Difference between revisions of "Baker - Building Generalizable Fine-grained Detectors"

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=== Hypotheses ===
 
=== Hypotheses ===
  
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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.
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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.
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H3: We hypothesize that these affect models will become a valuable component of future research in the M&M and CMDM thrusts.
  
 
=== Independent Variables ===
 
=== Independent Variables ===

Revision as of 15:27, 4 December 2009

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

Gaming the system

Off-Task Behavior

Affect

Frustration

Boredom

Flow

Engaged Concentration

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.

Independent Variables

Dependent Variables

Planned Studies

Explanation

Further Information

Connections

Annotated Bibliography

References

Future Plans