Integrating Learning, Problem Solving, and Engagement in Narrative-Centered Learning Environments

By Jonathan P. Rowe, Lucy R. Shores, Bradford W. Mott, James C. Lester. International Journal of Artificial Intelligence in Education, 2011

348 citations recorded by the source corpus.

A key promise of narrative-centered learning environments is the ability to make learning engaging. However, there is concern that learning and engagement may be at odds in these game-based learning environments. This view suggests that, on the one hand, students interacting with a game-based learning environment may be engaged but unlikely to learn, while on the other hand, traditional learning technologies may promote deep learning but provide limited engagement. This paper presents findings from a study with human participants that challenges the view that engagement and learning need be opposed. A study was conducted with 153 middle school students interacting with a narrative-centered learning environment. Rather than finding an oppositional relationship between learning and engagement, the study found a strong positive relationship between learning outcomes, in-game problem solving and increased engagement. Furthermore, the relationship between learning outcomes and engagement held even when controlling for students' background knowledge and game-playing experience. Additional analyses revealed that males tended to report significantly greater presence in the virtual environment than girls, and students with more game-playing experience reported significantly greater presence in the virtual environment than students with minimal game-playing experience. Follow up analyses suggested that differences in presence may be more strongly associated with game-playing experience than gender.

Subfields: Artificial Intelligence, Developmental and Educational Psychology

View Paper: https://www.semanticscholar.org/paper/173e5d51d24b783973c55cac9e0637bade891a43

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In Artificial Intelligence

In Developmental and Educational Psychology

Citations in the corpus

Handbook of Research on Learning and Instruction (2011, background): “Thus, children were taught to move based on elite, adult movement models rather than developmentally appropriate patterns such as those that Roberton (1982) described.”
Artificial Intelligence in Education: A Review (2020, background): “A key characteristic and tenet of AI, as the name intimates, is having some level of intelligence, a characteristic that has only been the preserve of human beings until the onset of AI [4], [5], [7], [11], [16], [24], [35].”
Interactive Narrative: An Intelligent Systems Approach (2012, background): “In science, technology, engineering, and mathematics (STEM) education, interactive narrative shares many parallels with problem-based inquiry learning, which involves guiding a student through a succession of problems that build off one another (Rowe et al. 2011).”
Pedagogical Agents: Back to the Future (2018, positive): “For example, the Crystal Island game-based learning environment for middle grades microbiology and literacy (Rowe et al. 2011) features as pedagogical agent a virtual nurse named Kim (figure 2).”
The Nature of Problem Solving (2017, positive): “These methods have been tested for the development of problem solving in a number of different areas and contexts (for example Chang et al., 2012; Rowe et al., 2011; Spires et al., 2011).”