Challenges And Propositions For Developing Effective Team Training With Adaptive Tutors

Keywords

Intelligent tutors; Learner modeling; Measurement; Team leadership; Team performance; Team training; Teamwork

Abstract

A key challenge for cost-effective Intelligent Tutoring Systems (ITSs) is the ability to create generalizable domain, learner, and pedagogical models so they can be re-used many times over. Investment in this technology will be needed to succeed in developing ITSs for team training. The purpose of this chapter is to propose an instructional framework for guiding team ITS researchers in their development of these models for reuse. We establish a foundation for the framework with three propositions. First, we propose that understanding how teams develop is needed to establish a science-based foundation for modeling. Toward this end, we conduct a detailed exploration of the Kozlowski, Watola, Jensen, Kim, and Botero (2009) theory of team development and leadership, and describe a use case example to demonstrate how team training was developed for a specific stage in their model. Next, we propose that understanding measures of learning and performance will inform learner modeling requirements for each stage of team development. We describe measures developed for the use case and how they were used to understand teamwork skill development. We then discuss effective team training strategies and explain how they were implemented in the use case to understand their implications for pedagogical modeling. From this exploration, we describe a generic instructional framework recommending effective training strategies for each stage of team development. To inform the development of reusable models, we recommend selecting different team task domains and varying team size to begin researching commonalities and differences in the instructional framework.

Publication Date

1-1-2018

Publication Title

Research on Managing Groups and Teams

Volume

19

Number of Pages

75-97

Document Type

Article; Book Chapter

Personal Identifier

scopus

DOI Link

https://doi.org/10.1108/S1534-085620180000019008

Socpus ID

85067082794 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85067082794

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