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

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

2 Citationer (Scopus)

294 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.

Originalsprog	Engelsk
Titel	The Interplay of Data, Technology, Place and People for Smart Learning : Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development
Redaktører	Hendrik Knoche, Elvira Popescu, Antonio Cartelli
Antal sider	10
Forlag	Springer
Publikationsdato	2018
Sider	167-176
ISBN (Trykt)	978-3-319-92021-4
ISBN (Elektronisk)	978-3-319-92022-1
DOI	https://doi.org/10.1007/978-3-319-92022-1_15
Status	Udgivet - 2018
Begivenhed	3rd International Conference on Smart Learning Ecosystems and Regional Development: The interplay of data, technology, place and people - Aalborg University, Aalborg, Danmark Varighed: 23 maj 2018 → 25 maj 2018 http://slerd.org/2018

Konference

Konference	3rd International Conference on Smart Learning Ecosystems and Regional Development
Lokation	Aalborg University
Land/Område	Danmark
By	Aalborg
Periode	23/05/2018 → 25/05/2018
Internetadresse	http://slerd.org/2018

Navn	Smart Innovation, Systems and Technologies
Vol/bind	95
ISSN	2190-3018

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

Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study ActivitiesAccepteret manuskript, 340 KB

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2 Afsluttet

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

2 Citationer
1 Konferenceartikel i proceeding
1 Tidsskriftartikel

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. (red.). Cham: Springer, s. 406-414 9 s. (Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Bind 328).
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2 Citationer (Scopus)

1301823 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, I: Interaction Design and Architecture(s). 39, s. 44-60 17 s.
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Citationsformater

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. I H. Knoche, E. Popescu, & A. Cartelli (red.), The Interplay of Data, Technology, Place and People for Smart Learning: Proceedings of the 3rd International Conference on Smart Learning Ecosystems and Regional Development (s. 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. red. / Hendrik Knoche ; Elvira Popescu ; Antonio Cartelli. Springer, 2018. s. 167-176 (Smart Innovation, Systems and Technologies, Bind 95).

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

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.",

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",

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doi = "10.1007/978-3-319-92022-1_15",

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booktitle = "The Interplay of Data, Technology, Place and People for Smart Learning",

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note = "3rd International Conference on Smart Learning Ecosystems and Regional Development : The interplay of data, technology, place and people, SLERD2018 ; Conference date: 23-05-2018 Through 25-05-2018",

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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. i H Knoche, E Popescu & A Cartelli (red), 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, bind 95, s. 167-176, 3rd International Conference on Smart Learning Ecosystems and Regional Development, Aalborg, Danmark, 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. red. / Hendrik Knoche; Elvira Popescu; Antonio Cartelli. Springer, 2018. s. 167-176 (Smart Innovation, Systems and Technologies, Bind 95).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › 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

Y1 - 2018

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.

KW - Academic performance

KW - Student retention

KW - Learning Management System

KW - Learning Tools Interoperability

KW - Problem-Based Learning

KW - Flipped learning

U2 - 10.1007/978-3-319-92022-1_15

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

M3 - Article in proceeding

SN - 978-3-319-92021-4

T3 - Smart Innovation, Systems and Technologies

SP - 167

EP - 176

BT - The Interplay of Data, Technology, Place and People for Smart Learning

A2 - Knoche, Hendrik

A2 - Popescu, Elvira

A2 - Cartelli, Antonio

PB - Springer

T2 - 3rd International Conference on Smart Learning Ecosystems and Regional Development

Y2 - 23 May 2018 through 25 May 2018

ER -

Christensen BC, Bemman B, Knoche H , Gade R. Identifying Students Struggling in Courses by Analyzing Exam Grades, Self-reported Measures and Study Activities. I Knoche H, Popescu E, Cartelli A, red., 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. s. 167-176. (Smart Innovation, Systems and Technologies, Bind 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

Konference

Adgang til dokumentet

AUB Link

Fingeraftryk

Projekter

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

Dropout prediction from digital learning for retention

Publikation

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

Citationsformater