Understanding international benchmarks on student engagement: awareness and research alignment from a computer science perspective

Michael Morgan, Neena Thota, Jane Sinclair, Janet Fraser, Jana Jackova, Matthew Butler, Gerry Cross

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

5 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationITiCSE-WGR 2017 - Proceedings of the 2017 ITiCSE Conference on Working Group Reports
Subtitle of host publicationJuly 3–5, 2017 Bologna, Italy
EditorsJudithe Sheard, Ari Korhonen
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1-24
Number of pages24
ISBN (Electronic)9781450356275
DOIs
Publication statusPublished - 2017
EventAnnual Conference on Innovation and Technology in Computer Science Education - Working Groups 2017 - Bologna, Italy
Duration: 3 Jul 20175 Jul 2017
Conference number: 22nd
https://iticse.acm.org/ITiCSE2017/

Conference

ConferenceAnnual Conference on Innovation and Technology in Computer Science Education - Working Groups 2017
Abbreviated titleITiCSE2017
CountryItaly
CityBologna
Period3/07/175/07/17
Internet address

Keywords

  • Computing education
  • Higher education
  • International benchmarks
  • Student engagement

Cite this

Morgan, M., Thota, N., Sinclair, J., Fraser, J., Jackova, J., Butler, M., & Cross, G. (2017). Understanding international benchmarks on student engagement: awareness and research alignment from a computer science perspective. In J. Sheard, & A. Korhonen (Eds.), ITiCSE-WGR 2017 - Proceedings of the 2017 ITiCSE Conference on Working Group Reports: July 3–5, 2017 Bologna, Italy (pp. 1-24). New York NY USA: Association for Computing Machinery (ACM). https://doi.org/10.1145/3174781.3174782
Morgan, Michael ; Thota, Neena ; Sinclair, Jane ; Fraser, Janet ; Jackova, Jana ; Butler, Matthew ; Cross, Gerry. / Understanding international benchmarks on student engagement : awareness and research alignment from a computer science perspective. ITiCSE-WGR 2017 - Proceedings of the 2017 ITiCSE Conference on Working Group Reports: July 3–5, 2017 Bologna, Italy. editor / Judithe Sheard ; Ari Korhonen. New York NY USA : Association for Computing Machinery (ACM), 2017. pp. 1-24
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Morgan, M, Thota, N, Sinclair, J, Fraser, J, Jackova, J, Butler, M & Cross, G 2017, Understanding international benchmarks on student engagement: awareness and research alignment from a computer science perspective. in J Sheard & A Korhonen (eds), ITiCSE-WGR 2017 - Proceedings of the 2017 ITiCSE Conference on Working Group Reports: July 3–5, 2017 Bologna, Italy. Association for Computing Machinery (ACM), New York NY USA, pp. 1-24, Annual Conference on Innovation and Technology in Computer Science Education - Working Groups 2017, Bologna, Italy, 3/07/17. https://doi.org/10.1145/3174781.3174782

Understanding international benchmarks on student engagement : awareness and research alignment from a computer science perspective. / Morgan, Michael; Thota, Neena; Sinclair, Jane; Fraser, Janet; Jackova, Jana; Butler, Matthew; Cross, Gerry.

ITiCSE-WGR 2017 - Proceedings of the 2017 ITiCSE Conference on Working Group Reports: July 3–5, 2017 Bologna, Italy. ed. / Judithe Sheard; Ari Korhonen. New York NY USA : Association for Computing Machinery (ACM), 2017. p. 1-24.

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AU - Cross, Gerry

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AB - 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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Morgan M, Thota N, Sinclair J, Fraser J, Jackova J, Butler M et al. Understanding international benchmarks on student engagement: awareness and research alignment from a computer science perspective. In Sheard J, Korhonen A, editors, ITiCSE-WGR 2017 - Proceedings of the 2017 ITiCSE Conference on Working Group Reports: July 3–5, 2017 Bologna, Italy. New York NY USA: Association for Computing Machinery (ACM). 2017. p. 1-24 https://doi.org/10.1145/3174781.3174782