Understanding Student Navigation Patterns in Game-Based Learning

Abstract

Research on learning analytics (LA) has focused mostly at the university level. LA research in the K–12 setting is needed. This study aimed to understand 4,115 middle school students’ learning paths based on their behavioural patterns and the relationship with performance levels when they used a digital learning game as their science curriculum. The findings showed significant positive relationships between various tool uses and performance measures and varied tool use patterns at different problem-solving phases by high- and low-performing students. The results indicated that students who used tools appropriately and wisely, given the phase they were at, were more likely to succeed. The findings offered an insightful glimpse of learners’ navigation patterns in relation to their performance and provided much-needed empirical evidence to support using analytics for game-based learning in K–12 education. The findings also revealed that log data cannot explain all learners’ actions. Implications for both research and practice are discussed.

Understanding Middle School Students’ Self-Efficacy and Performance in a Technology-Enriched Problem-Based Learning Program: A Learning Analytics Approach

“…Students are asked to provide a rationale for why they plan to launch a probe and what data the probe is to gather. Their probe justifications were categorized into five types (i.e., random input, vague inquiry, vague claim, specific inquiry, and reasoning; Liu et al, 2022). Even though the Mann-Whitney U tests showed no significant differences among the three self-efficacy groups in the total numbers of probes launched, significant differences were found in the two high-level types of probe justification (specific inquiry and reasoning; see Table 4).…”

“…Also, they explored different tools, such as the Solar System Database, Spectra, and the Periodic Table . Similarly, in this phase, the low-low science knowledge students frequently went back and forth between the Alien Information Center and Notebook. However, the low-low group visited the Mission Control Center and the Probe Design Center, which are the places to design and launch probes and are not expected at this early stage (Liu et al, 2022). In the second stage, the most frequent patterns in the low-high group was Alien Information Center ←→ Notebook, and frequent patterns of Notebook ←→ Concept Database, and Notebook ←→ Solar System Database were also observed.…”

Learning analytics for student homework activities during a long break: Evidence from K-12 education in Japan

“…Other studies on the use of learning analytics in the K-12 context include a study that examined the correlation between log data and performance in digital learning tools in middle school science (Liu et al, 2022), and a study that used smart glasses to visualize student behavior (Holstein et al, 2018). However, to the best of our knowledge, no examples of learning analytics being applied to learning during the summer vacation period were found.…”