Technological acceptance of LINE in flipped EFL oral training

Computers in Human Behavior

Volumn 70, Issue , 2017, Pages 178-190

  Chen Hsieh, Jun Scott ^a   Huang, Yong Ming ^b   Wu, Wen Chi Vivian ^c  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b CHIA NAN UNIVERSITY OF PHARMACY AND SCIENCE   (Taiwan)

^c ASIA UNIVERSITY   (Taiwan)

Author keywords

Flipped learning; LINE; Oral training; TAM

Indexed keywords

LEARNING SYSTEMS; SURVEYS;

BEHAVIORAL INTENTION; CONVENTIONAL APPROACH; DIVERSE APPLICATIONS; FLIPPED LEARNING; LEARNING EXPERIENCES; LINE; ORAL TRAININGS; TECHNOLOGY ACCEPTANCE MODEL;

ENGINEERING EDUCATION;

HUMAN; HUMAN EXPERIMENT; INTERVIEW; LANGUAGE DEVELOPMENT; MODEL; PERCEPTION; QUESTIONNAIRE; STUDENT;

EID: 85008932282     PISSN: 07475632     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.chb.2016.12.066     Document Type: Article

References (146)

1. Abachi, H.R., Muhammad, G., The impact of m-learning technology on students and educators. Computers in Human Behavior 30 (2014), 491–496.
2. Abdullah, F., Ward, R., Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior 56 (2016), 238–256.
3. Abeysekera, L., Dawson, P., Motivation and cognitive load in the flipped classroom: Definition, rationale and a call for research. Higher Education Research & Development 34 (2015), 1–14.
4. Adams, D.A., Nelson, R.R., Todd, P.A., Perceived usefulness, ease of use, and usage of information. MIS Quarterly 16:2 (1992), 227–250.
5. Al-Gahtani, S.S., Hubona, G.S., Wang, J., Information technology (IT) in Saudi Arabia: Culture and the acceptance and use of IT. Information & Management 44:8 (2007), 681–691.
6. Amer, M., A study of learners’ usage of a mobile learning application for learning idioms and collocations. CALICO Journal 31:3 (2014), 285–302.
7. Asl, F.M., The impact of context on learning idioms in EFL classes. TESOL Journal, 37(1), 2013, 2.
8. Bakker, G., Sloep, P., Jochems, W., Students and instant messaging: A survey of current use and demands for higher education. Research in Learning Technology 15:2 (2007), 143–153.
9. Bandyopadhyay, K., Fraccastoro, K.A., The effect of culture on user acceptance of information technology. Communications of the Association for Information Systems 19:1 (2007), 522–543 based-practices/fi nalreport.pdf.
10. Blasco-Arcas, L., Buil, I., Hernández-Ortega, B., Sese, F.J., Using clickers in class. The role of interactivity, active collaborative learning and engagement in learning performance. Computers & Education 62 (2013), 102–110.
11. Boucher, B., Robertson, E., Wainner, R., Sanders, B., “Flipping” Texas State University's physical therapist musculoskeletal curriculum: Implementation of a hybrid learning model. Journal of Physical Therapy Education 27:3 (2013), 72–77.
12. Bourgonjon, J., De Grove, F., De Smet, C., Van Looy, J., Soetaert, R., Valcke, M., Acceptance of game-based learning by secondary school teachers. Computers & Education 67 (2013), 21–35.
13. Butt, A., Student views on the use of a flipped classroom approach: Evidence from Australia. Business Education & Accreditation 6 (2014), 33–43.
14. Cavus, N., Ibrahim, D., M-learning: An experiment in using SMS to support learning new English language words. British Journal of Educational Technology 40:1 (2009), 78–91.
15. Chau, P.Y.K., An empirical assessment of a modified technology acceptance model. Journal of Management Information Systems 12:2 (1996), 185–204.
16. Chen Hsieh, J.S., Wu, W.-C.V., Marek, M.W., Using the flipped classroom to enhance EFL learning. Computer Assisted Language Learning, 2016, 10.1080/09588221.2015.1111910 Retrieved from.
17. Cheng, B., Wang, M., Moormann, J., Olaniran, B.A., Cheng, N.S., The effects of organizational learning environment factors on e-learning acceptance. Computers & Education 58:3 (2012), 885–899.
18. Chen, H.R., Tseng, H.F., Factors that influence acceptance of web-based elearning system for the in-service education of junior high school teachers in Taiwan. Evaluation and Program Planning 35 (2012), 398–406.
19. Chi, M.T., Active-constructive-interactive: A conceptual framework for differentiating learning activities. Topics in Cognitive Science 1:1 (2009), 73–105.
20. Chin, W.W., Newsted, P.R., Structural equation modeling analysis with small samples using partial least squares. Hoyle, R., (eds.) Statistical strategies for small sample research, 1999, Sage Publications, California, 307–341.
21. Chua, A.Y.K., Banerjee, S., Pee, L.G., An m-learning game for the study of humanities. IEEE Conference on e-Learning, e-Management and e-Services, 2015, 137–142.
22. Chun, D., Smith, B., Kern, R., Technology in language use, language teaching, and language learning. The Modern Language Journal 100:S1 (2016), 64–80.
23. Chwo, S.M.G., Marek, M.W., Wu, W.C.V., Curriculum integration of MALL in L1/L2 Pedagogy: Perspectives on research. Educational Technology & Society 19:2 (2016), 340–354 Retrieved from http://www.ifets.info/download_pdf.php?j_id=71&a_id=1720.
24. Coniam, D., Wong, R., Internet Relay Chat as a tool in the autonomous development of ESL learners’ English language ability: An exploratory study. System 32 (2004), 321–335.
25. Creswell, J.W., Research design: Qualitative, quantitative, and mixed methods approaches. 2013, SAGE, Los Angeles, Calif.
26. Davies, R.S., Dean, D.L., Ball, N., Flipping the classroom and instructional technology integration in a college-level information systems spreadsheet course. Educational Technology Research and Development 61:4 (2013), 563–580.
27. Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (1989), 319–340.
28. Davis, F.D., Bagozzi, R.P., Warshaw, P.R., User acceptance of computer technology: A comparison of two theoretical models. Management Science 35:8 (1989), 982–1003.
29. Dicheva, D., Dichev, C., Agre, G., Angelova, G., Gamification in education: A systematic mapping study. Educational Technology & Society 18:3 (2015), 75–88.
30. Dill, E.M., The impact of flip teaching on student homework completion, behavior, engagement, and proficiency. 2012 Completed to meet the requirements of University of New England MS Ed. Program. Retrieved from http://www.lessonpaths.com/learn/mmHosted/64517.
31. Dörnyei, Z., Individual differences in second language acquisition. AILA Review 19 (2006), 42–68.
32. Drucker, M.J., Commentary: Crossing the digital divide: How race, class, and culture matter. Contemporary Issues in Technology and Teacher Education 6:1 (2006), 43–45.
33. El-Gayar, O., Moran, M., Hawkes, M., Students’ acceptance of tablet PCs and implications for educational institutions. Educational Technology & Society 14:2 (2011), 58–70.
34. Enfield, J., Looking at the impact of the flipped classroom model of instruction on undergraduate multimedia students at CSUN. Tech Trends 57 (2013), 14–27.
35. Escudeiro, P., Carvalho, C., Game-based language learning. International Journal of Information and Education Technology 3:6 (2013), 643–647.
36. Eun-ji, B., Number of LINE users to top 700 mil. this year. 2015 Retrieved from http://www.koreatimes.co.kr/www/news/tech/2015/02/419_173201.html.
37. Flipped Learning Network, The four pillars of F-L-I-P™. 2014 Retrieved from http://flippedlearning.org/cms/lib07/VA01923112/Centricity/Domain/46/FLIP_handout_FNL_Web.pdf.
38. Fornell, C., Larcker, D.F., Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18:1 (1981), 39–50.
39. Frohberg, D., Göth, C., Schwabe, G., Mobile Learning projects. A critical analysis of the state of the art. Journal of Computer Assisted Learning 25 (2009), 307–331.
40. Fulton, K., Upside down and inside out: Flip your classroom to improve student learning. Learning & Leading with Technology 39:8 (2012), 12–17.
41. Godwin-Jones, R., Emerging technologies: Mobile apps for language learning. Language Learning & Technology 15:2 (2011), 2–11.
42. Granena, G., Explicit and implicit cognitive aptitudes and information-processing styles: An individual differences study. Applied Psycholinguistics, FirstView, 2015, 1–14.
43. Grey, S., Williams, J.N., Rebuschat, P., Individual differences in incidental language learning: The role of learning styles, personality, and working memory. Learning and Individual Differences 38 (2015), 44–53.
44. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L., Multivariate data analysis. 6th ed., 2006, Prentice-Hall, New Jersey.
45. Han, Y.J., Successfully flipping the ESL classroom for learner autonomy. NYS TESOL Journal 2:98 (2015), 98–109.
46. Han, J.H., Finkelstein, A., Understanding the effects of professors’ pedagogical development with Clicker Assessment and Feedback technologies and the impact on students’ engagement and learning in higher education. Computers & Education 65 (2013), 64–76.
47. Hassanzadeh, A., Kanaani, F., Elahi, S., A model for measuring e-learning systems success in universities. Expert Systems with Applications 39:12 (2012), 10959–10966.
48. Hidayanto, A.N., Febriawan, D., Sucahyo, Y.G., Purwandari, B., Factors influencing the use of E-Class. Journal of Industrial and Intelligent Information 2:2 (2014), 121–125.
49. Hou, H., Wu, S., Analyzing the social knowledge construction behavioral patterns of an online synchronous collaborative discussion instructional activity using an instant messaging tool: A case study. Computers & Education 57:2 (2011), 1459–1468.
50. Hsia, J., Chang, C., Tseng, A., Effects of individuals’ locus of control and computer Self-Efficacy on their e-learning acceptance in high-tech companies. Behaviour & Information Technology 33:1 (2014), 51–64.
51. Huang, Y.M., Exploring the factors that affect the intention to use collaborative technologies: The differing perspectives of sequential/global learners. Australasian Journal of Educational Technology 31:3 (2015), 278–292.
52. Huang, Y.M., The factors that predispose students to continuously use cloud services: Social and technological perspectives. Computers & Education 97 (2016), 86–96.
53. Huang, Y.M., Huang, Y.M., Huang, S.H., Lin, Y.T., A ubiquitous English vocabulary learning system: Evidence of active/passive attitudes vs. usefulness/ease-of-use. Computers & Education 58:1 (2012), 273–282.
54. Huang, H.M., Liaw, S.S., Exploring users’ attitudes and intentions toward the web as a survey tool. Computers in Human Behavior 21:5 (2005), 729–743.
55. Hughes, J.E., Ooms, A., Content-focused technology inquiry groups: Preparing urban teachers to integrate technology to transform student learning. Journal of Research on Technology in Education 36:4 (2004), 397–411.
56. Hung, H.T., Flipping the classroom for English language learners to foster active learning. Computer Assisted Language Learning 28:1 (2015), 81–96.
57. Hwang, G.J., Tsai, C.C., Research trends in mobile and ubiquitous learning: A review of publications in selected journals from 2001 to 2010. British Journal of Educational Technology 42 (2011), 65–70.
58. Im, I., Hong, S., Kang, M.S., An international comparison of technology adoption: Testing the UTAUT model. Information & Management 48:1 (2011), 1–8.
59. Jahangir, N., Begum, N., The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking. African Journal of Business Management 2:2 (2008), 32–40.
60. Jeong, W., Instant messaging in on-site and online classes in higher education. Educause Quarterly 1 (2007), 30–36.
61. Johnson, L., Adams Becker, S., Estrada, V., Freeman, A., NMC horizon report: 2014 K-12 edition., 2014, The New Media Consortium, Austin, Texas.
62. Kahari, L., The effects of cell phone use on the study habits of University of Zimbabwe first year Faculty of Arts students. International Journal of Education and Research 1:10 (2013), 1–12.
63. Kay, R.H., LeSage, A., Examining the benefits and challenges of using audience response systems: A review of the literature. Computers & Education 53:3 (2009), 819–827.
64. Ke, C.H., Sun, H.M., Yang, Y.C., Effects of user and system characteristics on perceived usefulness and perceived ease of use of the web-based classroom response system. The Turkish Online Journal of Educational Technology 11:3 (2012), 128–143.
65. Khan, Ö., Daşkin, N.C., “You reap what you sow” idioms in materials designed by EFL teacher-trainees. Novitas-ROYAL (Research on Youth and Language) 8:2 (2014), 97–118.
66. Kim, M.K., Kim, S.M., Khera, O., Getman, J., The experience of three flipped classrooms in an urban university: An exploration of design principles. Internet and Higher Education 22 (2014), 37–50.
67. Kim, H., Kwon, Y., Exploring smartphone applications for effective mobile-assisted language learning. Multimedia-Assisted Language Learning 15:1 (2012), 31–57.
68. Kim, K., Trimi, S., Park, H., Rhee, S., The impact of CMS quality on the outcomes of e-learning systems in higher education: An empirical study. Decision Sciences Journal of Innovative Education 10:4 (2012), 575–587.
69. King, W.R., He, J., A meta-analysis of the technology acceptance model. Information & Management 43:6 (2006), 740–755.
70. Knutsen, L.A., M-service expectancies and attitudes: Linkages and effects of first impressions. Proceedings of the 38th annual Hawaii international conference on system sciences, 2005, January, IEEE 84a-84a.
71. Kukulska-Hulme, A., Will mobile learning change language learning?. ReCALL 21:2 (2009), 157–165.
72. Lauricella, S., Kay, R., Exploring the use of text and instant messaging in higher education. Research in Learning Technology, 21, 2013, 19061.
73. Lee, Y.H., Hsiao, C., Purnomo, S.H., An empirical examination of individual and system characteristics on enhancing e-learning acceptance. Australasian Journal of Educational Technology 30:5 (2014), 561–579.
74. Lee, Y., Kozar, K.A., Larsen, K.R., The technology acceptance model: Past, present, and future. Communications of the Association for Information Systems 12:1 (2003), 752–780.
75. Li, J.P., Kishore, R., How robust is the UTAUT instrument?: a multigroup invariance analysis in the context of acceptance and use of online community weblog systems. Proceedings of the 2006 ACM SIGMIS CPR conference on computer personnel research: Forty four years of computer personnel research: Achievements, challenges & the future, 2006, April, ACM, 183–189.
76. Liu, Y., Li, H., Carlsson, C., Factors driving the adoption of m-learning: An empirical study. Computers & Education 55 (2010), 1211–1219.
77. Love, B., Hodge, A., Grandgenett, N., Swift, A., Student learning and perceptions in a flipped linear algebra course. International Journal of Mathematical Education in Science and Technology 45:3 (2014), 317–324.
78. Lu, M., Effectiveness of vocabulary learning via mobile phone. Journal of Computer Assisted Learning 24 (2008), 515–525.
79. Lu, J., Yu, C.S., Liu, C., Yao, J.E., Technology acceptance model for wireless Internet. Internet Research 13:3 (2003), 206–222.
80. Marangunić, N., Granić, A., Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society 14:1 (2015), 81–95.
81. Marchewka, J.T., Liu, C., Kostiwa, K., An application of the UTAUT model for understanding student perceptions using course management software. Communications of the IIMA 7:2 (2007), 93–104.
82. Markett, C., Arnedillo Sánchez, I., Weber, S., Tangney, B., Using short message service to encourage interactivity in the classroom. Computers & Education 46:3 (2006), 280–293.
83. Marks, R.B., Sibley, S.D., Arbaugh, J.B., A structural equation model of predictors for effective online learning. Journal of Management Education 29:4 (2005), 531–563.
84. McLaughlin, J.E., Griffin, L.M., Esserman, D.A., Davidson, C.A., Glatt, D.M., Roth, M.T., et al. Pharmacy student engagement, performance, and perception in a flipped satellite classroom. American Journal of Pharmaceutical Education 77:9 (2013), 1–8.
85. McLaughlin, J.E., Roth, M.T., Glatt, D.M., Gharkholonarehe, N., Davidson, C.A., Griffin, L.M., et al. The flipped classroom: A course redesign to foster learning and engagement in a health professions school. Academic Medicine 89:2 (2014), 236–243.
86. Means, B., Toyama, Y., Murphy, R., Bakia, M., Jones, K., Evaluation of evidence-based practices in online learning: a meta-analysis and review of online learning studies. 2009, US Department of Education Retrieved from https://www2.ed.gov/rschstat/eval/tech/evidence.
87. Mohammadi, H., Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in Human Behavior 45 (2015), 359–374.
88. Moon, J.W., Kim, Y.G., Extending the TAM for a world-wide-web context. Information & Management 38:4 (2001), 217–230.
89. Motiwalla, L.F., Mobile learning: A framework and evaluation. Computers & Education 49:3 (2007), 581–596.
90. Murdock, J.L., Williams, A.M., Creating an online learning community: Is it possible?. Innovative Higher Education 36:5 (2011), 305–315.
91. Nassuora, A.B., Students acceptance of mobile learning for higher education in Saudi Arabia. American Academic & Scholarly Research Journal 4:2 (2012), 1–9.
92. Ng'ambi, D., Brown, I., Intended and unintended consequences of student use an online questioning environment. British Journal of Educational Technology 40:2 (2009), 316–328.
93. Ning, H., Adapting cooperative learning in tertiary ELT. ELT Journal 65:1 (2011), 60–70.
94. Ozdamli, F., Uzunboylu, H., M-learning adequacy and perceptions of students and teachers in secondary schools. British Journal of Educational Technology 46:1 (2015), 159–172.
95. Palloff, R.M., Pratt, K., Building learning communities in cyberspace: Effective strategies for the online classroom. 1999, Jossey-Bass Publishers, San Francisco, CA.
96. Palomo-Duarte, M., Berns, A., Cejas, A., Dodero, J.M., Caballero, J.A., Ruiz-Rube, I., Assessing foreign language learning through mobile game-based learning environments. International Journal of Human Capital and Information Technology Professionals (IJHCITP) 7:2 (2016), 53–67.
97. Park, S.Y., An Analysis of the Technology Acceptance Model in Understanding University Students’ Behavioral Intention to Use e-Learning. Journal of Educational Technology & Society 12:3 (2009), 150–162.
98. Park, S.Y., Nam, M.W., Cha, S.B., University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology 43:4 (2011), 592–605.
99. Parnell, J.A., Carraher, S., The management education by internet readiness (MEBIR) Scale: Developing a scale to assess personal readiness for internet-mediated management education. Journal of Management Education 27:4 (2003), 431–446.
100. Pew Research Center, U.S. smartphone use in 2015. 2015 Retrieved from http://www.pewinternet.org/2015/04/01/us-smartphone-use-in-2015/.
101. Pimmer, C., Mobile learning as boundary crossing: An alternative route to technology-enhanced learning?. Interactive Learning Environments 24:5 (2016), 979–990.
102. Pituch, K.A., Lee, Y.-K., The influence of system characteristics on e-learning use. Computers & Education 47:2 (2006), 222–244.
103. Poole, D., The impact of anonymous and assigned use of student response systems on student achievement. Journal of Interactive Learning Research 23:2 (2012), 101–112.
104. Read, W., Robertson, N., McQuilken, L., A novel romance: The technology acceptance model with emotional attachment. Australasian Marketing Journal 19:4 (2011), 223–229.
105. Ros, S., Hernández, R., Caminero, A., Robles, A., Barbero, I., Maciá, A., et al. On the use of extended TAM to assess students’ acceptance and intent to use third-generation learning management systems. British Journal of Educational Technology 46:6 (2015), 1250–1271.
106. Saga, V., Zmud, R., The nature and determinants of it acceptance, routinization, routinization, and infusion. Levine, L., (eds.) Diffusion, transfer and implementation of information technology, 1994, ElsevierScience Publications, Minneapolis, MN, USA, 67–86.
107. Sahilu, W.S., Ahmad, W.F.W., Haron, N.S., Development and usability evaluation of platform independent mobile learning tool (MLT). World Academy of Science, Engineering and Technology 5:8 (2011), 1139–1150.
108. Sahin, A., Cavlazoglu, B., Zeytuncu, Y.E., Flipping a college calculus course: A case study. Journal of Educational Technology & Society 18:3 (2015), 142–152.
109. Saran, M., Seferoğlu, G., Supporting foreign language vocabulary learning through multimedia messages via mobile phones. Hacettepe University Journal of Education 38 (2010), 252–266.
110. Sarawagi, N., Flipped CS0 classroom: Applying Bloom's taxonomy to algorithmic thinking. Journal of Computing Sciences in Colleges 29 (2014), 21–28.
111. Selim, H.M., An empirical investigation of student acceptance of course websites. Computers & Education 40:4 (2003), 343–360.
112. Sevian, H., Robinson, W.E., Clickers promote learning in all kinds of classes: Small and large, graduate and undergraduate, lecture and lab. Journal of College Science Teaching 40:3 (2011), 14–18.
113. Shen, H., Luo, L., Sun, Z., What affect lower grade learner's perceived usefulness and perceived ease of use of mobile digital textbook learning system? An empirical factor analyses investigation in China. International Journal of Multimedia and Ubiquitous Engineering 10:1 (2015), 33–46.
114. Shipee, M., Keengwee, J., M-learning: Anytime, anywhere learning transcending the boundaries of the educational box. Education and Information Technologies 19 (2014), 103–113.
115. Shroff, R.H., Deneen, C.C., Ng, E.M.W., Analysis of the Technology Acceptance Model in Examining Students’ Behavioural Intention to Use an e-portfolio System. Australasian Journal of Educational Technology 27 (2011), 600–618.
116. Smith, M.K., Trujillo, C., Su, T.T., The benefits of using clickers in small-enrollment seminar-style biology courses. CBE-Life Sciences Education 10:1 (2011), 14–17.
117. Spires, H.A., Digital game-based learning. Journal of Adolescent & Adult Literacy 59:2 (2015), 125–130.
118. Stevens, J., Applied multivariate statistics for the social sciences. 2002, Lawrence Erlbaum Associates, Mahwah, N.J.
119. Stockwell, G., Tracking learner usage of mobile phones for language learning outside of the classroom. Hubbard, P., Schulze, M., Smith, B., (eds.) Learner-computer interaction in language education: A festschrift in honor of robert fischer, 2013, CALICO, 118–136.
120. Strayer, J., How learning in an inverted classroom influences cooperation, innovation and task orientation. Learning Environments Research 15 (2012), 171–193.
121. Sumak, B., Hericko, M., Punik, M., A meta-analysis of e-learning technology acceptance: The role of user types and e-learning technology types. Computers in Human Behavior 27:6 (2011), 2067–2077.
122. Sweeny, S.M., Writing for the instant messaging and text messaging generation: Using new literacies to support writing instruction. Journal of Adolescent & Adult Literacy 54 (2010), 121–130.
123. Tagarelli, K.M., Borges Mota, M., Rebuschat, P., Working memory, learning context, and the acquisition of L2 syntax. Zhisheng, W., Borges Mota, M., McNeill, A., (eds.) Working memory in second language acquisition and processing: Theory, research and commentary, 2015, Multilingual Matters, Bristol, 224–247.
124. Tarhini, A., Hone, K., Liu, X., The effects of individual differences on elearning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior 41 (2014), 153–163.
125. Thorne, S.L., Black, R., Sykes, J., Second language use, socialization, and learning in internet interest communities and online games. Modern Language Journal 93 (2009), 802–821.
126. Timmis, S., Constant companions: Instant messaging conversations as sustainable supportive study structures amongst undergraduate peers. Computers & Education 59:1 (2012), 3–18.
127. Traxler, J., Riordan, B., Evaluating the effectiveness of retention strategies using SMS, WAP and WWW student support. Proceedings of 4th annual conference, 2003, LTSN Centre for Information and Computer Science, Galway, Ireland, 54–55.
128. Tărcăoanu, M.C., Teaching and learning idioms in English (theoretical and practical considerations). Scientific Journal of Humanistic Studies 4:7 (2012), 220–228.
129. Venkatesh, V., Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research 11:4 (2000), 342–365.
130. Venkatesh, V., Davis, F.D., A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science 46:2 (2000), 186–204.
131. Venkatesh, V., Morris, M.G., Why don't men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior. MIS Quarterly 24:1 (2000), 115–139.
132. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D., User acceptance of information technology: Toward a unified view. MIS Quarterly 27:3 (2003), 425–478.
133. Wagner, E.D., Enabling mobile learning. EDUCAUSE Review 40 (2005), 41–42.
134. Wang, Y.S., Shih, Y.W., Why do people use information kiosks? A validation of the Unified Theory of Acceptance and Use of Technology. Government Information Quarterly 26:1 (2009), 158–165.
135. Wang, J.Y., Wu, H.K., Hsu, Y.S., Using mobile applications for learning: Effects of simulation design, visual-motor integration, and spatial ability on high school students’ conceptual understanding. Computers in Human Behavior 66 (2017), 103–113.
136. Wauters, K., Desmet, P., Van Den Noortgate, W., Adaptive item-based learning environments based on the item response theory: Possibilities and challenges. Journal of Computer Assisted Learning 26:6 (2010), 549–562.
137. White, P., Syncox, D., Alters, B., Clicking for grades? Really? Investigating the use of clickers for awarding grade-points in post-secondary education. Interactive Learning Environments 19:5 (2011), 551–561.
138. Wu, W.H., Wu, Y.-C.J., Chen, C.Y., Kao, H.Y., Lin, C.H., Huang, S.H., Review of trends from mobile learning studies: A meta-analysis. Computers & Education 59:2 (2012), 817–827.
139. Wu, P.-H.N., Marek, M.W., Incorporating LINE Smartphone app affordances: Cross-cultural collaboration, willingness to communicate, and language learning. International Journal of Computer Assisted Language Learning and Teaching (IJCALLT) 6:2 (2016), 56–73, 10.4018/IJCALLT.2016040104 Available from: http://www.igi-global.com/article/incorporating-line-smartphone-affordances/153895.
140. Wu, W.-C.V., Yen, L.L., Marek, M.W., Using online EFL interaction to increase confidence, motivation, and ability. Journal of Educational Technology & Society 14:3 (2011), 118–129.
141. Wu, B., Zhang, C., Empirical study on continuance intentions towards Elearning 2.0 systems. Behaviour & Information Technology 33:10 (2014), 1027–1038.
142. Yu, Z., Chen, W., Kong, Y., Sun, X.L., Zheng, J., The impact of clickers instruction on cognitive loads and listening and speaking skills in college English class. PloS One, 9(9), 2014, e106626.
143. Zhang, H., Song, W., Burston, J., Reexamining the effectiveness of vocabulary learning via mobile phones. The Turkish Online Journal of Educational Technology 10:3 (2011), 203–214.
144. Yu, Z.G., The impact of clickers use on vocabulary knowledge and satisfaction in an EFL class in China. International Journal of Information and Communication Technology Education 10:3 (2014), 47–59.
145. Yu, Z.G., Liu, C., The influence of clickers use on metacognition and learning outcomes in College English Classroom. International Journal of Information and Communication Technology Education (IJICTE) 10:2 (2014), 50–61.
146. Zydney, J.M., Warner, Z., Mobile apps for science learning: Review of research. Computers & Education 94 (2016), 1–17.