| 研究実績の概要 |
This study has been able to identify nonhuman actors and their roles by using Actor-Network Theory in a blended learning environment for L2 education. The most prominent nonhuman actors were computers as preferred tools for L2 learning, however, apart from this, learners adopted smartphones as tools to conduct designated in-class tasks as dictionaries and online searching tool for the information needed for task completion. The identified black-boxed tasks were mainly collaborative tasks: tasks that needed prior preparation or had assigned roles for each participant. Some learners did not prepare for the collaborative part of the task so that they stalled their group task progress. Through this study, problems with existing CAF became salient, especially when applied to the data collected from L2 speakers of low proficiency. In this study, the researcher created a new set of measuring system that enables to categorise such oral outputs according to their corresponding levels of grammatical forms. This “hierarchical C-Unit” system allows us to assign numerical values to the most basic utterance like one word sentence.
For low proficient students, it took longer to give instructions for each task during each class. Flipped classroom was, therefore, introduced to create more class time for activities. The researcher made video lectures for learners to be studied before the actual class time. This actually made the time for task instruction time shorter (10-20 minutes depending on tasks) and the students were able to teach each other by referring to the video during the class.
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