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Measuring Quality in Blended Learning: A Multimodal of the Sloan Consortium, Key Success Indicators and Transformative Driven Mechanism

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Published:03 May 2020Publication History

ABSTRACT

Blended learning epitomizes a periphery notion; an entity that ties spheres from unique and diverse attributes and conditions. The concept of evaluating and defining the quality of student learning experiences in a blended environment is complex, subjective and multi-perspective. Nonetheless, this concept is vital and beneficial in assessing and refining the performance and potential of blended learning approaches which is consequential to the learning outcomes, various needs and objectives of the real-world applications. Moreover, the optics of quality technological deployment remain a debatable issue by way of limited evidences in substantiating operational procedures. Thus, this study proposes the adaptation of Sloan Consortium (Sloan-C) quality indicators in appraising the quality of blended learning approaches. In particular, the Five Pillars of Quality which includes (a) Learning Effectiveness, (b) Access, (c) Cost-Effectiveness and Institutional Commitment, (d) Faculty Satisfaction, and (e) Student Satisfaction were conceptualized to be adapted into an operating framework, offering a means of implementing and continuously improving the aspect of quality in blended learning by converging individual institutional aims, objectives as well as other elements including (a) Goal, (b) Process, (c) Metric and (d) Progress. The untapped potential of Sloan-C in the corroboration of Key Success Indicators (KSIs), sequentially elucidated in the Transformative Driven Mechanism Framework (TDM) by Mahmud (2017), in uncovering the notions quality in blended learning are also discussed.

References

  1. Richardson, J. C., & Swan, K. (2003). Examing social presence in online courses in relation to students' perceived learning and satisfaction. Journal of Asynchronous Learning Networks, 7(1), 68--84.Google ScholarGoogle Scholar
  2. Sun, P. C., Tsai, R. J., Finger, G., Chen, Y. Y., & Yeh, D. (2008). What drives a successful e-Learning? An empirical investigation of the critical factors influencing learner satisfaction. Computers & education, 50(4), 1183--1202.Google ScholarGoogle Scholar
  3. Bowyer, J., & Chambers, L. (2017). Evaluating blended learning: Bringing the elements together. Research Matters: A Cambridge Assessment Publication, 23, 17--26.Google ScholarGoogle Scholar
  4. Pawlowski, J. M. (2007). The quality adaptation model: adaptation and adoption of the quality standard ISO/IEC 19796-1 for learning, education, and training. Journal of Educational Technology & Society, 10(2), 3--16.Google ScholarGoogle Scholar
  5. Ehlers, U. D. (2004). Quality in e-learning from a learner's perspective. European Journal for Distance and Open Learning, 1, 73--90.Google ScholarGoogle Scholar
  6. Baldazzi, A., Ricci, L., & Baroz, V. V. (Eds.). (2015). E-learning quality assurance: a multi perspective approach. Gangemi Editore spa.Google ScholarGoogle Scholar
  7. Deepwell, F. (2007). Embedding Quality in e-Learning Implementation through Evaluation. Educational Technology & Society, 10 (2), 34--43.Google ScholarGoogle Scholar
  8. Mahmud, M. M. (2017). Transformative driven mechanism framework as key success indicators for blended learning. Unpublished doctoral dissertation. University of Technology MARA, Shah Alam, Malaysia.Google ScholarGoogle Scholar
  9. Mahmud, M. M. (2018). Technology and Language-What Works and What Does Not: A Meta-analysis of Blended Learning Research. Journal of Asia TEFL, 15(2), 365.Google ScholarGoogle Scholar
  10. Lim, D. H., & Johnson, S. (2002). Trainee Perceptions of factors that influence learning transfer. International Journal of Training and Development, 6(1), 36--48.Google ScholarGoogle ScholarCross RefCross Ref
  11. Sullivan, R. (2002). Transfer of learning: A guide for strengthening the performance of health care workers. Retrieved from http://www.reproline.jhu.edu/ english/6read/6training/tol/index.htm.Google ScholarGoogle Scholar
  12. Gan, B., Menkhoff, T., & Smith, R. (2015). Enhancing students' learning process through interactive digital media: New opportunities for collaborative learning. Computers in Human Behavior, 51, 652--663.Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Laurillard, D. (1993). Rethinking University Teaching: A Framework for the Effective Use of Educational Technology. London: Routledge.Google ScholarGoogle Scholar
  14. Lim, D. H., & Kim, H. J. (2003). Motivation and learner characteristics affecting online learning and learning application. Journal of Educational Technology Systems, 31(4), 423--439.Google ScholarGoogle ScholarCross RefCross Ref
  15. Osguthorpe, R. T., & Graham, C. R. (2003). Blended Learning Environments: Definitions and Directions. Quarterly Review of Distance Education, 4(3), 227--33.Google ScholarGoogle Scholar
  16. Chung, J., & Davis, I. K. (1995). An instructional theory for learner control: Revisited. In M. R. Simonson (Ed.), Proceedings of the 1995 Annual National Convention of the Association for Educational Communications and Technology, Anaheim, CA: AACE, 72--86.Google ScholarGoogle Scholar
  17. Bielawski, L., & Metcalf, D. S. (2003). Blended e-learning: Integrating knowledge, performance, support, and online learning. Human Resource Development. Amherst: HRD Press.Google ScholarGoogle Scholar
  18. Chuang, C. P., Zheng, B. Y., Chien, H. C., Huang, Y. J., & Hsieh, N. T. (2012). Learning effects of blended learning at different ratios with SP chart. Int. J. Inf. Technol. Comput. Sci, 6, 143--151.Google ScholarGoogle Scholar
  19. Bersin, J. (2004). The blended learning book: Best practices, proven methodologies, and lessons learned. San Francisco, CA: John Wiley & Sons.Google ScholarGoogle Scholar
  20. Ellis, R. A., & Calvo, R. A. (2007). Minimum indicators to assure quality of LMS-supported blended learning. Journal of Educational Technology & Society, 10(2), 60--70.Google ScholarGoogle Scholar
  21. Biggs, J. (2003). Aligning teaching and assessing to course objectives. Teaching and Learning in Higher Education: New Trends and Innovations, 2, 13--17.Google ScholarGoogle Scholar
  22. Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., & Sicilia, N. (2018). Blended learning: the new normal and emerging technologies. International Journal of Educational Technology in Higher Education, 15(1), 3.Google ScholarGoogle ScholarCross RefCross Ref
  23. Ramsden, P. (2003). Learning to teach in higher education. London: Routledge.Google ScholarGoogle ScholarCross RefCross Ref
  24. Rutkowski, J., Moscinska, K., & Klosowski, P. (2005). Redevelopment of a Large Engineering Course from a Traditional to Blended Model-Circuit Theory Example. In The Proceedings of The 8th IASTED International Conference on Computers And Advanced Technology in Education.Google ScholarGoogle Scholar
  25. Vrasidas, C. (2000). Constructivism versus objectivism: Implications for interaction, course design, and evaluation in distance education. International Journal of Educational Telecommunications, 6(4), 339--362.Google ScholarGoogle Scholar
  26. Bourne, J. R., Mayadas, A. F., & Campbell, J. O. (2000). Asynchronous learning networks: an information-technology-based infrastructure for engineering education. Proceedings of the IEEE, 88(1), 63--71.Google ScholarGoogle ScholarCross RefCross Ref
  27. Coryell, J. E. & Chlup, D. T. (2007). Implementing e-learning components with adult English language learners: Vital factors and lessons learned. Computer Assisted Language Learning, 20(3), 263--278.Google ScholarGoogle ScholarCross RefCross Ref
  28. Hong, K. H. & Samimy, K. K. (2010). The influence of L2 teachers' use of CALL modes on language learners' reactions to blended learning. CALICO Journal, 27(2), 328--248.Google ScholarGoogle ScholarCross RefCross Ref
  29. Neumeier, P. (2005). A closer look at blended learning: Parameters for designing a blended learning environment for language teaching and learning. ReCALL 17(2), 163--178.Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Stracke, E. (2007). A road to understanding: A qualitative study into why learners drop out of a blended language learning (BLL) environment. ReCALL, 19(1), 57--78.Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Moore, J. C. (2005). The Sloan Consortium quality framework and the five pillars. The Sloan Consortium. Retrieved July, 15, 2007.Google ScholarGoogle Scholar
  32. Bourne, J. R., & Moore, J. C. (Eds.). (2004). Elements of quality online education: Into the mainstream (No. 5). Olin College-Sloan-C.Google ScholarGoogle Scholar
  33. Moore, J. C. (2012). A Synthesis of Sloan-C Effective Practices, December 2011. Journal of Asynchronous Learning Networks, 16(1), 91--115.Google ScholarGoogle Scholar

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  1. Measuring Quality in Blended Learning: A Multimodal of the Sloan Consortium, Key Success Indicators and Transformative Driven Mechanism

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    • Published in

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      IC4E '20: Proceedings of the 2020 11th International Conference on E-Education, E-Business, E-Management, and E-Learning
      January 2020
      441 pages
      ISBN:9781450372947
      DOI:10.1145/3377571

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      • Published: 3 May 2020

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