Journals / The Eurasia Proceedings of Educational and Social Sciences / 2016 / Volume: 5

PREDICTORS OF VOCATIONAL COLLEGE STUDENTS’ BEHAVIORAL INTENTION TO USE WEB-BASED LISTENING TEST

Pages
—
Publication Date
2016-09-01
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Students’ perceived playfulness appeared to be the strongest predictor of their behavioral intention to use within the web-based listening test. 

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Higher Education Institutions extensively use web-based testing applications to support their teaching and learning activities.
📊
Key Findings & Data:The participants of the study comprised 929 post-secondary students enrolled in an “English Foreign Language” course during 2015-2016 fall semester.
💡
Conclusion & Impact:Students’ perceived playfulness appeared to be the strongest predictor of their behavioral intention to use within the web-based listening test. 
🔬Design:Survey & Empirical Study
📍Scope:2015-2016 Verileri
⚙️Variables:Web-based listening test · technology acceptance · vocational college

Abstract

Higher Education Institutions extensively use web-based testing applications to support their teaching and learning activities. Despite myriad benefits of these applications, students and institutions do not take full advantage of web-based testing applications due to a number of factors influencing behavioral intention. The purpose of this study was to examine vocational college students’ behavioral intention of a web-based listening test administered in a vocational collage. The participants of the study comprised 929 post-secondary students enrolled in an “English Foreign Language” course during 2015-2016 fall semester. Computer Based Assessment Acceptance Model was adopted to determine predictors of participants’ behavioral intention to use web-based listening test. The data were collected via an online questionnaire and analyzed using Pearson correlation coefficients and linear regression analyses. The results of the study revealed that students’ behavioral intention to use web-based listening test had significantly positive relationship with their computer self-efficacy, facilitating conditions, social influence, goal expectancy, content of the test, perceived ease of use, perceived usefulness and perceived playfulness. Results also showed that social influence, goal expectancy, perceived usefulness, perceived ease of use and perceived playfulness exerted a direct influence on behavioral intention to use web-based listening test. Students’ perceived playfulness appeared to be the strongest predictor of their behavioral intention to use within the web-based listening test. 

🔖

Cite This Article

Erdogan, T., Cigdem, H., Yildirim, O. G. (2016). PREDICTORS OF VOCATIONAL COLLEGE STUDENTS’ BEHAVIORAL INTENTION TO USE WEB-BASED LISTENING TEST. The Eurasia Proceedings of Educational and Social Sciences, Volume: 5. https://ajindex.com/articles/1550302

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (10)

  1. Abedi, J. (2014). The use of computer technology in designing appropriate test accommodations for English language learners. Applied Measurement in Education, 27(4), 261-272.
  2. Cigdem, H., & Oncu, S. (2015). E-assessment adaptation at a military vocational college: student perceptions. Eurasia Journal of Mathematics, Science & Technology Education, 11(5), 971-988.
  3. Cigdem, H., & Tan, S. (2014). Students' opinions on administering optional online quizzes in a two-year college Mathematics course. Journal of Computer and Education Research, 2(4), 51e73. Bull, J., & McKenna, C. (2004). Blueprint for computer-assisted assessment. London: Routledge-Falmer. Chou, C., Moslehpour, M., & Le Huyen, N. T. (2014). Concurrent and predictive validity of computer-adaptive freshman English test for college freshman English in Taiwan. International Journal of English Language Education, 2(1), 143-156.
  4. Jeong, H. (2014). A comparative study of scores on computer-based tests and paper-based tests. Behaviour & Information Technology, 33(4), 410-422.
  5. Llamas-Nistal, M., Fernández-Iglesias, M. J., González-Tato, J., & Mikic-Fonte, F. A. (2013). Blended e-assessment: Migrating classical exams to the digital world. Computers & Education, 62, 72-87.
  6. Morris, D. (2008). Economics of scale and scope in e-learning. Teaching in Higher Education, 33(3), 331–343.
  7. Pino-Silva, J. (2008). Student perception of computerized tests. ELT Journal, 62(2). Prensky, M. (2001). Digital natives, digital immigrants. On the Horizon, 9(5), 1-6.
  8. Terzis, V., & Economides, A. A. (2011). The acceptance and use of computer based assessment. Computers & Education, 56(4), 1032–1044.
  9. Terzis, V., Moridis, C. N., & Economides, A. A. (2012). How Student’s Personality Traits Affect Computer Based Assessment Acceptance: Integrating BFI with CBAAM. Computers in Human Behavior 28(5), 1985–1996.
  10. Zakrzewski, S., & Steven, C. (2000). A model for computer-based assessment: The catherine wheel principle. Assessment & Evaluation in Higher Education 25 (2): 201–15.