Is Online Learning Able to Effectively Decrease Tuition and Miscellaneous Fees Debt of Higher Education in Taiwan

Ming-Yuan Hsieh; Yung-Kuan Chan

doi:10.12973/eurasia.2016.1515a

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Ming-Yuan Hsieh ¹, Yung-Kuan Chan ² ^*

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¹ National Taichung University of Education² National Chung-Hsing University^* Corresponding Author

Abstract

As to rapid development of higher education, a majority of higher-education students have been suffering the serious pressure of tuition and miscellaneous fee debt. Therefore, this research utilizes Factor Analysis (FA) approach to analyze the weight-questionnaire of random Taiwanese college students and furthermore, employ Technology Acceptance Model (TAM) and Analytical Network Process (ANP) model to verify interplays and correlations between online learning and Tuition and Miscellaneous Fee Debt (TMFD) through a series of assessed weight-measurements of higher-education students’ and experts’ questionnaires. As for the increment of research reliability and validity, Fuzzy Set Qualitative Comparative Analysis (fsQCA) method is significantly applied to approve the weight-measurement and evaluate-results. Consequently, the most valuable conclusion is that “online learning is able to passively decrease Tuition and Miscellaneous Fee Debt (TMFD) in higher education (PDTMFD)” that induces the best solution for research topic in order to re-supplies research gap in online learning relative research fields.

Keywords

online learning
tuition and miscellaneous fee debt (TMFD)
technology acceptance model (TAM)
factor analysis (FA)
analytical network process (ANP)
fuzzy set qualitative comparative analysis (“fsQCA”) method

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Research Article

EURASIA J Math Sci Tech Ed, 2016, Volume 12, Issue 5, 1313-1326

https://doi.org/10.12973/eurasia.2016.1515a

Publication date: 20 Mar 2016

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Article Downloads: 1484

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References

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. Management Information System Quarterly, 16, 227–247.
Anderson, T., & McGreal, R. (2012). Disruptive pedagogies and technologies in universities. Education, Technology and Society, 15(4), 380–389.
Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the Association for Information Systems, 8(4), 244–254.
Bain, O. (2001). The cost of higher education to students and parents in Russia: Tuition policy issues. Peabody Journal of Education, 76(3&4), 57–80.
Basurto, X. (2013). Linking multi-level governance to local common-pool resource theory using fuzzy-set qualitative comparative analysis: Insights from twenty years of biodiversity conservation in Costa Rica. Global Environmental Change, 23(3), 573–587. doi: 10.1016/j.gloenvcha.2013.02.011
Bell, F. (2010a). Connectivism: Its place in theory-informed research and innovation in technology-enabled learning. The International Review of Research in Open and Distance Learning, 12(3), 98–118.
Bell, F. (2010b). Network theories for technology-enabled learning and social change: Connectivism and actor network theory. In L. Dirckinck-Holmfeld, V. Hodgson, C. Jones, M. de Laat, D. McConnell & T. Ryberg (Eds.), Proceedings of the 7th International Conference on Networked Learning 2010 (pp. 526–533).
Benbasat, I., & Barki, H. (2007). Quo vadis, TAM? Journal of the Association of Information Systems, 8(4), 211–218.
Blackman, T. (2013). Exploring explanations for local reductions in teenage pregnancy rates in England: An approach using qualitative comparative analysis. Social Policy and Society, 12(1), 61–72.
Budak, I., & Kaygin, B. (2015). An investigation of mathematically promising students’ cognitive abilities and their contributions to learning environment. Eurasia Journal of Mathematics, Science & Technology Education, 11(1), 25–36.
Carlson, D. E., Farmer, J., & Weathersby, G. (1974). A framework for analyzing postsecondary education financing policies. Washington, DC: U.S. Government Printing Office.
Carswell, L., Thomas, P., Petre, M., Price, B., & Richards, M. (2000). Distance education via the Internet: the student experience. British Journal of Educational Technology, 31(1), 29– 46.
Casey, D. M. (2008). A Journey to Legitimacy: The historical development of distance education through technology. TechTrends, 52(2), 45–51. doi: 10.1007/s11528-008- 0135-z
Christensen, C. M., Johnson, C. W., & Horn, M. B. (2008). Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns. Retrieved from http://tinyurl.com/apnfhls
Chuttur, M. Y. (2009). Overview of the technology acceptance model: Origins, developments and future directions. Indiana University, USA. Sprouts: Working Papers on Information Systems, 9(37).
Cubillo, J., Sanchez, J., & Cervino, J. (2006). International students’ decision-making process. International Journal of Educational Management, 20(2), 101–115. doi: 10.1108/09513540610646091
Debenham, M. (2001). Computer mediated communication and disability support: addressing barriers to study for undergraduate distance learners with long-term health problems, unpublished doctoral dissertation. Milton, Keynes: The Open University.
Downes, S. (2007). Models for sustainable open educational resources. Interdisciplinary Journal of Knowledge and Learning Objects, 3, 29–44.
Eilks, I. (2015). Science education and education for sustainable development – Justification, model, practices and perspectives. Eurasia Journal of Mathematics, Science & Technology Education, 11(1), 149–158.
Fini, A. (2009). The technological dimension of a massive open online course: The Case of the CCK08 course tools. The International Review of Research in Open and Distance Learning, 10(5), 1–26. Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/643/1410
Foster, S., Long, G., & Snell, K. (1999) Inclusive instruction and learning for deaf students in postsecondary education. Journal of Deaf Studies and Deaf Education, 4(3), 225–235.
Fournier, H., Kop, R., & Sitlia, H. (2011). The value of learning analytics to networked learning on a personal learning environment. Proceedings of the 1st International Conference on Learning Analytics and Knowledge, 104–109. doi: 10.1145/2090116.2090131
Han, S., Yalvac, B., Capraro, M. M., & Capraro, R. M. (2015). In-service teachers’ implementation and understanding of STEM project based learning. Eurasia Journal of Mathematics, Science & Technology Education, 11(1), 63–76. doi: 10.12973/eurasia.2015.1306a
Harman, H. H. (1976). Modern Factor Analysis. Chicago, IL: University of Chicago Press.
Hazel, E., Conrad, L., & Martin, E. (1997). Exploring gender and phenomenography. Higher Education Research and Development, 16(2), 213–226.
Hu, S., & Kuh, G. D. (2002). Being (dis)engaged in educationally purposeful activities: the influences of student and institutional characteristics. Research in Higher Education, 43(5), 555–575.
Hyde, W. D. (1978). The effect of tuition and financial aid on access and choice in postsecondary education. Postsecondary Education Finance Conference 1977. Denver, CO: Education Commission of the States.
Jackson, G. A. (1978). Financial aid and student enrollment. Journal of Higher Education, 49(6), 548–574.
Johnstone, B. D. (2003). Cost sharing in higher education: Tuition, financial assistance, and accessibility in a comparative perspective. Sociologický časopis/Czech Sociological Review, 39(3), 351–374.
Johnstone, D. B. (2002). Challenges of financial austerity: Imperatives and limitations of revenue diversification in higher education. The Welsh Journal of Education, 11(1), 18– 36.
Johnstone, D. B., & Shroff-Mehta, P. (2003). Higher education finance and accessibility: An international comparative examination of tuition and finance assistance policies. In H. Eggins (Ed.) Globalization and Reform in Higher Education. London, England: Society for Research into Higher Education. Retrieved from http://www.gse.buffalo.edu/org/IntHigherEdFinance
Kelland, J. H. (2006). Constructivist theories and online learning best practices: A discourse analysis. Canadian Association for the Study of Adult Education (CASAE) 2006 National Conference On-Line Proceedings. Toronto, Ontario: York University.
Kember, D. (1995). Open learning courses for adults: a model of student progress. Englewood Cliffs, NJ: Educational Technology.
Keskin, N. O., & Metcalf, D. (2011). The current perspectives, theories and practices of mobile learning. The Turkish Online Journal of Educational Technology, 10(2), 202–208.
King, W. R., & He, J. (2006). A meta-analysis of the technology acceptance model. Information & Management, 43(6), 740–755. doi:10.1016/j.im.2006.05.003
Knudsen, O. K., & Servelle, P. J. (1978). The demand for higher education at private institutions of moderate selectivity. American Economist, 22 (2), 30–34.
Kop, R., & Carroll, F. (2012). Cloud computing and creativity: Learning on a massive open online course. European Journal of Open, Distance and E-Learning. Retrieved from http://www.eurodl.org/index.php?article=457
Kop, R., & Hill, A. (2008). Connectivism: Learning theory of the future or vestige of the past? International Review of Research in Open and Distance Learning, 9(3), 1–13.
Kop, R., Fournier, H., & Mak, J. S. F. (2011). A pedagogy of abundance or a pedagogy to support human beings? Participant support on massive open online courses. International Review of Research in Open and Distance Learning, Special Issue - Emergent Learning, Connections, Design for Learning, 12(7), 74–93.
Larsen, R., & Warne, R. T. (2010). Estimating confidence intervals for eigenvalues in exploratory factor analysis. Behavior Research Methods, 42(3), 871–876.
Lawless, C. J., & Richardson, J. T. E. (2002). Approaches to studying and perceptions of academic quality in distance education. Higher Education, 44(2), 257–282. doi: 10.1023/A:1016315114558
Ledesma, R. D., & Valero-Mora, P. M. (2007). Determining the number of factors to retain in EFA: An easy-to-use computer program for carrying out Parallel Analysis. Practical Assessment Research & Evaluation, 12(2), 1–11.
Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & Management, 40(3), 191–204. doi:10.1016/S0378-7206(01)00143-4
Leslie, L. L. (1984). Changing patterns in student financing of higher education. Journal of Higher Education, 55(3), 313–346.
Liyanagunawardena, T. R. (2012). Information communication technologies and distance education in Sri Lanka: A case study of two universities (PhD Thesis). University of Reading, Reading, UK
Martin, F. G. (2012). Will massive open online courses change how we teach? Communications of the ACM, 55(8), 26–28. doi:10.1145/2240236.2240246
Mehaffy, G. L. (2012). Challenge and change. EDUCAUSE Review, 47(5), 25–41.
Mendel, J. M., & Korjani, M. M. (2012). Charles Ragin’s Fuzzy Set Qualitative Comparative Analysis (fsQCA) used for linguistic summarizations, Information Sciences, 202(20), 1– 23. doi:10.1016/j.ins.2012.02.039
Pikkarainen, T., Pikkarainen, K., & Karjaluoto, H. (2004). Consumer acceptance of online banking: An extension of the Technology Acceptance Model. Internet ResearchElectronic Networking Applications and Policy, 14(3), 224–235.
Price, I., Matzdorf, F., & Agathi, H. (2003). The impact of facilities on student choice of university. Facilities, 21(10), 212–222.
Ragin, C. C. (2006). User's guide to fuzzy-Set/Qualitative comparative analysis 2.0. Tucson, Arizona: Department of Sociology, University of Arizona.
Ramsden, P. (1991). A performance indicator of teaching quality in higher education: the Course Experience Questionnaire. Studies in Higher Education, 16(2), 129–150. doi:10.1080/03075079112331382944
Ravenscroft, A. (2011). Dialogue and connectivism: A new approach to understanding and promoting dialogue-rich networked learning. International Review of Research in Open and Distance Learning, 12(3), 140–160.
Richardson, J. T. E. (1999). The concepts and methods of phenomenographic research. Review of Educational Research, 69(1), 53–82.
Richardson, J. T. E., Long, G. L., & Foster, S. B. (2004). Academic engagement in students with a hearing loss in distance education. Journal of Deaf Studies and Deaf Education, 9(1), 68–85.
Rumble, G. (1999) Cost analysis of distance learning. Performance Improvement Quarterly, 12(2), 122–137. doi: 10.1111/j.1937-8327.1999.tb00133.x
Saaty, T. L. (1996). Decision making with dependence and feedback: The analytic network process. Pittsburgh, PA: RWS Publications.
Saaty, T. L., & Özermir, S. M. (2005). The encyclicon: A dictionary of decisions with dependence and feedback based on the analytic network process. Pittsburgh, PA: RWS Publications.
Saaty, T. L., & Vargas, L. G. (2006). Decision making with the analytic network process: economic, political, social and technological applications with Benefits, opportunities, costs and risks. New York, NY: Springer.
Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2(1), 3–10.
Taylor, R. E. & Darling, J. R. (1991). Perceptions towards marketing higher education: do academic disciplines make a difference? In T. J. Hayes (Ed.), New Strategies in Higher Education Marketing (pp. 17–38). New York, NY: Haworth Press.
Tekleselassie, A. A. (2001). Targeting subsidies to higher education: Means testing in comparative perspective. Buffalo: Center for comparative and global studies in Education (International Comparative Higher Education Finance and Accessibility Project). Retrieved from http://www.gse.buffalo.edu/org/IntHig herEdFinance
Tierney, M. L. (1980). The impact of financial aid on student demand for public/private higher education. Journal of Higher Education, 51(5), 527–45.
Traxler, J. (2010). The learner experience of mobiles, mobility and connectedness. Retrieved from http://www.helenwhitehead.com/elesig/ELESIG%20Mobilities%20ReviewPDF.pdf
Velicer, W. F. (1976). Determining the number of components from the matrix of partial correlations. Psychometrika, 41(3), 321–327.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science, 46(2), 186–204.
Venkatesh, V., Morris, M. G., Davis, F. D., & Davis, G. B. (2003). User acceptance of information technology: Toward a unified view. Management Information Systems Quarterly, 27(3), 425–478.
Warne, R. T., & Larsen, R. (2014). Evaluating a proposed modification of the Guttman rule for determining the number of factors in an exploratory factor analysis. Psychological Test and Assessment Modeling, 56(1), 104–123.
Wilson, K. L., Lizzio, A., & Ramsden, P. (1997). The development, validation and application of the Course Experience Questionnaire. Studies in Higher Education, 22(1), 33–53.
Woodhall, M. (1988). Designing a student loan programme for a developing country: The relevance of international experience. Economics of Education Review, 7(1), 153–161.
Woodhall, M. (1992). Changing sources and patterns of finance for higher education: A review of international trends. Higher Education in Europe, 17(1), 141–149. doi:10.1080/0379772920170110
Workman, M. (2007). Advancements in technology: New opportunities to investigate factors contributing to differential technology and information use. International Journal of Management and Decision Making, 8(2&3&4), 318–342.
Yuan, L., MacNeill, S., & Kraan, W. (2008, September 9). Open Educational ResourcesOpportunities and challenges for higher education. Retrieved from http://wiki.cetis.ac.uk/images/0/0b/OER_Briefing_Paper.pdf 1-34
Ziderman, A. (2002). Alternative objectives of national student loan schemes: Implications for design, implementation, and policy. The Welsh Journal of Education, 11(1), 37–47.

How to cite this article

APA

Hsieh, M.-Y., & Chan, Y.-K. (2016). Is Online Learning Able to Effectively Decrease Tuition and Miscellaneous Fees Debt of Higher Education in Taiwan. Eurasia Journal of Mathematics, Science and Technology Education, 12(5), 1313-1326. https://doi.org/10.12973/eurasia.2016.1515a

Vancouver

Hsieh MY, Chan YK. Is Online Learning Able to Effectively Decrease Tuition and Miscellaneous Fees Debt of Higher Education in Taiwan. EURASIA J Math Sci Tech Ed. 2016;12(5):1313-26. https://doi.org/10.12973/eurasia.2016.1515a

AMA

Hsieh MY, Chan YK. Is Online Learning Able to Effectively Decrease Tuition and Miscellaneous Fees Debt of Higher Education in Taiwan. EURASIA J Math Sci Tech Ed. 2016;12(5), 1313-1326. https://doi.org/10.12973/eurasia.2016.1515a

Chicago

Hsieh, Ming-Yuan, and Yung-Kuan Chan. "Is Online Learning Able to Effectively Decrease Tuition and Miscellaneous Fees Debt of Higher Education in Taiwan". Eurasia Journal of Mathematics, Science and Technology Education 2016 12 no. 5 (2016): 1313-1326. https://doi.org/10.12973/eurasia.2016.1515a

Harvard

Hsieh, M.-Y., and Chan, Y.-K. (2016). Is Online Learning Able to Effectively Decrease Tuition and Miscellaneous Fees Debt of Higher Education in Taiwan. Eurasia Journal of Mathematics, Science and Technology Education, 12(5), pp. 1313-1326. https://doi.org/10.12973/eurasia.2016.1515a

MLA

Hsieh, Ming-Yuan et al. "Is Online Learning Able to Effectively Decrease Tuition and Miscellaneous Fees Debt of Higher Education in Taiwan". Eurasia Journal of Mathematics, Science and Technology Education, vol. 12, no. 5, 2016, pp. 1313-1326. https://doi.org/10.12973/eurasia.2016.1515a