Abstract
With this study, we aim to explore the learning analytics framework that was based on the learning theory and combine data mining approaches. It is expected to explore the spontaneous learning behavior and cognitive process of the concept of geometric area during the learning process and present interesting results. This pilot study recruited 160 11-year-old children in Grade 5 of elementary schools in urban and rural areas of Taiwan. The simulation-based environment embedded four instructional designs to support students learning geometric area, which we named the simulation-based assist area concept learning environment (SAACLE). According to the statistical analyses, we found the pilot results showed that the indigenous children seemed to outperform the nonindigenous children in highly directed learning environments; in contrast, the urban children outperformed the indigenous children in learning environments with little direction. Interestingly, such a performance did not exist during the learning processes, and the indigenous and nonindigenous children demonstrated different learning patterns in retention- and transfer-level posttests. Furthermore, we explored the learning analytics framework to analyze the leaning process log file and clarify the learning patterns of children with different sociocultural backgrounds.
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Hsu, Y., Hsu, SK. (2020). A Pilot Study of the Effects of Instructional Design with Learning Analytics on a Computer Simulation-Based Learning Environment. In: Shen, J., Chang, YC., Su, YS., Ogata, H. (eds) Cognitive Cities. IC3 2019. Communications in Computer and Information Science, vol 1227. Springer, Singapore. https://doi.org/10.1007/978-981-15-6113-9_32
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