Resumé

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

Fingerprint

Students
student
Ecosystems
Ecosystem
Learning
regression
technical education
Problem-Based Learning
self-study
learning success
quiz
self-assessment
drop-out
decision-making process
learning
performance
Decision Making
programming
Education
Decision making

Citer dette

@article{c88cdb31999445ad92fb1c7d6e94e47a,
title = "Pass or Fail? Prediction of Students’ Exam Outcomes from Self-reported Measures and Study Activities",
abstract = "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.",
keywords = "Academic performance, Student retention, Learning Management System, Learning Tools Interoperability, Problem-Based Learning, Flipped learning",
author = "Christensen, {Bianca Clavio} and Brian Bemman and Hendrik Knoche and Rikke Gade",
note = "Special issue on 'Smart Learning Ecosystems - technologies, places, and human-centered design'",
year = "2019",
language = "English",
volume = "39",
pages = "44--60",
journal = "ID&A Interaction design & architecture(s)",
issn = "1826-9745",
publisher = "Interaction Design and Architecture(s)",

}

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 -