Analysis of Gamification Elements. A Case Study in a Computer Science Course

Iruela, Miguel García; Fonseca, Manuel J.; Neira, Raquel Hijón; Chambel, Teresa

doi:10.1007/978-3-030-23207-8_17

Cited by 3 publications

(1 citation statement)

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“…Gamified education materials inevitably provoke competition and cooperation amongst students with design elements like scores, leaderboards, teams, and missions. These game elements have varying effects on student satisfaction (Agapito et al, 2018 ; García Iruela et al, 2019 ). Similar incentives have been introduced in Fortune 500 companies to induce interteam competition (Young et al, 1993 ).…”

Section: Discussionmentioning

confidence: 99%

Friend or foe: classifying collaborative interactions using fNIRS

Hayne,

Grant,

Hirshfield

et al. 2023

Front. Neuroergonomics

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To succeed, effective teams depend on both cooperative and competitive interactions between individual teammates. Depending on the context, cooperation and competition can amplify or neutralize a team's problem solving ability. Therefore, to assess successful collaborative problem solving, it is first crucial to distinguish competitive from cooperative interactions. We investigate the feasibility of using lightweight brain sensors to distinguish cooperative from competitive interactions in pairs of participants (N=84) playing a decision-making game involving uncertain outcomes. We measured brain activity using functional near-infrared spectroscopy (fNIRS) from social, motor, and executive areas during game play alone and in competition or cooperation with another participant. To distinguish competitive, cooperative, and alone conditions, we then trained support vector classifiers using combinations of features extracted from fNIRS data. We find that features from social areas of the brain outperform other features for discriminating competitive, cooperative, and alone conditions in cross-validation. Comparing the competitive and alone conditions, social features yield a 5% improvement over motor and executive features. Social features show promise as means of distinguishing competitive and cooperative environments in problem solving settings. Using fNIRS data provides a real-time measure of subjective experience in an ecologically valid environment. These results have the potential to inform intelligent team monitoring to provide better real-time feedback and improve team outcomes in naturalistic settings.

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Section: Discussionmentioning

confidence: 99%

Friend or foe: classifying collaborative interactions using fNIRS

Hayne,

Grant,

Hirshfield

et al. 2023

Front. Neuroergonomics

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Positive Artificial Intelligence in Education (P-AIED): A Roadmap

Bittencourt

Chalco

Santos

et al. 2023

Int J Artif Intell Educ

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Validating the Effectiveness of Data-Driven Gamification Recommendations: An Exploratory Study

Toda¹,

Palomino²,

Rodrigues³

et al. 2019

Anais Do XXX Simpósio Brasileiro De Informática Na Educação (SBIE 2019)

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Gamification design has benefited from data-driven approaches to creating strategies based on students' characteristics. However, these strategies need further validation to verify their effectiveness in e-learning environments. The exploratory study presented in this paper thus aims at verifying how data-driven gamified strategies are perceived by the students, i.e., the users of e-learning environments. In this study, we conducted a survey presenting 25 predefined strategies, based on a previous study, to students and analysed each strategy's perceived relevance, instanced in an e-learning environment. Our results show that students perceive Acknowledgement, Objective and Progression as important elements in a gamified e-learning environment. We also provide new insights about existing elements and design recommendations for domain specialists.

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Analysis of Gamification Elements. A Case Study in a Computer Science Course

Cited by 3 publications

References 4 publications

Friend or foe: classifying collaborative interactions using fNIRS

Friend or foe: classifying collaborative interactions using fNIRS

Positive Artificial Intelligence in Education (P-AIED): A Roadmap

Validating the Effectiveness of Data-Driven Gamification Recommendations: An Exploratory Study

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