skip to main content
10.1145/3027385.3027448acmotherconferencesArticle/Chapter ViewAbstractPublication PageslakConference Proceedingsconference-collections
short-paper

Using learning analytics to explore help-seeking learner profiles in MOOCs

Published:13 March 2017Publication History

ABSTRACT

In online learning environments, learners are often required to be more autonomous in their approach to learning. In scaled online learning environments, like Massive Open Online Courses (MOOCs), there are differences in the ability of learners to access teachers and peers to get help with their study than in more traditional educational environments. This exploratory study examines the help-seeking behaviour of learners across several MOOCs with different audiences and designs. Learning analytics techniques (e.g., dimension reduction with t-sne and clustering with affinity propagation) were applied to identify clusters and determine profiles of learners on the basis of their help-seeking behaviours. Five help-seeking learner profiles were identified which provide an insight into how learners' help-seeking behaviour relates to performance. The development of a more in-depth understanding of how learners seek help in large online learning environments is important to inform the way support for learners can be incorporated into the design and facilitation of online courses delivered at scale.

References

  1. Khaled M Alraimi, Hangjung Zo, and Andrew P Ciganek. 2015. Understanding the MOOCs continuance: The role of openness and reputation. Computers & Education 80 (2015), 28--38. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Anthony R Artino. 2007. Self-regulated learning in online education: A review of the empirical literature. International journal of Instructional Technology and Distance Learning 4, 6 (2007), 3--18.Google ScholarGoogle Scholar
  3. Aneesha Bakharia, Linda Corrin, Paula de Barba, Gregor Kennedy, Dragan Gašević, Raoul Mulder, David Williams, Shane Dawson, and Lori Lockyer. 2016. A conceptual framework linking learning design with learning analytics. In Proceedings of the Sixth International Conference on Learning Analytics & Knowledge. ACM, 329--338. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. J Broadbent and WL Poon. 2015. Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education 27 (2015), 1--13.Google ScholarGoogle ScholarCross RefCross Ref
  5. Moon-Heum Cho and Jin Soung Yoo. 2016. Exploring online students' self-regulated learning with self-reported surveys and log files: a data mining approach. Interactive Learning Environments (2016), 1--13.Google ScholarGoogle Scholar
  6. Carleton Coffrin, Linda Corrin, Paula de Barba, and Gregor Kennedy. 2014. Visualizing patterns of student engagement and performance in MOOCs. In Proceedings of the fourth international conference on learning analytics and knowledge. ACM, 83--92. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Rebecca Ferguson and Doug Clow. 2015. Examining engagement: analysing learner subpopulations in massive open online courses (MOOCs). In Proceedings of the Fifth International Conference on Learning Analytics And Knowledge. ACM, 51--58. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Brendan J Frey and Delbert Dueck. 2007. Clustering by passing messages between data points. science 315, 5814 (2007), 972--976.Google ScholarGoogle Scholar
  9. Nabeel Gillani, Rebecca Eynon, Michael Osborne, Isis Hjorth, and Stephen Roberts. 2014. Communication communities in MOOCs. arXiv preprint arXiv:1403.4640 (2014).Google ScholarGoogle Scholar
  10. Carolyn Hart. 2012. Factors associated with student persistence in an online program of study: A review of the literature. journal of Interactive Online Learning 11, 1 (2012), 19--42.Google ScholarGoogle Scholar
  11. Nina Hood, Allison Littlejohn, and Colin Milligan. 2015. Context counts: How learners' contexts influence learning in a MOOC. Computers & Education 91 (2015), 83--91. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Stuart A Karabenick and Myron H Dembo. 2011. Understanding and facilitating self-regulated help seeking. New Directions for Teaching and Learning 2011, 126 (2011), 33--43.Google ScholarGoogle ScholarCross RefCross Ref
  13. René F Kizilcec, Mar Pérez-Sanagustín, and Jorge J Maldonado. 2017. Self-regulated learning strategies predict learner behavior and goal attainment in Massive Open Online Courses. Computers & Education 104 (2017), 18--33. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. journal of Machine Learning Research 9, Nov (2008), 2579--2605.Google ScholarGoogle Scholar
  15. Negin Mirriahi, Daniyal Liaqat, Shane Dawson, and Dragan Gašević. 2016. Uncovering student learning profiles with a video annotation tool: reflective learning with and without instructional norms. Educational Technology Research and Development 64, 6 (2016), 1083--1106.Google ScholarGoogle ScholarCross RefCross Ref
  16. Paul R Pintrich. 2000. The role of goal orientation in self-regulated learning. Academic Press.Google ScholarGoogle Scholar
  17. Minna Puustinen and Jean-François Rouet. 2009. Learning with new technologies: Help seeking and information searching revisited. Computers & Education 53, 4 (2009), 1014--1019. Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Carolyn Penstein Rosé, Oliver Ferschke, Gaurav Tomar, Diyi Yang, Iris Howley, Vincent Aleven, George Siemens, Matthew Crosslin, Dragan Gasevic, and Ryan Baker. 2015. Challenges and opportunities of dual-layer MOOCs: Reflections from an edX deployment study. In Proceedings of the 11th International Conference on Computer Supported Collaborative Learning (CSCL 2015), Vol. 15. 848--851.Google ScholarGoogle Scholar
  19. Shu-Fen Tseng, Yen-Wei Tsao, Liang-Chih Yu, Chien-Lung Chan, and K Robert Lai. 2016. Who will pass? Analyzing learner behaviors in MOOCs. Research and Practice in Technology Enhanced Learning 11, 1 (2016), 8.Google ScholarGoogle ScholarCross RefCross Ref
  20. Pascal Van Hentenryck and Carleton Coffrin. 2014. Teaching creative problem solving in a MOOC. In Proceedings of the 45th ACM technical symposium on Computer science education. ACM, 677--682. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. George Veletsianos, Justin Reich, and Laura A Pasquini. 2016. The Life Between Big Data Log Events" Learners' Strategies to Overcome Challenges in MOOCs. AERA Open 2, 3 (2016), 1--10.Google ScholarGoogle Scholar

Index Terms

  1. Using learning analytics to explore help-seeking learner profiles in MOOCs

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      LAK '17: Proceedings of the Seventh International Learning Analytics & Knowledge Conference
      March 2017
      631 pages
      ISBN:9781450348706
      DOI:10.1145/3027385

      Copyright © 2017 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 13 March 2017

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • short-paper

      Acceptance Rates

      LAK '17 Paper Acceptance Rate36of114submissions,32%Overall Acceptance Rate236of782submissions,30%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader