Improving student academic emotions and learning satisfaction in lectures in a foreign language with speech-enabled language translation technology
DOI:
https://doi.org/10.14742/ajet.7428Keywords:
speech-enabled language translation, academic emotions, learning satisfaction, English as a medium of instructionAbstract
Some students are unable to fully comprehend lecture content delivered in English as a medium of instruction (EMI) because of their low linguistic competence. This negatively impacts their academic emotions and learning satisfaction. Speech-enabled language translation (SELT) technology was applied in this study to simultaneously translate lecture content from English into native language of students and show translated text to students. This study investigated the effectiveness of a SELT application on perceived academic emotions and learning satisfaction of students attending lectures in EMI. Thirty-three university students participated, and perceived academic emotions and learning satisfaction were measured and compared across two groups of students: low language ability (LLA), and high language ability (HLA). The level of perceived academic emotions of LLA students was low before lectures but high during and after lectures. HLA students perceived high levels of academic emotions before lectures and low during and after lectures. LLA students’ level of learning satisfaction was much better in comparison to HLA students. The results suggest that SELT technology is beneficial for learning for LLA students. Therefore, educators and researchers may apply SELT technology to EMI lectures in order to facilitate LLA students levels of perceived academic emotions and learning satisfaction.
Implications for practice or policy:
- SELT technology is beneficial for learning of the low linguistic competency students.
- Educators and researcher may apply SELT technology to EMI lectures in order to facilitate positive academic emotions and learning satisfaction of the low linguistic competency students.
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Copyright (c) 2022 Rustam Shadiev, Yueh Min Huang
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