Lessons learned from the first worldwide accessible e-learning in Landscape Ecology
Massive open online courses (MOOCs) are distance learning tools for individualized learning. They allow students to learn at their own pace in a virtual classroom. We describe success and pitfalls of the MOOC Landscape Ecology, designed as an undergraduate University course taught by an international consortium of Professors covering theory and application of the field. The paper describes course performance with summary metrics, illustrates contents and didactic tools, and provides a list of suggestions for instructors who engage in distant learning. We identify the following five key success factors for this and related MOOCs: (1) commitment and passion of an international consortium of lecturers; (2) a sound mixture of theory and practice; (3) numerous field-videos; (4) content and skill-oriented practicums (here using R, GIS, remote sensing); and (5) interactive formats where students discuss and share their opinions. In all runs of our MOOC we experienced some difficulties with peer-assessed writing tasks due to widely differing “review cultures”. The instructor-paced MOOC attracted over 3500 students in 2018 and 2019, and had comparably high completion rates (14% and 11%, respectively), compared to typical MOOC completion rates ranging from 5% to 15%. Completion rates in our self-paced run in 2020 were 8-9% only.
Bloom, B.S., Engelhart, M.D., Furst, E.J., Hill, W.H., Krathwohl, D.R., 1956. Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. McKay, New York.
Bonafini, F.C., Chae, C., Park, E., Jablokow, K.W., 2017. How Much Does Student Engagement with Videos and Forums in a MOOC Affect Their Achievement? Online Learning 21 (4), 223-240. DOI:10.24059/olj.v21i4.1270
Branch, R.M., 2009. Instructional design. The ADDIE Approach. Springer, New York. Conole, G., 2015. Designing effective MOOCs. Educational Media International 52 (4), 239-252. DOI:10.1080/09523987.2015.1125989
Diver, P., Martinez, I., 2015. MOOCs as a massive research laboratory: opportunities and challenges. Distance Education, 36 (1), 5-25. DOI:10.1080/01587919.2015.101996
Eriksson, T., Adawi, T., Stohr, C., 2017. Time is the bottleneck: a qualitative study exploring why learners drop out of MOOCs. Journal of computing in higher education 29 (1), 133-146. DOI:10.1007/s12528-016-9127-8
Evans, B.J., Baker, R.B., Dee, T.S., 2016. Persistence Patterns in Massive Open Online Courses (MOOCs). Journal of Higher Education 87 (2), 206-242. DOI:10.1353/jhe.2016.0006
Henderikx, M.A., Kreijns, K., Kalz, M., 2017. Refining success and dropout in massive open online courses based on the intention-behavior gap. Distance Education 38 (3), 353-368. DOI:10.1080/01587919.2017.1369006
Hew, K.F., 2016. Promoting engagement in online courses: What strategies can we learn from three highly rated MOOCS. British Journal of Educational Technology 47 (2), 320-341. DOI:10.1111/bjet.12235
Jentges, E., Kölbel, J., 2016. Training Critical Thinking Skills:The 6SA Method. http://blogs.ethz.ch/refreshteaching/files/2016/01/The_6SA_Method.pdf [Accessed 31. March].
Kienast, F., Ghosh, R., Wildi, O., (eds.) 2007. A changing world: Challenges for landscape research. Landscape Series. Springer.
Kulkarni, C., Wei, K.P., Le, H., Chia, D., Papadopoulos, K., Cheng, J., Koller, D., Klemmer, S.R., 2013. Peer and Self Assessment in Massive Online Classes. ACM Transactions on Computer-Human Interaction 20 (6), 33. DOI:10.1145/2505057
Loizzo, J., Ertmer, P.A., Watson, W.R., Watson, S.L., 2017. Adults as self-directed and determined to set and achieve personal learning goals in MOOCs: learners’ perceptions of MOOC motivation, success, and completion. Online Learning 21 (2) (online). DOI:10.24059/olj.v21i2.889
Lopez-Goni, I., Sanchez-Angulo, M., 2018. Social networks as a tool for science communication and public engagement: focus on Twitter. FEMS Microbiology Letters 365 (2), 246. DOI:10.1093/femsle/fnx246
Maartje A. Henderikx, K.K., Kalz, M., 2017. Refining success and dropout in massive open online courses based on the intention–behavior gap. Distance Education 38(3), 353-368. DOI:10.1080/01587919.2017.1369006
Shah, D., 2018. By The Numbers: MOOCs in 2018. https://www.classcentral.com/report/moocstats-2018/ [Accessed 31. March].
Sunar, A.S., White, S., Abdullah, N.A., Davis, H.C. 2017. How Learners’ Interactions Sustain Engagement: A MOOC Case Study. IEEE Transactions on Learning Technologies 10(4), 475-487. DOI:10.1109/TLT.2016.2633268
Tinto, V., 1975. Dropout from higher education: A theoretical synthesis of recent research. Review of Educational Research 45, 89–125.DOI:10.2307/1170024
Tinto, V., 1982. Defining dropout: A matter of perspective. New Directions for Institutional Research 1982, 3–15. DOI:10.1002/ir.37019823603
Xing, W.L., Chen, X., Stein, J., Marcinkowski, M. 2016. Temporal predication of dropouts in MOOCs: Reaching the low hanging fruit through stacking generalization. Computers in human behavior 58, 119-129. DOI:10.1016/j.chb.2015.12.007
Zhu, M., Bonk, C.J., & Sari, A.R., 2018. Instructor experiences designing MOOCs in higher education: Pedagogical, resource, and logistical considerations and challenges. Online Learning, 22(4), 203-241. DOI:10.24059/olj.v22i4.1495
Copyright (c) 2020 Felix Kienast, Selina Gosteli, Thomas C. Jr. Edwards, Gregor Martius
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