Research

According to the landmark study by Bassok, Reimann and Glaser, learning by doing is the most effective way to increase learning retention, especially for skills-based training and problem-solving.

Chi, M.T.H., Bassok, M., Lewis, M. W., Reimann, P., & Glaser, R. (1989) Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145-182

Despite the research, the traditional teaching model does not employ for this ‘learning by doing’ strategy. Instead, most often corporate training departments and academia use the ‘expert-based teaching model’ in which students watch passively as content is presented. After this learning experience, students demonstrate their competency by completing some form of assessment.

In this traditional model, the student does not search and retrieve this content until after the learning experience – when they’ve entered the ‘real world.’

The major problem with this traditional model is that students do not have the opportunity to apply the learning and rehearse and fail. When the learning environment unable to provide this practice, students will not achieve mastery until it is too late!

The game-based learning model revolves around rehearsal. This ‘learning by doing’ style has been proven to promote mastery. Students access the content in order to complete the ‘level’ or the challenge. When they fail, gaming promotes rehearsal, encouraging students to retry until they have successfully completed the level.

This model can also provide simulated real-world ‘cues,’ so that students become aware of stimuli that triggers their need to employ a ‘search’ and ‘retrieval’ process, applying their knowledge to overcome the challenge.

While not every learning objective requires rehearsal, game-based learning will increase the amount of interactivity and increase student retention.

For more information on the game-based continuum and Digitec’s game-based learning tools, click here to contact us.

* Special thanks to Will Thalheimer at the Work-Learning Research for data on the search retrieval learning process.