Chapter 11 Automated Analysis and Visualization of Online Learning Experiences and Engagement

In: Handbook of Research in Online Learning
Authors:
Sanghoon Park
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,
David H. Tai
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, and
Stephen Allen
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Type:
Chapter
Pages:
260–275
DOI:
https://doi.org/10.1163/9789004702813_011

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Abstract

In this chapter, we introduce an innovative approach to analyzing and visualizing online students’ learning experiences and engagement using an automated visualization system. As learners progress through an online course, they engage in cognitive learning activities and emotional stimulation while interacting with learning contents, and ultimately accumulate a series of learning experiences as they complete learning tasks throughout the semester. Hence, the analysis of online learning experiences needs to consider not only learners’ behavioral interactions within the course but also their cognitive and emotional engagement with learning content and tasks. We have developed an automated system, Course Activity Pulse for Students’ Understanding of Learning Ecology (CAPSULE), to efficiently analyze online students’ learning experiences and engagement on both the course and individual levels. We also illustrated how the analysis results can be presented in a visual dashboard, allowing course instructors to tailor online teaching strategies, or providing instructional designers with insights to reconsider course design. Specifically, the chapter provides the framework of online learning experience analysis, summaries of previous studies on online learning experience analysis, the design and development of an automated online learning experience analysis and visualization system, and the implications of the system for online learning practice, online course design, and online learning research.

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Cover Handbook of Research in Online Learning
E-Book ISBN:
9789004702813
Publisher:
Brill
Print Publication Date:
25 Sep 2024
Front Matter
Chapter 1 Lessons Learned in Conducting a Systematic Review in Online Learning
Chapter 2 The Study of Adult Learners in Distance Education
Chapter 3 Theories and Models to Frame, Inform, and Stimulate Online Learning
Chapter 4 What We Know about Online Teacher Education
Chapter 5 A Responsive, Consultative Professional Development Model for Online Engagement
Chapter 6 Active Learning by Design
Chapter 7 An Integrative Review of Adult Learners’ Informal Learning in Online Settings
Chapter 8 Metatextual Dimensions of Multimodal Literacy Engagement
Chapter 9 21 Years of Worldwide Learning Online
Chapter 10 A Critical Review of Two Automated Adaptive Teaching Systems in Mathematics and Chemistry
Chapter 12 Evidence-based Practices for Developing a Community of Inquiry
Chapter 13 Pre-service Teachers’ Preparation for Teaching Online
Chapter 14 Micro Investment, Big Reward
Chapter 15 Selecting Media and Methods to Facilitate Blended Learning
Chapter 16 The Impact of Digital Learning on Racially Minoritized and Poverty-Affected College Students
Chapter 17 Remote Academic Advising for Colleges and Universities
Chapter 18 Online Mentoring in the Digital Age
Chapter 19 Mental Fatigue in Online Learning
Chapter 20 Analysis of Patterns in Time for Evaluating Online Instruction
Chapter 21 The Difference between Emergency Remote Teaching and Online Learning
Chapter 22 Exploring the Impact of E-Texts on Student Learning in Online Education
Chapter 23 Supporting AI-Enhanced Active Online Learning Experiences
Chapter 24 Student Perceptions of Learning with Instructional AI Assistance in Online Discussions
Chapter 25 Gamification for Higher Education Applications
Chapter 26 Developing a Quality Assurance Approach for an Online Professional Military Education Institution
Chapter 27 Skills for Student Success in Online Learning
Back Matter

  • Bassi, M., Ferrario, N., Ba, G., Fave, A. D., & Viganò, C. (2012). Quality of experience during psychosocial rehabilitation: A real-time investigation with experience sampling method. Psychiatric Rehabilitation Journal, 35(6), 447–453. https://doi.org/10.1037/h0094578

    • Search Google Scholar
    • Export Citation

  • Borch, I., Sandvoll, R., & Risør, T. (2020). Discrepancies in purposes of student course evaluations: What does it mean to be “satisfied”? Educational Assessment, Evaluation, and Accountability, 32, 83–102. https://doi.org/10.1007/s11092-020-09315-x

    • Search Google Scholar
    • Export Citation

  • Bovill, C. (2011). Sharing responsibility for learning through formative evaluation: Moving to evaluation as learning. Practice and Evidence of Scholarship of Teaching and Learning in Higher Education, 6(2), 96–109.

    • Search Google Scholar
    • Export Citation

  • Carbone, A., Ross, B., Phelan, L., Lindsay, K., Drew, S., Stoney, S., & Cottman, C. (2015). Course evaluation matters: Improving students’ learning experiences with a peer-assisted teaching program. Assessment & Evaluation in Higher Education, 40(2), 165–180. https://doi.org/10.1080/02602938.2014.895894

    • Search Google Scholar
    • Export Citation

  • Ebner-Priemer, U. W., & Trull, T. J. (2009). Ecological momentary assessment of mood disorders and mood dysregulation. Psychological Assessment, 21(4), 463–475. https://doi.org/10.1037/a0017075

    • Search Google Scholar
    • Export Citation

  • Grace, L. J., Maheswari, V., & Nagamalai, D. (2011). Web log data analysis and mining. Proceedings of Advanced Computing First International Conference on Computer Science and Information Technology, Bangalore, India.

    • Search Google Scholar
    • Export Citation

  • Hektner, J. M., Schmidt, J. A., & Csikszentmihalyi, M. (2007). Experience sampling method: Measuring the quality of everyday life. Sage.

    • Search Google Scholar
    • Export Citation

  • Herrington, J., Reeves, T. C., & Oliver, R. (2006). Authentic tasks online: A synergy among learner, task, and technology. Distance Education, 27(2), 233–247. https://doi.org/10.1080/01587910600789639

    • Search Google Scholar
    • Export Citation

  • Illeris, K. (2003). Three dimensions of learning: Contemporary learning theory in the tension field between the cognitive, the emotional, and the social. Krieger.

    • Search Google Scholar
    • Export Citation

  • Kolb, A. Y., & Kolb, D. A. (2009). Experiential learning theory: A dynamic, holistic approach to management learning, education and development. In S. J. Armstrong & C. V. Fukami (Eds.), The Sage handbook of management learning, education and development (pp. 42–68). Sage.

    • Search Google Scholar
    • Export Citation

  • Kolb, D. A. (1984). Experiential learning: Experience as the source of learning and development. Prentice Hall.

  • Park, S. (2015). Examining learning experience in two online courses using Web logs and Experience Sampling Method (ESM). In B. Hokanson, G. Clinton, & M. W. Tracey (Eds.), The design of learning experience: Creating the future of educational technology. Springer-Verlag.

    • Search Google Scholar
    • Export Citation

  • Park, S. (2016). Analyzing and comparing online learning experiences through micro-level analytics. Journal of Educational Technology Development and Exchange, 8(2), 55–80. https://doi.org/10.18785/jetde.0802.04

    • Search Google Scholar
    • Export Citation

  • Park, S. (2017). Analysis of time-on-task, behavior experiences, and performance in two online courses with different authentic learning tasks. International Review of Research in Open and Distributed Learning, 18(2), 213–233. https://doi.org/10.19173/irrodl.v18i2.2433

    • Search Google Scholar
    • Export Citation

  • Park, S., & Yun, H. (2018). The influence of motivational regulation strategies on online students’ behavioral, emotional, and cognitive engagement. American Journal of Distance Education, 32(1), 43–56. https://doi.org/10.1080/08923647.2018.1412738

    • Search Google Scholar
    • Export Citation

  • Paas, F., Tuovinen, J. E., van Merriënboer, J. J. G., & Darabi, A. A. (2005). A motivational perspective on the relation between mental effort and performance: Optimizing learner involvement in instruction. Educational Technology Research & Development, 53(3), 25–34. https://doi.org/10.1007/BF02504795

    • Search Google Scholar
    • Export Citation

  • Pekrun, R. (1992). The impact of emotions on learning and achievement: Towards a theory of cognitive/motivational mediators. Applied Psychology, 41(4), 359–376. https://doi.org/10.1111/j.1464-0597.1992.tb00712.x

    • Search Google Scholar
    • Export Citation

  • Shearer, R. L., Aldemir, T., Hitchcock, J., Resig, J., Driver, J., & Kohler, M. (2020). What students want: A vision of a future online learning experience grounded in distance education theory. American Journal of Distance Education, 34(1), 36–52. https://doi.org/10.1080/08923647.2019.1706019

    • Search Google Scholar
    • Export Citation

  • Sozer, E. M., Zeybekoglu, Z., & Kaya, M. (2019). Using mid-semester course evaluation as a feedback tool for improving learning and teaching in higher education. Assessment & Evaluation in Higher Education, 44(7), 1003–1016. https://doi.org/10.1080/02602938.2018.1564810

    • Search Google Scholar
    • Export Citation

  • Tedesco, D., & Tullis, T. (2006). A comparison of methods for eliciting post-task subjective ratings in usability testing. Usability Professionals Association (UPA), 1–9.

    • Search Google Scholar
    • Export Citation

  • Turner, J. C., Cox, K. E., DiCintio, M., Meyer, D. K., Logan, C., & Thomas, C. T. (1998). Creating contexts for involvement in mathematics. Journal of Educational Psychology, 90(4), 730–745. https://doi.org/10.1037/0022-0663.90.4.730

    • Search Google Scholar
    • Export Citation

  • Uekawa, K., Borman, K., & Lee, R. (2007). Student engagement in U.S. urban high school mathematics and science classrooms: Findings on social organization, race, and ethnicity. The Urban Review, 39(1), 1–43. https://doi.org/10.1007/s11256-006-0039-1

    • Search Google Scholar
    • Export Citation

  • Weimer, M. (2002). Learner-centered teaching: Five key changes to practice. Jossey-Bass.

  • Woo, Y., & Reeves, T. C. (2008). Interaction in asynchronous Web-based learning environments: Strategies supported by educational research. Journal of Asynchronous Learning Networks, 12(3–4), 179–194.

    • Search Google Scholar
    • Export Citation

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