Year: 2014
Type: article
Study design: Clinical Trial
Abstract: Over the past couple decades, intelligent tutoring systems (ITSs) have become popular in education. ITSs are effective at helping students learn (VanLehn, 2011; Razzaq, Mendicino & Heffernan, 2008; Koedinger et al, 1997) and help researchers understand how students learn. Such research has included modeling how students learn (Corbett & Anderson, 1995), the effectiveness of help given within an ITS (Beck et al, 2008), the difficulty of different problems (Pardos & Heffernan, 2011), and predicting long-term outcomes like college attendance (San Pedro et al, 2013a), among many other studies.\nWhile most studies have focused on ITSs from a cognitive perspective, a growing number of researchers are paying attention to the motivational and affective aspects of tutoring, which have been recognized as important components of human tutoring (Lepper et al, 1993). Recent work has shown that student affect within an ITS can be detected, even without physical sensors or cameras (D’Mello et al, 2008; Conati & Maclaren, 2009; Sabourin et al, 2011; San Pedro et al, 2013b).\nInitial studies with these sensor-less affect detectors have shown that certain problematic affective states, such as boredom, confusion and frustration, are prevalent within ITSs (Baker et al, 2010b). Boredom in particular has been linked to negative learning outcomes (Pekrun et al, 2010; Farmer & Sundberg, 1986) and long-term disengagement (Farrell, 1988). Therefore, reducing or responding effectively to these affective states within ITSs may improve both short- and long-term learning outcomes.\nThis work is an initial attempt to determine what causes boredom in ITSs. First, we determine which is more responsible for boredom in ITSs: the content in the system, or the students themselves. Based on the findings of that analysis, we conduct a randomized controlled trial to determine the effects of monotony on student boredom. In addition to the work on boredom, we also perform analyses that concern student modeling, specifically how to improve Knowledge Tracing (Corbett & Anderson, 1995), a popular student model used extensively in real systems like the Cognitive Tutors (Koedinger et al, 1997) and in educational research.
Source: Digital WPI
Author: William J. Hawkins
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
Funder: National Science Foundation
Awards: Improving Formative Assessment Practices: Using Learning Trajectories to Develop Resources that Support Teacher Instructional Practice and Student Learning in CMP2, PIMSE: A GK-12 Partnership Implementing Mathematics and Science Education (PIMSE): Assisting Middle School Use of Tutoring Technology in the Classroom, Empirical Research, Contextual Research: Taking advantage of Cognitive Science Principles: Adding to a Computer-Based Tutor an Automatic Reassessment and Relearning System, Making Math Tutors More Engaging and Effective through Interaction Design Patterns and Educational Data Mining, Research: Predicting STEM Career Choice from Computational Indicators of Student Engagement within Middle School Mathematics Classes
OpenAlex ID: W2952019464