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
Hypotheses
H1: We hypothesize that it will be possible to develop reasonably accurate detectors of student affect for four LearnLabs, that detect affect using only the data from the interaction between the student and the keyboard/mouse.
H2: We hypothesize that models of behaviors such as gaming the system, and off-task behavior, in combination with models of affect/behavior dynamics, can make affect detectors more accurate.
H3: We hypothesize that these affect models will become a valuable component of future research in the M&M and CMDM thrusts.
