Learning Analytics

Learning analytics provides data-driven support essential for the complex, non-linear learning processes characteristic of CBL. Ifenthaler and Gibson (2019) found that engagement in challenge-based digital environments is positively related to learning performance across behavioral, cognitive, emotional, and motivational dimensions. More recent research confirms that learning analytics dashboards are shifting from purely descriptive tools toward pedagogically informed designs that actively support students' learning processes — a development well-aligned with CBL's emphasis on reflection and iteration (Paulsen & Lindsay, 2024). The predictive capabilities of learning analytics are particularly valuable in CBL contexts, where early identification of struggling students can prevent them from becoming overwhelmed by challenge complexity, and where timely, data-informed interventions have been shown to meaningfully improve outcomes (Ramaswami et al., 2023). Van den Beemt et al. (2022) emphasized that CBL increasingly relies on such analytics and evaluative dashboards to support active learning, requiring explicit institutional support beyond what traditional education typically provides.

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• Paulsen, L., & Lindsay, E. (2024). Learning analytics dashboards are increasingly becoming about learning and not just analytics. Education and Information Technologies, 29, 14279–14308. https://doi.org/10.1007/s10639-023-12401-4
• Ramaswami, G., Susnjak, T., & Mathrani, A. (2023). Effectiveness of a learning analytics dashboard for increasing student engagement levels. Journal of Learning Analytics, 10(3), 115–134. https://doi.org/10.18608/jla.2023.7935