Identifying Learning Activity Sequences that Are Associated with High Intention-Fulfillment in MOOCs

Sep 9, 2019·
Eyal Rabin
,
Vered Silber-Varod
,
Yoram Kalman
Marco Kalz
Marco Kalz
· 1 min read
Abstract
Learners join MOOCs (Massive Open Online Courses) with a variety of intentions. The fulfillment of these initial intentions is an important success criterion in self-paced and open courses. Using post course self-reported data enabled us to divide the participants to those who fulfilled the initial intentions (high-IF) and those who did not fulfill their initial intentions (low-IF). We used methods adapted from natural language processing (NLP) to analyze the learning paths of 462 MOOC participants and to identify activities and activity sequences of participants in the two groups. Specifically, we used n-gram analysis to identify learning activity sequences and keyness analysis to identify prominent learning activities. These measures enable us to identify the differences between the two groups. Differences can be seen at the level of single activities, but major differences were found when longer n-grams were used. The high-IF group showed more consistency and less divergent learning behavior. High-IF was associated, among other things, with study patterns of sequentially watching video lectures. Theoretical and practical suggestions are introduced in order to help MOOC developers and participants to fulfill the participants’ learning intentions.
Type
Publication
Transforming Learning with Meaningful Technologies, 224-235. Springer International Publishing
publications publications

Rabin, E., Silber-Varod, V., Kalman, Y.M., Kalz, M. (2019). Identifying Learning Activity Sequences that Are Associated with High Intention-Fulfillment in MOOCs. In Scheffel, M., Broisin, J., Pammer-Schindler, V., Ioannou, A., Schneider, J. (eds) Transforming Learning with Meaningful Technologies. (pp.224-235) EC-TEL 2019. Lecture Notes in Computer Science(), vol 11722. Springer, Cham. https://doi.org/10.1007/978-3-030-29736-7_17

MOOCS Intention-Fulfilment N-Gram Sequencing Learning Paths
Marco Kalz
Authors
Marco Kalz
Professor of Educational Technology

I am a researcher in educational technology whose work explores how digital technologies reshape learning, teaching, and educational institutions. My research combines educational technology, learning sciences, feedback research, and critical perspectives on digital transformation.Current areas of interest include peer feedback and feedback literacy, open and networked learning, AI and misinformation in education, digital learning ecologies, and the societal implications of data-driven and platform-based education. Methodologically, my work spans empirical learning research, psychometric scale development, design-oriented research, and conceptual analyses of digital transformation in education.

I am working as a full professor of educational technology and Chief Information/Chief Digital Officer (CIO/CDO) at the Heidelberg University of Education. I serve as associate editor of the International Journal of Artificial Intelligence in Education and editorial board member of the Journal of Computing in Higher Education. I am a senior-fellow of the Interuniversity Center for Educational Sciences (ICO) and the Dutch research school on information and knowledge systems (SIKS). I work as director of the study program E-Learning and Media Education and director of the Heidelberg Centre for Digital Transformation in Education. Over the years I could secure approx. 4 Mio EUR of research funding for my institutions from competitive projects with a total budget of 36 Mio EUR. I have been an invited keynote speaker on more than 60 conferences and events. Besides European projects I am regularly involved in educational innovation and consulting projects with partners inside and outside of my institutions including clients like the International Labour Organisation, United Nations Environment Program, the European Commission, UNESCO, OECD or other international and national organizations.