1. Using Learning Analytics to Investigate Learning Processes and Behavioural Patterns in an Augmented Reality Language Learning Environment
- Author
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Geng, Xuewang and Yamada, Masanori
- Abstract
Background: Augmented reality has been widely applied in various fields, and its benefits in language learning have been increasingly recognized. However, the investigation of effective learning behaviours and processes in augmented reality learning environments, taking into account temporality and analysis of differences in learning behaviours between learning achievements from the perspective of learning strategies, is still a topic that requires further research. Objectives: The goal of this research is to explore effective learning processes in AR language learning environments and learning behavioural patterns of learners with different performance levels via LA taking into account temporality. Methods: We used the learning application designed and developed in previous studies to understand the learning process and behavioural patterns of acquiring Japanese compound verbs with augmented reality. Lag sequential analyses were conducted to analyse the learning behavioural transitions using learning data. Additionally, frequent sequences were analysed using frequent sequence mining to compare the learning behavioural patterns of learners with different learning performances. Results and Conclusions: We investigated the learning process and behavioural patterns and discussed the language learning strategies employed in the augmented reality learning environment. The results indicated that learners primarily performed strategies of performed expressions, recombining, and structured reviewing in AR compound verb learning activities. Furthermore, the results revealed the different types of strategies applied by different learning performance learners, whereby the low group learners used the least variety of strategies, and the middle and high group learners employed repeating and deductive reasoning strategies that were not followed by the low group.
- Published
- 2023
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