Baker - Building Generalizable Fine-grained Detectors
Contents
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
Computational Modeling and Data Mining
