Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2018

Abstract

Engagement is instrumental to students’ learning and academic achievements. In this study, we model the engagement states of students who are working on programming exercises in an intelligent tutoring system. Head pose, keystrokes and action logs of students automatically captured within the tutoring system are fed into a Hidden Markov Model for inferring the engagement states of students. With the modeling of students’ engagement on a moment by moment basis, intervention measures can be initiated automatically by the system when necessary to optimize the students’ learning. This study is also one of the few studies that bypass the need for human data labeling by using unsupervised machine learning techniques to model engagement states.

Keywords

unsupervised, machine learning, engagement, intelligent tutoring, sensors

Discipline

Programming Languages and Compilers

Research Areas

Information Systems and Management

Publication

GSTF Journal on Computing

Volume

6

Issue

1

First Page

1

Last Page

6

ISSN

2251-3043

Identifier

10.5176/2251-2195_CSEIT17.4

Publisher

Global Science & Technology Forum

Comments

Not found at http://dl6.globalstf.org/index.php/joc/issue/view/195

Additional URL

https://doi.org/10.5176/2251-3043_6.1.114

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