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Design a MOOC – Think Granular!

Why do students drop-out of MOOCs? What can be done to improve MOOC completion rates?

Editor’s Note: This is a guest post by Elke Lackner (University of Graz), Martin Ebner and Mohammad Khalil (Graz University of Technology).

You sign up for a MOOC because the topic seems to be really interesting and you have some spare time left. The first week is highly interesting and you are very active and motivated. But from week to week your enthusiasm is constantly diminishing. Some unexpected duties drop in and your motivation keeps forfeiting. At a certain point you realize that you have already lost the track and you decide to give in.

Does this situation sound familiar to you? Have you ever started a MOOC and capitulated due to different reasons (e.g. personal, professional, and/or motivational)? You answer in the affirmative? You nod? Don’t be frustrated, you are in good company.

MOOCs and their drop-out problem

It has often been highlighted that MOOCs suffer from a high drop-out rate or, to put it the other way round, from low completion rates. There are thousands of interested learners who start a MOOC and only hundreds that complete it. Hollands and Tirthali (2014, 42) state that “3% to 15% of all enrollees” complete a course, whereas Jasnani (2013, 6) writes about “the typical completion rate of below 10%, approximately 7.5%”. The reasons to drop a course are manifold, as Colman (2013) resumes from a survey he conducted. They range from a lack of time, to the fact that participants are just interested in parts of a MOOC or signed up for it because they were curious about the MOOC or its topic but didn’t want to complete the course. Some of the interviewed people just completed parts of the MOOC and some dropped it because they were disappointed e.g. of the (instructional) course design, unexpected costs or technical problems. Others feel overwhelmed by the different requirements asked for in the MOOC or the information overload they cannot cope with. Others, finally, are not used to learn in a self-autonomous and self-regulated way and do not have appropriate learning strategies or competences to “survive” a MOOC journey.

Week 4 is crucial

What we have found out using Learning Analytics (LA) to collect and use the data of three of our courses at the Austrian MOOC platform iMooX (www.imoox.at) is that there seems to be a specific “crucial point” within a course that can be seen as a drop-out-point: week 4. In our paper MOOCs as granular systems: design patterns to foster participant activity (Lackner et al. 2015) we attempted to answer the question at which point of an xMOOC learners decide whether they will more likely complete or quit the course. LA as an interaction analysis of educational data aims at understanding and consequently improving learning behavior (Greller et al. 2014). Using LA to gather the enormous amount of data that arises within courses with a huge number of participants means to gather data from different sources behind the scenes. The participants do not know that their behavior is being tracked and act in a natural way. The collected data consists of different activities, such as, for example, simple log files, the number of posts written in discussion forums or the, the number of read posts (written by their colleagues). Additionally we could see how often the participants did the quizzes and which questions caused difficulties.

We observed two interesting aspects analyzing the data of our three courses:

a. Talking about completion rates and student activity has to be done in a differentiated way. There are three numbers that have to be considered: enrolled participants, active participants and “completers”. Figure 1 might help to visualize these different types of participants:

Students Summary in MOOCs-2
Figure 1: Different types of MOOC participants

An active participant is a participant who, at least, did one quiz or wrote one forum post within the course. A certified student passed all quizzes with at least 75% right answers and did a final feedback survey at the end of the course. Looking at the chart, you can identify a huge gap in our courses between those registered and those who completed the course or even got the confirmation of participation.

Let’s take a closer look at the course Gratis Online Lernen 2014 to nail this observation down: Calculating the completion rate on the basis of the registered participants, it amounts to 21.44% (with a certification rate of 17.49%). If we take the active participants as our sample, the completion rate is 45.30% and the certification rate 36.95%. The numbers double. [If you are interested in a more detailed analysis, you find different tables and visuals in our paper MOOCs as granular systems: design patterns to foster participant activity.]

b. The “drop-out point” in these three courses lies between the fourth and fifth week. Whereas the participants are highly active within the first (and even the second) week, their activity starts decreasing in week three and stays then, after week four, more or less constant.

Lessons learned

It is evident that our study provides only first results and is therefore biased. There are specific circumstanced that have to be considered: The Austrian MOOC platform offers, for example, only courses in German language and most of the participants have at least a bachelor’s degree (Neuböck et al. 2015). Nevertheless, keeping these first results in mind, the (instructional) course design of xMOOCs has to be reconsidered. It has already been stated that MOOCs need a special and elaborated instructional design (Guardià & Sangrà 2013; Hayes 2015; Jasnani 2013; Kopp & Lackner 2014; Richter 2013) as the course design is a crucial factor when deciding whether to complete or quit a MOOC, as Colman (2013) has shown. Here are three simple tips that might help to increase the completion rates of a MOOC:

1. Many of the MOOCs provided by well-known institutions, for example Coursera (www.coursera.org), edX (www.edx.org), FutureLearn (www.futurelearn.org), Mooin (https://mooin.oncampus.de) or iversity (www.iversity.org), last six to ten weeks. As we have seen, the “drop-out-point” – at least in the courses we scrutinized – can be identified between week four and week five. So a more granular course design should be taken into consideration. Think granular and design four-week courses. Split the content into different parts and offer rather a series of two or three MOOCs each lasting four weeks than one twelve-week MOOC (Hollands & Tirthali 2014, 92; Jasnani 2013, 14). [A tendency to offer shorter MOOCs can already be spotted when browsing different platforms. This tendency seems to be rather new as the first MOOCs mostly had an eight-week structure.]

2. Many participants do not want to do the entire course, are interested in some pieces of information or do not intend to get a certificate. To make their learning outcomes visible, implement a badge system (CEDEFOP 2009; Schön et al. 2013). Badges showcase the learning process. Even if participants do not want to have a certificate for the course, they may want to earn a badge on a specific topic, unit or part of the course.

3. Learners might overcome the drop-out-point when suspense is high or they interact actively (Khalil & Ebner 2014). Use traditional ways of creating suspense, stemming from narration (Bakker 2013), to sow the desire within your learners to “stay tuned”. Create different suspense peaks so the participants “have to” come back to the course to get the missing information or the solution of a (hidden) riddle.

These three lessons might help all those who want to design a MOOC to do it in way that participants stay tuned. All others interested in MOOCs and participating in MOOCs might see this post as an incentive to rethink their own behavior. When do you decide to quit a MOOC? Why do you drop out of a course? Are you an enrolled or an active participant who drops the course?

List of references

Bakker, E. J. (1993). Activation and Preservation: The Interdependence of Text and Performance in an Oral Tradition. In: Oral Tradition, 8 (1), 5-20.

CEDEFOP (2009). European guidelines for validating non-formal and informal learning, Luxembourg: Office for Official Publications of the European Communities. Retrieved from: http://www.cedefop.europa.eu/files/4054_en.pdf [2015/05/06].

Colman, D. (2013). MOOC Interrupted: Top 10 Reasons Our Readers Didn’t Finish a Massive Open Online Course. Retrieved from: http://www.openculture.com/2013/04/10_reasons_you_didnt_complete_a_mooc.html [2015/05/06].

Greller, W.; Ebner, M. & Schön, M. (2014). Learning Analytics: From Theory to Practice – Data Support for Learning and Teaching. In: M. Kalz & E. Ras (eds.), Computer Assisted Assessment. Research into E-Assessment. New York: Springer, 79-87.

Guàrdia, L.; Maina, M. & Sangrà, A. (2013). MOOC Design Principles. A Pedagogical Approach from the Learner’s Perspective. In: eLearning Papers, 33, 1-6. Retrieved from http://www.openeducationeuropa.eu/en/download/file/fid/27126 [2015/05/06].

Hayes, S. (2015). MOOCs and Quality: A Review of the Recent Literature. QAA MOOCs Network. Retrieved from: http://www.qaa.ac.uk/en/Publications/Documents/MOOCs-and-Quality-Literature-Review-15.pdf [2015/08/10].

Hollands, F. M. & Tirthali, D. (2014). MOOCs: expectations and reality. Full report. Center for Benefit-Cost Studies of Education, Teachers College, Columbia University, NY. Retrieved from: http://cbcse.org/wordpress/wpcontent/uploads/2014/05/MOOCs_Expectations_and_Reality.pdf [2015/05/06].

Jasnani, P. (2013). Designing MOOCs. A White Paper on Instructional Design for MOOCs, 1-35. Retrieved from http://www.tatainteractive.com/pdf/Designing_MOOCs-A_White_Paper_on_ID_for_MOOCs.pdf [2015/05/06].

Khalil, H. & Ebner, M. (2014). MOOCs Completion Rates and Possible Methods to Improve Retention – A Literature Review. In: Proceedings of World Conference on Educational Multimedia, Hypermedia and Telecommunications 2014. Chesapeake, VA: AACE, 1236-1244.

Kopp, M. & Lackner, E. (2014). Do MOOCs need a Special Instructional Design?. In:EDULEARN14 Proceedings, 7138-7147.

Lackner, E.; Ebner, M. & Khalil, M. (2015). “MOOCs as granular systems: design patterns to foster participant activity”, in: eLearning Papers n.º 42, https://www.openeducationeuropa.eu/en/article/Design-Patterns-for-Open-Online-Teaching-and-Learning-In-Depth-42-3, June 2015, 1-10.

Neuböck, K.; Kopp, M. & Ebner, M. (2015). What do we know about typical MOOC participants? First insights from the field, in: EMOOCS2015. Proceedings Papers. European Stakeholder Summit on Experiences and best practices in and around MOOCs, 18-20 May 2015, Université Catholique de Louvain, Mons (Belgium), 183-190.

Richter, S. (2013). Tips for Designing a Massive Open Online Course. Retrieved from http://facdevblog.niu.edu/tips-for-designing-a-massive-open-online-course-mooc [2015/05/06].

Schön, S.; Ebner, M.; Rothe, H.; Steinmann, R. & Wenger, F. (2013). Macht mit im Web! Anreizsysteme zur Unterstützung von Aktivitäten bei Community- und Content-Plattformen. Güntner, G. & Schaffert, S. (eds.). Salzburg: Salzburg Research.

(c) 2015 Elke Lackner, Martin Ebner & Mohammad Khalil, released under Creative Commons Attribution License CC-BY 4.0

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