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

The case for learning transfer

retention

MOOC environment

engagement

learning

…≠

knowledgeapplication in practice

“learning transfer”

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?

Main challenge: data

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accessible?

large-scale?

relevant?

longitudinal?

WebSocial Web

Beyond the MOOC environment hundreds of

millions of users

most focus on users’ private lifes

professional networks are becoming popular

MOOCenvironment

GitHub

10+ million registered users

hosting, collaboration and organisation

the most popular social coding platform

founded in 2007long-term

large-scale

detailed

detailed logs

code changes

project meta-data

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.

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

FP101x

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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).

A sanity check

FP101xbefore FP101x after FP101x

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.

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.

FP101x does not influence the amount of functional coding by our Expert learners.

Do Expert learners change?

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.

Learners are more likely to transfer when …

they are intrinsically motivated.

have high self-efficacy.

are experienced programmers.

Learners who transfer quickly move on

FP101x

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.

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Thank you.

c.hauff@tudelft.nl