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Research Article

Towards a model for online learning satisfaction (MOLS): re-considering non-linear relationships among personal innovativeness and modes of online interaction

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References

  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.
  • Al-Busaidi, K. A., & Al-Shihi, H. (2012). Key factors to instructors’ satisfaction of learning management systems in blended learning. Journal of Computing in Higher Education, 24(1), 18–39.
  • Ali, A., & Ahmad, I. (2011). Key factors for determining students’ satisfaction in distance learning courses: A study of Allama Iqbal Open University. Contemporary Educational Technology, 2(2), 118–134.
  • Aljaloud, M. (2012). Barriers to implementing learning management systems in Saudi Arabian higher education (Master’s thesis). Flinders University, Adelaide, South Australia.
  • Alqurashi, E. (2016). Self-efficacy in online learning environments: A literature review. Contemporary Issues in Education Research, 9(1), 45–52.
  • Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148.
  • Anderson, T. (2004). Towards a theory of online learning. Theory and Practice of Online Learning, 2, 109–119.
  • Arroway, P., Davenport, E., Guangning, X., & Updegrove, D. (2010). EDUCAUSE core data service fiscal year 2009 summary report. EDUCAUSE White Paper. EDUCAUSE.
  • Baker, C. (2010). The impact of instructor immediacy and presence for online student affective learning, cognition and motivation. The Journal of Educators Online, 7, n1.
  • Belanich, J., Wisher, R., . A., & Orvis, K. L. (2004). A question-collaboration approach to web-based learning. The American Journal of Distance Education, 18, 169–185.
  • Bervell, B., & Umar, I., . N. (2017). Validation of the UTAUT model: Re-considering non-linear relationships of exogeneous variables in higher education technology acceptance research. Eurasia Journal of Mathematics, Science and Technology Education, 13, 6471–6490.
  • Bolliger, D., & Martindale, T. (2004, January–March). Key factors for determining student satisfaction in online courses. International Journal on E-Learning, 1, 61–67.
  • Bray, E., Aoki, K., & Dlugosh, L. (2008). Predictors of learning satisfaction in Japanese online distance learners. The International Review of Research in Open and Distributed Learning, 9(3).
  • Byers, A. S. (2010). Examining learner-content interaction importance and efficacy in online, self-directed electronic professional development in science for elementary educators in grades three â six. Blacksburg, Virginia: Virginia Tech.
  • Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. The Chronicle of Higher Education, 46,39–41.
  • Chang, S.-H.-H., & Smith, R. A. (2008). Effectiveness of personal interaction in a learner-centered paradigm distance education class based on student satisfaction. Journal of Research on Technology in Education, 40(4), 407–426.
  • Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334.
  • Deperlioglu, O., Sarpkaya, Y., & Ergun, E. (2011). Development of a relational database for learning management systems. The Turkish Online Journal of Educational Technology (TOJET), 10(4), 107–120.
  • Donnelly, R. (2010). Harmonizing technology with interaction in blended problem-based learning. Computers & Education, 54(2), 350–359.
  • Duc, T. (2012). Designing distance learning for the 21st century: Constructivism, Moore’s transactional theory and Web 2.0 (Master’s thesis). Blekinge Institute of Technology, Karlskrona, Sweden.
  • Gallagher, E. (2014). The effects of teacher-student relationships: Social and academic outcomes of low-income middle and high school students. NYU Steinhardt, Department of Applied Psychology. Retrieved from https://steinhardt.nyu.edu/appsych/opus/issues/2013/fall/gallagher
  • Garrison, D. R. (1993). A cognitive constructivist view of distance education: An analysis of teaching-learning assumptions. Distance Education, 14(2), 199–211.
  • Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95–105.
  • Gautrea, C. (2011). Motivational factors affecting the integration of a learning management system by faculty. The Journal of Educators Online, 8(1), 1–25.
  • Gunawardena, C. N., Linder-VanBerschot, J. A., LaPointe, D. K., & Rao, L. (2010). Predictors of learner satisfaction and transfer of learning in a corporate online education program. American Journal of Distance Education, 24(4), 207–226.
  • Hagenauer, G., & Volet, S. E. (2014). Teacher-student relationship at university: An important yet under-researched field. Oxford Review of Education, 40(3), 370–388.
  • Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling. Thousand Oaks, CA: Sage.
  • Hamre, R. B., & Pianta, R. C. (2001). Early teacher-child relationships and the trajectory of children’s school outcomes through eighth grade. Child Development, 72(2), 625–638.
  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
  • Horvat, A., Dobrota, M., Krsmanovic, M., & Cudanov, M. (2015). Student perception of Moodle learning management system: A satisfaction and significance analysis. Interactive Learning Environments, 23(4), 515–527.
  • Hughes, J. N. (2012). Teacher-student relationships and school adjustment: Progress and remaining challenges. Attachment and Human Development, 14(3), 319–327.
  • Johnson, D. W. (1981). Student-student interaction: The neglected variable in education. Educational Researcher, 10(1), 5–10.
  • Jung, I., Choi, S., Lim, C., & Leem, J. (2002). Effects of different types of interaction on learning achievement, satisfaction and participation in web-based instruction. Innovations in Education and Teaching International, 39(2), 153–162.
  • Kanuka, H., & Anderson, T. (2007). Online social interchange, discord and knowledge construction. International Journal of E-Learning & Distance Education, 13(1), 57–74.
  • Kenny, J. (2003). Student perceptions of the use of online learning technology in their courses. Retrieved from http://ultibase.rmit.edu.au/Articles/march03/kenny2.pdf
  • Kerr, M. S., Rynearson, K., & Kerr, M. C. (2006). Student characteristics for online learning success. The Internet and Higher Education, 9(2), 91–105.
  • Kim, Y., & Glassman, M. (2013). Beyond search and communication: Development and validation of the Internet Self-efficacy Scale (ISS). Computers in Human Behavior, 29(4), 1421–1429.
  • Kline, R. B. (2015). Principles and practice of structural equation modeling methodology in the social sciences. New York, NY: The Guilford Press.
  • Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, (ijec), 11(4), 1–10.
  • Kock, N. (2016). Advantages of nonlinear over segmentation analyses in path models. International Journal of e-Collaboration (ijec), 12(4), 1–6.
  • Kock, N., & Gaskins, L. (2014). The mediating role of voice and accountability in the relationship between Internet diffusion and government corruption in Latin America and Sub-Saharan Africa. Information Technology for Development, 20(1), 23–43.
  • Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607–610.
  • Kundi, G. M., & Nawaz, A. (2011). Predictor of e-learning development and use practices in HEIs of NWFP, Pakistan. Turkish Online Journal of Distance Education (TOJDE), 12(1), 108–125.
  • Kuo, Y.-C., Walker, A. E., Belland, B. R., & Schroder, K. E. (2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distributed Learning, 14(1), 16–39.
  • Kuo, Y.-C., Walker, A. E., Schroder, K. E., & Belland, B. R. (2014). Interaction, Internet, self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education, 20, 35–50.
  • Ladyshewsky, R. (2013). Instructor presence in online courses and student satisfaction. The International Journal for the Scholarship of Teaching and Learning, 7(1), 1–23.
  • Liaw, -S.-S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51, 864–873.
  • Lim, J., Kim, M., Chen, S. S., & Ryder, C. E. (2008). An empirical investigation of student achievement and satisfaction in different learning environments. Journal of Instructional Psychology, 35(2), 113–119.
  • Lin, C. H., Zheng, B., & Zhang, Y. (2017). Interactions and learning outcomes in online language courses. British Journal of Educational Technology, 48(3), 730–748.
  • Lin, Y.-M., Lin, G.-Y., & Laffey, J. M. (2008). Building a social and motivational framework for understanding satisfaction in online learning. Journal of Educational Computing Research, 38(1), 1–27.
  • Martin, J. (2009). Developing course material for online instruction of adults. Journal of Online Teaching and Learning, 5(2), 364.
  • McLaughlin, M., McGrath, D. J., Burian-Fitzgerald, M. A., Lanahan, L., Scotchmer, M., Enyeart, C., & Salganik, L. (2005). Student content engagement as a construct for the measurement of effective classroom instruction and teacher knowledge. Washington D.C.: American Institutes for Research.
  • Miyazoe, T., & Anderson, T. (2010). Empirical research on learners’ perceptions: Interaction equivalency theorem in blended learning. European Journal of Open, Distance and E-Learning, 13, 1–9.
  • Moore, J. (2014). Effects of online interaction and instructor presence on students’ satisfaction and success with online undergraduate public relations courses. Journalism & Mass Communication Educator, 69(3), 271–288.
  • Moore, M., & Kearsley, G. (1996). Distance education. A systems view. Belmont, CA: Wadsworth.
  • Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1–7.
  • Murray, M., Pérez, J., Geist, D., & Hedrick, A. (2012). Student interaction with online course content: Build it and they might come. Journal of Information Technology Education: Research, 11, 125–140.
  • Murray, M., Pérez, J., Geist, D., & Hedrick, A. (2013). Student interaction with content in online and hybrid courses: Leading horses to the proverbial water. In Proceedings of the informing science and information technology education conference, Informing Science Institute 2013 (Vol. 1, pp. 99–115).
  • Naveh, G., Tubin, D., & Pliskin, N. (2012). Student satisfaction with learning management systems: A lens of critical success factors. Technology, Pedagogy and Education, 21(3), 337–350.
  • Ngafeeson, M. N., & Sun, J. (2015). The effects of technology innovativeness and system exposure on student acceptance of e-textbooks. Journal of Information Technology Education: Research, 14, 55–71.
  • Orth, U., Robins, R. W., & Widaman, K. (2012). Life-span development of self-esteem and its effects on important life outcomes. Journal of Personality and Social Psychology, 102(6), 1271–1288.
  • Owusu-Agyeman, Y., & Larbi-Siaw, O. (2018). Exploring the factors that enhance student-content interaction in a technology-mediated learning environment. Cogent Education, 5(1), 1456780.
  • Puzziferro, M. (2008). Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. The American. Journal. Of Distance Education, 22(2), 72–89.
  • Richardson, J. C., Besser, E., Koehler, A., Lim, J. E., & Strait, M. (2016). Instructors’ perceptions of instructor presence in online learning environments. The International Review of Research in Open and Distributed Learning, 17(4).
  • Roberts, W. L. (1986). Nonlinear models of development: An example from the socialization of competence. Child Development, 57(5), 1166–1178.
  • Rogers, M. E. (2003). Diffusions of innovations. New York, NY: Free Press.
  • Sher, A. (2009). Assessing the relationship of student-instructor and student-student interaction to student learning and satisfaction in web-based online learning environment. Journal of Interactive Online Learning, 8(2), 102–120.
  • Sheridan, K., & Kelly, M. A. (2010). The indicators of instructor presence that are important to students in online courses. Journal of Online Learning and Teaching, 6(4), 767–779.
  • Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2, 3–10.
  • Špilka, R. (2015). Learner-content interaction in flipped classroom model. International Journal of Information and Communication Technologies in Education, 4(3), 53–61.
  • Sze-Yeng, F., & Hussain, R. M. R. (2010). Self-directed learning in a socioconstructivist learning environment. Procedia - Social and Behavioral Sciences, 9, 1913–1917.
  • Tu, C.-H., Sujo-Montes, L., Yen, C.-J., Chan, J.-Y., & Blocher, M. (2012). The integration of personal learning environments & open network learning environments. TechTrends: Linking Research and Practice to Improve Learning, 56(3), 13–19.
  • Valasidou, A., Sidiropoulos, D., & Makridou-Bousiou, D. (2005, June). The constructivist perspective in distance learning environments. In P. Kommers & G. Richards Eds., EdMedia: World conference on educational multimedia, hypermedia & telecommunications 2005 (pp. 1932–1935). Montreal, Canada: Association for the Advancement of Computing in Education (AACE).
  • van Raaij, E. M., & Schepers, J. J. (2008). The acceptance and use of a virtual learning environment in China. Computers and Education, 50(3), 838–852.
  • Veletsianos, G. (2010). A definition of emerging technologies for education. In G. Veletsianos (Ed.), Emerging technologies in distance education (pp. 3–22). Edmonton, Canada: AU Press, Athabasca University.
  • Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.
  • Wang, C.-H., Shannon, D. M., & Ross, M. E. (2013). Students’ characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34(3), 302–323.
  • Watson, W. R., & Watson, S. L. (2007). An argument for clarity: What are learning management systems, what are they not, and what should they become? TechTrends, 51(2), 28–34.
  • Wu, D., & Hiltz, S. R. (2004). Predicting learning from asynchronous online discussions. Journal of Asynchronous Learning Networks, 8(2), 139–152.
  • Yueh, H.-P., & Hsu, S. (2008). Designing a learning management system to support instruction. Communications of the ACM, 51(4), 59–63.
  • Zimmerman, T. D. (2012). Exploring learner to content interaction as a success factor in online courses. The International Review of Research in Open and Distributed Learning, 13(4), 152–165.

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