Chapter

Intelligent Tutoring Systems

Volume 7315 of the series Lecture Notes in Computer Science pp 505-510

Relating Student Performance to Action Outcomes and Context in a Choice-Rich Learning Environment

  • James R. SegedyAffiliated withVanderbilt University
  • , John S. KinnebrewAffiliated withVanderbilt University
  • , Gautam BiswasAffiliated withVanderbilt University

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Abstract

This paper presents results from a recent classroom study using Betty’s Brain, a choice-rich learning environment in which students learn about a scientific domain (e.g., mammal thermoregulation) as they teach a virtual agent named Betty. The learning and teaching task combines reading and understanding a set of hypertext resources with constructing a causal map that accurately models the science phenomena. The open-ended nature of this task requires students to combine planning, targeted reading, teaching, monitoring their teaching, and making revisions, which presents significant challenges for middle school students. This paper examines students’ learning activity traces and compares learning behaviors of students who achieved success with those who struggled to complete their causal maps. This analysis focuses on students’ actions leading to changes in their causal maps. We specifically examine which actions led students to make correct versus incorrect changes to their causal map. The results of this analysis suggest future directions in the design and timing of feedback and support for similarly complex, choice-rich learning tasks.

Keywords

Metacognition Monitoring Learning Activity Traces Sequence Analysis Learning Environment