Evaluating Game-Based Learning in informal contexts with Data Science

Abstract

The emergence of digital Game-Based Learning (GBL) has sparked interest in assessing its efficacy. This assessment needs to consider the complex mix of narrative and interactivity typical of video games, which makes it difficult to evaluate to what extent a video game achieves its stated learning objectives. This challenge is exponentially increased when gaming sessions happen spontaneously in informal contexts, without any supervision by educators or the option to assess the players’ prior knowledge and skills. This work presents a methodology for analyzing GBL experiences based on data science and the data collection functionalities offered by current game development platforms. This strategy is applied to the analysis of a social media simulator designed to promote information literacy within the video game Julia: A Science Journey. The system collected data on 436 sessions from 112 unique players over six months. The records included information on replayability, identification of fake news, and reaction times. The results suggest that players become more adept and swifter at identifying fake news through repeated games. Success in identifying misinformation is also related to the topic, with hoaxes related to scientific content being more easily recognized than those associated with political controversies.

Keywords

• video games
• game-based learning
• information literacy
• misinformation
• data science
• critical thinking