Title:
A Real-Time Model to Assess Student Engagement During Interaction with Intelligent Educational Agents
A Real-Time Model to Assess Student Engagement During Interaction with Intelligent Educational Agents
Author(s)
Brown, LaVonda
Howard, Ayanna M.
Howard, Ayanna M.
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Abstract
Adaptive learning is an educational method that utilizes computers as an interactive teaching
device. Intelligent tutoring systems, or educational agents, use adaptive learning techniques to
adapt to each student’s needs and learning styles in order to individualize learning. Effective
educational agents should accomplish two essential goals during the learning process – 1)
monitor engagement of the student during the interaction and 2) apply behavioral strategies to
maintain the student’s attention when engagement decreases. In this paper, we focus on the first
objective of monitoring student engagement. Most educational agents do not monitor
engagement explicitly, but rather assume engagement and adapt their interaction based on the
student’s responses to questions and tasks. A few advanced methods have begun to incorporate
models of engagement through vision-based algorithms that assess behavioral cues such as eye
gaze, head pose, gestures, and facial expressions. Unfortunately, these methods require a heavy
computation load, memory/storage constraints, and high power consumption. In addition, these
behavioral cues do not correlate well with achievement of high-cognitive tasks, as we will
discuss in this paper. As an alternative, our proposed model of engagement uses physical events,
such as keyboard and mouse events. This approach requires fewer resources and lower power
consumption, which is also ideally suited for mobile educational agents such as handheld tablets
and robotic platforms. In this paper, we discuss our engagement model which uses techniques that determine behavioral
user state and correlate these findings to mouse and keyboard events. In particular, we observe
three event processes: total time required to answer a question; accuracy of responses; and proper
function executions. We evaluate the correctness of our model based on an investigation
involving a middle-school after-school program in which a 15-question math exam that varies in
cognitive difficulty is used for assessment. Eye gaze and head pose techniques are referenced for
the baseline metric of engagement. We then conclude the investigation with a survey to gather
the subject’s perspective of their mental state throughout the exam. We found that our model of engagement is comparable to the eye gaze and head pose techniques.
When high-level cognitive thinking is required, our model is more accurate than the eye gaze and
head pose techniques due to the use of outside variables for assistance and non-focused gazes
during questions requiring deep thought. The large time delay associated with the lack of eye
contact between the student and the computer screen causes the aforementioned algorithms to
incorrectly declare the subjects as being disengaged. Furthermore, speed and validity of
responses can help to determine how well the student understands the material, and this is
confirmed through the survey responses and video observations. This additional information will
be used in the future to better integrate instructional scaffolding and adaptation with the
educational agent.
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Date Issued
2014-06
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Proceedings