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Understanding International Benchmarks on Student Engagement: Awareness and Research Alignment from a Computer Science Perspective

Published: 30 January 2018 Publication History

Abstract

There is an increasing trend to use national survey instruments to measure student engagement. Unfortunately, Computer Science (CS) rates poorly on a number of measures in these surveys, even when compared to related STEM disciplines. Initial research suggests reasons for this poor performance may include a lack of awareness by CS academics of these instruments and the student engagement measures on which they are based, and a misalignment between these measures and the research focus (and teaching practice) of CS educators. This working group carried out an investigation of major engagement instruments to examine the measures they embody and track the achievement of CS with respect to the major international benchmarks. A comprehensive research mapping exercise was then conducted to examine the focus of current CS education research and its alignment to student engagement measures on which the instruments are based. The process enabled identification of examples of best practice in student engagement research in CS education. In order to better understand CS academics' perspectives on engagement a series of interviews were also conducted with CS staff. Our findings indicate that CS engagement results are, if anything, declining further. Analysis of CS education research literature shows that many authors refer to ``engagement'' (and their aim to increase it) but few attach a clear meaning to the term or offer evidence to support a link to improved engagement. Further, many initiatives reported would be unlikely to tick the boxes of the narrow, behaviourally-focussed measures covered by the major instruments. Staff interviews revealed a wide variety of beliefs about what student engagement means and what should be done to promote it in CS, including the view that many activities measured in the instruments are counter-productive for CS. This work aims to promote a greater awareness of the international benchmarks and the aspects of student engagement they measure. The results reported here can be used by CS educators to inform decisions on strategies to improve engagement and how these might relate to existing survey measures.

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    cover image ACM Conferences
    ITiCSE-WGR '17: Proceedings of the 2017 ITiCSE Conference on Working Group Reports
    January 2018
    162 pages
    ISBN:9781450356275
    DOI:10.1145/3174781
    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 ACM 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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    Published: 30 January 2018

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    Author Tags

    1. computing education
    2. higher education
    3. international benchmarks
    4. student engagement

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    • (2023)Leveraging Large Language Models for Analysis of Student Course FeedbackProceedings of the 16th Annual ACM India Compute Conference10.1145/3627217.3627221(76-79)Online publication date: 9-Dec-2023
    • (2023)Understanding Student Evaluation of Teaching in Computer Science CoursesProceedings of the 16th Annual ACM India Compute Conference10.1145/3627217.3627220(13-18)Online publication date: 9-Dec-2023
    • (2023)The Roles of Confidence and Perceived Usefulness in Female Student Engagement in High School Computing ScienceProceedings of the 18th WiPSCE Conference on Primary and Secondary Computing Education Research10.1145/3605468.3605497(1-9)Online publication date: 27-Sep-2023
    • (2023)Exploring Student Engagement, Confidence, and Usefulness for Female Students in CS Class at High School Using Machine Learning2023 IEEE Frontiers in Education Conference (FIE)10.1109/FIE58773.2023.10343402(1-9)Online publication date: 18-Oct-2023
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