LewiSpace: An Educational Puzzle Game Combined with a Multimodal Machine Learning Environment

Ghali, Ramla; Ouellet, Sébastien; Frasson, Claude

doi:10.1007/978-3-319-24489-1_23

Cited by 2 publications

(2 citation statements)

References 12 publications

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“…Others, like [Ghali et al 2015] and [Henry et al 2018], use sensors to perceive data from the user in order to adapt gamification features more efficiently. In [Ghali et al 2015] a machine learning model uses sensors like electroencephalography, eye tracking, and facial expression recognition to predict the actions of a player in an educational chemistry game called Lewispace.…”

Section: Resultsmentioning

confidence: 99%

“…What can be observed from all these different approaches is that there's no consensus as to what the adaptive quality of gamification should be. Some authors (as seen in [Smith et al 2017], [Ghali et al 2015], and [Kamnardsiri et al 2016]) believe it comes from IoT devices and sensors to detect user's input and predict behavior, while others (as seen in [Klock et al 2016], [Smith et al 2017], [Lavoué et al 2018], and [Paiva et al 2016]) believe it comes from detecting the user's profile and learning context in order to adapt gamification elements.…”

Section: Resultsmentioning

confidence: 99%

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Adaptive gamification strategies for education: a systematic literature review

Lopes

Reinheimer

Medina

et al. 2019

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

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Gamification elements are frequently used in learning environments as a means to motivate and engage students. Adaptive gamification, a more recent approach, attempts to maximize the expected learning objectives by adapting these gamification elements to the needs of each user. In this paper, we present a systematic review of adaptive gamification in educational contexts aiming to understand how these adaptive features work, what they adapt and which strategies they adopt. We identified 16 papers that describe different adaptive gamification strategies for different learning topics, based on factors like user input, user experience, and player typology.Resumo. Elementos de gamificação são frequentemente usados em ambientes de aprendizagem como método para motivar e engajar estudantes. Recentemente, a gamificação adaptativa vem surgindo como forma de maximizar os objetivos de aprendizagem esperados ao adaptar elementos de gamificaçãoàs necessidades dos usuários. Neste artigo,é apresentada uma revisão sistemática sobre gamificação adaptativa em ambientes educacionais, buscando compreender como essa adaptação acontece, o queé adaptado e quais estratégias são adotadas. Foram identificados 16 artigos que descrevem diferentes estratégias de gamificação adaptativa para diferentes tópicos de aprendizagem, baseandose em fatores como interações e experiência do usuário e perfis de jogador.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Adaptive gamification strategies for education: a systematic literature review

Lopes

Reinheimer

Medina

et al. 2019

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

View full text Add to dashboard Cite

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LewiSpace: an Exploratory Study with a Machine Learning Model in an Educational Game

Ghali

Ouellet

Frasson

2015

Journal of Education and Training Studies

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The use of educational games as a tool for providing learners with a playful and educational aspect is widespread. In this paper, we present an educational game that we developed to teach a chemistry lesson, namely drawing a Lewis diagram. Our game is a 3D environment known as LewiSpace and aims at balancing between playful and educational contents in order to increase engagement and motivation while learning. The game contains mainly five different missions aim at constructing Lewis diagram molecules which are organized in an ascending order of difficulty. We also conducted an experiment to gather data about learners' cognitive and emotional states as well as their behaviours through our game by using three types of sensors (electroencephalography, eye tracking, and facial expression recognition with an optical camera) and a self report personality questionnaire (the Big Five). Primary results show that a machine learning model namely logistic regression, can predict with some success whether the learner will success or fail in each mission of our game, and paves the way for an adaptive version of the game. This latter will challenge or assist learners based on some features extracted from our data. Feature extraction integrated into a machine learning model aims mainly at providing learners' with a real-time adaptation according to their performance and skills while progressing in our game.

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LewiSpace: An Educational Puzzle Game Combined with a Multimodal Machine Learning Environment

Cited by 2 publications

References 12 publications

Adaptive gamification strategies for education: a systematic literature review

Adaptive gamification strategies for education: a systematic literature review

LewiSpace: an Exploratory Study with a Machine Learning Model in an Educational Game

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