Engagement tracing: using response times to model student disengagement
J. Beck. Artificial Intelligence in Education: Supporting Learning Through Intelligent And Socially Informed Technology, (2005)
Abstract
Time on task is an important predictor for how much students learn. However, students must be focused on their learning for the time invested to be productive. Unfortunately, students do not always try their hardest to solve problems presented by computer tutors. This paper explores student disengagement and proposes an approach, engagement tracing, for detecting whether a student is engaged in answering questions. This model is based on item response theory, and uses as input the difficulty of the question, how long the student took to respond, and whether the response was correct. From these data, the model determines the probability a student was actively engaged in trying to
answer the question. The model has a reliability of 0.95, and its estimate of student engagement correlates at 0.25 with student gains on external tests. We demonstrate that simultaneously modeling student proficiency in the domain enables us to better model student engagement. Our model is sensitive enough to detect variations in student engagement within a single tutoring session. The novel aspect of this work is that it requires only data normally collected by a computer tutor, and the affective model is statistically validated against student performance on an external measure
%0 Journal Article
%1 joseph2005etu
%A Beck, Joseph E.
%D 2005
%I IOS Press
%J Artificial Intelligence in Education: Supporting Learning Through Intelligent And Socially Informed Technology
%K ITS gamingthesystem learner learning modelling response responsetime statistical time wleformativeeassessment
%T Engagement tracing: using response times to model student disengagement
%U http://www.cs.cmu.edu/~listen/pdfs/AIED2005-Beck-disengagement%20final%20version.pdf
%X Time on task is an important predictor for how much students learn. However, students must be focused on their learning for the time invested to be productive. Unfortunately, students do not always try their hardest to solve problems presented by computer tutors. This paper explores student disengagement and proposes an approach, engagement tracing, for detecting whether a student is engaged in answering questions. This model is based on item response theory, and uses as input the difficulty of the question, how long the student took to respond, and whether the response was correct. From these data, the model determines the probability a student was actively engaged in trying to
answer the question. The model has a reliability of 0.95, and its estimate of student engagement correlates at 0.25 with student gains on external tests. We demonstrate that simultaneously modeling student proficiency in the domain enables us to better model student engagement. Our model is sensitive enough to detect variations in student engagement within a single tutoring session. The novel aspect of this work is that it requires only data normally collected by a computer tutor, and the affective model is statistically validated against student performance on an external measure
@article{joseph2005etu,
abstract = {Time on task is an important predictor for how much students learn. However, students must be focused on their learning for the time invested to be productive. Unfortunately, students do not always try their hardest to solve problems presented by computer tutors. This paper explores student disengagement and proposes an approach, engagement tracing, for detecting whether a student is engaged in answering questions. This model is based on item response theory, and uses as input the difficulty of the question, how long the student took to respond, and whether the response was correct. From these data, the model determines the probability a student was actively engaged in trying to
answer the question. The model has a reliability of 0.95, and its estimate of student engagement correlates at 0.25 with student gains on external tests. We demonstrate that simultaneously modeling student proficiency in the domain enables us to better model student engagement. Our model is sensitive enough to detect variations in student engagement within a single tutoring session. The novel aspect of this work is that it requires only data normally collected by a computer tutor, and the affective model is statistically validated against student performance on an external measure},
added-at = {2008-09-17T02:32:49.000+0200},
author = {Beck, Joseph E.},
biburl = {https://www.bibsonomy.org/bibtex/2e99ca87b28dd2ecdc284318299f41788/yish},
interhash = {909ca77241d9cdc99acb9ba2d13ac4ed},
intrahash = {e99ca87b28dd2ecdc284318299f41788},
journal = {Artificial Intelligence in Education: Supporting Learning Through Intelligent And Socially Informed Technology},
keywords = {ITS gamingthesystem learner learning modelling response responsetime statistical time wleformativeeassessment},
publisher = {IOS Press},
timestamp = {2008-09-17T02:32:49.000+0200},
title = {Engagement tracing: using response times to model student disengagement},
url = {http://www.cs.cmu.edu/~listen/pdfs/AIED2005-Beck-disengagement%20final%20version.pdf},
year = 2005
}