Investigating the impact of context-awareness smart learning mechanism on EFL conversation learning

ABSTRACTABSTRACTDrama learning is helpful for English speaking, however, few studies provided students with opportunities to practice drama conversations individually. This study proposed a Context-Awareness Smart Learning Mechanism (CASLM) and integrated into SmartVpen that consisted of context-aware learning content, context-aware input assistance, oral recognition feedback, peer cooperative learning, and smart conversation robot. The participants were 68 eighth grade-students divided into three groups: an experimental group (EG) who used SmartVpen, a control group 1 (CG1) who used typical camera and voice recorder, and a control group 2 (CG2) who used papers and pencils. The results showed the EG outperformed the other groups concerning oral and conversational skills, which indicated the use of SmartVpen had significant effects in both English oral speaking and conversational skills. Additionally, the number of time to complete conversation practices can predict students' oral performance by 30%. Furthermore, the results also showed the EG tend to practice drama conversations more frequently than the CG1, which demonstrated practicing English drama conversations using SmartVpen can effectively improve students' learning motivation. Thus, we suggested English conversations practice activities should be conducted in authentic context with SmartVpen to support students' speaking and facilitate them to apply what they learned in real-life situations.KEYWORDS: Context-awarenesssmart learningchat robotdrama creationEnglish dialogue Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by National Science and Technology Council of the Republic of China [grant numbers MOST 111-2410-H-656-004, 109-2511-H-008-009-MY3 and 111-2410-H-008-061-MY3].Notes on contributorsYi-Fan LiuDr. Yi-Fan Liu is an Assistant Research Fellow with the Research Center for Testing and Assessment, National Academy for Educational Research, New Taipei City, Taiwan. His research interests include Human Factors, Technology Enhanced Language Learning, Note-taking Behaviors, and Mobile Learning.Wu-Yuin HwangDr. Wu-Yuin is a Professor affiliated with the department of Computer Science and Information Engineering, College of Science and Engineering, National Dong Hwa University, and the Institute of Network Learning Technology, National Central University, Taiwan. His current research interests are related to integration of IOT, AI and multimedia sensors of mobile devices for interactions among human and all things in AR contexts like smart agriculture, buildings and campus. Dr. Hwang received the Outstanding Research Award, Ministry of Science and Technology, Taiwan in 2021. He is also ranked in top 7 scholars of the world in terms of high quality journal publication performance of instructional design and technology.Chia-Hsuan SuChia-Hsuan Su is a Master Student of Graduate Institute of Network Learning Technology, National Central University, Taoyuan, Taiwan.