Impact of serious games on science learning achievement compared with more conventional instruction: an overview and a meta-analysis

ABSTRACT

Serious games have become increasingly available to educators. Empirical studies and meta-analyses have examined their impact on learning achievement. However, natural sciences could have a special relation to serious games by their systematic use of quantitative and predictive models that can generate microworlds and simulations. Since no known meta-analysis on serious games observed a significant impact in the specific context of science learning, the present meta-analysis synthesised results from 79 empirical studies that compared the impact on science learning achievement of instruction using serious games versus instruction using more conventional methods. Consistent with theory and past meta-analyses not specifically related to science learning, post-instruction learning achievement was weakly to moderately higher for declarative knowledge, knowledge retention and procedural knowledge for students taught with serious games. Furthermore, findings of the present work suggest that five moderator variables produced significant effects on the relationship between playing serious games and learning outcomes, and three showed consistent variations in mean effect size that could lead to significance, with more studies and larger samples. These findings are discussed in connection with previous meta-analyses’ findings, potential pedagogical implications and possible future research.

KEYWORDS:

• Science education
• learning achievement
• serious game
• conventional instruction
• meta-analysis
• overview

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. http://psych.wisc.edu/henriques/mediator.html

Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182.

2. In Vogel et al.’s meta-analysis, N represents the cumulative number of participants from primary studies on which overall effect sizes were computed.

3. In Sitzmann & Ely’s meta-analysis, N represents the cumulative number of participants from primary studies on which overall effect sizes were computed.

4. In Wouters et al.’s meta-analysis, N represents the cumulative number of participants from primary studies on which overall effect sizes were computed.

5. In Wouters et al.’s meta-analysis, k represents the cumulative number of effect sizes from primary studies on which overall effect sizes were computed.

6. In Clark et al.’s meta-analysis, N represents the cumulative number of effect sizes from primary studies, which lead to computation of overall effect sizes.

Additional information

Notes on contributors

Martin Riopel

Martin Riopel (Ph. D.) is a professor of science and technology education and vice-dean of research at the Université du Québec à Montréal (UQAM) in Canada. His research interests focus on computer-assisted learning, serious games, learning models and neuroeducation. He also holds the Research Chair on Educational Innovation (CRIP) at the Paris-Saclay University in France.

Lucian Nenciovici

Lucian Nenciovici (M. A.) is a Ph. D. student in science and technology education and a teacher at the Université du Québec à Montréal (UQAM) in Canada with research focus on neuroeducation and meta-analysis methodologies.

Patrice Potvin

Patrice Potvin (Ph. D.) is a professor of science and technology education at the Université du Québec à Montréal (UQAM) in Canada. Holder of the Research Chair on Interest of Youth toward Science and Technology (CRIJEST), director of the Science and Technology Education Research Team (EREST) and member of the Royal Society of Canada, his research and development interests focus on student interest in science, open science learning, computer-assisted learning, teacher training, and conceptual change through a neuro-educational approach.

Pierre Chastenay

Pierre Chastenay (Ph. D.) is an astronomer and a professor of science and technology education at the Université du Québec à Montréal (UQAM) in Canada. He mainly does research in science teaching and astronomy teaching at the elementary level. His current projects deal with spatial abilities and astronomy education, teaching the phases of the Moon, Teaching astronomy by doing astronomy like astronomers, and using a full-dome digital planetarium to teach astronomical concepts.

Patrick Charland

Patrick Charland (Ph. D.) is a professor of science and technology education at the Université du Québec à Montréal (UQAM) in Canada and co-holder of the UNESCO Research Chair in Curriculum Development (CUDC). He is specialized in the study of the dynamic of interactions between the dimensions of engagement and situational interest in real time with the collection and analysis of behavioral, cognitive and emotional data.

Jérémie Blanchette Sarrasin

Jérémie Blanchette Sarrasin (M. A.) is a Ph.D. student in the field of Mind, Brain and Education and a teacher at the Université du Québec à Montréal (UQAM) in Canada. Her research focus on the cerebral mechanisms involved in reasoning in science and mathematics using cognitive neuroscience methods.

Steve Masson

Steve Masson (Ph. D.) is a professor of neuroeducation at Université du Québec à Montréal (UQAM) in Canada and director of the Laboratory for Research in Neuroeducation (LRN). Using the functional magnetic resonance imaging, he studies the brain mechanisms related to school learning and teaching. His research interests focus on the effects of teaching practices on the brain, and the role of neuroscience in teaching.