Adaptive Learning: A Stabilizing Influence Across Disciplines and Universities

Charles Dziuban, Colm Howlin, Patsy Moskal, Connie Johnson, Liza Parker, Maria Campbell

Abstract


This study represents an adaptive learning partnership among The University of Central Florida, Colorado Technical University, and the platform provider Realizeit.  A thirteen-variable learning domain for students forms the basis of a component invariance study. The results show that four dimensions: knowledge acquisition, engagement activities, communication and growth remain constant in nursing and mathematics courses across the two universities, indicating that the adaptive modality stabilizes learning organization in multiple disciplines. The authors contend that similar collaborative partnerships among universities and vendors is an important next step in the research process.

Keywords


adaptive learning, learning analytics, online learning, digital learning, principal components analysis

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References


Adam, B. (2004). Time. Cambridge, IK: Polity.

Alli, N., Rajan, R., & Ratliff, G. (2016). How Personalized Learning Unlocks Student Success. EDUCAUSE Review Online. 51(2)

Argyris, C. (1960). Understanding organizational behavior. Oxford, England: Dorsey.

Association of Public & Land-Grant Universities. (2016). Personalizing Learning with Adaptive Courseware. Retrieved from http://www.aplu.org/projects-and-initiatives/personalized- learning-consortium/plc-projects/plc-adaptive-courseware/

Bailey, A., Vaduganathan, N., Henry, T., Laverdiere, R., Pugliese, L. (2018, March). Making Digital Learning Work: Success Strategies From Six Leading Universities and Community Colleges. The Boston Consulting Group.

Bastedo, K. & Cavanagh, T. (2016, April 19). Personalized Learning as a Team Sport: What IT Professionals Need to Know. EDUCAUSE Review.

Becker, S. A., Cummins, M., Davis, A., Freeman, A., Hall, C. G., & Ananthanarayanan, V. (2017). NMC horizon report: 2017 higher education edition (pp. 1-60). The New Media Consortium.

Betts, K., & Heaston, A. (2014). Build it but will they teach?: Strategies for increasing faculty participation & retention in online & blended education. Online Journal of Distance Learning Administration, 17(2), n2.

Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. US Department of Education, Office of Educational Technology, 1, 1-57.

Bill & Melinda Gates Foundation. (2014, November). Early progress: Interim research on personalized learning. Retrieved from http://collegeready.gatesfoundation.org/wp-content/uploads/2015/06/Early-Progress-on-Personalized-Learning-Full-Report.pdf

Bowker, G. C., & Star, S. L. (2000). Sorting things out: Classification and its consequences. Cambridge, MA: The MIT press.

Brown, J. (2015). Personalizing Post-Secondary Education: An Overview of Adaptive Learning Solutions for Higher Education. Retrieved from http://www.sr.ithaka.org/wp-content/uploads/2015/08/SR_Report_Personalizing_Post_Secondary_Education_31815_0.pdf

Buchanan, T., Sainter, P., & Saunders, G. (2013). Factors affecting faculty use of learning technologies: Implications for models of technology adoption. Journal of Computing in Higher Education, 25(1), 1-11.

Cahalan, M., & Perna, L. (2015). Indicators of Higher Education Equity in the United States: 45 Year Trend Report. Pell Institute for the Study of Opportunity in Higher Education.

Carnevale, A. P., Rose, S. J., & Cheah, B. (2011). The college payoff: Education, occupations, lifetime earnings. Washington, DC: Georgetown University Center on Education and the Workforce.

Caplan, B. (2018). The Case Against Education: Why the Education System is a Waste of Time and Money. Princeton: Princeton University Press.

Carroll, J. B. (1963). A model of school learning. Teachers college record.

Chan, W., Ho, R. M., Leung, K., Chan, D. K., & Yung, Y. (1999). An alternative method for evaluating congruence coefficients with Procrustes rotation: A bootstrap procedure. Psychological Methods, 4(4), 378-402. doi:10.1037/1082-989X.4.4.378

Chen, B., Bastedo, K., Kirkley, D., Stull, C., and Tojo, J. (2017). Designing personalized

adaptive learning courses at the University of Central Florida. ELI Brief.

Daines, J., Troka, T., & Santiago, J. (2016). Improving Performance in Trigonometry and Pre-Calculus by Incorporating Adaptive Learning Technology into Blended Models on Campus. In 123rd Annual ASEE Conference & Exposition, New Orleans, Louisiana.

Diamond, J. (2005). Guns, germs, and steel: The fates of human societies. New York: Norton.

du Boulay, B. (2006). Commentary on Kurt VanLehn's The behaviour of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 267-270.

Dziuban, C. (2017). The Technology of Adaptive Learning. Education Technology Insights.

Dziuban, C. D., Moskal, P. D., Cassisi, J., & Fawcett, A. (2016). Adaptive Learning in Psychology: Wayfinding in the Digital Age. Online Learning, 20(3), 74-96.

Dziuban, C., Howlin, C., Johnson, C., & Moskal, P. (2017, December 18). An Adaptive Learning Partnership. EDUCAUSE Review.

Dziuban, C., Moskal, P. & Hartman, J. (2016). Adapting to Learn, Learning to Adapt. EDUCAUSE Center for Analysis and Research (ECAR) Research Bulletin. Louisville, CO: ECAR, September 30, 2016. Retrieved from: https://library.educause.edu/resources/2016/9/adapting-to-learn-learning-toadapt.

Dziuban, C., Moskal, P., Johnson, C., & Evans, D. (2017). Adaptive learning: A tale of two contexts. Current Issues in Emerging eLearning, 4(1), 3.

Dziuban, C., Moskal, P., Kramer, L., & Thompson, J. (2013). Student satisfaction with online learning in the presence of ambivalence: Looking for the will-o'-the-wisp. The Internet and Higher Education, 17, 1-8.

Fay, B. Students & Debt. debt.org. Retrieved from: https://www.debt.org/students/ , May 2018

Floridi, L. (2013). Spreading ignorance equally. The Philosophers' Magazine, (63), 24-25.

Floridi, L. (2014). The 4th Revolution. Oxford: Oxford University Press

Gardner, H. (2011). Frames of mind: The theory of multiple intelligences. Basic books.

Gelsinger, P. (2018, March/April). Mind-Blowing to Mundane: How Tech is Reshaping Our Expectations. MIT Technology Review, 121(2), 7.

Goldberg, L. R. (1992). Goldberg's 100 Unipolar Big-Five Factor Markers. Psychological Assessment, 4(1), 26-42.

Hendrickson, A. E., & White, P. O. (1964). Promax: A quick method for rotation to oblique

simple structure. British Journal of Statistical Psychology, 17(1), 65-70. Retrieved from http://onlinelibrary.wiley.com/doi/10.1111/j.2044-8317.1964.tb00244.x/pdf

Howlin, C. P., & Lynch, D. (2014). A framework for the delivery of personalized adaptive content. 2014 International Conference on Web and Open Access to Learning (ICWOAL), (pp. 1- 5). doi:10.1109/ICWOAL.2014.7009203

Johnson, C., & Zone, E. (In review). Achieving a Scale Implementation of Adaptive Learning through Faculty Engagement: A Case Study. Current Issues in Emerging eLearning.

Johnson, D. (2017, June 15). Opening the Black Box of Adaptivity. EDUCASE Review.

Kaiser, H. F., & Rice, J. (1974). Little Jiffy, Mark IV. Educational and Psychological Measurement, 34, 111–117.

Kennedy, A. (2015). Faculty perceptions of the usefulness of and participation in professional development for online teaching: An analysis of faculty development and online teaching satisfaction (3722998). Retrieved from ProQuest Education Database.

Kibble, T. (2015). The standard model of particle physics. European Review, 23(1), 36-44. doi:10.1017/S1062798714000520

Lakoff, G. (2008). Women, fire, and dangerous things. Chicago: University of Chicago press.

Legon, R. & Garrett, R. (2018). The Changing Landscape of Online Education (Chloe) 2: A Deeper Dive. CHLOE2.

Lochner, L. (December 2010). Measuring the Impacts of the Tangelo Park Project on Local Residents. University of Western Ontario.

Lochner, L., & Monge-Naranjo, A. (2015). Student loans and repayment: Theory, evidence and policy (No. w20849). National Bureau of Economic Research.

Lorenzo-Seva, U., & ten Berge, J. F. (2006). Tucker's congruence coefficient as a meaningful index of factor similarity. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 2(2), 57-64. doi:10.1027/1614-2241.2.2.57

Moskal, P., Carter, D., & Johnson, D. (2017). 7 Things You Should Know About Adaptive Learning. ELI.

Mulaik, S.A. (2009). The foundations of factor analysis, second edition. London, United Kingdom: Chapman and Hall.

Mullainathan, S., & Shafir, E. (2013). Scarcity: Why having too little means so much. Macmillan.

Nakic, J., Granic, A., & Glavinic, V. (2015). Anatomy of student models in adaptive learning systems: A systematic literature review of individual differences from 2001 to 2013. Journal of Educational Computing Research, 51(4), 459-489. doi:10.2190/EC.51.4.e

Norberg, A., Dziuban, C., & Moskal, P. (2011). A time-based blended learning model. On the Horizon, 19(3), 207-216.

Office of Educational Technology. (2017, January). Reimagining the Role of Technology in Education: 2017 National Education Technology Plan Update. US Department of Education. Retrieved from: https://tech.ed.gov/files/2017/01/NETP17.pdf

Online Learning Consortium. (2016). Digital Learning Innovation Award. Retrieved from https://onlinelearningconsortium.org/about/olc-awards/dlia/

Page, S. E. (2010). Diversity and complexity. Princeton University Press.

Pugliese, L. (2016, October 17). Adaptive Learning Systems: Surviving the Storm. EDUCAUSE Review.

Quality Matters. (2014). Quality MattersTM Overview [PowerPoint slides]. Retrieved from: https://www.qualitymatters.org/sites/default/files/pd-docs-PDFs/QM-Overview-Presentation-2014.pdf

Rousseau, D. M. (1990). Normative beliefs in fund-raising organizations: Linking culture to organizational performance and individual responses. Group & Organization Studies, 15(4), 448-460.

Setenyi, J. (1995, May). Teaching democracy in an unpopular democracy. Paper presented at What to Teach about Hungarian Democracy Conference. 12 May 1995, Kossuth Klub, Hungary.

Schönemann, P. (1966). A generalized solution of the orthogonal procrustes problem. Psychometrika, 31(1), 1. doi:10.1007/BF02289451

Smith, D. (2013). An artificial intelligence-based distance learning system. Distance Learning. 10(3), 51-56.

Taleb, N.N. (2018). Skin in the Game: Hidden Asymmetries in Daily Life. New York: Random House.

Thurstone, L. L. (1938). Primary mental abilities. University of Chicago Press: Chicago.

Tupes, E. C., & Christal, R. E. (1992). Recurrent personality factors based on trait ratings. Journal Of Personality, 60(2), 225-251. doi:10.1111/j.1467-6494.1992.tb00973.x

Tyton Partners. (2016). Learning to Adapt 2.0: The Evolution of Adaptive Learning in Higher Education. Retrieved from http://tytonpartners.com/tyton-wp/wp-content/uploads/2016/04/Tyton-Partners-Learning-to-Adapt-2.0-FINAL.pdf

Vandewaetere, M., & Clarebout, G. (2014). Advanced Technologies for Personalized Learning, Instruction, and Performance. In Handbook of Research on Educational Communications and Technology (pp. 425-437).

VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227-265.

Weiss, E. (2013, July 13). Tangelo Park Program (Orlando, Florida). Broader, Bolder Approach to Education.

Wingo, N. P., Ivankova, N. V., & Moss, J. A. (2017). Faculty Perceptions about Teaching Online: Exploring the Literature Using the Technology Acceptance Model as an Organizing Framework. Online Learning, 21(1), 15-35.

Yarnall, L., Means, B., & Wetzel, T. (2016). Lessons learned from early implementations of adaptive courseware. SRI Education, April.




DOI: http://dx.doi.org/10.24059/olj.v22i3.1465