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Students' Achievement of Personalized Learning Objectives in MOOCs

Published:12 August 2020Publication History

ABSTRACT

Massive Open Online Courses (MOOCs) provide the opportunity to offer free and open education at scale. Thousands of students with different social and cultural backgrounds from all over the world can enroll for a course. This diverse audience comes with varying motivations and intentions from their personal or professional life. However, course instructors cannot offer individual support and guidance at this scale and therefore usually provide a one-size-fits-all approach. Students have to follow weekly-structured courses and their success is measured with the achievement of a certificate at the end. To better address the varying learning needs, technical support for goal-oriented and self-regulated learning is desired but very limited to date. Both learning strategies are proven to be key factors for students' achievement in large-scale online learning environments. Therefore, this paper presents a continuative study of personalized learning objectives in MOOCs to encourage goal-oriented and self-regulated learning. Based on the previously well-perceived acceptance and usefulness of the concept of personalized learning objectives, this study examines which learners select an objective and how successful they complete objectives. Concerning the learners' socio-demographic and geographical background, we could not identify any practical significant difference between students with selected learning objectives and the total course population. However, we have identified promising objective achievement rates, and we have observed a practical significant improvement of the certification rates comparing the total course population and students who selected an objective that included a graded certificate. This has also demonstrated a method for calculating more reasonable completion rates in MOOCs.

References

  1. Lori B. Breslow, David E. Pritchard, Jennifer DeBoer, Glenda S. Stump, Andrew D. Ho, and Daniel T. Seaton. 2013. Studying learning in the worldwide classroom: Research into edXtextquoterights first MOOC. Research & Practice in Assessment 8 (2013), 13--25. http://www.rpajournal.com/dev/wp-content/uploads/2013/05/SF2.pdfGoogle ScholarGoogle Scholar
  2. Jaclyn Broadbent and Walter Poon. 2015. Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education 27 (04 2015). http://dx.doi.org/10.1016/j.iheduc.2015.04.007Google ScholarGoogle Scholar
  3. George T. Doran. 1981. There's a SMART way to write management's goals and objectives. Management Review 70, 11 (1981), 35--36.Google ScholarGoogle Scholar
  4. Catrina Grella and Christoph Meinel. 2016. MOOCs as a Promoter of Gender Diversity in STEM?. In Proceedings of the 12th International Scientific Conference eLearning and Software for Education. http://dx.doi.org/10.12753/2066-026X-19-164Google ScholarGoogle Scholar
  5. Maartje Henderikx, Karel Kreijns, and Marco Kalz. 2017. Refining success and dropout in massive open online courses based on the intention-behavior gap. Distance Education 38, 3 (2017). http://dx.doi.org/10.1080/01587919.2017.1369006Google ScholarGoogle Scholar
  6. René F. Kizilcec, Mar Pérez-Sanagust'in, and Jorge J. Maldonado. 2017. Self-regulated Learning Strategies Predict Learner Behavior and Goal Attainment in Massive Open Online Courses. Comput. Educ. 104 (01 2017), 18--33. http://dx.doi.org/10.1016/j.compedu.2016.10.001Google ScholarGoogle Scholar
  7. René F. Kizilcec and Andrew J. Saltarelli. 2019. Can a Diversity Statement Increase Diversity in MOOCs?. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (L@S '19). ACM. http://dx.doi.org/10.1145/3330430.3333633Google ScholarGoogle Scholar
  8. René F. Kizilcec and Emily Schneider. 2015. Motivation as a Lens to Understand Online Learners: Toward Data-Driven Design with the OLEI Scale. ACM Trans. Comput.-Hum. Interact. 22 (2015), 6:1--6:24. http://dx.doi.org/10.1145/2699735Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. David R. Krathwohl. 2002. A Revision of Bloom's Taxonomy: An Overview. Theory Into Practice 41, 4 (2002), 212--218. http://dx.doi.org/10.1207/s15430421tip4104_2Google ScholarGoogle ScholarCross RefCross Ref
  10. Daeyeoul Lee, Sunnie Watson, and William Watson. 2018. Systematic literature review on self-regulated learning in massive open online courses. Australasian Journal of Educational Technology (03 2018). http://dx.doi.org/10.14742/ajet.3749Google ScholarGoogle Scholar
  11. Allison Littlejohn and Colin Milligan. 2015. Designing MOOCs for professional learners: Tools and patterns to encourage self-regulated learning. eLearning Papers 42 (06 2015), 38--45. http://oro.open.ac.uk/46385/Google ScholarGoogle Scholar
  12. Anoush Margaryan, Manuela Bianco, and Allison Littlejohn. 2015. Instructional quality of Massive Open Online Courses (MOOCs). Computers & Education 80 (01 2015), 77--83. http://dx.doi.org/10.1016/j.compedu.2014.08.005Google ScholarGoogle Scholar
  13. Colin Milligan and Allison Littlejohn. 2016. How health professionals regulate their learning in massive open online courses. The Internet and Higher Education 31 (2016), 113--121. http://dx.doi.org/10.1016/j.iheduc.2016.07.005Google ScholarGoogle ScholarCross RefCross Ref
  14. Colin Milligan and Allison Littlejohn. 2017. Why study on a MOOC? The motives of students and professionals. The International Review of Research in Open and Distributed Learning 18, 2 (2017). http://dx.doi.org/10.19173/irrodl.v18i2.3033Google ScholarGoogle ScholarCross RefCross Ref
  15. Daniel Onah and Jane Sinclair. 2017. Assessing Self-Regulation of Learning Dimensions in a Stand-alone MOOC Platform. International Journal of Engineering Pedagogy (iJEP) 7 (05 2017), 4. https://online-journals.org/index.php/i-jep/article/view/6511Google ScholarGoogle Scholar
  16. T. J. Park, H. J. Cha, and G. Y. Lee. 2016. A Study on Design Guidelines of Learning Analytics to Facilitate Self-Regulated Learning in MOOCs. Educational Technology International 17, 1 (2016), 117--150. http://www.kset.or.kr/eti_ojs/index.php/instruction/article/view/61Google ScholarGoogle Scholar
  17. Paul R. Pintrich. 2000. The Role of Goal Orientation in Self-Regulated Learning. In Handbook of Self-Regulation, Monique Boekaerts, Paul R. Pintrich, and Moshe Zeidner (Eds.). Academic Press, 451--502. http://dx.doi.org/10.1016/B978-012109890--2/50043--3Google ScholarGoogle Scholar
  18. Tobias Rohloff and Christoph Meinel. 2018. Towards Personalized Learning Objectives in MOOCs. In Lifelong Technology-Enhanced Learning. Springer International Publishing, 202--215. http://dx.doi.org/10.1007/978--3--319--98572--5_16Google ScholarGoogle Scholar
  19. Tobias Rohloff, Dominic Sauer, and Christoph Meinel. 2019a. On the Acceptance and Usefulness of Personalized Learning Objectives in MOOCs. In Proceedings of the Sixth (2019) ACM Conference on Learning @ Scale (L@S '19). ACM. http://dx.doi.org/10.1145/3330430.3333624Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Tobias Rohloff, Dominic Sauer, and Christoph Meinel. 2019b. Student Perception of a Learner Dashboard in MOOCs to Encourage Self-Regulated Learning. In 2019 IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE). in press.Google ScholarGoogle Scholar
  21. Dale H. Schunk. 2005. Self-Regulated Learning: The Educational Legacy of Paul R. Pintrich. Educational Psychologist 40, 2 (2005), 85--94. http://dx.doi.org/10.1207/s15326985ep4002_3Google ScholarGoogle ScholarCross RefCross Ref
  22. Daniel E. Steere and Domenico Cavaiuolo. 2002. Connecting Outcomes, Goals, and Objectives in Transition Planning. Teaching Exceptional Children 34, 6 (2002), 54--59. http://dx.doi.org/10.1177/004005990203400608Google ScholarGoogle ScholarCross RefCross Ref
  23. Julia Wilkowski, Amit Deutsch, and Daniel M. Russell. 2014. Student Skill and Goal Achievement in the Mapping with Google MOOC. In Proceedings of the First ACM Conference on Learning @ Scale Conference (L@S '14). ACM, 3--10. http://dx.doi.org/10.1145/2556325.2566240Google ScholarGoogle Scholar
  24. Saijing Zheng, Mary Beth Rosson, Patrick C. Shih, and John M. Carroll. 2015. Understanding Student Motivation, Behaviors and Perceptions in MOOCs. In Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing (CSCW '15). ACM, 1882--1895. http://dx.doi.org/10.1145/2675133.2675217Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Barry J. Zimmerman. 2000. Attaining Self-Regulation: A Social Cognitive Perspective. In Handbook of Self-Regulation, Monique Boekaerts, Paul R. Pintrich, and Moshe Zeidner (Eds.). Academic Press, Chapter 2, 13--39. http://dx.doi.org/10.1016/B978-012109890-2/50031--7Google ScholarGoogle Scholar

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        cover image ACM Other conferences
        L@S '20: Proceedings of the Seventh ACM Conference on Learning @ Scale
        August 2020
        442 pages
        ISBN:9781450379519
        DOI:10.1145/3386527

        Copyright © 2020 ACM

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        Publication History

        • Published: 12 August 2020

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