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

Autor/inn/en	Ghali, Ramla; Ouellet, Sébastien; Frasson, Claude
Titel	LewiSpace: An Exploratory Study with a Machine Learning Model in an Educational Game
Quelle	In: Journal of Education and Training Studies, 4 (2016) 1, S.192-201 (10 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2324-805X
Schlagwörter	Foreign Countries; College Students; Educational Games; Chemistry; Science Instruction; Visual Aids; Learner Engagement; Student Motivation; Psychological Patterns; Cognitive Processes; Medicine; Eye Movements; Photography; Personality Measures; Regression (Statistics); Models; Computer Assisted Instruction; College Science; Canada (Montreal) + Suchen Sie Ihr Suchwort? Ausland; Collegestudent; Educational game; Lernspiel; Chemie; Teaching of science; Science education; Natural sciences Lessons; Naturwissenschaftlicher Unterricht; Anschauungsmaterial; Schulische Motivation; Cognitive process; Kognitiver Prozess; Medizin; Augenbewegung; Fotografie; Regression; Regressionsanalyse; Analogiemodell; Computer based training; Computerunterstützter Unterricht
Abstract	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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01