Toward Performance Prediction Using In-Game Measures

Sylvester Arnab; Odafe Imiruaye; Fotis Liarokapis; Gemma Tombs; Petros Lameras; Angel Serrano-Laguna; Pablo Moreno-Ger

Toward Performance Prediction Using In-Game Measures

Sylvester Arnab, Odafe Imiruaye, Fotis Liarokapis, Gemma Tombs, Petros Lameras, Angel Serrano-Laguna, Pablo Moreno-Ger

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

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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 ingame 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 - 20 Apr 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/Territory	United States
City	Chicago, Illinois
Period	16/04/15 → 20/04/15

Bibliographical note

This paper was presented on the 20th April 2015 at the AERA (American Educational Research Association) conference, Chicago, Illinois.
Arnab, S. , Imiruaye, O. , Liarokapis, F. , Tombs, G. , Lameras, P. , Serrano-Laguna, Angel and Moreno-Ger, Pablo. 2015, 20 April. Toward Performance Prediction Using In-Game Measures. Paper presented at
the 2015 annual meeting of the American Educational Research
Association. Retrieved 18 August 2015, from the AERA Online Paper
Repository.

Keywords

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

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Arnab, S., Imiruaye, O., Liarokapis, F., Tombs, G., Lameras, P., Serrano-Laguna, A., & Moreno-Ger, P. (2015). Toward 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, 'Toward 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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title = "Toward Performance Prediction Using In-Game Measures",

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 ingame data, analyzed using Learning Analytics techniques, and discusses the potential of such an approach to predict learners{\textquoteright} 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{\textquoteright} progress and predict their performance, providing opportunities for individual support to be provided to learners. ",

keywords = "serious games, performance prediction, user studies, learning analytics, game-based learning",

author = "Sylvester Arnab and Odafe Imiruaye and Fotis Liarokapis and Gemma Tombs and Petros Lameras and Angel Serrano-Laguna and Pablo Moreno-Ger",

note = "This paper was presented on the 20th April 2015 at the AERA (American Educational Research Association) conference, Chicago, Illinois. Arnab, S. , Imiruaye, O. , Liarokapis, F. , Tombs, G. , Lameras, P. , Serrano-Laguna, Angel and Moreno-Ger, Pablo. 2015, 20 April. Toward Performance Prediction Using In-Game Measures. Paper presented at the 2015 annual meeting of the American Educational Research Association. Retrieved 18 August 2015, from the AERA Online Paper Repository.; American Educational Research Association annual meeting ; Conference date: 16-04-2015 Through 20-04-2015",

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T1 - Toward Performance Prediction Using In-Game Measures

AU - Arnab, Sylvester

AU - Imiruaye, Odafe

AU - Liarokapis, Fotis

AU - Tombs, Gemma

AU - Lameras, Petros

AU - Serrano-Laguna, Angel

AU - Moreno-Ger, Pablo

N1 - This paper was presented on the 20th April 2015 at the AERA (American Educational Research Association) conference, Chicago, Illinois. Arnab, S. , Imiruaye, O. , Liarokapis, F. , Tombs, G. , Lameras, P. , Serrano-Laguna, Angel and Moreno-Ger, Pablo. 2015, 20 April. Toward Performance Prediction Using In-Game Measures. Paper presented at the 2015 annual meeting of the American Educational Research Association. Retrieved 18 August 2015, from the AERA Online Paper Repository.

PY - 2015/4/20

Y1 - 2015/4/20

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 ingame 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 ingame 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.

KW - serious games

KW - performance prediction

KW - user studies

KW - learning analytics

KW - game-based learning

M3 - Paper

T2 - American Educational Research Association annual meeting

Y2 - 16 April 2015 through 20 April 2015

ER -

Toward Performance Prediction Using In-Game Measures

Abstract

Conference

Bibliographical note

Keywords

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