Toward a Scientifically Rooted Design Architecture of Team Process and Performance Modeling in Adaptive, Team-Based Intelligent Tutoring Systems
Abstract
The goal of this research was to support the development of a practical architecture for the computer-based tutoring of teams. This report examines the relationship of various team behaviors, attitudes, and cognition as antecedents to successful team performance, learning, and performance during adaptive instruction guided by Intelligent Tutoring Systems (ITSs). Adaptive instruction is a training or educational experience tailored by artificially intelligent, computer-based tutors with the goal of optimizing learner outcomes (e.g., knowledge and skill acquisition, performance, enhanced retention, accelerated learning, or transfer of skills from instructional environments to work environments). Teamwork focuses on the coordination, cooperation, and communication among individuals to achieve a shared goal. For ITSs to optimally tailor team instruction, tutors must have key insights about team members, roles, and team objectives. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors, attitudes, and cognition with team outcomes (learning and performance) as part of a large-scale meta-analysis of the ITS, team training, and team-performance literature. The goal is to develop and apply findings of this meta-analysis to guide instructional decisions by the Generalized Intelligent Framework for Tutoring, an open-source architecture for authoring, delivering, managing, and evaluating adaptive instruction.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jan 22, 2020
- Accession Number
- AD1090218
Entities
People
- Anne M. Sinatra
- Eduardo Salas
- Joan Johnston
- Robert Sottilare
- Shawn Burke