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

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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.
Original languageEnglish
Title of host publicationThe Interplay of Data, Technology, Place and People for Smart Learning : Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development
EditorsHendrik Knoche, Elvira Popescu, Antonio Cartelli
Number of pages10
PublisherSpringer
Publication date2018
Pages167-176
ISBN (Print)978-3-319-92021-4
ISBN (Electronic)978-3-319-92022-1
DOIs
Publication statusPublished - 2018
Event3rd International Conference on Smart Learning Ecosystems and Regional Development: The interplay of data, technology, place and people - Aalborg University, Aalborg, Denmark
Duration: 23 May 201825 May 2018
http://slerd.org/2018

Conference

Conference3rd International Conference on Smart Learning Ecosystems and Regional Development
LocationAalborg University
CountryDenmark
CityAalborg
Period23/05/201825/05/2018
Internet address
SeriesSmart Innovation, Systems and Technologies
Volume95
ISSN2190-3018

Fingerprint

student
programming
technical education
self-study
learning success
decision-making process
learning
experience
regression
university
performance

Keywords

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

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. Smart Innovation, Systems and Technologies, Vol.. 95 https://doi.org/10.1007/978-3-319-92022-1_15
Christensen, Bianca Clavio ; Bemman, Brian ; Knoche, Hendrik ; Gade, Rikke. / 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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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; Gade, Rikke.

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 proceedingArticle in proceedingResearchpeer-review

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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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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). https://doi.org/10.1007/978-3-319-92022-1_15