Toward a Deeper Understanding and Prediction of Achievement, Success,
and Affect of Students in Virtual High School Courses:
A Quantitative Analysis of Variation of Student Measures within Course by Type of Course

Notes for the MVHS Advisory Council Meeting
April 20, 2005
W. Patrick Dickson

Introduction. This project is focusing on the intensive analysis of the within class variation of individual achievement, attrition, and affective responses to online learning in a systematic sampling of the several types of courses offered by the Michigan Virtual High School. In addition to these analyses, building on ideas derived from Tufte's work on the visual display of quantitative data, we are attempting to go beyond the usual data analyses to explore ways of representing data in ways explicitly crafted to inform policy makers, teachers, students, and other stakeholders.


Key Points for Today

Beyond "No Significant Difference" or "Just as Good As".  Cathy Cavanaugh: Meta-analysis.

Organizing Existing Data at MVHS.

Reviewing Literature on Data-Driven Decision Making. Informed, not driven.

Conceptualizing the Multiple Meanings of D3. So many terms, so many meanings.

Data Visualization. "Edward Tufte" ".net"   Need theory. "Data inform, graphics persuade."

Dashboards "Tiger" "XcelsiusA metaphor whose time has come.

Balanced Scorecard. The business world.

Conceptualizing Different Markets, Different Audiences for D3.
Policy Makers, Teachers, Parents, Students, Administrators.

Issue: Home Grown Solutions or Commercial Packages for D3? So many.

Designing for Communicating via the Web. Optimizing Value of Analyses.

 

Some Questions

What Data, What Decisions?  Your examples?

What Software Do You Use?  Your experiences?

ROI: Making Data Investment Pay Off.  Priorities for MVHS?

References

Email me: pdickson@msu.edu

http://www.msu.edu/user/pdickson/talks/mvhsstudykeypoints.htm