Skip to main content
Have a personal or library account? Click to login
Making Barriers to Learning in MOOCs Visible. A Factor Analytical Approach Cover

Making Barriers to Learning in MOOCs Visible. A Factor Analytical Approach

Open Access
|Oct 2021

References

  1. Adamopoulos, P. (2013). What Makes a Great MOOC? An Interdisciplinary Analysis of Student Retention in Online Courses. In 34th International Conference on Information Systems: ICIS 2013. United States: Association for Information Systems. http://pages.stern.nyu.edu/~padamopo/What%20makes%20a%20great%20MOOC.pdf
  2. Antonaci, A., Klemke, R., Dirkx, K., & Specht, M. (2019). May the plan be with you! A usability study of the stimulated planning game element embedded in a MOOC platform. International Journal of Serious Games, 6(1), 4970. DOI: 10.17083/ijsg.v6i1.239
  3. Balfour, S. P. (2013). Assessing Writing in MOOCs: Automated Essay Scoring and Calibrated Peer Review. Research & Practice in Assessment, 8, 4048. https://eric.ed.gov/?id=EJ1062843
  4. Barnes, C. (2013). MOOCs: The challenges for academic librarians. Australian Academic & Research Libraries, 44(3), 163175. DOI: 10.1080/00048623.2013.821048
  5. Barnett, S. D., Hickling, E. J., & Sheppard, S. (2018). The impact of gender on the factor structure of PTSD symptoms among active duty United States military personnel. European Journal of Trauma & Dissociation, 2(3), 117124. DOI: 10.1016/j.ejtd.2018.01.002
  6. Belanger, Y., & Thornton, J. (2013). Bioelectricity: A quantitative approach. Durham, NC. http://dukespace.lib.duke.edu/dspace/bitstream/handle/10161/6216/Duke_Bioelectricity_MOOC_Fall2012.pdf?sequence=1
  7. Boyatt, R., Joy, M., Rocks, C., & Sinclair, J. (2013). What (Use) is a MOOC? In L. Uden, Y. Tao, H. Yang & I. Ting (Eds.), Springer proceedings in complexity: 2nd International Workshop on Learning Technology for Education in Cloud (pp. 133145). DOI: 10.1007/978-94-007-7308-0_15
  8. Bryant, F. B., & Yarnold, P. R. (1995). Principal-components analysis and exploratory and confirmatory factor analysis. In L. G. Grimm & P. R. Yarnold (Eds.), Reading and understanding multivariate statistics (p. 99136). American Psychological Association. https://psycnet.apa.org/record/1995-97110-004
  9. Byrne, B. M. (2005). Factor analytic models: Viewing the structure of an assessment instrument from three perspectives. Journal of personality assessment, 85(1), 1732. DOI: 10.1207/s15327752jpa8501_02
  10. Byrne, B. M. (2012). Structural equation modeling with Mplus: Basic concepts, applications, and programming. New York: Routledge. DOI: 10.4324/9780203807644
  11. Carr, S. (2000). As distance education comes of age, the challenge is keeping the students. The Chronicle of Higher Education, 4, A39A41. https://eric.ed.gov/?id=EJ601725
  12. Comrey, A. L., & Lee, H. B. (2013). A first course in factor analysis (2nd ed.). Psychology Press. DOI: 10.4324/9781315827506
  13. Drake, K. E., & Egan, V. (2017). Investigating gender differences in the factor structure of the Gudjonsson Compliance Scale. Legal and Criminological Psychology, 22(1), 8898. DOI: 10.1111/lcrp.12081
  14. Duffy, M. C., Lajoie, S. P., Pekrun, R., & Lachapelle, K. (2018). Emotions in medical education: Examining the validity of the Medical Emotion Scale (MES) across authentic medical learning environments. Learning and Instruction. DOI: 10.1016/j.learninstruc.2018.07.001
  15. Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 5683. DOI: 10.1080/10705519909540119
  16. Fishbein, M., & Ajzen, I. (2010). Predicting and changing behaviour: The reasoned action approach. Psychology Press (Taylor & Francis). DOI: 10.4324/9780203838020
  17. Gamage, D., Fernando, S., & Perera, I. (2015, August). Quality of MOOCs: A review of literature on effectiveness and quality aspects. In Ubi-Media Computing (UMEDIA), 2015 8th International Conference (pp. 224229). IEEE. DOI: 10.1109/UMEDIA.2015.7297459
  18. Greene, J. A., Oswald, C. A., & Pomerantz, J. (2015). Predictors of Retention and Achievement in a Massive Open Online Course. American Educational Research Journal, 52(5), 925955. DOI: 10.3102/0002831215584621
  19. Grover, S., Franz, P., Schneider, E., & Pea, R. (2013, June). The MOOC as distributed intelligence: Dimensions of a framework & evaluation of MOOCs. In Proceedings CSCL, 2, 425. https://repository.isls.org/bitstream/1/1940/1/42-45.pdf
  20. Harrington, D. (2009). Confirmatory factor analysis. Oxford New York Press. DOI: 10.1093/acprof:oso/9780195339888.001.0001
  21. Hastie, T., Tibshirani, R., & Friedman, J. (2001). The elements of statistical learning: Data mining, inference, and prediction. Springer. DOI: 10.1007/BF02985802
  22. Hayduk, L. A. (2014). Shame for disrespecting evidence: The personal consequences of insufficient respect for structural equation model testing. BMC Medical Research Methodology, 14, 124. DOI: 10.1186/1471-2288-14-124
  23. Henderikx, M. A. (2019). Mind The Gap: Unravelling learner success and behaviour in Massive Open Online Courses [doctoral dissertation]. Open Universiteit Netherlands. https://research.ou.nl/ws/files/11849471/DissertationMaartjeHenderikx.pdf
  24. Henderikx, M. A., Kreijns, K., & Kalz, M. (2017). Refining success and dropout in massive open online courses based on the intention–behavior gap. Distance Education, 38(3), 353368. DOI: 10.1080/01587919.2017.1369006
  25. Henderikx, M., Kreijns, K., & Kalz M. (2018a). A classification of barriers that influence intention achievement in MOOCs. In V. Pammer-Schindler, M. Pérez-Sanagustín, H. Drachsler, R. Elferink & M. Scheffel (Eds.), Lifelong technology-enhanced learning. EC-TEL 2018. LNCS, 11082, 315. DOI: 10.1007/978-3-319-98572-5_1
  26. Henderikx, M. A., Kreijns, C., & Kalz, M. (2018b). Intention – behavior dynamics in MOOCs Learning. What happens to good intentions along the way? In 2018 Learning With MOOCS (LWMOOCS): Proceedings of the Fifth Learning with MOOCs Conference (pp. 110112). IEEE. DOI: 10.1109/LWMOOCS.2018.8534595
  27. Henderikx, M., Kreijns, K., Castaño Muñoz, J., & Kalz, M. (2019). Factors influencing the pursuit of personal learning goals in MOOCs. Distance Education, 40(2), 187204. DOI: 10.1080/01587919.2019.1600364
  28. Ho, A. D., Reich, J., Nesterko, S., Seaton, D. T., Mullaney, T., Waldo, J., & Chuang, I. (2014). HarvardX and MITx: The first year of open online courses. HarvardX Working Paper No. 1. http://harvardx.harvard.edu/multiple-course-report. DOI: 10.2139/ssrn.2381263
  29. Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education, 98, 157168. DOI: 10.1016/j.compedu.2016.03.016
  30. Hooper, D., Coughlan, J., & Mullen, M. (2008). Structural equation modelling: Guidelines for determining model fit. Electronic Journal of Business Research Methods (EJBRM), 6(1), 5360. https://academic-publishing.org/index.php/ejbrm/article/view/1224
  31. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 155. DOI: 10.1080/10705519909540118
  32. Idrees, M., Hafeez, M., & Kim, J. Y. (2017). Workers’ age and the impact of psychological factors on the perception of safety at construction sites. Sustainability, 9(5), 745. https://www.mdpi.com/2071-1050/9/5/745/htm. DOI: 10.3390/su9050745
  33. Jansen, R. S., van Leeuwen, A., Janssen, J., Conijn, R., & Kester, L. (2020). Supporting learners’ self-regulated learning in Massive Open Online Courses. Computers & Education, 146, 103771. DOI: 10.1016/j.compedu.2019.103771
  34. Kenny, D. A., Kaniskan, B., & McCoach, D. B. (2015). The performance of RMSEA in models with small degrees of freedom. Sociological Methods & Research, 44(3), 486507. DOI: 10.1016/j.compedu.2019.103771
  35. Khalil, H., & Ebner, M. (2013). Interaction Possibilities in MOOCs – How Do They Actually Happen? International Conference on Higher Education Development (pp. 124). Egypt: Mansoura University. https://www.scribd.com/document/134249470/Interaction-Possibilities-in-MOOCs-How-Do-They-Actually-Happen. DOI: 10.1177/0049124114543236
  36. Khalil, H., & Ebner, M. (2014). MOOCs Completion Rates and Possible Methods to Improve Retention – A Literature Review. In World Conference on Educational Multimedia, Hypermedia and Telecommunications (pp. 12361244). Chesapeak, VA: AACE. https://www.learntechlib.org/p/147656
  37. Koller, D., Ng, A., Do, C., & Chen, Z. (2013). Retention and intention in massive open online courses: In depth. Educause Review Online. http://er.educause.edu/articles/2013/6/retention-and-intention-in-massive-open-online-courses-in-depth
  38. Mackness, J., Mak, S., & Williams, R. (2010). The ideals and reality of participating in a MOOC. In Proceedings of the 7th international conference on networked learning 2010 (pp. 266275). University of Lancaster. https://researchportal.port.ac.uk/portal/en/publications/the-ideals-and-reality-of-participating-in-a-mooc(067e281e-6637-423f-86a5-ff4d2d687af1).html
  39. Marsh, H. W., Balla, J. R., & McDonald, R. P. (1988). Goodness-of-fit indexes in confirmatory factor analysis: The effect of sample size. Psychological Bulletin, 103(3), 391410. DOI: 10.1037/0033-2909.103.3.391
  40. Marsh, H. W., Hau, K. T., & Grayson, D. A. (2005). Goodness of fit evaluation in structural equation modeling. In A. Maydeu-Olivares & J. J. McArdle (Eds.), Contemporary psychometrics. A festschrift to Roderick P. McDonald (pp. 225340). n. https://psycnet.apa.org/record/2005-04585-010
  41. Marsh, H. W., Hau, K. T., & Wen, Z. (2004). In search of golden rules: Comment on hypothesis-testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler’s (1999) Findings. Structural Equation Modeling: A Multidisciplinary Journal, 11(3), 320341. DOI: 10.1207/s15328007sem1103_2
  42. McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010). The MOOC model for digital practice. https://oerknowledgecloud.org/sites/oerknowledgecloud.org/files/MOOC_Final.pdf
  43. McNeish, D., An, J., & Hancock, G. R. (2018). The thorny relation between measurement quality and fit index cutoffs in latent variable models. Journal of personality assessment, 100(1), 4352. DOI: 10.1080/00223891.2017.1281286
  44. Misopoulos, F., Argyropoulou, M., & Tzavara, D. (2018). Exploring the factors affecting student academic performance in online programs: a literature review. In A. Khare & D. Hurst (Eds.), On the line (pp. 235249). DOI: 10.1007/978-3-319-62776-2_18
  45. Muilenburg, L. Y., & Berge, Z. L. (2005). Student barriers to online learning: A factor analytic study. Distance Education, 26(1), 2948. DOI: 10.1080/01587910500081269
  46. Muthén, L. K., & Muthén, B. O. (1998–2014). MPlus user’s guide (8th ed.). Los Angeles, CA. Retrieved from https://www.statmodel.com/download/usersguide/MplusUserGuideVer_8.pdf
  47. Ng, S.-M. (2013). Validation of the 10-item Chinese perceived stress scale in elderly service workers: one-factor versus two-factor structure. BMC Psychology, 9. https://link.springer.com/article/10.1186/2050-7283-1-9. DOI: 10.1186/2050-7283-1-9
  48. Onah, D. F., Sinclair, J., & Boyatt, R. (2014). Dropout rates of massive open online courses: behavioural patterns. In International conference on education and new learning technologies. EDULEARN14 proceedings, 1, 58255834. Barcelona. http://wrap.warwick.ac.uk/65543/
  49. Osborne, J. W., Costello, A. B., & Kellow, J. T. (2008). Best practices in exploratory factor analysis. Best practices in quantitative methods (pp. 8699). DOI: 10.4135/9781412995627.d8
  50. Preacher, K. J. (2006). Testing complex correlational hypotheses with structural equation models. Structural Equation Modeling, 13(4), 520543. DOI: 10.1207/s15328007sem1304_2
  51. Preacher, K. J., Zhang, G., Kim, C., & Mels, G. (2013). Choosing the optimal number of factors in exploratory factor analysis: A model selection perspective. Multivariate Behavioral Research, 48, 2856. DOI: 10.1080/00273171.2012.710386
  52. Prudon, P. (2014). Confirmatory factor analysis: a brief introduction and critique. Tilgængelig på. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.476.6207&rep=rep1&type=pdf
  53. Reich, J. (2014). MOOC completion and retention in the context of student intent. Educause Review Online. http://er.educause.edu/articles/2014/12/mooc-completion-and-retention-in-the-context-of-student-intent
  54. Reich, J., & Ruipérez-Valiente, J. A. (2019). The MOOC pivot. Science, 363(6423), 130131. DOI: 10.1126/science.aav7958
  55. Rhemtulla, M., Brosseau-Liard, P. E., & Savalei, V. (2012). When can categorical variables be treated as continuous? A comparison of robust continuous and categorical SEM estimation methods under suboptimal conditions. Psychological methods, 17(3), 354373. DOI: 10.1037/a0029315
  56. Saleh, A., & Bista, K. (2017). Examining factors impacting online survey response rates in educational research: Perceptions of graduate students. Journal of MultiDisciplinary Evaluation, 13(29), 6374. https://files.eric.ed.gov/fulltext/ED596616.pdf
  57. Shapiro, H. B., Lee, C. H., Roth, N. E. W., Li, K., Çetinkaya-Rundel, M., & Canelas, D. A. (2017). Understanding the massive open online course (MOOC) student experience: An examination of attitudes, motivations, and barriers. Computers & Education, 110, 3550. DOI: 10.1016/j.compedu.2017.03.003
  58. Schmitt, T. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29, 304321. DOI: 10.1177/0734282911406653
  59. Schmitt, T. A., Sass, D. A., Chappelle, W., & Thompson, W. (2018). Selecting the best factor structure and moving measurement validation forward: An illustration. Journal of personality assessment, 100(4), 345362. DOI: 10.1080/00223891.2018.1449116
  60. Shin, N. (2003). Transactional presence as a critical predictor of success in distance learning. Distance Education, 24(1), 6968. DOI: 10.1080/01587910303048
  61. Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics (6th Ed., New International Edition). Pearson Education Limited.
  62. Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in Science Education, 48(6), 12731296. DOI: 10.1007/s11165-016-9602-2
  63. Urushihata, T., Kinugasa, T., Soma, Y., & Miyoshi, H. (2010). Aging effects on the structure underlying balance abilities tests. Journal of the Japanese Physical Therapy Association, 13(1), 18. DOI: 10.1298/jjpta.13.1
  64. Wegener, D. T., & Fabrigar, L. R. (2000). Analysis and design for nonexperimental data addressing casual and noncausal hypotheses. In H. T. Reis & C. M. Judd (Eds.), Handbook of research methods in social and personality psychology (pp. 412450). Cambridge University Press. DOI: 10.1017/CBO9780511996481.024
  65. Yu, C.-Y. (2002). Evaluating cutoff criteria of model fit indices for latent variable models with binary and continuous outcomes. Los Angeles: University of California. https://pdfs.semanticscholar.org/7a22/ae22553f78582fc61c6cab4567d36998293b.pdf
Language: English
Page range: 143 - 159
Submitted on: Oct 18, 2020
Accepted on: May 31, 2021
Published on: Oct 20, 2021
Published by: International Council for Open and Distance Education (ICDE)
In partnership with: Paradigm Publishing Services

© 2021 Maartje Henderikx, Karel Kreijns, Kate M. Xu, Marco Kalz, published by International Council for Open and Distance Education (ICDE)
This work is licensed under the Creative Commons Attribution 4.0 License.