Factors Influencing Students' Listening Learning Performance in Mobile Vocabulary-Assisted Listening Learning: An Extended Technology Acceptance Model
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| Title: | Factors Influencing Students' Listening Learning Performance in Mobile Vocabulary-Assisted Listening Learning: An Extended Technology Acceptance Model |
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| Language: | English |
| Authors: | Hui-Tzu Hsu (ORCID |
| Source: | Journal of Computer Assisted Learning. 2024 40(4):1511-1525. |
| Availability: | Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us |
| Peer Reviewed: | Y |
| Page Count: | 15 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Information Analyses Reports - Research |
| Education Level: | Higher Education Postsecondary Education |
| Descriptors: | Intention, Vocabulary Development, Handheld Devices, College Students, Predictor Variables, Student Behavior, Listening Skills, English (Second Language), Electronic Learning, Models, Second Language Learning, Performance, Listening Comprehension, Educational Technology |
| DOI: | 10.1111/jcal.12969 |
| ISSN: | 0266-4909 1365-2729 |
| Abstract: | Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning performance (LLP) through vocabulary learning performance (VLP) in the context of mobile vocabulary-assisted listening learning by using two mobile learning tools. Methods: A total of 129 college students with a pre-intermediate level of English were recruited as participants, and a 10-week mobile vocabulary-assisted, listening-learning course was conducted in 2022. In each task of this course, the students had to learn target words from a listening passage on Quizlet and then engage in listening activities on Randall's ESL Cyber Listening Lab. Quantitative responses obtained through an online questionnaire were analysed through partial-least-squares structural equation modelling. Results: The analysis results indicated that BI significantly predicted LLP through VLP. Perceived ease of use (PEU) and perceived usefulness (PU) were significant antecedents of BI. However, PEU did not significantly predict PU because of the difficulty of navigating between the two technological tools used in this study. The extended model demonstrated its effectiveness in explaining listening learning performance, as evidenced by an explained variance (R[superscript 2]) of 69%. Conclusion: The extended model validates the influence of BI on learning performance and it can also draw teachers' focus toward the significance of enhancing students' BI to improve their listening learning performance. Pedagogical implications based on the results are provided in this paper. |
| Abstractor: | As Provided |
| Entry Date: | 2024 |
| Accession Number: | EJ1432006 |
| Database: | ERIC |
| Abstract: | Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning performance (LLP) through vocabulary learning performance (VLP) in the context of mobile vocabulary-assisted listening learning by using two mobile learning tools. Methods: A total of 129 college students with a pre-intermediate level of English were recruited as participants, and a 10-week mobile vocabulary-assisted, listening-learning course was conducted in 2022. In each task of this course, the students had to learn target words from a listening passage on Quizlet and then engage in listening activities on Randall's ESL Cyber Listening Lab. Quantitative responses obtained through an online questionnaire were analysed through partial-least-squares structural equation modelling. Results: The analysis results indicated that BI significantly predicted LLP through VLP. Perceived ease of use (PEU) and perceived usefulness (PU) were significant antecedents of BI. However, PEU did not significantly predict PU because of the difficulty of navigating between the two technological tools used in this study. The extended model demonstrated its effectiveness in explaining listening learning performance, as evidenced by an explained variance (R[superscript 2]) of 69%. Conclusion: The extended model validates the influence of BI on learning performance and it can also draw teachers' focus toward the significance of enhancing students' BI to improve their listening learning performance. Pedagogical implications based on the results are provided in this paper. |
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| ISSN: | 0266-4909 1365-2729 |
| DOI: | 10.1111/jcal.12969 |
