Clustering student participation: implications for education

Shadi Esnashari; Lesley Gardner; Paul Watters

doi:10.1109/WAINA.2018.00104

Clustering student participation: implications for education

Shadi Esnashari, Lesley Gardner, Paul Watters

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

5 Citations (Scopus)

Abstract

Increasing educational attainment from a broader and more diverse student population is a policy goal for many governments. Yet increased enrolments brings many challenges for faculty members trying to track and predict academic performance. One possible mechanism for prediction is to use in-class participation data to determine whether participation is linked to academic performance. In this study, we combined in-class and out-of-class (e.g., Learning Management System) data with a range of qualitative and quantitative self-report measures. We then used a range of data mining (DM) algorithms to predict final course outcomes. We found that students who participated more and thought that the tool helped them to learn, engaged and increased their interest in the course more, and eventually achieved the highest scores. This finding supports the view that in-class participation is critical to learning and academic success.

Original language	English
Title of host publication	Proceedings: 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops
Subtitle of host publication	IEEE WAINA 2018
Editors	Leonard Barolli, Makoto Takizawa, Tomoya Enokido, Marek R. Ogiela, Lidia Ogiela, Nadeem Javaid
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	313-318
Number of pages	6
ISBN (Electronic)	9781538653944
DOIs	https://doi.org/10.1109/WAINA.2018.00104
Publication status	Published - 2018
Externally published	Yes
Event	32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018 - Krakow, Poland Duration: 16 May 2018 → 18 May 2018

Conference

Conference	32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018
Country/Territory	Poland
City	Krakow
Period	16/05/18 → 18/05/18

Keywords

Audience participation tools
Cluster analysis
Learning analytics

Access to Document

10.1109/WAINA.2018.00104

Cite this

Esnashari, S., Gardner, L., & Watters, P. (2018). Clustering student participation: implications for education. In L. Barolli, M. Takizawa, T. Enokido, M. R. Ogiela, L. Ogiela, & N. Javaid (Eds.), Proceedings: 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops: IEEE WAINA 2018 (pp. 313-318). Article 8418089 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/WAINA.2018.00104

Esnashari, Shadi ; Gardner, Lesley ; Watters, Paul. / Clustering student participation : implications for education. Proceedings: 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops: IEEE WAINA 2018. editor / Leonard Barolli ; Makoto Takizawa ; Tomoya Enokido ; Marek R. Ogiela ; Lidia Ogiela ; Nadeem Javaid. Institute of Electrical and Electronics Engineers (IEEE), 2018. pp. 313-318

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abstract = "Increasing educational attainment from a broader and more diverse student population is a policy goal for many governments. Yet increased enrolments brings many challenges for faculty members trying to track and predict academic performance. One possible mechanism for prediction is to use in-class participation data to determine whether participation is linked to academic performance. In this study, we combined in-class and out-of-class (e.g., Learning Management System) data with a range of qualitative and quantitative self-report measures. We then used a range of data mining (DM) algorithms to predict final course outcomes. We found that students who participated more and thought that the tool helped them to learn, engaged and increased their interest in the course more, and eventually achieved the highest scores. This finding supports the view that in-class participation is critical to learning and academic success.",

keywords = "Audience participation tools, Cluster analysis, Learning analytics",

author = "Shadi Esnashari and Lesley Gardner and Paul Watters",

year = "2018",

doi = "10.1109/WAINA.2018.00104",

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editor = "Leonard Barolli and Makoto Takizawa and Tomoya Enokido and Ogiela, \{Marek R.\} and Lidia Ogiela and Nadeem Javaid",

booktitle = "Proceedings: 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

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note = "32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018 ; Conference date: 16-05-2018 Through 18-05-2018",

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Esnashari, S, Gardner, L & Watters, P 2018, Clustering student participation: implications for education. in L Barolli, M Takizawa, T Enokido, MR Ogiela, L Ogiela & N Javaid (eds), Proceedings: 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops: IEEE WAINA 2018., 8418089, Institute of Electrical and Electronics Engineers (IEEE), pp. 313-318, 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018, Krakow, Poland, 16/05/18. https://doi.org/10.1109/WAINA.2018.00104

Clustering student participation: implications for education. / Esnashari, Shadi; Gardner, Lesley; Watters, Paul.
Proceedings: 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops: IEEE WAINA 2018. ed. / Leonard Barolli; Makoto Takizawa; Tomoya Enokido; Marek R. Ogiela; Lidia Ogiela; Nadeem Javaid. Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 313-318 8418089.

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

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T1 - Clustering student participation

T2 - 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops, WAINA 2018

AU - Esnashari, Shadi

AU - Gardner, Lesley

AU - Watters, Paul

PY - 2018

Y1 - 2018

N2 - Increasing educational attainment from a broader and more diverse student population is a policy goal for many governments. Yet increased enrolments brings many challenges for faculty members trying to track and predict academic performance. One possible mechanism for prediction is to use in-class participation data to determine whether participation is linked to academic performance. In this study, we combined in-class and out-of-class (e.g., Learning Management System) data with a range of qualitative and quantitative self-report measures. We then used a range of data mining (DM) algorithms to predict final course outcomes. We found that students who participated more and thought that the tool helped them to learn, engaged and increased their interest in the course more, and eventually achieved the highest scores. This finding supports the view that in-class participation is critical to learning and academic success.

AB - Increasing educational attainment from a broader and more diverse student population is a policy goal for many governments. Yet increased enrolments brings many challenges for faculty members trying to track and predict academic performance. One possible mechanism for prediction is to use in-class participation data to determine whether participation is linked to academic performance. In this study, we combined in-class and out-of-class (e.g., Learning Management System) data with a range of qualitative and quantitative self-report measures. We then used a range of data mining (DM) algorithms to predict final course outcomes. We found that students who participated more and thought that the tool helped them to learn, engaged and increased their interest in the course more, and eventually achieved the highest scores. This finding supports the view that in-class participation is critical to learning and academic success.

KW - Audience participation tools

KW - Cluster analysis

KW - Learning analytics

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U2 - 10.1109/WAINA.2018.00104

DO - 10.1109/WAINA.2018.00104

M3 - Conference proceeding contribution

AN - SCOPUS:85056274240

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EP - 318

BT - Proceedings: 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops

A2 - Barolli, Leonard

A2 - Takizawa, Makoto

A2 - Enokido, Tomoya

A2 - Ogiela, Marek R.

A2 - Ogiela, Lidia

A2 - Javaid, Nadeem

PB - Institute of Electrical and Electronics Engineers (IEEE)

Y2 - 16 May 2018 through 18 May 2018

ER -

Esnashari S, Gardner L, Watters P. Clustering student participation: implications for education. In Barolli L, Takizawa M, Enokido T, Ogiela MR, Ogiela L, Javaid N, editors, Proceedings: 32nd IEEE International Conference on Advanced Information Networking and Applications Workshops: IEEE WAINA 2018. Institute of Electrical and Electronics Engineers (IEEE). 2018. p. 313-318. 8418089 doi: 10.1109/WAINA.2018.00104

Clustering student participation: implications for education

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