Chinese Language Teaching Methodology and Technology
Author Affiliation
Harvard University
Author Affiliation in Chinese
哈佛大学
English Abstract
With technological advancement and the COVID pandemic, online speaking assessment is increasingly used in language teaching. Two modes are developed: online synchronous testing (direct human-to-human interview) and online asynchronous testing (semi-direct human-to-machine interview). Ample literature has explored how each of the two online modes differs from traditional face-to-face speaking assessments. However, few studies have investigated the differences between the two modes, especially in terms of examinees’ affective preferences. This study, therefore, compares the extent to which each mode is accepted and favored by test takers and explores why such an affective preference emerges. The participants are 46 college students enrolled in an Elementary Chinese course. They completed a survey that investigates their level of motivation, self-confidence, and anxiety in the two types of online speaking tests. An open-ended question item solicited further explanations from test-takers. Results showed a strong affective preference for synchronous assessment, as manifested by a higher level of motivation and self-confidence and a lower level of anxiety. Possible reasons are discussed based on students’ written responses. The study is theoretically significant as it identifies factors on student experience and performance in online speaking assessments. It also provides practical guidance for language teachers in optimizing online oral tests.
Manuscript Language
English
Recommended Citation
Du, Yuxiao and Zhang, Fangzheng
(2022)
"Examinees’ Affective Preference for Online Speaking Assessment: Synchronous VS Asynchronous,"
Chinese Language Teaching Methodology and Technology: Vol. 5:
Iss.
1, Article 3.
Available at:
https://engagedscholarship.csuohio.edu/cltmt/vol5/iss1/3
Included in
Chinese Studies Commons, Educational Assessment, Evaluation, and Research Commons, Online and Distance Education Commons