learning transfer: does it take place in moocs
TRANSCRIPT
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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
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The case for learning transfer
retention
MOOC environment
engagement
learning
…≠
knowledgeapplication in practice
“learning transfer”
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Our questionsTo what extent does the transfer of learned conceptstake place?
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What type of learners are most likely to make thetransfer?
How does the transfer manifest itself over time?
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Main challenge: data
@flickr:360around
accessible?
large-scale?
relevant?
longitudinal?
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WebSocial Web
Beyond the MOOC environment hundreds of
millions of users
most focus on users’ private lifes
professional networks are becoming popular
MOOCenvironment
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GitHub
10+ million registered users
hosting, collaboration and organisation
the most popular social coding platform
founded in 2007long-term
large-scale
detailed
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detailed logs
code changes
project meta-data
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Hypotheses grounded in prior works
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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.
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From hypotheses to measurements
FP101xlogs surveys coding
activities++
email@address
Are changes made in a
functional language?
3 months 2.5 years + 0.5 years
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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).
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A sanity check
FP101xbefore FP101x after FP101x
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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.
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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.
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FP101x does not influence the amount of functional coding by our Expert learners.
Do Expert learners change?
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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.
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Learners are more likely to transfer when …
they are intrinsically motivated.
have high self-efficacy.
are experienced programmers.
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Learners who transfer quickly move on
FP101x
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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