Tracing Knowledge and Engagement in Parallel by Observing Behavior in Intelligent Tutoring Systems
Year: 2015
Type: dissertation
Abstract: Two of the major goals in Educational Data Mining are determining students’ state of knowledge and determining their affective state. It is useful to be able to determine whether a student is engaged with a tutor or task in order to adapt to his/her needs and necessary to have an idea of the students' knowledge state in order to provide material that is appropriately challenging. These two problems are usually examined separately and multiple methods have been proposed to solve each of them. However, little work has been done on examining both of these states in parallel and the combined effect on a student’s performance. The work reported in this thesis explores ways to observe both behavior and performance in order to more fully understand student state.
Source: Digital WPI
Author: Sarah E. Schultz
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: green
OpenAlex ID: W2941852018