[Colleague version - this one hides all of the underlying R code] Using feedback analytics data (UQMarkUp) including feedback provision, feedback use and marks with demographic data (from SAP BI), to predict whether students will achieve an A (over 85%) on their final report.

Support vector machine (SVM) using feedback analytics data (UQMarkUp) including feedback provision, feedback use and marks with demographic data (from SAP BI), to predict whether students will achieve an A (over 85%) on their final report. [Coder version - this prints out all of the underlying R code]

from initial idea, through drafting of manuscript to post-review amendments

Data from training runs

quick and dirty output from today's analysis using open bins from Report 0 to categorise students and frame the analysis of their academic performance on each report

inputting, checking, cleaning and outputting

a quick mock-up of figures on the proportion of audio annotations students played

Working cleaned data into paper text. Using UQM paper version KC11 (26 June 1pm)

Latest outputs for Kay: Relationships between report final grade and open bin

25 May 2015 version

boxplots for each report from BIOL1040 and BIOM2011 in Sem 1 and 2, 2013

Loading UQM data for 2013 BIOL1040, BIOM2011 and BIOM2013, and demographic data for 2013 BIOL1040 and BIOM2011

PHYL1007 Metab prac

PHYL1007 Metabolic systems practical

3 clusters instead of 5

draft analyses of LMS analytics