Game-based learning (GBL) has increasingly been used to promote students’ learning engagement. Although prior GBL studies have highlighted the significance of learning engagement as a mediator of students’ meaningful learning, the existing accounts failed to capture specific evidence of how exactly students’ in-game actions in GBL enhance learning engagement. Hence, this mixed-method study was designed to examine whether middle school students’ in-game actions are likely to promote certain types of learning engagement (i.e., content and cognitive engagement). This study used and examined the game E-Rebuild, a single-player three-dimensional architecture game that requires learners’ application of math knowledge. Using in-depth gameplay behavior analysis, this study sampled a total of 92 screen-recorded and video-captured gameplay sessions attended by 25 middle school students. We adopted two analytic approaches: sequential analysis and thematic analysis. Whereas sequential analysis explored which in-game actions by students were likely to promote each type of learning engagement, the thematic analysis depicted how certain gameplay contexts contributed to students’ enhanced learning engagement. The study found that refugee allocation and material trading actions promoted students’ content engagement, whereas using in-game building tools and learning support boosted their cognitive engagement. This study also found that students’ learning engagement was associated with their development of mathematical thinking in a GBL context.
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Modeling Player Engagement with Bayesian Hierarchical Models
Modeling player engagement is a key challenge in games. However, the gameplay signatures of engaged players can be highly context-sensitive, varying based on where the game is used or what population of players is using it. Traditionally, models of player engagement are investigated in a particular context, and it is unclear how effectively these models generalize to other settings and populations. In this work, we investigate a Bayesian hierarchical linear model for multi-task learning to devise a model of player engagement from a pair of datasets that were gathered in two complementary contexts: a Classroom Study with middle school students and a Laboratory Study with undergraduate students. Both groups of players used similar versions of Crystal Island, an educational interactive narrative game for science learning. Results indicate that the Bayesian hierarchical model outperforms both pooled and context-specific models in cross-validation measures of predicting player motivation from in-game behaviors, particularly for the smaller Classroom Study group. Further, we find that the posterior distributions of model parameters indicate that the coefficient for a measure of gameplay performance significantly differs between groups. Drawing upon their capacity to share information across groups, hierarchical Bayesian methods provide an effective approach for modeling player engagement with data from similar, but different, contexts.
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- Award ID(s):
- 1661202
- NSF-PAR ID:
- 10091350
- Date Published:
- Journal Name:
- Proceedings of the Fourteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
- Page Range / eLocation ID:
- 215-221
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Background. Middle school students’ math anxiety and low engagement have been major issues in math education. In order to reduce their anxiety and support their math learning, game-based learning (GBL) has been implemented. GBL research has underscored the role of social dynamics to facilitate a qualitative understanding of students’ knowledge. Whereas students’ peer interactions have been deemed a social dynamic, the relationships among peer interaction, task efficiency, and learning engagement were not well understood in previous empirical studies.
Method. This mixed-method research implemented E-Rebuild, which is a 3D architecture game designed to promote students’ math problem-solving skills. We collected a total of 102 50-minutes gameplay sessions performed by 32 middle school students. Using video-captured and screen-recorded gameplaying sessions, we implemented behavior observations to measure students’ peer interaction efficiency, task efficiency, and learning engagement. We used association analyses, sequential analysis, and thematic analysis to explain how peer interaction promoted students’ task efficiency and learning engagement.
Results. Students’ peer interactions were negatively related to task efficiency and learning engagement. There were also different gameplay patterns by students’ learning/task-relevant peer-interaction efficiency (PIE) level. Students in the low PIE group tended to progress through game tasks more efficiently than those in the high PIE group. The results of qualitative thematic analysis suggested that the students in the low PIE group showed more reflections on game-based mathematical problem solving, whereas those with high PIE experienced distractions during gameplay.
Discussion. This study confirmed that students’ peer interactions without purposeful and knowledge-constructive collaborations led to their low task efficiency, as well as low learning engagement. The study finding shows further design implications: (1) providing in-game prompts to stimulate students’ math-related discussions and (2) developing collaboration contexts that legitimize students’ interpersonal knowledge exchanges with peers.
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