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Using Learning Analytics to Investigate Patterns of Performance and Engagement in Large Classes

Authors:

Hassan Khosravi,

Kendra M.L. CooperAuthors Info & Claims

SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education

Pages 309 - 314

https://doi.org/10.1145/3017680.3017711

Published: 08 March 2017 Publication History

Abstract

Educators continue to face significant challenges in providing high quality, post-secondary instruction in large classes including: motivating and engaging diverse populations (e.g., academic ability and backgrounds, generational expectations); and providing helpful feedback and guidance. Researchers investigate solutions to these kinds of challenges from alternative perspectives, including learning analytics (LA). Here, LA techniques are applied to explore the data collected for a large, flipped introductory programming class to (1) identify groups of students with similar patterns of performance and engagement; and (2) provide them with more meaningful appraisals that are tailored to help them effectively master the learning objectives. Two studies are reported, which apply clustering to analyze the class population, followed by an analysis of a subpopulation with extreme behaviours.

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Cited By

Roski MSebastian REwerth RHoppe ANehring A(2024)Learning analytics and the Universal Design for Learning (UDL)Computers & Education10.1016/j.compedu.2024.105028214:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.compedu.2024.105028
Nguyen BNguyen NChen H(2023)Utilizing Learning Analytics in an Automated Programming Assessment System to Enhance Software Practice EducationPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch017(332-362)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch017
Poitras EDempsey DCrane BSimpson SSiegel A(2023)The Applications of Learning Analytics to Enhance Learning and Engagement in Introductory Programming InstructionPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch005(89-108)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch005
Show More Cited By

Index Terms

Using Learning Analytics to Investigate Patterns of Performance and Engagement in Large Classes
1. Social and professional topics
  1. Professional topics
    1. Computing education
      1. Student assessment

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Published In

cover image ACM Conferences

SIGCSE '17: Proceedings of the 2017 ACM SIGCSE Technical Symposium on Computer Science Education

March 2017

838 pages

ISBN:9781450346986

DOI:10.1145/3017680

General Chairs:
Michael E. Caspersen
Aarhus University
,
Stephen H. Edwards
Virginia Tech
,
Program Chairs:
Tiffany Barnes
North Carolina State University
,
Daniel D. Garcia
University of California, Berkeley

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGCSE: ACM Special Interest Group on Computer Science Education

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Association for Computing Machinery

New York, NY, United States

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Published: 08 March 2017

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SIGCSE '17

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SIGCSE '17: The 48th ACM Technical Symposium on Computer Science Education

March 8 - 11, 2017

Washington, Seattle, USA

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SIGCSE '17 Paper Acceptance Rate 105 of 348 submissions, 30%;

Overall Acceptance Rate 1,595 of 4,542 submissions, 35%

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SIGCSE TS 2025

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The 56th ACM Technical Symposium on Computer Science Education

February 26 - March 1, 2025

Pittsburgh , PA , USA

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Cited By

Roski MSebastian REwerth RHoppe ANehring A(2024)Learning analytics and the Universal Design for Learning (UDL)Computers & Education10.1016/j.compedu.2024.105028214:COnline publication date: 1-Jun-2024
https://dl.acm.org/doi/10.1016/j.compedu.2024.105028
Nguyen BNguyen NChen H(2023)Utilizing Learning Analytics in an Automated Programming Assessment System to Enhance Software Practice EducationPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch017(332-362)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch017
Poitras EDempsey DCrane BSimpson SSiegel A(2023)The Applications of Learning Analytics to Enhance Learning and Engagement in Introductory Programming InstructionPerspectives on Learning Analytics for Maximizing Student Outcomes10.4018/978-1-6684-9527-8.ch005(89-108)Online publication date: 24-Oct-2023
https://doi.org/10.4018/978-1-6684-9527-8.ch005
Aggarwal APuthanveetil NGardner-Mccune CDoyle MStephenson BDorn BSoh LBattestilli L(2023)Who Attempts Optional Practice Problems in a CS1 Course?Proceedings of the 54th ACM Technical Symposium on Computer Science Education V. 110.1145/3545945.3569854(1055-1061)Online publication date: 2-Mar-2023
https://dl.acm.org/doi/10.1145/3545945.3569854
Labrović JPetrović NAnđelković JMeršnik M(2023)Patterns of behavioral engagement in an online english language course: cluster analysisJournal of Computing in Higher Education10.1007/s12528-023-09382-1Online publication date: 19-Aug-2023
https://doi.org/10.1007/s12528-023-09382-1
Kaur AChahal K(2023)A learning analytics dashboard for data-driven recommendations on influences of non-cognitive factors in introductory programmingEducation and Information Technologies10.1007/s10639-023-12125-529:8(9221-9256)Online publication date: 7-Sep-2023
https://doi.org/10.1007/s10639-023-12125-5
Sahlaoui HAlaoui EAgoujil SNayyar A(2023)An empirical assessment of smote variants techniques and interpretation methods in improving the accuracy and the interpretability of student performance modelsEducation and Information Technologies10.1007/s10639-023-12007-w29:5(5447-5483)Online publication date: 17-Jul-2023
https://doi.org/10.1007/s10639-023-12007-w
Kaur AChahal K(2023)A Review on the Impact of Cognitive Factors in Introductory ProgrammingProceedings of Fourth International Conference on Communication, Computing and Electronics Systems10.1007/978-981-19-7753-4_77(1019-1032)Online publication date: 15-Mar-2023
https://doi.org/10.1007/978-981-19-7753-4_77
Cevikbas MKaiser G(2022)Promoting Personalized Learning in Flipped Classrooms: A Systematic Review StudySustainability10.3390/su14181139314:18(11393)Online publication date: 10-Sep-2022
https://doi.org/10.3390/su141811393
Aggarwal AAshok A(2022)Exploring the Differences in Students’ Behavioral Engagement With Quizzes and Its Impact on their Performance in a Flipped CS1 CourseProceedings of the 22nd Koli Calling International Conference on Computing Education Research10.1145/3564721.3564740(1-11)Online publication date: 17-Nov-2022
https://dl.acm.org/doi/10.1145/3564721.3564740
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