Development Of Framework To Engage Student's Learning In Technical MOOC Using Wearable Technology

The low completion rate issue in MOOC has become one of the main highlights by researchers. It is reported that only 10 to 15 per cent of the students able to complete the MOOC. This low completion rate was due to the students are less engaged with the MOOC content causing them to demotivate to comp...

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Main Author: Ahmad Fesol., Siti Feirusz
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Language:English
English
Published: 2019
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institution Universiti Teknikal Malaysia Melaka
collection UTeM Repository
language English
English
advisor Salam, Sazilah

topic L Education (General)
LB Theory and practice of education
LB2300 Higher Education
spellingShingle L Education (General)
LB Theory and practice of education
LB2300 Higher Education
Ahmad Fesol., Siti Feirusz
Development Of Framework To Engage Student's Learning In Technical MOOC Using Wearable Technology
description The low completion rate issue in MOOC has become one of the main highlights by researchers. It is reported that only 10 to 15 per cent of the students able to complete the MOOC. This low completion rate was due to the students are less engaged with the MOOC content causing them to demotivate to complete the whole MOOC. Engaging students in a MOOC environment especially for non-technical subjects was achievable. However, for a technical MOOC it involved significant challenges. Researches highlighted that one of the requirements for designing an engaging practice-based MOOC or technical MOOC is to include practice-oriented learning mode into its course structure. Therefore, the aim of this study is to develop a framework to engage student’s learning in technical MOOC using wearable technology. This study adopted the case study methodology approach with qualitative and quantitative analysis which conducted at UTeM. The instruments used in this study include technical MOOC, wearable technology, and student engagement items. A total of 375 engineering students involved in this study and the data were analysed using descriptive and parametric testing. The survey results reflected that the learning materials produced by wearable technology do contribute towards positive effect in increasing the level of student’s engagement with the learning process. Among key recommendations for future study are to implement the proposed framework to design and develop other engineering and technical courses and to further explore other potential elements of wearable technology to enhance student engagement in online learning.
format Thesis
qualification_name Doctor of Philosophy (PhD.)
qualification_level Doctorate
author Ahmad Fesol., Siti Feirusz
author_facet Ahmad Fesol., Siti Feirusz
author_sort Ahmad Fesol., Siti Feirusz
title Development Of Framework To Engage Student's Learning In Technical MOOC Using Wearable Technology
title_short Development Of Framework To Engage Student's Learning In Technical MOOC Using Wearable Technology
title_full Development Of Framework To Engage Student's Learning In Technical MOOC Using Wearable Technology
title_fullStr Development Of Framework To Engage Student's Learning In Technical MOOC Using Wearable Technology
title_full_unstemmed Development Of Framework To Engage Student's Learning In Technical MOOC Using Wearable Technology
title_sort development of framework to engage student's learning in technical mooc using wearable technology
granting_institution Universiti Teknikal Malaysia Melaka
granting_department Faculty of Information and Communication Technology
publishDate 2019
url http://eprints.utem.edu.my/id/eprint/24578/1/Development%20Of%20Framework%20To%20Engage%20Student%27s%20Learning%20In%20Technical%20MOOC%20Using%20Wearable%20Technology.pdf
http://eprints.utem.edu.my/id/eprint/24578/2/Development%20Of%20Framework%20To%20Engage%20Student%27s%20Learning%20In%20Technical%20MOOC%20Using%20Wearable%20Technology.pdf
_version_ 1747834075832385536
spelling my-utem-ep.245782021-10-05T11:15:22Z Development Of Framework To Engage Student's Learning In Technical MOOC Using Wearable Technology 2019 Ahmad Fesol., Siti Feirusz L Education (General) LB Theory and practice of education LB2300 Higher Education The low completion rate issue in MOOC has become one of the main highlights by researchers. It is reported that only 10 to 15 per cent of the students able to complete the MOOC. This low completion rate was due to the students are less engaged with the MOOC content causing them to demotivate to complete the whole MOOC. Engaging students in a MOOC environment especially for non-technical subjects was achievable. However, for a technical MOOC it involved significant challenges. Researches highlighted that one of the requirements for designing an engaging practice-based MOOC or technical MOOC is to include practice-oriented learning mode into its course structure. Therefore, the aim of this study is to develop a framework to engage student’s learning in technical MOOC using wearable technology. This study adopted the case study methodology approach with qualitative and quantitative analysis which conducted at UTeM. The instruments used in this study include technical MOOC, wearable technology, and student engagement items. A total of 375 engineering students involved in this study and the data were analysed using descriptive and parametric testing. The survey results reflected that the learning materials produced by wearable technology do contribute towards positive effect in increasing the level of student’s engagement with the learning process. Among key recommendations for future study are to implement the proposed framework to design and develop other engineering and technical courses and to further explore other potential elements of wearable technology to enhance student engagement in online learning. 2019 Thesis http://eprints.utem.edu.my/id/eprint/24578/ http://eprints.utem.edu.my/id/eprint/24578/1/Development%20Of%20Framework%20To%20Engage%20Student%27s%20Learning%20In%20Technical%20MOOC%20Using%20Wearable%20Technology.pdf text en public http://eprints.utem.edu.my/id/eprint/24578/2/Development%20Of%20Framework%20To%20Engage%20Student%27s%20Learning%20In%20Technical%20MOOC%20Using%20Wearable%20Technology.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=117188 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Salam, Sazilah 1. 3M Corporation, 2001. The Power of Color in Presentations. 3M Meeting Network Articles and Advice. 2. Ab Jalil, H., 2016. Evaluation of Malaysia Pilot MOOC (Final Report), Serdang: CADe UPM. 3. Abeer, W. and Miri, B., 2014. Students Preferences and Views about Learning in a MOOC. Procedia-Social and Behavioral Sciences, 152, pp. 318-323. 4. Ahmad, Y., 2013. Instructional Design and Motivation in Computer-Based Learning Environment. IOSR Journal of Computer Engineering, 8(3), pp. 9-12. 5. Al-Rahmi, W. M., Alias, N., Othman, M. S., Alzahrani, A. I., Alfarraj, O., Saged, A. A., and Rahman, N. S. A., 2018. Use of E-Learning by University Students in Malaysian Higher Educational Institutions: A Case in Universiti Teknologi Malaysia. IEEE Access, 6, pp. 14268-14276. 6. Alcock, S. E., Dufton, J. A. and Durusu-Tanr.over, M., 2016. Archaeology and the MOOC: Massive, open, online, and opportunistic. Journal of Social Archaeology, 16(1), pp. 3-31. 7. Alducin-Ochoa, J. M. and Vazquez-Martinez, A. I., 2017. Learning Styles, Socio- Demographic Variables and Academic Performance of Building Engineering Students. Revista Electronica Educare, 21(1), pp. 350-380. 8. Alharbi, A., Paul, D., Henskens, F. and Hannaford, M., 2011. An investigation into the learning styles and self-regulated learning strategies for computer science students. Proceedings Ascilite. pp. 156-173. 9. Ali, S. and Ali, L., 2016. Efficacy of Gagnes nine events of instructions in improving the performance of undergraduate final year medical students. Advances in Health Professions Education, 1(2), pp. 65-68. 10. Ali, S., Farshid, M. and Kelsey, R., 2014. Utilizing MOOCs for blended learning in higher education. Frontiers in Education Conference (FIE), 2014 IEEE, pp. 1-4. 11. Allen, M., 2017. Designing Online Asynchronous Information Literacy Instruction Using the ADDIE Model. Distributed Learning, pp. 69-91. 12. Allen, W. C., 2006. Overview and evolution of the ADDIE training system. Advances in Developing Human Resources, 8(4), pp. 430-441. 13. Alumu, S. and Thiagarajan, P., 2016. Massive open online courses and E-learning in higher education. Indian Journal of Science and Technology, 9(6), pp. 1-10. 14. Andersen, R. and Ponti, M., 2014. Participatory pedagogy in an open educational course: challenges and opportunities. Distance Education, 35(2), pp. 234-249. 15. Anderson, D., Sweeney, D. and Williams, T., 2011. Fundamentals of Business Statistics. 6th Edition: Cengage Learning. 16. Appleton, J. J., Christenson, S. L., Kim, D. and Reschly, A. L., 2006. Measuring cognitive and psychological engagement: Validation of the Student Engagement Instrument. Journal of School Psychology, 44(5), pp. 427-445. 17. Aragues, R., Gil, E., Igual, R., Medrano, C., Delgado, J., Albiol, S., Romero, F., Garcia, J.C. and Perez, R., 2017. Blended learning in Electronics and Automation Engineering: a study of software and hardware needs for practical teaching. EDULEARN17 Proceedings, pp. 274-283. 18. Arriaga, A.F., Gawande, A.A., Raemer, D.B., Jones, D.B., Smink, D.S., Weinstock, P., Dwyer, K., Lipsitz, S.R., Peyre, S., Pawlowski, J.B. and Muret-Wagstaff, S., 2014. Pilot testing of a model for insurer-driven, large-scale multicenter simulation training for operating room teams. Annals of Surgery, 259(3), pp. 403-410. 19. Axisa, F., Schmitt, P.M., Gehin, C., Delhomme, G., McAdams, E. and Dittmar, A., 2005. Flexible technologies and smart clothing for citizen medicine, home healthcare, and disease prevention. IEEE Transactions on information technology in biomedicine, 9(3), pp. 325-336. 20. Baeten, M., Dochy, F. and Struyven, K., 2013. The effects of different learning environments on students motivation for learning and their achievement. British Journal of Educational Psychology, 83(3), pp. 484-501. 21. Balakrishnan, B., 2015. Online computer supported collaborative learning (CSCL) for engineering students: a case study in Malaysia. Computer Applications in Engineering Education, 23(3), pp. 352-362. 22. Balfanz, R., 2009. Can the American high school become an avenue of advancement for all? The Future of Children, 19(1), pp. 17-36. 23. Balfaqeeh, M., Hassan, A. and Berkett, T., 2017. Emirati Engineering Students Learning Styles: A Longitudinal Study. PEOPLE: International Journal of Social Sciences, 3(2), pp. 533-551. 24. Barak, M. and Watted, A., 2017. Project-Based MOOC: Enhancing Knowledge Construction. Digital Tools and Solutions for Inquiry-Based STEM Learning. pp. 282-307. 25. Barlett, J. E., Kotrlik, J. W. and Higgins, C. C., 2001. Organizational research: Determining appropriate sample size in survey research. Information, Technology, Learning, and Performance Journal, 19(1), p. 43-57. 26. Bastian, J., Sieberts, T. and Niklas, B., 2014. Media Literacy Lab - Project-based Open Online Learning in Media Education. Proceedings of EdMedia 2014-World Conference on Educational Media and Technology, pp. 1114-1120. 27. Benson, L., Kirn, A. and Faber, C., 2014. CAREER: Student motivation and learning in engineering. Indianapolis, ASEE Annual Conference Proceedings. Pp. 101-111. 28. Bernard, J., Chang, T. W., Popescu, E. and Graf, S., 2017. Learning style Identifier: Improving the precision of learning style identification through computational intelligence algorithms. Expert Systems with Applications, 75, pp. 94-108. 29. Bevan, N., 2009. Usability. Encyclopedia of Database System, pp. 3247-3251. 30. Bhagat, A., Vyas, R. and Singh, T., 2015. Students awareness of learning styles and their perceptions to a mixed method approach for learning. International Journal of Applied and Basic Medical Research, 5(Suppl 1), pp. 58-65. 31. Bird, A., Mendenhall, M., Stevens, M. and Oddou, G., 2010. Defining the content domain of intercultural competence for global leaders. Journal of Managerial Psychology, 25(8), pp. 810-828. 32. Bloodworth, A., Hollowgrass, R., Ogle, D. and Stern, J., 2010. Usability Testing as an Assessment Technique, ETS. 33. BMW Group, 2014. BMW Group, Visual inspection with memory function: BMW Group tests smart eyewear for quality assurance in production. [Online] Available at: https://www.press.bmwgroup.com [Accessed 30 May 2017]. 34. Bolliger, D. U. and Shepherd, C. E., 2018. Instructor and adult learner perceptions of the use of Internet ]enabled devices in residential outdoor education programs. British Journal of Educational Technology, 49(1), pp. 78-87. 35. Bowen, G. A., 2009. Document Analysis as a Qualitative Research Method. Qualitative Research Journal, 9(2), pp. 27-40. 36. Bower, M. and Sturman, D., 2015. What are the educational affordances of wearable technologies? Computers and Education, 88, pp. 343-353. 37. Brand, B., Valent, A. and Browning, A., 2013. How career and technical education can help students be college and career ready: A primer. 38. Branson, R.K., Rayner, G.T., Cox, J.L., Furman, J.P. and King, F.J., 1975. Interservice Procedures for Instructional Systems Development. Phase 4 and 5. Implement and Control, Florida State Univ Tallahassee Center for Educational Technology. 39. Braun, V. and Clarke, V., 2006. Using thematic analysis in psychology. Qualitative research in psychology, 3(2), pp. 77-101. 40. Brewin, J., Tang, J., Dasgupta, P., Khan, M.S., Ahmed, K., Bello, F., Kneebone, R. and Jaye, P., 2015. Full immersion simulation: validation of a distributed simulation environment for technical and non technical skills training in Urology. BJU International, 116(1), pp. 156-162. 41. Bryman, A., 2017. Quantitative and qualitative research: further reflections on their integration. Mixing methods: Qualitative and quantitative research, pp. 57-78. 42. Buchem, I., Merceron, A., Kreutel, J., Haesner, M. and Steinert, A., 2015. Designing for User Engagement in Wearable-technology Enhanced Learning for Healthy Ageing. Intelligent Environments (Workshops), pp. 314-324. 43. Buckley, P. and Doyle, E., 2017. Individualising gamification: an investigation of the impact of learning styles and personality traits on the efficacy of gamification using a prediction market. Computers and Education, 106, pp. 43-55. 44. Caple, J. and Martin, P., 1994. Reflections of Two Pragmatists: A Critique of Honey and Mumfords Learning Styles. Industrial and Commercial Training, 26(1), pp. 16-20. 45. Carrera, J., Chiota-McCollum, N., Mantri, S., Clark, W., Wang, C., Odell, V., Berthaud, J., Gunnell, B., McMurry, T., Worrall, B. and Southerland, A., 2017. Feasibility of Google Glass for Remote Resident Supervision and Evaluation, pp. 39-43. 46. Cassidy*, S., 2004. Learning styles: An overview of theories, models, and measures. Educational Psychology, 24(4), pp. 419-444. 47. Cassidy, S., 2006. Developing employability skills: Peer assessment in higher education. Education & Training, 48(7), pp. 508-517. 48. Castro, M., Tawfik, M., Garcia-Loro, F., Sancristobal, E., Mur, F. and Diaz, G., 2014. Combining Remote Laboratories and Massive Open Online Courses (MOOCs) for Teaching Electronics. Society for Information Technology and Teacher Education International Conference, pp. 2086-2090. 49. Chaballout, B., Molloy, M., Vaughn, J., Brisson III, R. and Shaw, R., 2016. Feasibility of augmented reality in clinical simulations: using Google Glass with manikins. JMIR Medical Education, 2(1). pp. 123-131. 50. Chang, R. I., Hung, Y. H. and Lin, C. F., 2015. Survey of learning experiences and influence of learning style preferences on user intentions regarding MOOCs. British Journal of Educational Technology, 46(3), pp. 528-541. 51. Chen, X., Xu, L., Wang, Y., Wang, H., Wang, F., Zeng, X., Wang, Q. and Egger, J., 2015. Development of a surgical navigation system based on augmented reality using an optical see-through head-mounted display. Journal of Biomedical Informatics, 55, pp. 124-131. 52. Clark, D. R., 2014. The Dick and Carey Model - 1978. [Online] Available at: http://www.nwlink.com/~donclark/history_isd/carey.html [Accessed 15 January 2016]. 53. Clark, D. R., 2015. Instructional Design. [Online] Available at: http://www.nwlink.com/ ~donclark/hrd/learning/development.html [Accessed 6 May 2016]. 54. Clark, D., 2014. Robert Gagne's Nine Steps of Instruction. [Online] Available at: http://www.nwlink.com/~donclark/hrd/learning/id/nine_step_id.html [Accessed 6 May 2016]. 55. Coffield, F., Moseley, D., Hall, E. and Ecclestone, K., 2004. Learning styles and pedagogy in post-16 learning: A systematic and critical review. Trowbridge, Wiltshire: Learning and Skills Research Centre. 56. Cohen, J. W., 1988. Statistical power analysis for the behavioral science. Hillsdale: Lawrence Erlbaum Associates. 57. Collien, D., 2017. OpenLearning Educational Values. [Online] Available at: https://www.openlearning.com/ Pedagogy [Accessed 10 April 2017]. 58. Conijn, R., Van den Beemt, A. and Cuijpers, P., 2018. Predicting student performance in a blended MOOC. Journal of Computer Assisted Learning, pp. 1-14. 59. Conole, G., 2014. A new classification schema for MOOCs. The International Journal for Innovation and Quality in Learning, 2(3), pp. 65-77. 60. Creswell, J. W. and Clark, V. L. P., 2011. Designing and conducting mixed methods research. 2nd ed. United States of America: Sage Publications. 61. Creswell, J., 1994. Research design: Qualitative and quantitative approaches. Sage Publications, Inc. Thousand Oaks, CA, US: Sage Publications, Inc. 62. Crockett, K., Latham, A. and Whitton, N., 2017. On predicting learning styles in conversational intelligent tutoring systems using fuzzy decision trees. International Journal of Human-Computer Studies, 97, pp. 98-115. 63. Daradoumis, T., Bassi, R., Xhafa, F. and Caballe, S., 2013. A review on massive elearning (MOOC) design, delivery and assessment. P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), 2013 Eighth International Conference, pp. 208-213. 64. De Bello, T. C., 1990. Comparison of eleven major learning styles models: Variables, appropriate populations, validity of instrumentation, and the research behind them. Reading, Writing, and Learning Disabilities, 6(3), pp. 203-222. 65. De Vaus, D. and De Vaus, D., 2001. Research design in social research. London: SAGE. 66. Deci, E. L., Vallerand, R. J., Pelletier, L. G. and Ryan, R. M., 1991. Motivation and education: The self-determination perspective. Educational psychologist, 26(3-4), pp. 325- 346. 67. DeCoux, V. M., 2016. Kolb's learning style inventory: A review of its applications in nursing research. Journal of Nursing Education, 29(5), pp. 202-207. 68. Dick, W. and Carey, L., 2000. The Systematic Design of Instruction. Glenview(IL): Scott, Foresman, and Company. 69. Dissanayaka, T. D., 2014. The learning styles and the preferred teaching-learning strategies of first year physiotherapy students. International Journal of Scientific and Research Publications, 4(7), pp. 1-3. 70. Doughty, J. and Hong, M.H., The handbook of second language acquisition. Oxford, UK: Blackwell Publishing Ltd, pp. 589-630. 71. Douglas, K., Bermel, P., Alam, M. and Madhavan, K., 2016. Big data characterization of learner behaviour in a highly technical MOOC engineering course. Journal of Learning Analytics, 3(3), pp. 170-192. 72. Douglas, S.S., Aiken, J.M., Greco, E., Schatz, M. and Lin, S.Y., 2017. Do-It-Yourself Whiteboard-Style Physics Video Lectures. The Physics Teacher, 55(1), pp. 22-24. 73. Dumas, J.S. and Redish, J., 1999. A practical guide to usability testing. Revised ed. Portland: Intellect. 74. Dunn, R. and Dunn, K., 1972. Practical Approaches to Individualizing Instruction: Contract and Other Effective Teaching Strategies. Englewood Cliffs, NJ: Parker: Division of Prentice Hall. 75. Dunn, R., 1984. Learning Style: State of the Science. Theory into Practice, Matching Teaching and Learning Styles, 23(1). pp. 115-121. 76. Dunn, R., 1990. Understanding the Dunn and Dunn learning styles model and the need for individual diagnosis and prescription. Reading, Writing, and Learning Disabilities, 6(3), pp. 223-247. 77. Elgan, M., 2013. Why 2014 is the 'year of smart glasses'? [Online] Available at: http://www.computerworld.com/article/2487076/mobile-wireless/why-2014-is-the--yearof- smart-glasses-.html. [Accessed 21 November 2015]. 78. Evans, H.L., O'Shea, D.J., Morris, A.E., Keys, K.A., Wright, A.S., Schaad, D.C. and Ilgen, J.S., 2016. A comparison of Google Glass and traditional video vantage points for bedside procedural skill assessment. The American Journal of Surgery, 211(2), pp. 336-342. 79. Everett, S. and Oswald, G., 2018. Engaging and training students in the development of inclusive learning materials for their peers. Teaching in Higher Education, pp. 1-16. 80. Fidalgo-Blanco, A., Sein-Echaluce, M. L. and Garcia-Penalvo, F. J., 2015. Methodological approach and technological framework to break the current limitations of MOOC model. Journal of Universal Computer Science, 21(5), pp. 712-734. 81. Fleming, N. D., 2001. Teaching and learning styles: VARK strategies. IGI Global. 82. Fleming, N., 2012. Facts, fallacies and myths: VARK and learning preferences. [Online] Available at: http://vark-learn.com/wp-content/uploads/2014/08/Some-Facts-About- VARK.pdf. [Accessed 28 November 2014]. 83. Formanek, M., Wenger, M.C., Buxner, S.R., Impey, C.D. and Sonam, T., 2017. Insights about large-scale online peer assessment from an analysis of an astronomy MOOC. Computers and Education, 113, pp. 243-262. 84. Fredricks, J. A., Blumenfeld, P. C. and Paris, A. H., 2004. School engagement: Potential of the concept, state of the evidence. Review of educational research, 74(1), pp. 59-109. 85. Freitas, S. I., Morgan, J. and Gibson, D., 2015. Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision. British Journal of Educational Technology, 46(3), pp. 455-471. 86. Furini, M., 2016. On gamifying the transcription of digital video lectures. Entertainment Computing, 14, pp. 23-31. 87. Gagne, R., 1985. The Conditions of Learning and the Theory of Instruction. 4th Edition ed. New York: Wadsworth Pub Co. 88. Gamage, D., Fernando, S. and Perera, I., 2015b. Quality of MOOCs: A review of literature on effectiveness and quality aspects. Ubi-Media Computing (UMEDIA), 2015 8th International Conference, pp. 224-229. 89. Gamage, D., Perera, I. and Fernando, S., 2015a. A framework to analyse effectiveness of learning in MOOC: Learners perspective. 8th International Conference on Ubi-Media Computing (UMEDIA), pp. 236-241. 90. Ganesh, A., 2014. Alignment of teaching style to learning preferences: Impact on student learning. Training and Development Journal, 5(2), pp. 119-131. 91. Gangwer, T. and Rzadko-Henry, G., 2001. Why Visual Teaching. [Online] Available at: http://visualteachingalliance.com/ [Accessed 2 June 2015]. 92. Gao, Y., Li, H. and Luo, Y., 2015. An empirical study of wearable technology acceptance in healthcare. Industrial Management and Data Systems, 115(9), pp. 1704-1723. 93. Garcia, F., Diaz, G., Tawfik, M., Martin, S., Sancristobal, E. and Castro, M., 2014. A practice-based MOOC for learning electronics. Global Engineering Education Conference (EDUCON), 2014 IEEE, pp. 969-974. 94. Garcia-Penalvo, F. J., Fidalgo-Blanco, A. and Sein-Echaluce, M. L., 2017. An adaptive hybrid MOOC model: Disrupting the MOOC concept in higher education. Telematics and Informatics. pp.213-234. 95. Gene, O. B., Nunez, M. M. and Blanco, A. F., 2014. Gamification in MOOC: challenges, opportunities and proposals for advancing MOOC model. Proceedings of the Second International Conference on Technological Ecosystems for Enhancing Multiculturality, pp. 215-220. 96. George, D. and Mallery, M., 2010. SPSS for Windows Step by Step: A Simple Guide and Reference. Boston: Pearson. 97. Glauser, W., 2013. Doctors among early adopters of Google Glass. Canadian Medical Association Journal, 109. pp. 173-184. 98. Gokdemir, A., Akdemir, O. and Vural, O. F., 2015. Using Gagnes nine events in learning management systems. Cypriot Journal of Educational Sciences, 10(1), pp. 18-31. 99. Golafshani, N., 2003. Understanding reliability and validity in qualitative research. The qualitative report, 8(4), pp. 597-606. 100. Gonzalez, M.A., Gonzalez, M.A., Martin, M.E., Llamas, C., Martinez, O., Vegas, J., 101. Herguedas, M. and Hernandez, C., 2017. Teaching and learning physics with smartphones. Blended Learning: Concepts, Methodologies, Tools, and Applications, pp. 866-885. 102. Gregorc, A. F. and Butler, K. A., 1984. Learning is a matter of style. VocEd, 59(3), pp. 27- 29. 103. Gregorc, A. F., 1985. Inside styles: Beyond the basics. Maynard: MA: Gabriel Systems. 104. Guo, P. J., Kim, J. and Rubin, R., 2014. How video production affects student engagement:An empirical study of MOOC videos. Proceedings of the First ACM Conference on Learning@ Scale Conference, pp. 41-50. 105. Gustafson, K. and Branch, R., 1997. Instructional Design Models. Syracuse(NY): ERIC Clearinghouse on Information and Technology. 106. Gutierrez, K., 2014. Studies confirm the power of visuals in eLearning. Shift: Disruptive eLearning. 107. Gutl, C., Rizzardini, R. H., Chang, V. and Morales, M., 2014. Attrition in MOOC: Lessons learned from drop-out students. International Workshop on Learning Technology for Education in Cloud, pp. 37-48. 108. Gynther, K., 2016. Design Framework for an Adaptive MOOC Enhanced by Blended Learning: Supplementary Training and Personalized Learning for Teacher Professional Development. Electronic Journal of e-Learning, 14(1), pp. 15-30. 109. Harasim, L., 2017. Learning Theory and Online Technologies. Taylor and Francis. 110. Hashimoto, D., Phitayakorn, R., Fernandez-del Castillo, C. and Meireles, O., 2016. A blinded assessment of video quality in wearable technology for telementoring in open surgery: The Google Glass experience. Surgical Endoscopy, 30(1), pp. 372-378. 111. Haslam, P. and Mafeld, S., 2013. Google Glass: Finding True Clinical Value. [Online] Available at: http://www.whichmedicaldevice.com/editorial/article/390/google-glassfinding- true-clinical-value. [Accessed 8 May 2015]. 112. Hasler, B., Major, L. and Hennessy, S., 2016. Tablet use in schools: a critical review of the evidence for learning outcomes. Journal of Computer Assisted Learning, 32(2), pp. 139- 156. 113. Hawk, T. F. and Shah, A. J., 2007. Using learning style instruments to enhance student learning. Decision Sciences Journal of Innovative Education, pp. 1-19. 114. Heck, P. and Zaidman, A., 2018. A systematic literature review on quality criteria for agile 115. requirements specifications. Software Quality Journal, 26(1), pp. 127-160. 116. Hew, K. F., 2015. Towards a model of engaging online students: lessons from MOOCs and four policy documents. International Journal of Information and Education Technology, 5(6), pp. 425-431. 117. Hew, K. F., 2016. Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology, 47(2), pp. 320-341. 118. Hill, F., Tomkinson, B., Hiley, A. and Dobson, H., 2016. Learning style preferences: an examination of differences amongst students with different disciplinary backgrounds. Innovations in Education and Teaching International, 53(2), pp. 122-134. 119. Hills, C., Levett-Jones, T., Warren-Forward, H. and Lapkin, S., 2016. Teaching and learning preferences of Generation Y occupational therapy students in practice education. International Journal of Therapy and Rehabilitation, 23(8), pp. 371-379. 120. Hinde, T., Gale, T., Anderson, I., Roberts, M. and Sice, P., 2016. A study to assess the influence of interprofessional point of care simulation training on safety culture in the operating theatre environment of a university teaching hospital. Journal of Interprofessional Care, 30(2), pp. 251-253. 121. Hoffman, E. S., 2014. Beyond the flipped classroom: Redesigning a research methods course for e3 instruction. Contemporary Issues in Education Research, 7(1), pp. 51-63. 122. Honan, E. and Bright, D., 2016. Writing a thesis differently. International Journal of Qualitative Studies in Education, 29(5), pp. 731-743. 123. Hone, K. S. and El Said, G. R., 2016. Exploring the factors affecting MOOC retention: A survey study. Computers and Education, 98, pp. 157-168. 124. Honey, P. and Mumford, A., 1992. The manual of learning styles. Peter Honey. 125. Hood, N., Littlejohn, A. and Milligan, C., 2015. Context counts: How learners' contexts influence learning in a MOOC. Computers and Education, 91, pp. 83-91. 126. Hospel, V. and Galand, B., 2016. Are both classroom autonomy support and structure equally important for students' engagement? A multilevel analysis. Learning and Instruction, 41, pp. 1-10. 127. Hossain, M.S., Islam, M.S., Glinsky, J.V., Lowe, R., Lowe, T. and Harvey, L.A., 2015. A massive open online course (MOOC) can be used to teach physiotherapy students about spinal cord injuries: a randomized trial. Journal of Physiotherapy, 61(1), pp. 21-27. 128. Hricko, M., 2008. Gagne's Nine Events of Instruction. Encyclopedia of Information Technology Curriculum Integration, pp. 353-356. 129. Hsu, T. C., Lee-Hsieh, J., Turton, M. A. and Cheng, S. F., 2014. Using the ADDIE model to develop online continuing education courses on caring for nurses in Taiwan. The Journal of Continuing Education in Nursing, 45(3), pp. 124-131. 130. Huang, L., Zhang, J. and Liu, Y., 2017. Antecedents of student MOOC revisit intention: Moderation effect of course difficulty. International Journal of Information Management, 37(2), pp. 84-91. 131. Huisman, B., Admiraal, W., Pilli, O., van de Ven, M. and Saab, N., 2018. Peer assessment in MOOCs: The relationship between peer reviewers' ability and authors' essay performance. British Journal of Educational Technology, 49(1), pp. 101-110. 132. Hwang, G., 2014. Definition, framework and research issues of smart learning environments-a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), pp. 4-12. 133. Imenda, S., 2014. Is there a conceptual difference between theoretical and conceptual frameworks? Journal of Social Sciences, 38(2), pp. 185-195. 134. Isave, M. and Dani, K., 2017. MOOCS through e-learning: an innovation in teachinglearning process. Special Issue on Equity and Quality in Higher Education: In Perspective 135. of New Education Policy 2016, 4(33), pp. 6-12. 136. Jabatan Pendidikan Tinggi, 2017. Garis Panduan Pembangunan dan Penyampaian MOOC Malaysia. Putrajaya: Jabatan Pendidikan Tinggi. 137. James Cook University, 2013. Visual, Auditory and Kinesthetic (VAK) learning style model. [Online] Available at: http://www.jcu.edu.au/wiledpack/modules/fsl/ JCU_090460.html [Accessed 25 March 2015]. 138. Jin, L. and Cortazzi, M., 2017. Practicing cultures of learning in internationalising universities. Journal of Multilingual and Multicultural Development, 38(3), pp. 237-250. 139. Jonassen, D., Tessmer, M. and Hannum, W., 1998. Task analysis methods for instructional design. s.l.:Routledge. 140. Jou, M., Tennyson, R. D., Wang, J. and Huang, S. Y., 2016. A study on the usability of Ebooks and APP in engineering courses: A case study on mechanical drawing. Computers and Education, 92, pp. 181-193. 141. Kahu, E. R., 2013. Framing student engagement in higher education. Studies in Higher Education, 38(5), pp. 758-773. 142. Kayisoglu, N., 2015. Validity and reliability studies for scale of evaluating physical education teachers based on student ratings (SEPETBSR). Journal of Physical Education and Sport Management, 6(8), pp. 60-69. 143. Keefe, J. W., 1987. Learning style theory and practice. National Association of Secondary School Principals, 1904 Association Dr., Reston, VA 22091. 144. Kelleher, J., 2017. Current State of Massive Open Online Courses in Malaysia. [Online] Available at: https://www.opengovasia.com/articles/6606-current-state-of-massive-openonline- courses-in-malaysia [Accessed 2 June 2018]. 145. Keller, J. and Suzuki, K., 1988. Use of the ARCS motivation model in courseware design. Instructional designs for microcomputer courseware ed. Hillsdale(NJ): Lawrence Erlbaum. 146. Kementerian Pendidikan Malaysia, 2015. Malaysia Education Blueprint 2015 - 2025 (Higher Education). Presint 5 (Putrajaya): Kementerian Pendidikan Malaysia. 147. Khadjooi, K., Rostami, K. and Ishaq, S., 2011. How to use Gagne's model of instructional design in teaching psychomotor skills. Gastroenterology and Hepatology From Bed to Bench, 4(3), pp. 116-119. 148. Khalil, H. and Ebner, M., 2014. MOOCs completion rates and possible methods to improve retention-a literature review. World Conference on Educational Multimedia, Hypermedia and Telecommunications, 2014(1), pp. 1305-1313. 149. Kim, M., Jung, E., de Siqueira, A. and Huber, L., 2016. An investigation into effective pedagogies in a flipped classroom: A case study. International Journal of E-Learning and Distance Education, 31(2), pp. 1-15. 150. Kim, P., 2015. Massive Open Online Courses: The MOOC revolution. New York, NY: Taylor and Francis. 151. Kirkwood, A. and Price, L., 2014. Technology-enhanced learning and teaching in higher education: what is enhanced and how do we know? A critical literature review. Learning, Media and Technology, 39(1), pp. 6-36. 152. Kleinbaum, D., Kupper, L., Nizam, A. and Rosenberg, E., 2013. Applied Regression Analysis and Other Multivariable Methods. 5th Edition ed. United States of America: Cengage Learning. 153. Kloos, C.D., Munoz-Merino, P.J., Alario-Hoyos, C., Ayres, I.E. and Fernandez-Panadero, C., 2015. Mixing and blending MOOC Technologies with face-to-face pedagogies. Global Engineering Education Conference (EDUCON), pp. 967-971. 154. Koh, Y. Y. and Yaw, L. C., 2012. The Study of Learning Styles among Mechanical Engineering Students from Different Institutions in Malaysia. Procedia-Social and Behavioral Sciences, 56 (2012), pp. 636-642. 155. Kolb, A. and Kolb, D., 2005. The Kolb learning style inventory-version 3.1 2005 technical specifications. 200 ed. Boston(MA): Hay Resources Direct. 156. Kolb, D. A., 1984. Experiential learning: experience as the source of learning and development. Englewood Cliffs, N.J.: Prentice-Hall. 157. Kolodzey, L., Grantcharov, P.D., Rivas, H., Schijven, M.P. and Grantcharov, T.P., 2016. Wearable technology in the operating room: a systematic review. BMJ Innovations, pp. 1- 10. 158. Kong, Q. P., Wong, N. Y. and Lam, C. C., 2003. Student engagement in mathematics: Development of instrument and validation of construct. Mathematics Education Research Journal, 15(1), pp. 4-21. 159. Kothari, C. R., 1996. Research Methodology. 2nd ed. New Age International. 160. Krejcie, R. and Morgan, D. W., 1970. Determining sample size for research activities. Educational and Psychological Measurement, 30(3), pp. 607-610. 161. Kruse, K., 2009. Gagne's Nine Events of Instruction: An Introduction. [Online] Available at: http://kvccdocs.com/online-certification/content/L-09/Gagne.pdf 162. Labus, A., Milutinovic, M., Stepanic, D., Stevanovic, M. and Milinovic, S., 2015. Wearable computing in e-education. Journal of Universal Excellence, 4(1), pp. A39-A51. 163. Lai, C. and Liou, W., 2007. Rapid ADDIE curriculums design model based on the heterogeneous multimedia information integration. IEEE, pp. 485-490. 164. Lam, S., Wong, B., Yang, H. and Liu, Y., 2012. Understanding student engagement with a contextual model. Handbook of Research on Student Engagement, pp. 403-419. 165. Lee, C.K., Kim, Y., Lee, N., Kim, B., Kim, D. and Yi, S., 2017. Feasibility study of utilization of action camera, GoPro Hero 4, Google Glass, and Panasonic HX-A100 in spine surgery. Spine, 42(4), pp. 275-280. 166. Lee, C. K. and Sidhu, M. S., 2015. Engineering students learning preferences in UNITEN: Comparative study and patterns of learning styles. Journal of Educational Technology and Society, 18(3), pp. 266-271. 167. Lester, D., 2013. A Review of the Student Engagement Literature. Focus on Colleges, Universities and Schools, 7(1), pp. 1-8. 168. Li, B., Wang, X. and Tan, S., 2018. What makes MOOC users persist in completing MOOCs? A perspective from network externalities and human factors. Computers in Human Behavior, pp. 385-395. 169. Lo, C. K., Lie, C. W. and Hew, K. F., 2018. Applying First Principles of Instruction as a design theory of the flipped classroom: Findings from a collective study of four secondary school subjects. Computers and Education, 118, pp. 150-165. 170. Loizzo, J., Ertmer, P., Watson, W. and Watson, S., 2017. Adult MOOC Learners as Self- Directed: Perceptions of Motivation, Success, and Completion. Online Learning, 21(2), pp. 80-103. 171. Lorenzo-Romero, C. and Gomez-Borja, M. A., 2014. Learning styles and Web technology use in business and economics university students. Procedia-Social and Behavioral Sciences, 141, pp. 1281-1290. 172. Lowenthal, P. and Hodges, C., 2015. In search of quality: Using Quality Matters to analyze the quality of massive, open, online courses (MOOCs). The International Review of Research in Open and Distributed Learning, 16(5), pp. 83-101. 173. Malaysia Department of Higher Education, 2017. Massive Open Online Courses (MOOCs). [Online] Available at: http://jpt.mohe.gov.my/index.php/pelajar/massive-openonline- courses-moocs. [Accessed 23 September 2017]. 174. Malaysia MOOC Dashboard, 2018. Malaysia MOOC Analytics. [Online] Available at: http://mooc.utem.edu.my/mymooc. [Accessed 28 May 2018]. 175. Malaysian Technical University Network, 2017. Fast Fact. [Online] Available at: https://mtun.uthm.edu.my/en/ [Accessed 23 January 2018]. 176. Maloshonok, N., 2014. Vygotsky s Theory: Lessons for Student Engagement Research. SERU International Research Conference. pp.203-214. 177. Mann, S., 2014. Wearable Computing. [Online] Available at: https://www.interactiondesign.org/enc [Accessed June 2016]. 178. Mansouri, S. A. and Piki, A., 2016. An exploration into the impact of blogs on students learning: case studies in postgraduate business education. Innovations in Education and Teaching International, 53(3), pp. 260-273. 179. Maric, M., Penger, S., Todorovic, I. and Djurica, N., 2015. Differences in learning styles: a comparison of Slovenian Universities. Procedia-Social and Behavioral Sciences, 197, pp. 175-183. 180. Martin, A. J., 2009. Motivation and engagement across the academic life span: A developmental construct validity study of elementary school, high school, and university/ college students. Educational and Psychological Measurement, 69(5), pp. 794-824. 181. Mayo-Malasky, P., Koenig, S., Narasimhan, M., Lakticova, V., Singas, E., Mayo, P.H. and Makaryus, M., 2015. Standardization of urgent Endotracheal Intubation Using Simulation Training and Checklist. Approach to Train Pulmonary And Critical Care Fellows, 191, pp. A5684-A5691 . 182. McCormick, N. J., Clark, L. M. and Raines, J. M., 2015. Engaging students in critical thinking and problem solving: A brief review of the literature. Journal of Studies in Education, 5(4), pp. 100-113. 183. McNaney, R., Vines, J., Roggen, D., Balaam, M., Zhang, P., Poliakov, I. and Olivier, P., 2014. Exploring the acceptability of google glass as an everyday assistive device for people with parkinson's. Proceedings of the 32nd Annual ACM Conference on Human Factors in Computing Systems, pp. 2551-2554. 184. Meier, D., 2000. The Accelerated Learning Handbook. New York: McGraw-Hill. 185. Meixner, B., 2017. Hypervideos and interactive multimedia presentations. ACM Computing Surveys (CSUR), 50(1), pp. 9-19. 186. Merrill, D., 1983. Component Display Theory. Instructional Design Theories and Models: An Overview of their Current States ed. Hillsdale(NJ): Lawrence Erlbaum. 187. Merrill, M., 2002. First principles of instruction. Educational Technology, Research and Development, 50(3), pp. 43-59. 188. Merrill, M., Drake, L., Lacy, M. and Pratt, J., 1996. Reclaiming Instructional Design. Educational Technology, 36(5), pp. 5-7. 189. Miller, P., 2001. Learning Styles: The Multimedia of the Mind. Research Report., ERIC. 190. Mihalec-Adkins, B., Hicks, N., Douglas, K.A., Diefes-Dux, H., Bermel, P. and Madhavan, K., 2016. Surveying the motivations of groups of learners in highly-technical STEM 191. MOOCs. Frontiers in Education Conference (FIE), pp. 1-6. 192. MIT Office of Digital Learning, 2017. Study of MOOCs offers insights into online learner engagement and behavior, Cambridge: MIT News. 193. Mkonto, N. P., 2016. Students Voice in Learning Style Assessment: The Innovative Learning Experiences Tool (ILE). pp. 180-198. 194. Moghavvemi, S., Sulaiman, A., Jaafar, N. I. and Kasem, N., 2018. Social media as a complementary learning tool for teaching and learning: The case of youtube. The International Journal of Management Education, 16(1), pp. 37-42. 195. MoocLab, 2017. MoocLab Report: The Global MOOC Landscape - 2017 A look at the numbers, the geography and the language of MOOCs. [Online] Available at: https://www.mooclab.club/resources/mooclab-report-the-global-mooc-landscape- 2017.214/ [Accessed 4 March 2018]. 196. Moradi, R., 2017. Design and Development of E-learning Content Based on Gagnes Model of Instructional Design and its Impact on Motivation and Academic Achievement. Iranian Journal of Medical Education, 17, pp. 359-369. 197. Muensterer, O.J., Lacher, M., Zoeller, C., Bronstein, M. and Kubler, J., 2014. Google Glass in pediatric surgery: an exploratory study. International journal of surgery, 12(4), pp. 281-289. 198. Nagtegaal, F., Verzijl, D. and Dervojeda, K., 2015. Internet of Things: Wearable Technology, s.l.: European Commission. 199. Nai, F. A., Degeng, I. N. S., Setyosari, P. and Widiati, U., 2017. Teaching Material Development 0f Learning and Teaching Course Through Lesson Study Application for University Students. International Conference on Education, pp. 273-283. 200. Nasir, A., Ali, D., Noordin, M. and Nordin, M., 2011. Technical skills and non-technical skills: predefinition concept. Proceedings of the IETEC 11 Conference, pp. 1-18. 201. Neuman, W., 2013. Social research methods: Qualitative and quantitative approaches. Pearson education. 202. NIST, 2012. Kolmogorov-Smirnov Goodness-of-Fit Test. [Online] Available at: https://www.itl.nist.gov/div898/handbook/eda/section3/eda35g.htm [Accessed 30 May 2018]. 203. Noble, A. R., 2017. Developing General Chemistry II Online: Successes and Challenges of Online Chemistry at a Primarily Undergraduate Institution. Online Approaches to Chemical Education, pp. 71-80. 204. Nordin, N., Norman, H., Embi, M.A., Mansor, A.Z. and Idris, F., 2016. Factors for development of learning content and task for MOOCs in an Asian context. International Education Studies, 9(5), pp. 48-61. 205. O'Mahony, S.M., Sbayeh, A., Horgan, M., O'Flynn, S. and O'Tuathaigh, C.M., 2016. Association between learning style preferences and anatomy assessment outcomes in graduate entry and undergraduate medical students. Anatomical Sciences Education, 9(4), pp. 391-399. 206. Ocepek, U., Bosni., Z., .erbec, I. N. and Rugelj, J., 2013. Exploring the relation between learning style models and preferred multimedia types. Computers and Education, 69, pp. 343-355. 207. Okubo, N., Nara, K., Takemura, S. and Ueda, Y., 2016. Applying an instructional design process to development of an independent verification and validation training program. Software Engineering Education and Training (CSEET), 2016 IEEE 29th International Conference, pp. 237-240. 208. Olivos, P., Santos, A., Martin, S., Canas, M., Gomez-Lazaro, E. and Maya, Y., 2016. The relationship between learning styles and motivation to transfer of learning in a vocational training programme. Suma Psicologica, 23(1), pp. 25-32. 209. OpenLearning Global, 2012. What is OpenLearning? [Online] Available at: https://www.openlearning.com/About [Accessed 17 March 2017]. 210. OpenLearning Global, 2014. OpenLearning Selected as Malaysias National MOOC Platform. [Online] Available at: https://learninghub.openlearning.com/2014/09/26/ 211. openlearning-selected-as-malaysias-national-mooc-platform/ [Accessed 2 June 2017]. Ortensi, A., Panunzi, A., Trombetta, S., Cattaneo, A., Sorrenti, S. and D'Orazi, V., 2017. Advancement of thyroid surgery video recording: A comparison between two full HD head mounted video cameras. International Journal of Surgery, 41, pp. S65-S69. 212. Ostashewski, N., Howell, J. and Dron, J., 2016. Crowdsourcing MOOC interactions: Using a social media site cMOOC to engage students in university course activities. Pan- Commonwealth Forum 8, pp. 258-264. 213. Pallant, J., 2007. SPSS survival manual: A step-by-step guide to data analysis using SPSS version 15. Nova Iorque: McGraw Hill. 214. Pan, D., Dhall, R., Lieberman, A. and Petitti, D. B., 2015. A mobile cloud-based Parkinsons disease assessment system for home-based monitoring. JMIR mHealth and uHealth, 3(1), pp. 29-34. 215. Parker, C., Smith, E. L., McKinney, D. and Laurier, A., 2016. The application of the engineering design process to curriculum revision: A collaborative approach to STEM curriculum refinement in an urban district. School Science and Mathematics, 116(7), pp. 399-406. 216. Parkes, M. and Fletcher, P., 2017. Student Preparedness for Interaction in an Online Learning Environment. EdMedia: World Conference on Educational Media and Technology, pp. 662-665. 217. Paro, J.A., Nazareli, R., Gurjala, A., Berger, A. and Lee, G.K., 2015. Video-based selfreview: comparing Google Glass and GoPro technologies. Annals of plastic surgery, 74, pp. 71-74. 218. Parslow, G., 2014. Commentary: Google glass: A head up display to facilitate teaching and learning. Biochemistry and Molecular Biology Education, 42(1), pp. 91-92. 219. Parsons, J. and Taylor, L., 2011. Improving student engagement. Current issues in education, 14(1), pp. 1-32. 220. Penger, S., Tekav.i., M. and Dimovski, V., 2008. Comparison, validation and implications of learning style theories in higher education in Slovenia: an experiential and theoretical case. International Business and Economics Research Journal, 7(12), pp. 25-44. 221. Peri, S., 2012. Factor Analysis - KMO-Bartlett's Test and Rotated Component Matrix. [Online] Available at: http://badmforum.blogspot.com/2012/08/factor-analysis-kmobartletts- test.html [Accessed 30 May 2018]. 222. Phan, T., McNeil, S. G. and Robin, B. R., 2016. Students patterns of engagement and course performance in a Massive Open Online Course. Computers and Education, 95, pp. 36-44. 223. Pope, C., Ziebland, S. and Mays, N., 2000. Qualitative research in health care: analysing qualitative data. BMJ: British Medical Journal, 320(7227), pp. 114-116. 224. Preece, R. A. and Cope, A. C., 2016. Are surgeons born or made? A comparison of personality traits and learning styles between surgical trainees and medical students. Journal of surgical education, 73(5), pp. 768-773. 225. Pruet, P., Ang, C. S. and Farzin, D., 2016. Understanding tablet computer usage among primary school students in underdeveloped areas: Students technology experience, learning styles and attitudes. Computers in Human Behavior, 55, pp. 1131-1144. 226. Pusat Sumber and Teknologi Pengajaran, UTeM, 2017. ULearn @ UTeM Semester 1 2017/18. [Online] Available at: http://ulearn.utem.edu.my/sem1201718/course/ management.php?categoryid=285andcourseid=5352 [Accessed 10 October 2017]. 227. Radcliffe, B., 2014. Wearable Technology. [Online] Available at: http://www.investopedia.com/terms/w/wearable-technology.asp [Accessed 15 September 2016]. 228. Rajaram, S., 2017. Predominance of Multi-Modality Preferences in both Males and Female I Year Medical Students, Using VARK Questionnaire. International Journal of Physiology, 5(2), pp. 123-127. 229. Ranginwala, S. and Towbin, A. J., 2017. Use of social media in radiology education. Journal of the American College of Radiology, pp. 190-200. 230. Rauh, S., Zsebedits, D., Tamplon, E., Bolch, S. and Meixner, G., 2015. Using Google Glass for mobile maintenance and calibration tasks in the AUDI A8 production line. Emerging Technologies and Factory Automation (ETFA), 2015 IEEE 20th Conference, pp. 1-4. 231. Raykov, T. and Marcoulides, G., 2011. Introduction to psychometric theory. Routledge. 232. Razali, N. and Wah, Y., 2011. Power comparisons of shapiro-wilk, kolmogorov-smirnov, lilliefors and anderson-darling tests. Journal of statistical modeling and analytics, 2(1), pp. 21-33. 233. Razali, S. N. and Shahbodin, F., 2015. The development of online project based collaborative learning using ADDIE Model. Procedia-Social and Behavioral Sciences, 195, pp. 1803-1812. 234. Razali, S. N., 2016. Online project based collaborative learning model to enhance students' soft skills. Ayer Keroh: Universiti Teknikal Malaysia Melaka. 235. Reid, N. and Shah, I., 2007. The role of laboratory work in university chemistry. Chemistry Education Research and Practice, 8(2), pp. 172-185. 236. Reigeluth, C. M. and Stein, F. S., 1983. The Elaboration Theory of Instruction. Instructional Design Theories and Models: An Overview of their Current States ed. Hillsdale(NJ): Lawrence Erlbaum. 237. Reigeluth, C. M., 1983. Instructional design theories and models: An overview of their current status. Hillsdale(NJ): Lawrence Erlbaum. 238. Reilly, P., 2012. Understanding and Teaching Generation Y. English teaching forum. 50(1), pp. 2-11. 239. Reiser, R. and Dempsey, J., 2007. Trends and Issues in Instructional Design. 2nd Edition ed. Upper Saddle River(NJ): Pearson Education, Inc. 240. Reiser, R. A., 2001. A history of instructional design and technology: Part II: A history of instructional design. Educational Technology Research and Development, 49(2), pp. 57- 67. 241. Ribeiro, I. B., Ngu, J. M., Lam, B. K. and Edwards, R. A., 2017. Simulation-Based Skill Training for Trainees in Cardiac Surgery: A Systematic Review. The Annals of Thoracic Surgery, 1015(3), pp. 972-982. 242. Romanelli, F., Bird, E. and Ryan, M., 2009. Learning styles: a review of theory, application, and best practices. American Journal of Pharmaceutical Education, 73(1), pp. 9-16. 243. Saekhow, J. and Kittisunthonphisarn, N., 2015. The development of communicative english lessons for webquest-based instruction through social networking. Procedia-Social and Behavioral Sciences, 197, pp. 1489-1493. 244. Sahib, S. and Tapsir, S. H., 2015. Regional expert meeting on massive open online courses. MOOCs for higher education in Asia and the Pacific, Chengdu: UNESCO. 245. Sahin, N., Keshav, N., Salisbury, J. and Vahabzadeh, A., 2018. second version of google glass as a wearable socio-affective aid: positive school desirability, high usability, and theoretical framework in a sample of children with autism. JMIR Human Factors, 5(1), pp. e1. 1-11. 246. Sapargaliyev, D., 2015. Wearable technology in education: From handheld to hands-free learning. Technology in Education. Transforming Educational Practices with Technology, pp. 55-60. 247. Sari, D. P. and Surya, E., 2017. Development the module of mathematics statistics 1 by using the model of Dick and Carey Design. International Journal of Sciences: Basic and Applied Research (IJSBAR), 34(1), pp. 237-246. 248. Schaufeli, W.B., Martinez, I.M., Pinto, A.M., Salanova, M. and Bakker, A.B., 2002. Burnout and engagement in university students: A cross-national study. Journal of Cross- Cultural Psychology, 33, pp. 464-481. 249. Schlegel, M. J., 1995. A Handbook of Instructional and Training Program Design, ERIC. 250. Shah, D., 2016. Class Central. [Online] Available at: https://www.classcentral. com/report/mooc-stats-2016/ [Accessed 17 June 2017]. 251. Shah, D., 2018. By The Numbers: MOOCS in 2017. [Online] Available at: https://www.class-central.com/report/mooc-stats-2017/ [Accessed 20 March 2018]. 252. Shah, D., French, J., Rankin, J. and Breslow, L., 2013. Using video to tie engineering themes to foundational concepts. Proceedings of the ASEE Annual Conference. pp.97-116. 253. Shamma, T., 2017. Google Glass Didn't Disappear. You Can Find it On The Factory Floor. [Online] Available at: https://www.npr.org/sections/alltechconsidered/ 2017/03/18/514299682/google-glass-didnt-disappear-you-can-find-it-on-the-factory-floor [Accessed 25 January 2018]. 254. Shinnick, M. A. and Woo, M. A., 2015. Learning style impact on knowledge gains in human patient simulation. Nurse Education Today, 35(1), pp. 63-67. 255. Shuib, M., Abdullah, A., Azizan, S. and Gunasegaran, T., 2015. Designing an intelligent mobile learning tool for grammar learning (i-MoL). International Journal of Interactive Mobile Technologies (iJIM), 9(1), pp. 41-46. 256. Simon, J., 2015. PowerPoint and Concept Maps: A Great Double Act. Accounting Education, 24(2), pp. 146-151. 257. Sivanandan, P., Letchumanan, T., Ramayah, M., Nasrijal, N.H. and Lim, C.L., 2014. Learning style preferences: Influence of cultural background among business students. International Journal of Arts and Commerce, 3(5), pp. 87-98. 258. Son, D., Lee, J., Qiao, S., Ghaffari, R., Kim, J., Lee, J.E., Song, C., Kim, S.J., Lee, D.J., Jun, S.W. and Yang, S., 2014. Multifunctional wearable devices for diagnosis and therapy of movement disorders. Nature Nanotechnology, 9(5), pp. 397-345. 259. Son, E., Halbert, A., Abreu, S., Hester, R., Jefferson, G., Jennings, K., Pine, H. and Watts, T., 2017. Role of Google Glass in improving patient satisfaction for otolaryngology residents: a pilot study. Clinical Otolaryngology, 42(2), pp. 433-438. 260. Spyropoulou, N., Pierrakeas, C. and Kameas, A., 2014. Creating MOOC Guidelines based on best practices. EDULEARN14 Proceedings, pp. 6981-6990. 261. Srinivasan, R., 2016. Emerging Shifts in Learning Paradigms-From Millenials to the Digital Natives. International Journal of Applied Engineering Research, 11(5), pp. 3616- 3618. 262. Sterling, M., Leung, P., Wright, D. and Bishop, T. F., 2107. The use of social media in graduate medical education: a systematic review. Academic Medicine, 92(7), pp. 1043- 1056. 263. Stilman, B., 2017. The Primary Language of the Human Brain. Procedia Computer Science, 111, pp. 448-462. 264. Stirling, B. and Alquraini, W., 2017. Using VARK to assess Saudi nursing students' learning style preferences: Do they differ from other health professionals? Journal of Taibah University Medical Sciences, 12(2), pp. 125-130. 265. Stirling, B. V., 2017. Results of a study assessing teaching methods of faculty after measuring student learning style preference. Nurse Education Today, 55, pp. 107-111. 266. Stojanova, A., 2017. Application of VARK learning model on Data Structures and Algorithms course. Global Engineering Education Conference (EDUCON), 2017 IEEE, pp. 613-620. 267. Svinicki, M. D. and Dixon, N. M., 1987. The Kolb model modified for classroom activities. College Teaching, 35(4), pp. 141-146. 268. Swan, M., 2013. The quantified self: Fundamental disruption in big data science and biological discovery. Big Data, 1(2), pp. 85-99. 269. Swinnerton, B. J., Morris, N. P., Hotchkiss, S. and Pickering, J. D., 2017. The integration of an anatomy massive open online course (MOOC) into a medical anatomy curriculum. Anatomical Sciences Education, 10(1), pp. 53-67. 270. Tabachnick, B. G. and Fidell, L. S., 2007. Using multivariate statistics. 5th ed. Boston: Pearson Education. 271. Talib, O., 2016. SPSS: Analisis Data Kuantitatif Untuk Penyelidik Muda. Bangi: MPWS Rich Publication. 272. Tavakol, M. and Dennick, R., 2011. Making sense of Cronbach's alpha. International Journal of Medical Education, 2, pp. 53-62. 273. Telaumbanua, Y. N. and Surya, B. S. M. E., 2017. Development of Mathematics Module Based on Metacognitive Strategy in Improving Students' Mathematical Problem Solving Ability at High School. Journal of Education and Practice, 8(19), pp. 73-80. Thirouard, M., Bernaert, O. and Dhorne, L., 2016. How can motivation and completion rates be improved in a MOOC? Data analysis of IFP Schools first two interactive MOOCs. Proceedings of the European Stakeholder Summit on Experiences and Best Practices In and Around MOOCs (EMOOCS 2016), pp. 329-338. 274. Tinio, M. F., 2009. Academic Engagement Scale for Grade School Students. The Assessment Handbook, 2, pp. 64-75. 275. Tough, D., 2012. A focus on Robert Gagne's instructional theories: application to teaching audio engineering. MEIEA Journal, 12(1), pp. 209-220. 276. Traxler, J., 2018. Learning with Mobiles in Developing Countries: Technology, Language, and Literacy. Information and Technology Literacy: Concepts, Methodologies, Tools, and Applications, pp. 774-790. 277. Trowler, V., 2010. Student engagement literature review. The Higher Education Academy, pp. 1-70. 278. Tully, J., Dameff, C., Kaib, S. and Moffitt, M., 2015. Recording medical students encounters with standardized patients using google glass: Providing end-of-life clinical education. Academic Medicine, 90(3), pp. 314-316. 279. Ungerleider, N., 2014. The Surprising Reason Oil Companies Love Google Glass. [Online] Available at: https://www.fastcompany.com/3031171/the-surprising-reason-oil-companieslove- google-glass [Accessed 10 May 2015]. 280. Universiti Teknikal Malaysia Melaka, 2015. Corporate Info. [Online] Available at: http://www.utem.edu.my/portal/vision,-mission-and-motto.html [Accessed 19 March 2016]. 281. Vaibhav, A. and Gupta, P., 2014. Gamification of MOOCs for increasing user engagement. MOOC, Innovation and Technology in Education (MITE) 2014 IEEE International Conference, pp. 290-295. 282. Vallurupalli, S., Paydak, H., Agarwal, S.K., Agrawal, M. and Assad-Kottner, C., 2013. Wearable technology to improve education and patient outcomes in a cardiology fellowship program- A feasibility study. Health and Technology, 3(4), pp. 267.270. 283. van Merrienboer, J. J. G., 1997. Training Complex Cognitive Skills: A Four-Component Instructional Design Model for Technical Training. Englewood Cliffs(NJ): Educational Technology Publications. 284. Veiga, F., Reeve, J., Wentzel, K. and Robu, V., 2014. Assessing students engagement: A review of instruments with psychometric qualities. I Congresso Internacional Envolvimento dos Alunos na Escola: Perspetivas da Psicologia e Educacao, pp. 38-57. 285. Ventura, P., Barcena, E. and Martin-Monje, E., 2014. Analysis of the impact of social feedback on written production and student engagement in Language MOOCs. Procedia- Social and Behavioral Sciences, 141, pp. 512-517. 286. Virtanen, T., Lerkkanen, M., Poikkeus, A. and Kuorelahti, M., 2015. The relationship between classroom quality and students engagement in secondary school. Educational Psychology, 35(8), pp. 963-983. 287. Vishnevsky, T. and Beanlands, H., 2004. Qualitative research. Nephrology Nursing Journal. 31(2), pp. 234-241. 288. Vizeshfar, F. and Torabizadeh, C., 2018. The effect of teaching based on dominant learning style on nursing students' academic achievement. Nurse Education in Practice, 28, pp. 103-108. 289. Wang, M. T., Willet, J. B. and Eccles, J. S., 2011. The assessment of school engagement: Examining dimensionality and measurement invariance by gender and race/ethnicity. Journal of School Psychology, 49(4), pp. 465-480. 290. Wang, Y. and Baker, R., 2015. Content or platform: Why do students complete MOOCs?. Journal of Online Learning and Teaching, 11(1), pp. 17-26. 291. Wang, Y., Wu, W. and Lou, Y., 2014. MOOC-DASH: A DASH system for delivering high-quality MOOCs videos. Multimedia (ISM), 2015 IEEE International Symposium, pp. 113-119. 292. Ward, M., 2016. Using animated visualisation in Computer Assisted Language Learning. Human System Interactions (HSI), 2016 9th International Conference, pp. 38-44. 293. Waterworth, N., 2013. Generation X, Generation Y, Generation Z and Baby Boomers. [Online] Available at: http://www.talentedheads.com/2013/04/09/generation-confused/ [Accessed 7 July 2015]. 294. Watted, A. and Barak, M., 2018. Motivating factors of MOOC completers: Comparing between university-affiliated students and general participants. The Internet and Higher Educaiton, 37, pp. 11-20. 295. West, J. and Turner, W., 2015. Enhancing the assessment experience: improving student perceptions, engagement and understanding using online video feedback. Innovations in Education and Teaching International Ahead-of-print, pp. 1-11. 296. Williams, D., Bond, A. and Baab, L., 2015. Improving the efficiency and effectiveness of tool training via self-paced instructional module: A case study using Camtasia. Proceedings of E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education, pp. 1205-1206. 297. Wiphasith, H., Narumol, R. and Sumalee, C., 2016. The design of the contents of an elearning for teaching M. 5 English language using ADDIE model. International Journal of Information and Education Technology, 6(2), pp. 127-131. 298. Wong, L., 2013. Student Engagement with Online Resources and Its Impact on Learning Outcomes. Journal of Information Technology Education: Innovations in Practice, 12, pp. 1-18. 299. Woodberry, E., 2015. The use of a wearable camera improves autobiographical memory in patients with Alzheimer's disease. Memory, 23(3), pp. 340-349. 300. WorldSIM, 2017. Action Cameras - What Are They and Why You Need One!. [Online] Available at: https://www.worldsim.com/blog/buying-action-cameras?___store=usa [Accessed 24 January 2018]. 301. Wu, T., Dameff, C. and Tully, J., 2014. Integrating Google Glass into simulation-based training: experiences and future directions. Journal of Biomedical Graphics and Computing, 4(2), pp. 49-54. 302. Wynd, W. R. and Bozman, C. S., 1996. Student learning style: A segmentation strategy for higher education. Journal of Education for Business, 71(4), pp. 232-235. 303. Xing, W., Chen, X., Stein, J. and Marcinkowski, M., 2016. Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in Human Behavior, 58, pp. 119-129. 304. Yamazaki, Y. and Umemura, H., 2017. Learning Style Differences between Undergraduates, MBAs, Nonmanagement Workers, and Managers in Japan. Journal of 305. Business Administration Research, 6(1), pp.137-149. Yang, T. and Choi, Y. M., 2015. Study on the design characteristics of head mounted displays (HMD) for use in guided repair and maintenance. International Conference on 306. Virtual, Augmented and Mixed Reality, pp. 535-543. 307. Yang, T. and Koszalka, T. A., 2016. Level of engagement and its application to learning resources. RIDLR, pp. 1-8. 308. Yang, T., Jiang, X., Zhong, Y., Zhao, X., Lin, S., Li, J., Li, X., Xu, J., Li, Z. and Zhu, H., 2017. A wearable and highly sensitive graphene strain sensor for precise home-based pulse wave monitoring. ACS sensors, 2(7), pp. 967-974. 309. Yee, M.H., Yunos, J.M., Othman, W., Hassan, R., Tee, T.K. and Mohamad, M.M., 2015. Disparity of learning styles and higher order thinking skills among technical students. Procedia-Social and Behavioral Sciences, 204, pp. 143-152. 310. Yoon, J.W., Chen, R.E., Han, P.K., Si, P., Freeman, W.D. and Pirris, S.M., 2017. Technical feasibility and safety of an intraoperative head up display device during spine instrumentation. The International Journal of Medical Robotics and Computer Assisted Surgery, 13(3), pp. 1770-1778. 311. Yousef, A. M. F., Chatti, M. A., Schroeder, U. and Wosnitza, M., 2014. What drives a successful MOOC? An empirical examination of criteria to assure design quality of MOOCs. Advanced Learning Technologies (ICALT), 2014 IEEE 14th International Conference, pp. 44-48. 312. Yousef, A. M. F., Chatti, M. A., Schroeder, U. and Wosnitza, M., 2015. A usability evaluation of a blended MOOC environment: An experimental case study. The International Review of Research in Open and Distributed Learning, 16(2), pp.1-14. 313. Yousef, D. A. and Yousef, D. A., 2016. The use of the learning styles questionnaire (LSQ) in the United Arab Emirates. Quality Assurance in Education, 24(4), pp. 490-506. 314. Yuen, S., Gallayanee, Y. and Erik, J., 2011. Augmented reality: An overview and five directions for AR in education. Journal of Educational Technology Development and Exchange, 4(1), pp. 119-140. 315. Zahl, D. A., Schrader, S. M. and Edwards, P. C., 2016. Student perspectives on using egocentric video recorded by smart glasses to assess communicative and clinical skills with standardized patients. European Journal of Dental Education, pp.122-131. 316. Zajdel, T. J. and Maharbiz, M. M., 2016. Introducing Electronics at Scale with a Massive Online Circuits Lab. Proceedings of the 123rd ASEE Annual Conference and Exposition, pp. 209-218. 317. Zamanzadeh, V., Ghahramanian, A., Rassouli, M., Abbaszadeh, A., Alavi-Majd, H. and Nikanfar, A.R., 2015. Design and implementation content validity study: development of an instrument for measuring patient-centered communication. Journal of caring sciences, 4(2), pp. 165-178. 318. Zhang, J., 2016. Can MOOCs be interesting to students? An experimental investigation from regulatory focus perspective. Computers and Education, 95, pp. 340-351. 319. Zhang, W., Wu, Y., Song, W. and Jia, X., 2010. Study on Design of the US Army Military Training System. Journal of the Academy of Equipment Command and Technology, 1, pp. 13-22. 320. Zheng, X.S., Foucault, C., Matos da Silva, P., Dasari, S., Yang, T. and Goose, S., 2015. Eye-wearable technology for machine maintenance: Effects of display position and handsfree operation. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 2125-2134. 321. Zhou, D., Zhou, T. and Zhou, A., 2015. Wearable augmented reality eyeglass communication device including mobile phone and mobile computing via virtual touch screen gesture control and neuron command. U.S., Patent No. 9,153,074. 322. Zhou, M., 2016. Chinese university students' acceptance of MOOCs: A self-determination perspective. Computers and Education, 92, pp. 194-203. 323. Zhu, Y., Pei, L. and Shang, J., 2017. Improving video engagement by gamification: A proposed design of MOOC videos. International Conference on Blended Learning, pp. 433-444.