Abstracts
Abstract
The aim of the study was to examine the determining factors of students' video usage and their learning satisfaction relating to the supplementary application of educational videos, accessible in a Moodle environment in a Business Mathematics Course. The research model is based on the extension of Technology Acceptance Model (TAM), in which the core TAM constructs – perceived usefulness, perceived ease of use, attitude – and internet self-efficacy were included as the explanatory factors of video usage. As regards the determinants of learning satisfaction, beside the core TAM constructs, the role of learning performance, learner-learner interaction, and learner-teacher interaction was examined. Data were collected from 89 students using a questionnaire, on which the partial least-squares structural equation modelling approach was used to evaluate the research model. The results confirmed that perceived usefulness, attitude, and internet self-efficacy had a direct effect on the video usage. Learning satisfaction was directly influenced by learner-learner interaction, perceived ease of use, and learning performance. Furthermore, the results indicated that video usage had a significant effect both on learning performance and on learning satisfaction. The findings show that the extended TAM model can be applied for predicting the university students' video technology usage and their learning satisfaction regarding the usage.
Keywords:
- video usage,
- learning satisfaction,
- learning performance,
- technology acceptance model
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Bibliography
- Abdous, M., & Yen, C.-J. (2010). A predictive study of learner satisfaction and outcomes in face-to-face, satellite broadcast, and live video-streaming learning environments. Internet and Higher Education, 13, 248-257.
- Abdullah, F., & Ward, R. (2016). Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256.
- Abbad, M. (2010). Learning from group interviews: Exploring dimensions of learning management system acceptance. International Journal of Instructional Technology and Distance Learning, 7(3), 25-39.
- Abbad, M., Morris, D., & Nahlik, C. D. (2009). Looking under the bonnet: Factors affecting student adoption of E-learning systems in Jordan. International Review of Research in Open and Distance Learning, 10(2), 1-25. Retrieved from https://library3.hud.ac.uk/summon/
- Al-Assaf, N., Almarabeh, T., & Eddin, L. N. (2015). A study on the impact of learning management system on students of the University of Jordan. Journal of Software Engineering and Applications, 8, 590-601.
- Al-Gahtani, S. S. (2016). Empirical investigation of e-learning acceptance and assimilation: A structural equation model. Applied Computing and Informatics, 12, 27-50.
- Alharbi, S. & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143-155.
- Ali, A., & Ahmad, I. (2011). Key factors for determining students' satisfaction in distance learning courses: A study of Allama Iqbal Open University. Contemporary Educational Technology, 2(2), 118-134.
- Bandura, A. (1997). Self-Efficacy: The exercise of control. New York, NY: Freeman.
- Bray, E., Aoki, K., & Dlugosh, L. (2008). Predictors of learning satisfaction in Japanese online distance learners. International Review of Research in Open & Distributed Learning, 9(3), 1-24.
- Chester, A. Buntine A., Hammond, K., & Atkinson, L. (2011). Podcasting in education: Student attitudes, behaviour and self-efficacy. Educational Technology & Society, 14(2), 236-247.
- Chin, W. (1998). Issues and opinion on structural equation modeling. MIS Quarterly, 22(1), 7-16.
- Choi, H. J., & Johnson, S. D. (2007). The effect of problem-based video instruction on learner satisfaction, comprehension and retention in college courses. British Journal of Education Techology, 38(5), 885-895.
- Copley, J. (2007). Audio and video podcasts of lectures for campus-based students: Production and evaluation of student use. Innovations in Education and Teaching International, 44(4), 387-399.
- Croxton, R. (2014). The role of interactivity in student satisfaction and persistence in online learning. MERLOT Journal of Online Learning and Teaching, 10(2), 314-325.
- Dalal, M. (2014). Pilot impact of multi-media tutorials in a computer science laboratory course - An empirical study. The Electronic Journal of e-Learning, 12( 4), 366-374.
- Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation), Cambridge, MA: Sloan School of Management, Massachusetts Institute of Technology.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
- Davis, F., Bagozzi, R., & Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
- Day, J., & Foley, J. (2006). Evaluating web lectures: A case study from HCI. Paper presented at the Conference on Human Factors in Computing Systems (CHI EA '06), Montreal, Canada.
- Del Barrio, S., Romero-Frías, E., & Arquero, J. (2013). The role of e-learning satisfaction in the acceptance of technology for educational purposes: a competing models analysis. Proceedings of The Open and Flexible Higher Education Conference 2013, EADTU (pp. 36-49). Paris, France: European Association of Distance Teaching Universities.
- DeLone, W., & McLean, E. (2003). The DeLone and McLean model of information systems success: A ten year update. Journal of Management Information Systems, 19(4), 9-30.
- DeVaney, T. A. (2009). Impact of video tutorials in an online educational statistics course. MERLOT Journal of Online Learning and Teaching, 5(4), 600-608.
- Donkor, F. (2010). The comparative instructional effectiveness of print-based and video-based instructional materials for teaching practical skills at a distance. The International Journal Review of Research in Open and Distance Learning, 11(1), 96-116. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/792/1506
- Donkor, F. (2011). Assessment of learner acceptance and satisfaction with video-based instructional materials for teaching practical skills at a distance. The International Review of Research in Open and Distributed Learning, 12(5), 74-92. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/953/1891
- Dupagne, M., Millette, D. M., & Grinfeder, K. (2009). Effectiveness of video podcast use as a revision tool. Journalism & Mass Communication Educator, 64(1), 54-70.
- Dupuis, J., Coutu, J., & Laneuville, O. (2013). Application of linear mixed-effect models for the analysis of exam scores: Online video associated with higher scores for undergraduate students with lower grades. Computers & Education, 66, 64-73.
- Eastin, M., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer-Mediated Communication, 6(1). Retrieved from http://jcmc.indiana.edu/vol6/issue1/eastin.html
- Evans, C. (2008). The effectiveness of m-learning in the form of podcast revision lectures in higher education. Computers & Education, 50(2), 491-498.
- El-Sayed, R., E. & El-Sayed, S. E. A. E. (2013). Video-based lectures: An emerging paradigm for teaching human anatomy and physiology to student nurses. Alexandria Journal of Medicine, 49, 215-222.
- Figlio, D., Rush, M., & Yin, L. (2010). Is it live or is it Internet? Experimental estimates of the effects of online instruction on student learning (Working Paper No. 16089). Retrieved from http://www.nber.org/papers/w16089
- Fornell, C., & Larcker, F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
- Giannakos, M. N., Chorianopoulos, K., & Chrisochoides, N. (2015). Making sense of video analytics: Lessons learned from clickstream interactions, attitudes, and learning outcome in a video-assisted course. International Review of Research in Open and Distance Learning, 16(1). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1976/3198
- Gosper, M., McNeill, M.,Woo, K., Phillips, R., Preston, G., & Green, D. (2007). Web-based lecture recording technologies - Do students learn from them? Paper presented at EDUCAUSE Australasia. Melbourne, Australia.
- Grandon, E., Alshare, O., & Kwan, O. (2005). Factors influencing student intention to adopt online classes: A cross-cultural study. Journal of Computing Sciences in Colleges, 20(4), 46-56.
- Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate data analysis (7th ed.). Upper Saddle River, NJ: Prentice Hall.
- Henseler, J., Ringle, C., & Sinkovics, R. (2009). The use of partial least squares path modeling in international marketing. Advences in International Marketing, 20, 277-319.
- Hill, J. L., & Nelson, A. (2011). New technology, new pedagogy? Employing video podcasts in learning and teaching about exotic ecosystems. Environmental Education Research, 17(3), 393-408.
- Hsu, H. H., & Chang, Y. Y. (2013). Extended TAM model: Impacts of convenience on acceptance and use of moodle. US-China Education Review, 3(4), 211-218.
- Hui, W., Hu, P., Clark, T., Tam, K., & Milton, J. (2008). Technology-assisted learning: A longitudinal field study of knowledge category, learning effectiveness, and satisfaction in language learning. Journal of Computer Assisted Learning, 24(3), 245-259.
- Islam, A. (2013). Investigating e-learning system usage outcomes in the university context. Computers & Education, 69, 387-399.
- Jung, I., Choi, S., Lim, C., & Leem, J. (2002). Effects of different types of interaction on learning achievement, satisfaction and participation in web-based instruction. Innovations in Education and Teaching International, 39(2), 153-162.
- Kay, R., & Kletskin, I. (2012). Evaluating the use of problem-based video podcasts to teach mathematics in higher education. Computers & Education, 59, 619-627.
- Keller, J. (1983). Motivational design of instruction. In C. Reigeluth, (Ed.), Instructional design theories and models: An overview of their current status ( pp. 386-434). Hillsdale, NJ: Erlbaum.
- Kelly, M., Lyng, C., McGrath, M., & Cannon, G. (2009). A multi-method study to determine the effectiveness of, and student attitudes to, online instructional videos for teaching clinical nursing skills. Nurse Education Today, 29(3), 292-300.
- Kim, J., & Chen, C.-Y. (2011). The influence of integrating pre-online lecture videos in classrooms: A case study. In Bonk, C. J., & Ho, C. P. (Ed.) Proceedings of World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 244-249). Chesapeake: VA: Association for the Advancement of Computing in Education.
- King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information and Management, 43(6), 740-755.
- Koohang, A., & Durante, A. (2003). Learners’ perceptions toward the web-based distance learning activities/assignments portion of an undergraduate hybrid instructional model. Journal of Informational Technology Education, 2, 105-113.
- Kuo, Y., Walker, A., & Schroder, K. (2010). Interaction and other variables as predictors of student satisfaction in online learning environment. Paper presented at the annual meeting of the Society for Information Technology & Teacher Education (SITE). San Diego, California.
- Kuo, Y.-C., Walker, A., Schroder, K., & Belland, B. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35-50.
- Kurtz, B. L., Fenwick Jr., J. B., & Ellsworth C. C. (2007). Using podcasts and tablet PCs in computer science. Proceedings of the 45th annual ACM Southeast regional conference (pp. 484-489). Winston-Salem, NC, USA: Association for Computing Machinery.
- Laurel, B. (1993). Computer as theatre. New York, NY.: Addison-Wesley.
- Lee, J. (2012). Patterns of interaction and participation in a large online course: Strategies for fostering sustainable discussion. Educational Technology & Society, 15(1), 260-272.
- Lee, D., & Lehto, M. (2013). User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers & Education, 193-208.
- Lee, Y.-H., Hsiao, C., & Purnomo, S. (2014). An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australasian Journal of Educational Technology, 30(5), 561-579.
- Liang, J., & Tsai, C. (2008). Internet self-efficacy and preferences toward constructivist Internet-based learning environments: A study of pre-school teachers in Taiwan. Educational Technology & Society, 11(1), 226-237.
- Laing, C., & Wootton, A. (2007). Using podcasts in higher education. Health Information on the Internet, 60, 7-9.
- Liao, C., Palvia, P., & Chen, J.-L. (2009). Information technology adoption behavior life cycle: Toward a technology continuance theory (TCT). International Journal of Information Management, 29(4), 309-320.
- Liu, S. (2008). Student interaction experiences in distance learning courses a phenomenological study. Online Journal of Distance Learning Administration, 11(1).
- Lloyd, S. A., & Robertson, C. L. (2012). Screencast tutorials enhance student learning of statistics. Teaching of Psychology, 39(1), 67-71.
- Lonn, S. & Teasley, S. D. (2009). Podcasting in higher education: What are the implications for teaching and learning? Internet and Higher Education, 12(2), 88-92.
- Majdalawi, Y. Kh., Almarabeh, T., & Mohammad, H. (2014). Factors affecting students' usage of learning management system at the University of Jordan. Life Science Journal, 11(6), 666-671.
- Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173-191.
- Marks, R., Sibley, S., & Arbaugh, J. (2005). A structural equation model of predictors for effective online learning. Journal of Management Education, 29, 531- 565.
- McConville, S. A., & Lane, A. M. (2006). Using on-line video clips to enhance self- efficacytoward dealing with difficult situations among nursing students. Nurse Education Today, 26(3), 200-208.
- McElroy, J. & Blount, Y. (2006). You, me and iLecture. In L. Markauskaite, P. Goodyear, & Reimann (Eds.). Proceedings of the 23rd Annual Conference of the Australasian Society for Computers in Learning in Tertiary Education: Who's Learning? Whose Technology? (pp. 549-558). Sydney: Sydney University Press.
- Moore, M. (1989). Three types of interactions. The American Journal of Distance Education, 3(2), 1-6.
- Noel-Levitz, R. (2011). National online learners priorities report (PDF). Retrieved from http://www.noellevitz.com/upload/Papers_and_Research/2011/PSOL_report%202011.pdf
- Padilla-Meléndez, A., Aguila-Obra, A. R. D., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306-317.
- Palmer, A., & Koenig-Lewis, N. (2012). The effects of pre-enrolment emotions and peer group interaction on students' satisfaction. Journal of Marketing Management, 27, 1208-1231.
- Rezaei, M., Mohammadi, H., Asadi, A., & Kalanta, K. (2008). Predicting e-learning application in agricultural higher education using technology acceptance model. Turkish Online Journal of Distance Education-TOJDE, 98(1), 85-95.
- Ringle, C., Wende, S., & Will, A. (2005). Smartpls. Hamburg, Germany: University of Hamburg.
- Roca, J., Chiu, C.-M., & Martinez, F. (2006). Understanding e-learning continuance intention: an extension of the Technology Acceptance Model. International Journal of Human-Computer Studies, 64(8), 683-696.
- Rodriguez Robles, F. M. (2006). Learner characteristic, interaction and support service variables as predictors of satisfaction in Web-based distance education. Dissertation Abstracts International, 67(07).
- Sahin, I. (2007). Predicting student satisfaction in distance education and learning environments. Turkish Online Journal of Distance Education-TOJDE, 8(2), 113-119.
- Sanchez, R. A. & Hueros, A. D. (2010). Motivational factors that influence the acceptance of Moodle using TAM. Computers in Human Behavior, 26, 1632-1640.
- Schepers, J., & Wetzels, W. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & Managment, 44(1), 90-103.
- Shi, J., Chen, Z., & Tian, M. (2011). Internet self-efficacy, the need for cognition, and sensation seeking as predictors of problematic use of the Internet. CyberPsychology, Behavior, and Social Networking, 14(4), 213-234.
- Soong, S. K. A., Chan, L. K., Cheers, C., & Hu., C. (2006). Impact of video recorded lectures among students. In L. Markauskaite, P. Goodyear, & P. Reimann (Eds.) Proceedings of ASCILITE (pp. 789-793). Sydney, Australia.
- Sun, P.-C., Tsai, R., Finger, G., Chen, Y.-Y., & Yeh, D. (2008). What drives a successful e-learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & Education, 50(4), 1183-1202.
- Sun, Y., Bhattacherjee, A., & Ma, W. (2009). Extending technology usage to work settings: The role of perceived work compatibility in ERP implementation. Information & Management, 46(6), 351-356.
- Sumak, B., Hericko, M., & Pusnik, M. (2011). A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior, 27, 2067-2077.
- Traphagan, T., Kucsera, J., & Kishi, K. (2010). Impact of class lecture webcasting on attendance and learning. Educational Technology Research & Development, 58(1), 19-37.
- Tsai, M.-J., & Tsai, C.-C. (2003). Information searching strategies in web-based science learning: The role of internet self-efficacy. Innovations in Education and Teaching International, 40(1), 43-50.
- Vajoczk, S., Watt, S., Marquis, N., & Holshausen, K. (2010). Podcasts: Are they an effective tool to enhance student learning? A case study. Journal of Educational Multimedia and Hypermedia, 19(3), 349-362.
- van Raaij, E., & Schepers, J. (2008). The acceptance and use of a virtual learning environment in China. Computers & Education, 50, 838-852.
- Venkatesh, V., & Davis, F. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27, 451-481.
- Venkatesh, V., & Davis, F. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186-204.
- Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27, 425-478.
- Venkatesh, V., Thong, J. Y. L., & Xu X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly. 36(1), 157-178.
- Wieling, M., & Hofman, W. (2010). The impact of online video lecture recordings and automated feedback on student performance. Computers & Education, 54(4), 992-998.
- Williams, A., Birch, E., & Hancock, P. (2012). The impact of online lecture recordings on student performance. Australasian Journal of Educational Technology, 28(2), 199-213.
- Williams, J., & Fardon, M. (2007, September). Recording lectures and the impact on student attendance. Paper presented at the ALT-C, Nottingham, UK.
- Womble, J. (2007). E-learning: The relationship among learner satisfaction, self-efficacy, and usefulness (Doctoral dissertation). San Diego: Alliant International University.
- Yukselturk, E., & Yildirim, Z. (2008). Investigation of interaction, online support, course structure and flexibility as the contributing factors to students' satisfaction in an online certificate program. Journal of Educational Technology & Society, 11(4), 51-65.
- Yunus, A. S., Kasa, Z., Asmuni, A., Samah, B. A., Napis, S., Yusoff, M. Z. M.,... & Wahab, H. A. (2006). Use of webcasting technology in teaching higher education. International Education Journal, 7(7), 916-923.
- Zhang, D., Zhou, L., Briggs, R. O., & Nunamaker Jr., J. F. (2006). Instructional video in e-learning: Assessing the impact of interactive video on learning effectiveness. Information & Management, 43, 15-27.
- Zhao, L., Lu, Y., Huang, W., & Wang, Q. (2010). Internet inequality: The relationship between high school students’ Internet use in different locations and their Internet self-efficacy. Computers & Education, 55(4), 1405-1423.