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Journal Article

An Approach to Scoring Collaboration in Online Game Environments  pp335-342

Claire Scoular, Esther Care, Nafisa Awwal

© Aug 2017 Volume 15 Issue 4, Editor: Elizabeth Boyle and Thomas Connolly, pp281 - 366

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Abstract

With technological advances, it is now possible to use games to capture information‑rich behaviours that reveal processes by which players interact and solve problems. Recent problem‑based games have been designed to assess and record detailed interactions between the problem solver and the game environment, and thereby capture salient solution processes in an unobtrusive way (Zoanetti, 2010; Bennett et al., 2003; Shute & Wang, 2009). The Assessment and Teaching of 21st Century Skills project used an innovative approach to capture these processes through responses (activities and communication patterns) within collaborative games (Griffin & Care, 2015). Game player response patterns are identified as behaviours that are indicative of skills and are captured in real‑time game play within time‑structured log files. The analysis of these log files allows for inferences to be drawn in regard to the efficiency and quality of player performance. A concern with this approach is that game development for this purpose is complex, time consuming and expensive, with unique scoring applied to each game. This paper presents another approach that identifies, across games, common behaviours. A systematic scoring system for assessing player behaviours in games could provide access to useful data not only from new games but from existing games. This paper presents such an approach using collaborative games situated in a problem‑solving context.

 

Keywords: Collaboration, problem solving, online assessment, log stream data, measurement, e-learning

 

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Journal Issue

Volume 15 Issue 4 / Aug 2017  pp281‑366

Editor: Elizabeth Boyle, Thomas Connolly

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Editorial

 

Keywords: Sign Language; American Sign Language; Recognition System; Kinect; Expert System; Game-Based Learning; Knowledge Engineering, Visual programming, Education, Computational thinking, K-12, Lightbot, Scratch, Microgames, learning, gender, culture, Multiple intelligences; Game preferences; Game mechanics; Evidence-based; Game design; Learning games, Collaboration, problem solving, online assessment, log stream data, measurement, e-learning, Educational Video Games; TAM (Technology Acceptance Model); Higher Education; Behavioural intention; Age

 

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