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TOWARDS AN APPROACH FOR MEASURING DIFFERENT TYPES AND INTENSITIES OF INTERACTION IN MASSIVE OPEN ONLINE COURSES (MOOCs)

P. Barbosa Cabral, S. Topolovec, J.P. van Staalduinen

Delft University of Technology (The Netherlands)

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Structure

• Background• Research Proposal• Challenges

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TU Delft’s MOOCs@pedrobcabral

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Background• MOOCs generates massive data on

behaviour

• Anderson’s Interaction Equivalency Theorem (IET) still lacks enough research

• Measuring interaction seems easier in an online environment

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Interaction Equivalency Theorem (IET) (Anderson, 2003)

• Deep and meaningful learning can be achieved if one of the interactions is in a high level

• Increasing the level in one of the modes will leadto more satisfaction butless cost or time-effective

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Research Proposal

• Identify different types of interaction and its intensity in a MOOC

• Identify possibilities and limitation of measuring it within the MOOC environment

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Student-content interaction• edX’s Course tab

– Videos– Assignments / exercises,– Text,– Handouts,– Textbook,– Images,– Interactive images,– Simulations,– Games, etc.

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S-C interaction – challenges

• Course design ≠ Students’ learning experience

• Passive = Active interaction ?

• 1 click = 1 S-C interaction?– Click on an unit?– Click on a video or a quiz question or …?

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Student-Student & S-Teacher interaction

• edX’s Discussion tab

• Other places to interact– Inside edX environment (e.g. Teams tool)– Social media– F2F– …

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S-S & S-T interaction – challenges

• Course design ≠ Students’ learning experience

• Passive = Active interaction ?

• Lonely posts ?

• Necessity to do a content analysis

• Up-votes, endorsements, marked as answered ?

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Interaction intensity• What is a high, medium or low level?

• Time? Learning sequence? Average interaction per student?

• Counting clicks ≠ counting messages

• Length and time effort of courses

• Students’ perception about intensity

• Absolut numbers or proportional values

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Putting all together

• Focus on IET’s second thesis(raise interaction = increase satisfaction, costs and time)

• Only students who have finished the MOOC– Average interaction– Average satisfaction– Compare different MOOCs

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Conclusions• Factors which influence the measure of

interaction– Course design– The platform– Data availability– What and how to measure

• Next phase: set the parameters and test IET’s second thesis.

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Thankyou@pedrobcabralhttp://www.slideshare.net/pmcabral https://online-learning.tudelft.nl/