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Week 1, video 4:

Week 1, video 4:. Classifying in RapidMiner 5.3. Hands-On Activity. Running algorithm in RapidMiner 5.3 Follow along on your own Data set is on Coursera SaoPedroetal(2013)_UMUAI_DesigningControlledExperiments_cummandlocalfeatures.csv. Data Comes From.

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Week 1, video 4:

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  1. Week 1, video 4: Classifying in RapidMiner5.3

  2. Hands-On Activity • Running algorithm in RapidMiner 5.3 • Follow along on your own • Data set is on Coursera • SaoPedroetal(2013)_UMUAI_DesigningControlledExperiments_cummandlocalfeatures.csv

  3. Data Comes From • Sao Pedro, Baker, Gobert, Montalvo, & Nakama (2013) Leveraging Machine-Learned Detectors of Systematic Inquiry Behavior to Estimate and Predict Transfer of Inquiry Skill. User Modeling and User-Adapted Interaction, 23 (1), 1-39. 

  4. Data Comes From • Predicting whether students correctly design controlled experiments when learning in a science inquiry microworld, Inq-ITS

  5. Let’s Build Some Models

  6. Open RapidMiner 5.3 • And open a new process

  7. You may need to install the WEKA expansion pack…

  8. Use this to set up student-level/demographic-level/content-level cross-validation

  9. Try it yourself with other algorithms! • W-JRip • W-KStar • Linear Regression (implements Step Regression)

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