Publication Information

Authors:

• Xinying Hou, Carnegie Mellon University
• Huy Anh Nguyen, Carnegie Mellon University
• J. Elizabeth Richey, Carnegie Mellon University
• Erik Harpstead, Carnegie Mellon University
• Jessica Hammer, Carnegie Mellon University
• Bruce M. McLaren, Carnegie Mellon University

Pages:

• 120-150

Keywords:

• Digital learning game, Decimal numbers, Mediation analysis, Student modeling, Open learner model, Enjoyment

Abstract:

• Digital learning games are designed to foster both student learning and enjoyment. Given this goal, an interesting research topic is whether game mechanics that promote learning and those that promote enjoyment have different effects on students’ experience and learning performance. We explored these questions in Decimal Point, a digital learning game that teaches decimal numbers and operations to 5th and 6th graders, through a classroom study with 159 students and two versions of the game. One version encouraged playing and learning through an open learner model (OLM, N = 55), while one encouraged playing for enjoyment through an analogous open enjoyment model (OEM, N = 54). We compared these versions to a control version that is neutral with respect to learning and enjoyment (N = 50). While students learned in all three conditions, our results indicated no significant condition differences in learning outcomes, enjoyment, or engagement. However, the learning-oriented group engaged more in re-practicing, while the enjoyment-oriented group demonstrated more exploration of different mini-games. Further analyses of students’ interactions with the open learner and enjoyment models revealed that students who followed the learner model demonstrated better in-game learning and test performance, while following the enjoyment model did not impact learning outcomes. These findings indicate that emphasizing learning or enjoyment can lead to distinctive game play behaviors, and that open learner models can be helpful in a learning game context. In turn, our analyses have led to preliminary ideas about how to use AI to provide recommendations that are more aligned with students’ dynamic learning and enjoyment states and preferences.