Computer Assisted Language Learning Accuracy of student transcriptions on routine L2 conversations more
Sat, Jul 9, 12:55-13:20 Asia/Tokyo
In an ongoing Kaken-funded research project, an online software was developed that supports learners as they progress through the steps of a group oral discussion task. During the activity, students transcribed their own voices. Later, they were presented with metrics regarding their contributions to the conversation including number of words spoken, number of turns taken, average words spoken per turn, number of words spoken during longest turn, number of pre-selected target words spoken, number of questions asked, and an accuracy percentage based on an automatic speech recognition (ASR) technology. Learners were prompted to use these data for self-assessment and goal setting, while teachers and researchers could access the data for pedagogical and research agendas. Data from a previous study (Author, 2019) using the same task sequence showed growth over time for total words spoken and average turn length. In this presentation, the principal investigator discusses some of the preliminary results gleaned from new research, particularly regarding the accuracy of students’ transcriptions. Attendees interested in using this online software for their own classes or participating in future research projects are offered free access and support.
College and University Educators Learner Profiling to Support Student-Centered Learning Environments more
Sat, Jul 9, 16:00-16:40 Asia/Tokyo
Advocates for student-centered learning environments (Hoidn, 2017; Hoidn & Klemenčič, 2020) have suggested that access to detailed information about students’ backgrounds and beliefs can help teachers prepare more individualized and appropriate lessons. For language educators at Japanese universities, provisions of student information are often limited to simple personal information and perhaps a test score. The action research project presented here set out to develop a system that could 1) collect both quantitative and qualitative information about learner backgrounds and beliefs, 2) process and report data to teachers in a timely manner, and 3) provide learners with the opportunity to contribute to the construction of their own learning environments through the completion of reflective tasks. To accomplish these goals, a learner profiling system was created using Microsoft Forms and Excel. Teachers were provided with reports of their classes within 24 hours, and students were able to use their submissions as benchmarks during reflective tasks at the end of the course. Alongside previously published and current research findings, the instruments are presented in this session as freely available resources for individual language educators and/or program directors looking to achieve similar agendas in their own teaching contexts.