Large Scale Randomized Control Trials to Validate Design of Digital Learning Platforms

Votes: 21
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The design and configuration of platforms for online learning such as MOOCs is an untested knowledge domain. We conceptualise a platform for knowledge gifting - whereby subject matter experts can be quickly trained to create and share their knowledge and receivers who are in need of such knowledge can interact. We propose that multiple, repeated Randomised Control Trials (RCTs) ) be conducted with the careful design of MOOC platforms and slection of measureable outcomes of effectiveness.

Please see illustration of RCT Experimental Design

The RCTs could proceed as follows. Standardizing a given number of subject or topics of impact (say, 3 diverse courses in Health & Nutrition, Python Programming, Parenting Skills for Beginners) and a controlled number of knowledge givers with the requisite expertise (say, 2 instructors per knowledge stock), we create a learning pathway or instructional design for a given time period (say, 12 weeks). Both content and assessments are part of this instructional design. Each course could be limited to, again say, 100 knowledge receivers who will be equally but randomly assigned to either a MOOC-delivered or F2F-delivered pathway at the start (split half). The MOOC platform to be used will comprise varying functionalities to be subject to experimentation whereas F2F delivery will be in a traditional classroom setting. At the mid-point (ie 6 weeks), an assessment of learning, a satisfaction survey, and measures of student engagement will be taken.

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  • ABOUT THE ENTRANT

  • Name:
    Ravi Sharma
  • Type of entry:
    team
    Team members:
    Kevin Jones, Ravi Sharma & Maryam Tabatabae
  • Profession:
    Not for Profit Advocate
  • Ravi is inspired by:
    My team and I have been gutted by the plight of students in the current Covid-19 pandemic.
  • Software used for this entry:
    NA
  • Patent status:
    none