An Asynchronous, Personalized Learning Platform―Guided Learning Pathways (GLP)


The authors propose that personalized learning can be brought to traditional and non-traditional learners through a synchronous learning platform that recommends to individual learners the learning materials best suited for him or her. Such a platform would allow learners to advance towards individual learning goals at their own pace, with learning materials catered to each learner’s interests and motivations. This paper describes the authors’ vision and design for a modular, personalized learning platform called Guided Learning Pathways (GLP), and its characteristics and features. We provide detailed descriptions of and propose frameworks for critical modules like the Content Map, Learning Nuggets, and Recommendation Algorithms. A threaded user scenario is provided for each module to help the reader visualize different aspects of GLP. We discuss work done at MIT to support such a platform.

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Shaw, C. , Larson, R. and Sibdari, S. (2014) An Asynchronous, Personalized Learning Platform―Guided Learning Pathways (GLP). Creative Education, 5, 1189-1204. doi: 10.4236/ce.2014.513135.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Belanger, Y. (2012). Evaluating the MITx Experience.
[2] Bloom, B. S. (1984). The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring. Educational Researcher, 13, 4-16.
[3] CAST (n.d.). About UDL.
[4] Christensen, C., Johnson, C., & Horn, M. (2008). Disrupting Class: How Disruptive Innovation Will Change the Way the World Learns. New York, NY: McGraw-Hill.
[5] Corbett, A., & Anderson, J. (1994). Knowledge Tracing: Modeling the Acquisition of Procedural Knowledge. User Modeling and User-Adapted Interaction, 4, 253-278.
[6] D’Mello, S., Lehman, B., Sullins, J., Daigle, R., Combs, R., Vogt, K. et al. (2010). A Time for Emoting: When Affect-Sensitivity Is and Isn’t Effective at Promoting Deep Learning. Lecture Notes in Computer Science 2010: Intelligent Tutoring Systems, 6094, 245-254.
[7] Daro, P., Mosher, F., & Corcoran, T. (2011). Learning Trajectories in Mathematics: A Foundation for Standards, Curriculum, Assessment, and Introduction. Consortium for Policy Research in Education.
[8] Dede, C., & Richards, J. (Eds.) (2012). Digital Teaching Platforms: Customizing Classroom Learning for Each Student. New York, NY: Teachers College Press, Columbia University.
[9] EdReNe (2011). Current State of Educational Repositories—National Overview: United Kingdom.
[10] edX (2012). 6.00x Syllabus.
[11] Fischer, K., Rose, L., & Rose, S. (2006). Growth Cycles of Mind and Brain: Analyzing Developmental Pathways of Learning Disorders. In K. Fischer, J. Bernstein, & M. Immordino-Yang (Eds.), Mind, Brain, and Education in Reading Disorders (pp.101-123). Cambridge, U.K.: Cambridge University Press.
[12] Hefferman, N., Hefferman, C., & Brest, A. (n.d.). ASSISTment Skill Diagram.
[13] Khan Academy Dashboard (n.d.). Exercise Dashboard.
[14] Khan Academy Homepage (n.d.).
[15] Knewton (n.d.). Personalized Education for the World.
[16] Lenning, O., & Ebbers, L. (1999). The Powerful Potential of Learning Communities: Improving Education for the Future. Washington, DC: The George Washington University, Graduate School of Education and Human Development.
[17] Lord, M. (2012). Teaching Toolbox.
[18] Merriman, J. (n.d.). Core Concept Catalog.
[19] MIT Crosslinks (n.d.). Crosslinks.
[20] MIT (n.d.).
[21] Nadolski, R., van den Berg, B., Berlanga, A., Drachsler, H., Hummel, H., Koper, R. et al. (2009). Simulating Light-Weight Personalised Recommender Systems in Learning Networks: A Case for Pedagogy-Oriented and Rating-Based Hybrid Recommendation Strategies. Journal of Artificial Societies and Social Simulation, 12.
[22] National Atlas of the United States (2003). Printable States Map (Unlabeled).
[23] National Research Council (2010). BIO 2010: Transforming Undergraduate Education for Future Research Biologists. Washington, DC: National Academies Press.
[24] Pashler, H., McDaniel, M., Rohrer, D., & Bjork, R. (2008). Learning Styles: Concepts and Evidence. Psychological Science in the Public Interest, 9, 105-119.
[25] Picciano, A., Seaman, J., & Allen, I. (2010). Educational Transformation through Online Learning: To Be or Not to Be. Journal of Asynchronous Learning Networks, 14, 17-35.
[26] Recker, M., Walker, A., & Lawless, K. (2003). What Do You Recommend? Implementation and Analysis of Collaborative Filtering of Web Resources for Education. Instructional Science, 31, 299-316.
[27] Romero, C., Ventura, S., Delgado, J., & De Bra, P. (2007). Personalized Links Recommendation Based on Data Mining in Adaptive Educational Hypermedia Systems. Lecture Notes in Computer Science, 4753, 292-306.
[28] Rose, D., & Meyer, A. (2012). Teaching Every Student in the Digital Age: Universal Design for Learning. Association for Supervision and Curriculum Development.
[29] Siemens, G., Gasevic, D., Haythornthwaite, C., Dawson, S., Shum, S., Ferguson, R. et al. (2011). Open Learning Analytics: an Integrated & Modularized Platform. Society for Learning Analytics Research.
[30] Time To Know (n.d.). Overview.
[31] Tsai, K., Chiu, T., Lee, M., & Wang, T. (2006). A Learning Objects Recommendation Model Based on the Preference and Ontological Approaches. Sixth International Conference on Advanced Learning Technologies, Kerkrade, 5-7 July 2006, 36-40.
[32] Tullis, J., & Benjamin, A. (2011). On the Effectiveness of Self-Paced Learning. Journal of Memory and Language, 64, 109-118.
[33] US Department of Education (2010). Transforming American Education: Learning Powered by Technology. Washington, DC: US Department of Education, Office of Educational Technology,
[34] Vander Ark, T. (2012). Getting Smart: How Digital Learning Is Changing the World. San Francisco, CA: Jossey-Bass.

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