Self-Regulated Learning in Exploratory Game-Based Learning Environment

• In Event: Measuring Self-Regulated Learning With Multi-Agent Learning Environments

Abstract

Self-regulated learning behaviors such as goal setting and monitoring have been found to be crucial to students’ success in computer-based learning environments. Consequently, understanding students’ self-regulated learning (SRL) behavior has been the subject of increasing interest. SRL is particularly important in exploratory learning environments where goals may be less well defined and students do not necessarily have a clear indicator of their progress. In order to be successful in this type of learning environment, students must actively identify and select their own goals and evaluate their progress accordingly. While the nature of the learning task may have implicit overarching goals such as ‘completing the task’ or ‘learning a lot,’ it is important for students to set more specific, concrete and measurable goals.
This paper presents an initial investigation into self-regulated learning in the exploratory game-based learning environment, Crystal Island. Crystal Island features a science mystery set on a recently discovered volcanic island. Students play the role of the protagonist, who is attempting to discover the identity and source of an unidentified disease plaguing a newly established research station by learning information, forming hypotheses, and performing investigations in the environment. A total of 260 middle school students interacted with Crystal Island. During this interaction student were encouraged to report on their mood and status through an in-game smart phone. This report was described to the students as being part of a new social network for Crystal Island. These status reports were later tagged for SRL evidence use using the following four ranked classifications: (1) specific reflection, (2) general reflection, (3) non-reflective statement, or (4) unrelated. This ranking is motivated by the observation that setting and reflecting upon goals is a hallmark of self-regulatory behavior and that specific goals are more beneficial than those that are more general. Students were then given an overall SRL score based on the average score of their statements. An even tertiary split was then used to assign the students to a High, Medium, and Low SRL category.
Our presentation will describe the methodology used to classify students and discuss initial analyses demonstrating the different learning and gameplay behaviors across students in different SRL-use categories. Student learning as measured by normalized learning gains from pre-test to post-test was compared for the three SRL groups. An ANOVA indicated a difference in learning gains between the group. Post-hoc comparisons indicate that both High and Medium SRL students experienced significantly better learning gains than Low SRL students at the α = 0.05 level. Analyses also indicated that there were significant difference on pre-test scores between groups, suggesting that students with high SRL tendencies may be better students or perhaps their increased prior knowledge helped them to identify and evaluate their goals more efficiently. Analyses also indicate some significant differences in the types of curricular resources used by students. This work highlights the importance of self-regulation on learning in exploratory game-based learning environments.

Authors

• Jennifer Sabourin, North Carolina State University

• James Lester, North Carolina State University