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.

Creator

• Schultz, Sarah E

Contributors

• Arroyo, Ivon

Degree

• MS

Unit

• Computer Science

Publisher

• Worcester Polytechnic Institute

Language

• English

Identifier

• etd-012715-143042

Keyword

• student modeling
• Knowledge tracing
• Intelligent Tutoring System
• Educational data mining
• engagement
• Bayesian networks
• Affect

Advisor

• Arroyo, Ivon

Defense date

• 2015-01-26

Year

• 2015

Date created

• 2015-01-27

Resource type

• Thesis

Rights statement

• In Copyright

License

• All rights reserved