Bianca Clavio; Bemman, Brian; Knoche, Hendrik; Gade, Rikke.
I: Interaction Design and Architecture(s), Bind 39, 2019, s. 44-60.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › peer review
TY - JOUR
T1 - Pass or Fail? Prediction of Students’ Exam Outcomes from Self-reported Measures and Study Activities
AU - Christensen, Bianca Clavio
AU - Bemman, Brian
AU - Knoche, Hendrik
AU - Gade, Rikke
N1 - Special issue on 'Smart Learning Ecosystems - technologies, places, and human-centered design'
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Academic performance
KW - Student retention
KW - Learning Management System
KW - Learning Tools Interoperability
KW - Problem-Based Learning
KW - Flipped learning
M3 - Journal article
VL - 39
SP - 44
EP - 60
JO - ID&A Interaction design & architecture(s)
JF - ID&A Interaction design & architecture(s)
SN - 1826-9745
ER -