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Inferring learning and attitudes from a Bayesian Network of log file data
 

Summary: Inferring learning and attitudes from a
Bayesian Network of log file data
Ivon ARROYO, Beverly Park WOOLF
Departmentof Computer Science University of Massachusetts,
Amherst, MA. Contact: ivon@cs.umass.edu
Abstract. A student's goals and attitudes while interacting with a tutor are typically
unseen and unknowable. However their outward behavior (e.g. problem-solving
time, mistakes and help requests) is easily recorded and can reflect hidden affect
status. This research evaluates the accuracy of a Bayesian Network to infer a
student's hidden attitude toward learning, amount learned and perception of the
system from log-data. The long term goal is to develop tutors that self-improve
their student models and their teaching, dynamically can adapt pedagogical
decisions about hints and help improve student's affective, intellectual and learning
situation based on inferences about their goals and attitude.
1 Introduction
The advent of the Internet has promoted Web-based learning environments that facilitate
collection of enormous student data, as a result of centralized servers and databases. Log data
permit the analysis of fine-grained student actions that characterize fading of students'
mistakes or the reduction of time on task [1]. The analysis of learning curves may also show
how to structure and better understand the domain being taught [2]. Learning to profit from

  

Source: Arroyo, Ivon M. - Center for Knowledge Communication, University of Massachusetts at Amherst

 

Collections: Computer Technologies and Information Sciences