Towards performance prediction using in-game measures

Sylvester Arnab; O. Imiruaye; F. Liarokapis; Gemma Tombs; Petros Lameras; A. Serrano-Laguna; P. Moreno-Ger

Towards performance prediction using in-game measures

Sylvester Arnab, O. Imiruaye, F. Liarokapis, Gemma Tombs, Petros Lameras, A. Serrano-Laguna, P. Moreno-Ger

Research output: Contribution to conference › Paper › peer-review

Abstract

The efficacy of a learning process is influenced by the quality of teaching, learning support and environment. This requires effort in tracking how students learn. This paper explores the use serious games in order to help understand the learning process, where interaction data during a play-learn session can be captured. The focus is on the use of in-game data, analyzed using Learning Analytics techniques, and discusses the potential of such an approach to predict learners’ performance. Gameplay data were collected from various play-learn sessions based on a First Aid Game. Results indicate that in-game measures can help to understand students’ progress and predict their performance, providing opportunities for individual support to be provided to learners.

Original language	English
Publication status	Published - 2015
Event	American Educational Research Association annual meeting - Chicago, Illinois, Chicago, Illinois, United States Duration: 16 Apr 2015 → 20 Apr 2015

Conference

Conference	American Educational Research Association annual meeting
Country	United States
City	Chicago, Illinois
Period	16/04/15 → 20/04/15

Keywords

serious games
performance prediction
user studies
learning analytics
game-based learning

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Cite this

Arnab, S., Imiruaye, O., Liarokapis, F., Tombs, G., Lameras, P., Serrano-Laguna, A., & Moreno-Ger, P. (2015). Towards performance prediction using in-game measures. Paper presented at American Educational Research Association annual meeting, Chicago, Illinois, United States. http://www.aera.net/Publications/OnlinePaperRepository/AERAOnlinePaperRepository/tabid/12720/Default.aspx

Arnab, S, Imiruaye, O, Liarokapis, F, Tombs, G, Lameras, P, Serrano-Laguna, A & Moreno-Ger, P 2015, 'Towards performance prediction using in-game measures', Paper presented at American Educational Research Association annual meeting, Chicago, Illinois, United States, 16/04/15 - 20/04/15. <http://www.aera.net/Publications/OnlinePaperRepository/AERAOnlinePaperRepository/tabid/12720/Default.aspx>

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AU - Arnab, Sylvester

AU - Imiruaye, O.

AU - Liarokapis, F.

AU - Tombs, Gemma

AU - Lameras, Petros

AU - Serrano-Laguna, A.

AU - Moreno-Ger, P.

PY - 2015

Y1 - 2015

N2 - The efficacy of a learning process is influenced by the quality of teaching, learning support and environment. This requires effort in tracking how students learn. This paper explores the use serious games in order to help understand the learning process, where interaction data during a play-learn session can be captured. The focus is on the use of in-game data, analyzed using Learning Analytics techniques, and discusses the potential of such an approach to predict learners’ performance. Gameplay data were collected from various play-learn sessions based on a First Aid Game. Results indicate that in-game measures can help to understand students’ progress and predict their performance, providing opportunities for individual support to be provided to learners.

AB - The efficacy of a learning process is influenced by the quality of teaching, learning support and environment. This requires effort in tracking how students learn. This paper explores the use serious games in order to help understand the learning process, where interaction data during a play-learn session can be captured. The focus is on the use of in-game data, analyzed using Learning Analytics techniques, and discusses the potential of such an approach to predict learners’ performance. Gameplay data were collected from various play-learn sessions based on a First Aid Game. Results indicate that in-game measures can help to understand students’ progress and predict their performance, providing opportunities for individual support to be provided to learners.

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KW - performance prediction

KW - user studies

KW - learning analytics

KW - game-based learning

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