Applying Game Learning Analytics to a Voluntary Video Game: Intrinsic Motivation, Persistence, and Rewards in Learning to Program at an Early Age

Abstract

Learning to program at an early age has been shown to be a vehicle for the development of Computational Thinking. Game-based environments are often used to develop these skills, but they lack sufficient voluntariness to assess aspects related to intrinsic motivation, such as interests, skills, persistence in solving a problem and behavior in response to rewards. These aspects directly affect achievement and academic performance, so it is necessary to analyze possible age and gender differences in order to adjust Computational Thinking curricula. With this aim, we deployed a voluntary video game which addresses basic computational concepts, based on intrinsic motivation, and aimed at early ages. Data were collected and analyzed using game learning analytics for 15 months, during which 4124 users played more than 28187 games. The analysis shows significant age and gender differences in relation to interests, skills, achievement, and progression through attempts. It was observed that the concepts addressed were achievable between the ages of 3 and 6 years and full mastery was possible by the age of 4 years, regardless of gender, as children persist with the challenge, intrinsically motivated, until it is overcome. In terms of persistence, significantly different behaviors were observed in the face of the challenge, which can help us to adjust the different learning methodologies to each age group and gender, adapting the way we provide reinforcement and rewards, especially for boys in the more complex challenges and for girls from the age of 5 years onwards.

Keywords

• Computational thinking
• early ages
• game learning analytics
• interests
• intrinsic motivation
• learn to program