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

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

doi:10.1007/978-3-319-92022-1_15

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

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

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

2 Citations (Scopus)

246 Downloads (Pure)

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 instrument`s 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.

Original language	English
Title of host publication	The Interplay of Data, Technology, Place and People for Smart Learning : Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development
Editors	Hendrik Knoche, Elvira Popescu, Antonio Cartelli
Number of pages	10
Publisher	Springer
Publication date	2018
Pages	167-176
ISBN (Print)	978-3-319-92021-4
ISBN (Electronic)	978-3-319-92022-1
DOIs	https://doi.org/10.1007/978-3-319-92022-1_15
Publication status	Published - 2018
Event	3rd International Conference on Smart Learning Ecosystems and Regional Development: The interplay of data, technology, place and people - Aalborg University, Aalborg, Denmark Duration: 23 May 2018 → 25 May 2018 http://slerd.org/2018

Conference

Conference	3rd International Conference on Smart Learning Ecosystems and Regional Development
Location	Aalborg University
Country/Territory	Denmark
City	Aalborg
Period	23/05/2018 → 25/05/2018
Internet address	http://slerd.org/2018

Series	Smart Innovation, Systems and Technologies
Volume	95
ISSN	2190-3018

Keywords

Academic performance
Student retention
Learning Management System
Learning Tools Interoperability
Problem-Based Learning
Flipped learning

Access to Document

10.1007/978-3-319-92022-1_15

Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study ActivitiesAccepted author manuscript, 340 KB

AUB Link

Search for the material in Aalborg University Library's search engine

2 Finished

DigiCulture: Improving the Digital Competences and Social Inclusion of Adults in Creative Industries
Knoche, H., Hougaard, B. I., Rehm, M., Rossau, I. G. & Skovfoged, M. M.
01/09/2018 → 31/08/2021
Project: Research
Dropout prediction from digital learning for retention
Knoche, H., Bruun-Pedersen, J. R., Timcenko, O., Kofoed, L. B., Tvedebrink, T., Eriksen, S., Møller, B., Bemman, B., Christensen, B. C. & Hougaard, B. I.
01/01/2018 → 31/12/2018
Project: Research

2 Citations
1 Article in proceeding
1 Journal article

Stars, Crests and Medals: Visual Badge Design Framework to Gamify and Certify Online Learning
Hougaard, B. I. & Knoche, H., 28 Jul 2020, Interactivity, Game Creation, Design, Learning, and Innovation: 8th EAI International Conference, ArtsIT 2019, and 4th EAI International Conference, DLI 2019, Aalborg, Denmark, November 6–8, 2019, Proceedings. Brooks, A. & Brooks, E. I. (eds.). Cham: Springer, p. 406-414 9 p. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Vol. 328).
Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

Open Access
File
2 Citations (Scopus)

129 Downloads (Pure)
Pass or Fail? Prediction of Students’ Exam Outcomes from Self-reported Measures and Study Activities
Christensen, B. C., Bemman, B., Knoche, H. & Gade, R., 2019, In: Interaction Design and Architecture(s). 39, p. 44-60 17 p.
Research output: Contribution to journal › Journal article › Research › peer-review

Open Access
File

Cite this

Christensen, B. C., Bemman, B., Knoche, H., & Gade, R. (2018). Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities. In H. Knoche, E. Popescu, & A. Cartelli (Eds.), The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development (pp. 167-176). Springer. https://doi.org/10.1007/978-3-319-92022-1_15

Christensen, Bianca Clavio ; Bemman, Brian ; Knoche, Hendrik et al. / Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities. The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development. editor / Hendrik Knoche ; Elvira Popescu ; Antonio Cartelli. Springer, 2018. pp. 167-176 (Smart Innovation, Systems and Technologies, Vol. 95).

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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 instrument`s 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, BC, Bemman, B, Knoche, H & Gade, R 2018, Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities. in H Knoche, E Popescu & A Cartelli (eds), The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development. Springer, Smart Innovation, Systems and Technologies, vol. 95, pp. 167-176, 3rd International Conference on Smart Learning Ecosystems and Regional Development, Aalborg, Denmark, 23/05/2018. https://doi.org/10.1007/978-3-319-92022-1_15

Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities. / Christensen, Bianca Clavio; Bemman, Brian; Knoche, Hendrik et al.
The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development. ed. / Hendrik Knoche; Elvira Popescu; Antonio Cartelli. Springer, 2018. p. 167-176 (Smart Innovation, Systems and Technologies, Vol. 95).

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities

AU - Christensen, Bianca Clavio

AU - Bemman, Brian

AU - Knoche, Hendrik

AU - Gade, Rikke

PY - 2018

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N2 - 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 instrument`s 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.

AB - 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 instrument`s 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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KW - Problem-Based Learning

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M3 - Article in proceeding

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ER -

Christensen BC, Bemman B, Knoche H , Gade R. Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities. In Knoche H, Popescu E, Cartelli A, editors, The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development. Springer. 2018. p. 167-176. (Smart Innovation, Systems and Technologies, Vol. 95). doi: 10.1007/978-3-319-92022-1_15

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

Abstract

Conference

Keywords

Access to Document

AUB Link

Fingerprint

Projects

DigiCulture: Improving the Digital Competences and Social Inclusion of Adults in Creative Industries

Dropout prediction from digital learning for retention

Research output

Stars, Crests and Medals: Visual Badge Design Framework to Gamify and Certify Online Learning

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

Cite this