Learning Transfer: Does it take place in MOOCs
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Presented at LWMOOCs 2015 in New York City by Claudia Hauff
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Learning Transfer: Does it take place in MOOCs
- 1. Learning Transfer: does it take place? Guanliang Chen*, Dan Davis*, Claudia Hauff*, Georgios Gousios+ and Geert-Jan Houben* An Investigation into the Uptake of Functional Programming in Practice * Delft University of Technology, the Netherlands + Radboud University Nijmegen, the Netherlands
- 2. The case for learning transfer retention MOOC environment engagement learning … ≠ knowledge application in practice “learning transfer”
- 3. Our questions To what extent does the transfer of learned concepts take place? @flickr:eviloars What type of learners are most likely to make the transfer? How does the transfer manifest itself over time?
- 5. Web Social Web Beyond the MOOC environment hundreds of millions of users most focus on users’ private lifes professional networks are becoming popular MOOC environment
- 6. GitHub 10+ million registered users hosting, collaboration and organisation the most popular social coding platform founded in 2007
- 7. detailed logs code changes project meta-data
- 8. Hypotheses grounded in prior works @flickr:carbonnyc H1: Only a small fraction of engaged learners is likely to exhibit learning transfer. H3: Learners expressing high self-efficacy are more likely to actively apply their trained tasks in new contexts. H4: Learners exhibiting a high-spacing learning routine are more likely to exhibit learning transfer. H5: The amount of exhibited transfer decreases over time. H2: Intrinsically motivated learners with mastery goals are more likely to exhibit transfer than extrinsically motivated learners.
- 9. From hypotheses to measurements FP101x logs surveys coding activities++ email@address 3 months 2.5 years + 0.5 years
- 10. FP101x @flickr:christiaan_008 Course programming language: Haskell Run as a typical video-lecture based MOOC Assessment: 288 Multiple Choice questions Introduction to Functional Programming 37,485 learners registered. 41% engaged with the course. 5% completed the course. 33% were active on GitHub (1.1M events).
- 11. A sanity check FP101xbefore FP101x after FP101x
- 12. Are “GitHub learners” different? GitHub learners Non-GitHub learners #Learners 12,415 25,070 Completion rate 7.71% 4.03% Avg. time watching videos 49.1 min 27.7 min Avg. #questions attempted 31.3 17.5 Avg. accuracy of learners’ answers 23.4% 12.9% GitHub learners are more engaged than non-GitHub learners and exhibit higher levels of knowledge.
- 13. Are “Expert learners” different? Expert GH learners Novice GH learners #Learners 1,721 10,694 Completion rate 15.0% 6.5% Avg. time watching videos 78.6 min 44.4 min Avg. #questions attempted 57.9 27.0 Avg. accuracy of learners’ answers 38.0% 21.1% Expert learners are more engaged than Novice learners and exhibit higher levels of knowledge.
- 14. FP101x does not influence the amount of functional coding by our Expert learners. Do Expert learners change?
- 15. What happens to our Novice learners? Learning transfer occurs at a rate of 8.5%. Learning transfer does not decrease over time. Lack of learning transfer mainly due to a lack of opportunities.
- 16. Learners are more likely to transfer when … they are intrinsically motivated. have high self-efficacy. are experienced programmers.
- 17. Learners who transfer quickly move on FP101x
- 18. Conclusions Most transfer learning findings from the classroom hold. The observed transfer rate is low: 8.5%. Learners quickly moved on after the course to industrially-relevant functional languages. @flickr:torsten-reuschling
Notas del editor
- Existing investigations into student learning within MOOC environments are commonly based on pre- & post-course surveys and log traces generated within those environments by the individual learners While student learning is indeed an important measure of success, we argue that another key measure is the amount of learning transfer that is taking place: do learners actually utilize the newly gained knowledge in practice? Are learners expanding their knowledge in the area over time or do they eventually move back to their pre-course knowledge levels and behaviours?
- Research questions
- The main challenge researchers face in answering these questions is the lack of accessible , large-scale, relevant and longitudinal data traces outside the MOOC environments.
- MOOCs are embedded deeply into the Web. Social Web platforms (Twitter being the prime example) have become a mainstay of the Web. They are used by hundreds of millions of users around the world and often provide open access to some | if not all | of the data generated within them. While most of these platforms are geared towards people's private lives, in the past few years social Web platforms have also begun to enter our professional lives. We focus on one specific one: GitHub.
- GitHub is one of the most popular social coding platforms world-wide with more than 10 million registered users. As GitHub was founded in 2007, we have potential access to log traces reaching several years into the past; moreover, its continuously increasing popularity will enable us to observe our learners over years to come.
- An example GitHub page showing the type of data we can draw from them. GitHub’s API is open.
- Our five hypotheses. All of them are based on prior works in the classroom and workplace transfer learning literature. high self-efficacy: those learners that believe in themselves high-spacing learning routine: the learner learns over time instead of a single big “cramming” session (the so-called low-spacing learners)
- FP101x is a functional programming course [explained more in the next slide] We used GitHub data across 3 years: 2.5 years before the course and 6 months after the course. Linking between edX and GitHub through email addresses.
- FP101X = Introduction to Functional Programming is a MOOC offered by the Delft University of Technology on the edX platform. The course introduces learners to various functional programming concepts; all programming is performed in the functional language Haskell. The ~12,000 learners on GitHub created more than 1 million events (pushing code changes) The first iteration of the course ran between October 15, 2014 and December 31, 2014. The course was set up as an xMOOC: lecture videos were distributed throughout the 8 teaching weeks. Apart from lectures each week, exercises were distributed in the form of multiple choice (MC) questions. To enable fully automatic evaluations, all lab work was also assessed through MC questions. Each of the 288 MC questions was worth 1 point & could be attempted once. Answers were due 2 weeks after the release of the assignment. To pass the course, 60% of all MC questions had to be answered correctly. Overall, 37,485 users registered for the course. Fewer than half (41%) engaged with the course, watching at least one lecture video. The completion rate was 5.25%, in line with similar MOOC offerings. 33% of all registered learners were active on GitHub.
- If everything works as expected, Haskell should go up during the course months and then go down again. It does.
- Learners partitioned into those found on GitHub and those not found on GitHub. Significant differences are shown in bold.
- Experts: functional experience before FP101x. Novices: functional experience only after FP101x.
- Plotted here is the percentage of functional programming learners exhibit (the percentage of all programming). We do not observe an effect of FP101x - functional programming levels tend to increase over time.
- This plot shows all Novice learners that started programming afterwards. Substantial percentage of programming occurs in functional languages. Overall, only 8.5% of the Novice engaged learners actually started functional programming after the course. Learning transfer does not decrease over time. To learn why the transfer rate is so low, we run a survey among the Novice learners - 80% of the time, learners mentioned the lack of opportunities to apply that knowledge in their work setting.
- These three hypotheses from the literature are also confirmed for MOOCs (H1-H3).
- Here is a splitup of all functional languages: which one do learners turn to over time? We see that initially Haskell is important (course language), however, learners quickly turn to Scala, the industrially leading functional language.