A learning analytics approach towards understanding collaborative inquiry in a problem-based learning environment

Abstract

This exploratory article highlights how problem-based learning (PBL) provided the pedagogical framework used to design and interpret learning analytics from CRYSTAL ISLAND: ECOJOURNEYS, a collaborative game-based learning environment centered on supporting science inquiry. In CRYSTAL ISLAND: ECOJOURNEYS, students work in teams of four, investigate the problem individually, and then utilize a brainstorming board, an in-game PBL whiteboard that structured the collaborative inquiry process. The article addresses a central question: how can PBL support the interpretation of the observed patterns in individual actions and collaborative interactions in the collaborative game-based learning environment? Drawing on a mixed method approach, we first analyzed students’ pre-post test results to determine if there were learning gains. We then used principal component analysis (PCA) to describe the patterns in game interaction data and clustered students based on the PCA. Based on the pre-post test results and PCA clusters, we used interaction analysis to understand how collaborative interactions unfolded across selected groups. Results showed that students learned the targeted content after engaging with the game-based learning environment. Clusters based on the PCA revealed four main ways of engaging in the game-based learning environment: students engaged in low to moderate self-directed actions with (1) high and (2) moderate collaborative sensemaking actions, (3) low self-directed with low collaborative sensemaking actions, and (4) high self-directed actions with low collaborative sensemaking actions. Qualitative interaction analysis revealed that a key difference among four groups in each cluster was the nature of verbal student discourse: students in the low to moderate self-directed and high collaborative sensemaking cluster actively initiated discussions and integrated information they learned to the problem whereas students in the other clusters required more support. These findings have implications for designing adaptive support that responds to students’ interactions with in-game activities.

Keywords

**Learning Analytics, Problem-based learning, Collaborative learning, Games, Quantitative Analysis, Qualitative research **

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