An Analysis of Course Evaluation Questionnaire on UCAS Students’ Academic Performance by Using Data Clustering

Shadi Abudalfa; Mohammed Salem

doi:10.1007/978-3-031-08954-1_21

An Analysis of Course Evaluation Questionnaire on UCAS Students’ Academic Performance by Using Data Clustering

Shadi Abudalfa, Mohammed Salem^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Course evaluation by questionnaire is the most popular assessment tool used by various universities. However, they use traditional statistical methods such as average for analyzing the questionnaires. Thereby, such questionnaires have not been sufficiently utilized. In this paper, we address this issue by employing machine learning techniques for improving effectiveness of course evaluation. Specifically, we use data clustering for dividing students into groups according to their responses. This work will help higher education institutes to improve performance of course evaluation. As a case study, we employed the presented technique to evaluate Entrepreneurship course that is pursued by the University College of Applied Sciences (UCAS). Our findings show that the grading system used with the selected course needs more improvement. Moreover, the outcome of this work supports the UCAS by facilitating process of applying Universal Design for Learning (UDL).

Original language	English
Title of host publication	Explore Business, Technology Opportunities and Challenges ‎After the Covid-19 Pandemic
Editors	Bahaaeddin Alareeni, Allam Hamdan
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	231-240
Number of pages	10
ISBN (Print)	9783031089534
DOIs	https://doi.org/10.1007/978-3-031-08954-1_21
State	Published - 2023
Externally published	Yes
Event	International Conference on Business and Technology , ICBT 2021 - Istanbul, Turkey Duration: 6 Nov 2021 → 7 Nov 2021

Publication series

Name	Lecture Notes in Networks and Systems
Volume	495 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	International Conference on Business and Technology , ICBT 2021
Country/Territory	Turkey
City	Istanbul
Period	6/11/21 → 7/11/21

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

Course evaluation
Data clustering
Machine learning
Students’ academic performance
Universal Design for Learning

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-031-08954-1_21

Cite this

Abudalfa, S., & Salem, M. (2023). An Analysis of Course Evaluation Questionnaire on UCAS Students’ Academic Performance by Using Data Clustering. In B. Alareeni, & A. Hamdan (Eds.), Explore Business, Technology Opportunities and Challenges ‎After the Covid-19 Pandemic (pp. 231-240). (Lecture Notes in Networks and Systems; Vol. 495 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-08954-1_21

Abudalfa, Shadi ; Salem, Mohammed. / An Analysis of Course Evaluation Questionnaire on UCAS Students’ Academic Performance by Using Data Clustering. Explore Business, Technology Opportunities and Challenges ‎After the Covid-19 Pandemic. editor / Bahaaeddin Alareeni ; Allam Hamdan. Springer Science and Business Media Deutschland GmbH, 2023. pp. 231-240 (Lecture Notes in Networks and Systems).

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title = "An Analysis of Course Evaluation Questionnaire on UCAS Students{\textquoteright} Academic Performance by Using Data Clustering",

abstract = "Course evaluation by questionnaire is the most popular assessment tool used by various universities. However, they use traditional statistical methods such as average for analyzing the questionnaires. Thereby, such questionnaires have not been sufficiently utilized. In this paper, we address this issue by employing machine learning techniques for improving effectiveness of course evaluation. Specifically, we use data clustering for dividing students into groups according to their responses. This work will help higher education institutes to improve performance of course evaluation. As a case study, we employed the presented technique to evaluate Entrepreneurship course that is pursued by the University College of Applied Sciences (UCAS). Our findings show that the grading system used with the selected course needs more improvement. Moreover, the outcome of this work supports the UCAS by facilitating process of applying Universal Design for Learning (UDL).",

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doi = "10.1007/978-3-031-08954-1_21",

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series = "Lecture Notes in Networks and Systems",

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Abudalfa, S & Salem, M 2023, An Analysis of Course Evaluation Questionnaire on UCAS Students’ Academic Performance by Using Data Clustering. in B Alareeni & A Hamdan (eds), Explore Business, Technology Opportunities and Challenges ‎After the Covid-19 Pandemic. Lecture Notes in Networks and Systems, vol. 495 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 231-240, International Conference on Business and Technology , ICBT 2021, Istanbul, Turkey, 6/11/21. https://doi.org/10.1007/978-3-031-08954-1_21

An Analysis of Course Evaluation Questionnaire on UCAS Students’ Academic Performance by Using Data Clustering. / Abudalfa, Shadi; Salem, Mohammed.
Explore Business, Technology Opportunities and Challenges ‎After the Covid-19 Pandemic. ed. / Bahaaeddin Alareeni; Allam Hamdan. Springer Science and Business Media Deutschland GmbH, 2023. p. 231-240 (Lecture Notes in Networks and Systems; Vol. 495 LNNS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Course evaluation by questionnaire is the most popular assessment tool used by various universities. However, they use traditional statistical methods such as average for analyzing the questionnaires. Thereby, such questionnaires have not been sufficiently utilized. In this paper, we address this issue by employing machine learning techniques for improving effectiveness of course evaluation. Specifically, we use data clustering for dividing students into groups according to their responses. This work will help higher education institutes to improve performance of course evaluation. As a case study, we employed the presented technique to evaluate Entrepreneurship course that is pursued by the University College of Applied Sciences (UCAS). Our findings show that the grading system used with the selected course needs more improvement. Moreover, the outcome of this work supports the UCAS by facilitating process of applying Universal Design for Learning (UDL).

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Abudalfa S, Salem M. An Analysis of Course Evaluation Questionnaire on UCAS Students’ Academic Performance by Using Data Clustering. In Alareeni B, Hamdan A, editors, Explore Business, Technology Opportunities and Challenges ‎After the Covid-19 Pandemic. Springer Science and Business Media Deutschland GmbH. 2023. p. 231-240. (Lecture Notes in Networks and Systems). doi: 10.1007/978-3-031-08954-1_21

An Analysis of Course Evaluation Questionnaire on UCAS Students’ Academic Performance by Using Data Clustering

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

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Cite this