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Present: Cindy, Justin, Jutta, Michelle, Ned, Philip, Ted,

Video Recording

Google Doc

Agenda

  1. Jutta continues the talk she didn’t finish in the last meeting
  2. Discussion of a draft diagram of the high level implementation structure

Notes:

Requirement challenges:

  • Learners can control 3 parts to influence the matching: 
  1. Learning goals
  2. Configure the preprocessor generated summary of learning needs/preferences
  3. Feedback on matching results

Are these enough for the matching engine to be non-black box?

  • Large data sets to train algorithms
  • What other exploration tools can learners use to discover their learning needs?
  • Where to search OER material? Wild web?


Technical Challenges:

  • Preprocessor algorithm
  • Matching algorithm
  • How to identify each learner across all exploration tools and platforms


3 main direction of WeCount:

  1. Address data gaps through co-design, challenge workshops that data related problems are not addressed
  2. Identify accessibility issues of existing data science tools. Address these through co-design.
  3. Explore the possibilities of moving against the bias, especially deep learning / big data based systems.


Floe infrastructure is the Floe match within WeCount

Issues with the diagram:

  1. Instead of letting learners config a set of preferences, machine learning should be applied to understand how learners learn better
  2. Instead of watching learners to discover their preferences, learners should be given a tool to track and record their preferences


The actual understanding of how learners learn better, such as kids learning math thru dinosaurs 


Next meeting: Tomorrow 1-2PM


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