Subject: Summary and next steps - 03/16
From: Smeet Poladia <[email-masked]>
To: Brian Junker <[email-masked]>
Cc: Zhuyi Xu <[email-masked]>, Zhou Lu CMU MSP <[email-masked]>
Date: Tue, 16 Mar 2021 17:56:33 -0400

Summary - 03/16

Metric for success (if student has learned that KC):
Percent Correct
Assistant Score
Slope of leaning curve
EDA:
Correlation plots between KCs
Grouped box plot (grouped for skill A, showing performance on skill B)
Scatter plot showing assistant scores between KCs
Gaussian Graphical Models:
Review research paper and glasso documentation
Start with one workspace, one student
Then all students within one workspace
Then all workspaces combined
Learning Curves:
Slope involving error rate
Slope involving logistics regression model
Refer to the Journal of Statistical Computing - R packages and how they’ve been applied at other datasets
Ask Stephen when does a student finish workspace and moves onto the next one

Next Steps:
Smeet: GGM (start with a workspace and then build on)
Elaine: Learning Curves (start with slopes as error rates and then build on)
Zhou: Other EDA (box plots , scatter plots, correlation plots, etc)

Summary - 03/15

There are handful of KCs per workspace and KCs can go across multiple steps across workspaces
BUG error is a known misconception (a naive error)
Work the updated dataset only
KCs have similar meaning within problems and steps within the same workspace
Concatenate workspace and KCs
To get started initially:
Focus on transaction where KCs are coded
OK: 1 and {all other errors}: 0
Concept builders are workspaces without KCs
Investigate the role of concept builders

Regards,
Elaine, Smeet & Zhou