Bayesian networks and linear regression models of students’ goals, moods, and emotions

Ivon Arroyo, David G. Cooper, Winslow Burleson, Beverly P. Woolf

Research output: Chapter in Book/Report/Conference proceedingChapter


If computers are to interact naturally with humans, they should recognize students’ affect and express social competencies. Research has shown that learning is enhanced when empathy or support is provided and that improved personal relationships between teachers and students leads to increased student motivation.1-4 Therefore, if tutoring systems can embed affective support for students, they should be more effective. However, previous research has tended to privilege the cognitive over the affective and to view learning as information processing, marginalizing, or ignoring affect.5 This chapter describes two data-driven approaches toward the automatic prediction of affective variables by creating models from students’ past behavior (log-data). The first case study shows the methodology and accuracy of an empirical model that helps predict students’ general attitudes, goals, and perceptions of the software, and the second develops empirical models for predicting students’ fluctuating emotions while using the system. The vision is to use these models to predict students’ learning and positive attitudes in real time. Special emphasis is placed in this chapter on understanding and inspecting these models, to understand how students express their emotions, attitudes, goals, and perceptions while using a tutoring system.

Original languageEnglish (US)
Title of host publicationHandbook of Educational Data Mining
PublisherCRC Press
Number of pages16
ISBN (Electronic)9781439804582
ISBN (Print)9781439804575
StatePublished - Jan 1 2010

ASJC Scopus subject areas

  • General Computer Science
  • General Economics, Econometrics and Finance
  • General Business, Management and Accounting


Dive into the research topics of 'Bayesian networks and linear regression models of students’ goals, moods, and emotions'. Together they form a unique fingerprint.

Cite this