Baker - Building Generalizable Fine-grained Detectors

From LearnLab
Revision as of 10:23, 4 December 2009 by Ryan (Talk | contribs)

Jump to: navigation, search

Building Generalizable Fine-grained Detectors

Summary Table

Study 1

PIs Ryan Baker, Vincent Aleven
Other Contributers Sidney D'Mello (Consultant, University of Memphis), Ma. Mercedes T. Rodrigo (Consultant, Ateneo de Manila University)
Study Start Date February, 2010
Study End Date February, 2011
LearnLab Site TBD
LearnLab Course Algebra, Geometry, Chemistry, Chinese
Number of Students TBD
Total Participant Hours TBD
DataShop TBD

Abstract

This project, joint between M&M and CMDM, will create a set of fine-grained detectors of affect and M&M behaviors. These detectors will be usable by future projects in these two thrusts to study the impact of learning interventions on these dimensions of students’ learning experiences, and to study the inter-relationships between these constructs and other key PSLC constructs (such as measures of robust learning, and motivational questionnaire data). It will be possible to apply these detectors retrospectively to existing PSLC data in DataShop, in order to re-interpret prior work in the light of relevant evidence on students’ affect and M&M behaviors.

Background & Significance

Glossary

Metacognition and Motivation

Computational Modeling and Data Mining

Hypotheses

Independent Variables

Dependent Variables

Planned Studies

Explanation

Further Information

Connections

Annotated Bibliography

References

Future Plans