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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
Language: English
Authors: Hui-Tzu Hsu (ORCID 0000-0001-5375-7013), Chih-Cheng Lin
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
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  Data: Factors Influencing Students' Listening Learning Performance in Mobile Vocabulary-Assisted Listening Learning: An Extended Technology Acceptance Model
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  Data: <searchLink fieldCode="AR" term="%22Hui-Tzu+Hsu%22">Hui-Tzu Hsu</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-5375-7013">0000-0001-5375-7013</externalLink>)<br /><searchLink fieldCode="AR" term="%22Chih-Cheng+Lin%22">Chih-Cheng Lin</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Computer+Assisted+Learning%22"><i>Journal of Computer Assisted Learning</i></searchLink>. 2024 40(4):1511-1525.
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  Data: 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
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  Data: 15
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  Data: 2024
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  Data: Journal Articles<br />Information Analyses<br />Reports - Research
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  Data: <searchLink fieldCode="DE" term="%22Intention%22">Intention</searchLink><br /><searchLink fieldCode="DE" term="%22Vocabulary+Development%22">Vocabulary Development</searchLink><br /><searchLink fieldCode="DE" term="%22Handheld+Devices%22">Handheld Devices</searchLink><br /><searchLink fieldCode="DE" term="%22College+Students%22">College Students</searchLink><br /><searchLink fieldCode="DE" term="%22Predictor+Variables%22">Predictor Variables</searchLink><br /><searchLink fieldCode="DE" term="%22Student+Behavior%22">Student Behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Listening+Skills%22">Listening Skills</searchLink><br /><searchLink fieldCode="DE" term="%22English+%28Second+Language%29%22">English (Second Language)</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+Learning%22">Electronic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Models%22">Models</searchLink><br /><searchLink fieldCode="DE" term="%22Second+Language+Learning%22">Second Language Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Performance%22">Performance</searchLink><br /><searchLink fieldCode="DE" term="%22Listening+Comprehension%22">Listening Comprehension</searchLink><br /><searchLink fieldCode="DE" term="%22Educational+Technology%22">Educational Technology</searchLink>
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  Data: 10.1111/jcal.12969
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  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: 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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        Value: 10.1111/jcal.12969
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    Subjects:
      – SubjectFull: Intention
        Type: general
      – SubjectFull: Vocabulary Development
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      – SubjectFull: Handheld Devices
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      – SubjectFull: Predictor Variables
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      – SubjectFull: Student Behavior
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      – SubjectFull: Listening Skills
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      – SubjectFull: Electronic Learning
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      – SubjectFull: Second Language Learning
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      – SubjectFull: Performance
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      – SubjectFull: Listening Comprehension
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      – TitleFull: Factors Influencing Students' Listening Learning Performance in Mobile Vocabulary-Assisted Listening Learning: An Extended Technology Acceptance Model
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