دانلود رایگان مقاله انگلیسی طراحی قوانین سازگار برای تیم ها - اشپرینگر 2018

Abstract

The goal of this research was the development of a practical architecture for the computer-based tutoring of teams. This article examines the relationship of team behaviors as antecedents to successful team performance and learning 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). The core contribution of this research was the identification of behavioral markers associated with the antecedents of team performance and learning thus enabling the development and refinement of teamwork models in ITS architectures. 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 both the team and the learners on that team. To aid the modeling of teams, we examined the literature to evaluate the relationship of teamwork behaviors (e.g., communication, cooperation, coordination, cognition, leadership/coaching, and conflict) with team outcomes (learning, performance, satisfaction, and viability) as part of a large-scale meta-analysis of the ITS, team training, and team performance literature. While ITSs have been used infrequently to instruct teams, the goal of this meta-analysis make team tutoring more ubiquitous by: identifying significant relationships between team behaviors and effective performance and learning outcomes; developing instructional guidelines for team tutoring based on these relationships; and applying these team tutoring guidelines to the Generalized Intelligent Framework for Tutoring (GIFT), an open source architecture for authoring, delivering, managing, and evaluating adaptive instructional tools and methods. In doing this, we have designed a domain-independent framework for the adaptive instruction of teams.

Conclusions

The core contributions of this research provide: 1) evidence of the effect of teamwork behaviors in identifying antecedents of team performance and team learning; 2) structural equation models for the team performance and team learning for use in ITSs; and 3) identification of behavioral markers which can be used as measures for assessment of team performance and team learning during adaptive instruction. Each of the significant antecedents described herein form a model to support the adaptive tutoring of teams. Collective efficacy, cohesion, communication, and leadership were significant antecedents of team performance. Trust, cohesion, conflict, and conflict management were identified as significant antecedents of team learning. While direct antecedents of team performance and team learning were identified, indirect influencers were also identified in our ontologies. Separate ontologies for team performance and team learning were constructed based on our MASEM process, but an expansion of this analysis over the next few years as new studies are conducted should result in even higher degrees of confidence for some of the meta-analytic results shown herein. To provide a means to measure team outcomes, we identified six sets of behavioral markers: trust, collective efficacy, cohesion, communication, and conflict/conflict management. The markers formed the basis of a process to identify team member behaviors associated with antecedents of team performance and team learning. They also pave the way to identify methods to acquire team state data and classify team states. We also reviewed next steps for applying our meta-analytic findings to GIFT and discussed additional research and next steps needed to fully realize our vision for the adaptive tutoring of teams.