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Differences between mobile learning environmental preferences of high school teachers and students in Taiwan: a structural equation model analysis

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Abstract

Mobile technology has been increasingly applied to educational settings in the past decade. Although researchers have attempted to investigate both students’ and teachers’ preferences regarding mobile learning, few studies have investigated the differences between the two, an understanding of which is important for developing effective mobile learning environments. To address this issue, a mobile learning environmental preference survey (MLEPS) consisting of eight factors, “ease of use,” “continuity,” “relevance,” “adaptive content,” “multiple sources,” “timely guidance,” “student negotiation” and “inquiry learning,” was developed in this study. A total of 1239 students (609 males and 630 females) and 429 teachers (208 males and 221 females) who employed mobile technology to learn and teach in schools completed the questionnaire. From the structural equation models, it was found that the major difference between the preferences of teachers and students in learning with mobile technologies was that the teachers tended to focus more on the technical issues, while the students cared more about the richness and usefulness of the learning content. In addition, both the students and teachers considered that the “anytime” and “anywhere” support provided via the mobile technology played an important role during the learning activities, engaging them in searching for information, collecting data, interpreting data and summarizing findings. It is therefore suggested that learning environments which conform to both students’ and teachers’ preferences be developed in the future.

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References

  • Akinoglu, O. (2013). Effects of concept maps on students critical thinking skills in science education. Journal of Environmental Protection and Ecology, 14(3A), 1424–1431.

    Google Scholar 

  • Antonenko, P. D., Derakhshan, N., & Mendez, J. P. (2013). Pedagogy 2 go: student and faculty perspectives on the features of mobile learning management systems. International Journal of Mobile Learning and Organisation, 7(3–4), 197–209.

    Article  Google Scholar 

  • Baran, E. (2014). A review of research on mobile learning in teacher education. Educational Technology & Society, 17(4), 17–32.

    Google Scholar 

  • Bentsen, P., Schipperijn, J., & Jensen, F. S. (2013). Green space as classroom: outdoor school teachers’ use. Preferences and Ecostrategies. Landscape Research, 38(5), 561–575.

    Article  Google Scholar 

  • Beyers, R. N. (2009). A five dimensional model for educating the net generation. Educational Technology & Society, 12(4), 218–227.

    Google Scholar 

  • Bogdanović, Z., Barać, D., Jovanić, B., Popović, S., & Radenković, B. (2013). Evaluation of mobile assessment in a learning management system. British Journal of Educational Technology, 45(2), 231–244.

    Article  Google Scholar 

  • Briggs, S. R., & Cheek, J. M. (1986). The role of factor analysis in the development and evaluation of personality scales. Journal of Personality, 54(1), 106–148.

    Article  Google Scholar 

  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit. Sage Focus Editions, 154, 136.

    Google Scholar 

  • Cattell, R. B. (1978). The scientific use of factor analysis in behavioral and life sciences. New York: Plenum.

    Book  Google Scholar 

  • Chang, K. E., Lan, Y. J., Chang, C. M., & Sung, Y. T. (2010). Mobile-device-supported strategy for Chinese reading comprehension. Innovations in Education and Teaching International, 47(1), 69–84.

    Article  Google Scholar 

  • Chang, C. C., Liang, C. Y., Yan, C. F., & Tseng, J. S. (2013). The impact of college students’ intrinsic and extrinsic motivation on continuance intention to use english mobile learning systems. Asia-Pacific Education Researcher, 22(2), 181–192.

    Article  Google Scholar 

  • Chau, P. Y. K. (1997). Reexamining a model for evaluating information center success using a structural equation modeling approach. Decision Sciences, 28(2), 309–334.

    Article  Google Scholar 

  • Chen, C. M. (2013). An intelligent mobile location-aware book recommendation system that enhances problem-based learning in libraries. Interactive Learning Environments, 21(5), 469–495.

    Article  Google Scholar 

  • Chen, C. C., & Huang, T. C. (2012). Learning in a u-Museum: Developing a context-aware ubiquitous learning environment. Computers & Education, 59(3), 873–883.

    Article  Google Scholar 

  • Christensen, R. (2002). Effects of technology integration education on the attitudes of teachers and students. Journal of Research on Technology in Education, 34(4), 411–433.

    Article  Google Scholar 

  • Chu, H. C. (2014). Potential negative effects of mobile learning on students’ learning achievement and cognitive load—a format assessment perspective. Educational Technology & Society, 17(1), 332–344.

    Google Scholar 

  • Clark, L. A., & Watson, D. (1995). Constructing validity: Basic issues in objective scale development. Psychological Assessment, 7(3), 309–319.

    Article  Google Scholar 

  • Cochrane, T. D. (2014). Critical success factors for transforming pedagogy with mobile Web 2.0. British Journal of Educational Technology, 45(1), 65–82.

    Article  Google Scholar 

  • Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). New Jersey: Lawrence Erlbaum Associates.

    Google Scholar 

  • Comrey, A. L. (1973). A first course in factor analysis. New York: Academic.

    Google Scholar 

  • De Hei, M. S. A., Strijbos, J. W., Sjoer, E., & Admiraal, W. (2015). Collaborative learning in higher education: Lecturers’ practices and beliefs. Research Papers in Education, 30(2), 232–247.

    Article  Google Scholar 

  • Donnelly, R. (2010). Interaction analysis in a ‘Learning by Doing’ problem-based professional development context. Computers & Education, 55(3), 1357–1366.

    Article  Google Scholar 

  • Ekanayake, S. Y., & Wishart, J. (2014). Integrating mobile phones into teaching and learning: A case study of teacher training through professional development workshops. British Journal of Educational Technology,. doi:10.1111/bjet.12131.

    Google Scholar 

  • El-Bishouty, M. M., Ogata, H., Rahman, S., & Yano, Y. (2010). Social knowledge awareness map for computer supported ubiquitous learning environment. Educational Technology & Society, 13(4), 27–37.

    Google Scholar 

  • Ertmer, P. A., & Ottenbreit-Leftwich, A. (2013). Removing obstacles to the pedagogical changes required by Jonassen’s vision of authentic technology-enabled learning. Computers & Education, 64, 175–182.

    Article  Google Scholar 

  • Field, A. (2000). Discovering statistics using SPSS for windows: Advanced techniques for the beginner. London: Sage publications Ltd.

    Google Scholar 

  • Fraser, B. J. (1982). Differences between student and teacher perceptions of actual and preferred classroom learning environment. Educational Evaluation and Policy Analysis, 4(4), 511–519.

    Article  Google Scholar 

  • Gikas, J., & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18–26.

    Article  Google Scholar 

  • Hargis, J., Cavanaugh, C., Kamali, T., & Soto, M. (2013). Measuring the difficult to measure: Teaching and learning with an iPad. International Journal of Mobile and Blended Learning (IJMBL), 5(2), 60–77.

    Article  Google Scholar 

  • Herro, D., Kiger, D., & Owens, C. (2013). Mobile technology: Case-based suggestions for classroom integration and teacher educators. Journal of Digital Learning in Teacher Education, 30(1), 30–40.

    Article  Google Scholar 

  • Hsu, C. K. (2015). Learning motivation and adaptive video caption filtering for EFL learners using handheld devices. Recall, 27(1), 84–103.

    Article  Google Scholar 

  • Hung, P. H., Hwang, G. J., Lin, Y. F., Wu, T. H., & Su, I. H. (2013). Seamless connection between learning and assessment-applying progressive learning tasks in mobile ecology inquiry. Educational Technology & Society, 16(1), 194–205.

    Google Scholar 

  • Hung, I. C., Yang, X. J., Fang, W. C., Hwang, G. J., & Chen, N. S. (2014). A context-aware video prompt approach to improving students’ in-field reflection levels. Computers & Education, 70, 80–91.

    Article  Google Scholar 

  • Hwang, G. J. (2014). Definition, framework and research issues of smart learning environments—a context-aware ubiquitous learning perspective. Smart Learning Environments, 1(1), 4.

    Article  Google Scholar 

  • Hwang, G. J., Shi, Y. R., & Chu, H. C. (2011). A concept map approach to developing collaborative Mindtools for context-aware ubiquitous learning. British Journal of Educational Technology, 42(5), 778–789.

    Article  Google Scholar 

  • Hwang, G. J., Tsai, C. C., Chu, H. C., Kinshuk, & Chen, C. Y. (2012). A context-aware ubiquitous learning approach to conducting scientific inquiry activities in a science park. Australasian Journal of Educational Technology, 28(5), 931–947.

  • Hwang, G. J., Tsai, C. C., & Yang, S. J. H. (2008). Criteria, strategies and research issues of context-aware ubiquitous learning. Educational Technology & Society, 11(2), 81–91.

    Google Scholar 

  • Ismail, I., Azizan, S. N., & Azman, N. (2013). Mobile phone as pedagogical tools: Are teachers ready. International Education Studies, 6(3), 36–47.

    Article  Google Scholar 

  • Jan, S. L., & Shieh, G. (2014). Sample size determinations for Welch’s test in one-way heteroscedastic ANOVA. British Journal of Mathematical and Statistical Psychology, 67(1), 72–93.

    Article  Google Scholar 

  • Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural equation modeling with the SIMPLIS command language. Lincolnwood: Scientific Software International.

    Google Scholar 

  • Kafyulilo, A. (2014). Access, use and perceptions of teachers and students towards mobile phones as a tool for teaching and learning in Tanzania. Education and Information Technologies, 19(1), 115–127.

    Article  Google Scholar 

  • Kamarainen, A. M., Metcalf, S., Grotzer, T., Browne, A., Mazzuca, D., Tutwiler, M. S., & Dede, C. (2013). EcoMOBILE: Integrating augmented reality and probeware with environmental education field trips. Computers & Education, 68, 545–556.

    Article  Google Scholar 

  • Kang, Y., Harring, J. R., & Li, M. (2015). Reexamining the impact of nonnormality in two-group comparison procedures. Journal of Experimental Education, 83(2), 147–174.

    Article  Google Scholar 

  • Kearney, M., Burden, K., & Rai, T. (2015). Investigating teachers’ adoption of signature mobile pedagogies. Computers & Education, 80, 48–57.

    Article  Google Scholar 

  • Kelloway, E. K. (1998). Using LISREL for structural equation modeling: A researcher’s guide. Newbury Park: Sage.

    Google Scholar 

  • Kim, P., Suh, E., & Song, D. (2015). Development of a design-based learning curriculum through design-based research for a technology-enabled science classroom. Educational Technology Research and Development, 63(4), 575–602.

    Article  Google Scholar 

  • Kitsantas, A., & Chow, A. (2007). College students’ perceived threat and preference for seeking help in traditional, distributed, and distance learning environments. Computers & Education, 48(3), 383–395.

    Article  Google Scholar 

  • Kline, R. B. (1998). Principles and practise of structural equation modeling. New York: Guilford Press.

    Google Scholar 

  • Kollöffel, B. (2012). Exploring the relation between visualizer–verbalizer cognitive styles and performance with visual or verbal learning material. Computers & Education, 58(2), 697–706.

    Article  Google Scholar 

  • Kordaki, M. (2013). High school computing teachers’ beliefs and practices: A case study. Computers & Education, 68, 141–152.

    Article  Google Scholar 

  • Kukulska-Hulme, A. (2012). How should the higher education workforce adapt to advancements in technology for teaching and learning? The Internet and Higher Education, 15(4), 247–254.

    Article  Google Scholar 

  • Lai, C. L., & Hwang, G. J. (2015). High school teachers’ perspectives on applying different mobile learning strategies to science courses: The national mobile learning program in Taiwan. International Journal of Mobile Learning and Organization, 9(2), 124–145.

    Article  Google Scholar 

  • Lee, S., & Kim, B. G. (2015). Users’ preferential factors in Web-based e-learning systems for ease of workplace learning in Korea. Learning and Individual Differences, 39, 96–104.

    Article  Google Scholar 

  • Li, Q. (2007). Student and teacher views about technology. Journal of Research on Technology in Education, 39(4), 377–397.

    Article  Google Scholar 

  • Liaw, S. S., Hatala, M., & Huang, H. M. (2010). Investigating acceptance toward mobile learning to assist individual knowledge management: Based on activity theory approach. Computers & Education, 54(2), 446–454.

    Article  Google Scholar 

  • Looi, C. K., So, H. J., Toh, Y., & Chen, W. (2011). The Singapore experience: Synergy of national policy, classroom practice and design research. International Journal of Computer-Supported Collaborative Learning, 6(1), 9–37.

    Article  Google Scholar 

  • Looi, C. K., Sun, D., Wu, L., Seow, P., Chia, G., Wong, L. H., & Norris, C. (2014). Implementing mobile learning curricula in a grade level: Empirical study of learning effectiveness at scale. Computers & Education, 77, 101–115.

    Article  Google Scholar 

  • Marty, P. F., Alemanne, N. D., Mendenhall, A., Maurya, M., Southerland, S. A., Sampson, V., & Schellinger, J. (2013). Scientific inquiry, digital literacy, and mobile computing in informal learning environments. Learning Media and Technology, 38(4), 407–428.

    Article  Google Scholar 

  • Morrison, G. R., Morrison, G. R., & Lowther, D. L. (2009). Integrating computer technology into the classroom: Skills for the 21st century. London: Pearson.

    Google Scholar 

  • Newhouse, C. P. (2014). Learning with portable digital devices in Australian schools: 20 years on! Australian Educational Researcher, 41(4), 471–483.

    Article  Google Scholar 

  • O’Bannon, B. W., & Thomas, K. (2014). Teacher perceptions of using mobile phones in the classroom: Age matters! Computers & Education, 74, 15–25.

    Article  Google Scholar 

  • Onwuegbuzie, A. J., Bustamante, R. M., & Nelson, J. A. (2010). Mixed research as a tool for developing quantitative instruments. Journal of Mixed Methods Research, 4(1), 56–78.

    Article  Google Scholar 

  • Otter, R. R., Seipel, S., Graeff, T., Alexander, B., Boraiko, C., Gray, J., & Sadler, K. (2013). Comparing student and faculty perceptions of online and traditional courses. The Internet and Higher Education, 19, 27–35.

    Article  Google Scholar 

  • Ozdamli, F., & Uzunboylu, H. (2014). M-learning adequacy and perceptions of students and teachers in secondary schools. British Journal of Educational Technology, 46(1), 159–172.

    Article  Google Scholar 

  • Patchen, T., & Smithenry, D. W. (2015). More than just chemistry: The impact of a collaborative participant structure on student perceptions of science. Research in Science Education, 45(1), 75–100.

    Article  Google Scholar 

  • Ramos, A. J., & Triñona, J. (2010). Mobile technology in non-formal distance education. In J. Baggaley & T. Belawati (Eds.), Distance education technologies in Asia. New Delhi: SAGE Publications India.

    Google Scholar 

  • Roblyer, M. D., McDaniel, M., Webb, M., Herman, J., & Witty, J. V. (2010). Findings on Facebook in higher education: A comparison of college faculty and student uses and perceptions of social networking sites. The Internet and Higher Education, 13(3), 134–140.

    Article  Google Scholar 

  • Ruchter, M., Klar, B., & Geiger, W. (2010). Comparing the effects of mobile computers and traditional approaches in environmental education. Computers & Education, 54(4), 1054–1067.

    Article  Google Scholar 

  • Saavedra, A. R., & Opfer, V. D. (2012). Learning 21st-century skills requires 21st-century teaching. Phi Delta Kappan, 94(2), 8–13.

    Article  Google Scholar 

  • Şad, S. N., & Göktaş, O. (2014). Preservice teachers’ perceptions about using mobile phones and laptops in education as mobile learning tools. British Journal of Educational Technology, 45(4), 606–618.

    Article  Google Scholar 

  • Schuck, S., Aubusson, P., Kearney, M., & Burden, K. (2012). Mobilising teacher education: a study of a professional learning community. Teacher Development, 17(1), 1–18.

    Article  Google Scholar 

  • Schultz, W. P. (2000). Empathizing with nature: The effects of perspective taking on concern for environmental issues. Journal of Social Issues, 56(3), 391–406.

    Article  Google Scholar 

  • Sharples, M., Corlett, D., & Westmancott, O. (2002). The design and implementation of a mobile learning resource. Personal and Ubiquitous Computing, 6, 220–234.

    Article  Google Scholar 

  • Shea, P., & Bidjerano, T. (2009). Community of inquiry as a theoretical framework to foster “epistemic engagement” and “cognitive presence” in online education. Computers & Education, 52(3), 543–553.

    Article  Google Scholar 

  • Shieh, G., & Jan, S. L. (2015). Optimal sample size allocation for Welch’s test in one-way heteroscedastic ANOVA. Behavior Research Methods, 47(2), 374–383. doi:10.3758/s13428-014-0477-8.

    Article  Google Scholar 

  • Sung, H. Y., Hwang, G. J., & Chang, Y. C. (2013). Development of a mobile learning system for local culture courses based on a collaborative problem-posing strategy. Interactive Learning Environments. doi:10.1080/10494820.2013.867889.

    Google Scholar 

  • Tan, T. H., Lin, M. S., Chu, Y. L., & Liu, T. Y. (2012). Educational affordances of a ubiquitous learning environment in a natural science course. Educational Technology & Society, 15(2), 206–219.

    Google Scholar 

  • Tsai, C. C. (2008). The preferences toward constructivist Internet-based learning environments among university students in Taiwan. Computers in Human Behavior, 24(1), 16–31.

    Article  Google Scholar 

  • Tsai, P. S., Tsai, C. C., & Hwang, G. H. (2011). College students’ conceptions of context-aware ubiquitous learning: A phenomenographic analysis. The Internet and Higher Education, 14(3), 137–141.

    Article  Google Scholar 

  • Tsai, P. S., Tsai, C. C., & Hwang, G. J. (2012). Developing a survey for assessing preferences in constructivist context-aware ubiquitous learning environments. Journal of Computer Assisted Learning, 28(3), 250–264.

    Article  Google Scholar 

  • Vermunt, J. D. (2007). The power of teaching-learning environments to influence student learning. BJEP Monograph Series II Number 4-Student Learning and University Teaching, 1(1), 72–89.

    Google Scholar 

  • Vermunt, J. D., & Verloop, N. (1999). Congruence and friction between learning and teaching. Learning and Instruction, 9(3), 257–280.

    Article  Google Scholar 

  • Vogel, B., Kurti, A., Milrad, M., Johansson, E., & Muller, M. (2014). Mobile inquiry learning in sweden: Development insights on interoperability, extensibility and sustainability of the LETS GO software system. Educational Technology & Society, 17(2), 43–57.

    Google Scholar 

  • Webster, T. E., & Son, J. B. (2015). Doing what works: A grounded theory case study of technology use by teachers of English at a Korean university. Computers & Education, 80, 84–94.

    Article  Google Scholar 

  • Wen, M. L., Tsai, C. C., Lin, H. M., & Chuang, S. C. (2004). Cognitive–metacognitive and content-technical aspects of constructivist Internet-based learning environments: A LISREL analysis. Computers & Education, 43(3), 237–248.

    Article  Google Scholar 

  • Wong, L. H., & Looi, C. K. (2011). What seams do we remove in mobile-assisted seamless learning? A critical review of the literature. Computers & Education, 57(4), 2364–2381.

    Article  Google Scholar 

  • Wu, P. H., Hwang, G. J., Tsai, C. C., Chen, Y. C., & Huang, Y. M. (2011). A pilot study on conducting mobile learning activities for clinical nursing courses based on the repertory grid approach. Nurse Education Today, 31(8), e8–e15.

    Article  Google Scholar 

  • Zhu, C. (2013). Students’ and teachers’ thinking styles and preferred teacher interpersonal behavior. Journal of Educational Research, 106(5), 399–407.

    Article  Google Scholar 

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Acknowledgments

This study is supported in part by the National Science Council of the Republic of China under contract numbers NSC 101-2511-S-011 -005 -MY3 and NSC 102-2511-S-011 -007 -MY3. The authors would like to declare that there is no conflict of interest in this study.

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  1. Graduate Institute of Digital Learning and Education, National Taiwan University of Science and Technology, #43, Sec.4, Keelung Rd., Taipei, 106, Taiwan

    Chiu-Lin Lai, Gwo-Jen Hwang, Jyh-Chong Liang & Chin-Chung Tsai

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  1. Chiu-Lin Lai
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  2. Gwo-Jen Hwang
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  3. Jyh-Chong Liang
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Correspondence to Gwo-Jen Hwang.

Appendix

Appendix

Appendix 1

See Table 6.

Table 6 The MLEPS questionnaire items
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Appendix 2

See Table 7.

Table 7 The EFA results of the teachers’ and the students’ MLEPS
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Appendix 3

See Table 8.

Table 8 The CFA result for the teachers’ and the students’ MLEPS
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Lai, CL., Hwang, GJ., Liang, JC. et al. Differences between mobile learning environmental preferences of high school teachers and students in Taiwan: a structural equation model analysis. Education Tech Research Dev 64, 533–554 (2016). https://doi.org/10.1007/s11423-016-9432-y

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