Designing Adaptive Instruction for Teams: a Meta-Analysis

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

Bringing Authoring Tools for Intelligent Tutoring Systems and Serious Games Closer Together: Integrating GIFT with the Unity Game Engine

In an effort to bring intelligent tutoring system (ITS) authoring tools closer to content authoring tools, the authors are working to integrate GIFT with the Unity game engine and editor. The paper begins by describing challenges faced by modern intelligent tutors and the motivation behind the integration effort, with special consideration given to how this work will better meet the needs of future serious games. The next three sections expand on these major hurdles more thoroughly, followed by proposed design enhancements that would allow GIFT to overcome these issues. Finally, an overview is given of the authors’ current progress towards implementing the proposed design. The key contribution of this work is an abstraction of the interface between intelligent tutoring systems and serious games, thus enabling ITS authors to implement more complex training behaviors

Five Lenses on Team Tutor Challenges: A Multidisciplinary Approach

This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs

Towards using a physio-cognitive model in tutoring for psychomotor tasks.

We report our exploratory research of psychomotor task training in intelligent tutoring systems (ITSs) that are generally limited to tutoring in the desktop learning environment where the learner acquires cognitively oriented knowledge and skills. It is necessary to support computer-guided training in a psychomotor task domain that is beyond the desktop environment. In this study, we seek to extend the current capability of GIFT (Generalized Intelligent Frame-work for Tutoring) to address these psychomotor task training needs. Our ap-proach is to utilize heterogeneous sensor data to identify physical motions through acceleration data from a smartphone and to monitor respiratory activity through a BioHarness, while interacting with GIFT simultaneously. We also uti-lize a computational model to better understand the learner and domain. We focus on a precision-required psychomotor task (i.e., golf putting) and create a series of courses in GIFT that instruct how to do putting with tactical breathing. We report our implementation of a physio-cognitive model that can account for the process of psychomotor skill development, the GIFT extension, and a pilot study that uses the extension. The physio-cognitive model is based on the ACT-R/Φ architecture to model and predict the process of learning, and how it can be used for improving the fundamental understanding of the domain and learner model. Our study contributes to the use of cognitive modeling with physiological con-straints to support adaptive training of psychomotor tasks in ITSs

Methods to Improve the Field of Intelligent Tutoring Systems using Emotion-based Agents

The aim of this paper is to review select current methods used in the field of Intelligent Tutoring Systems (ITS) with respect to the use of emotion-based agents and how those systems interact with the learner to capture criti-cal data, store the data, and effectively process the data to produce valuable feedback. From this data collected, proposed methods are presented on how to improve existing ITS systems and how to make new ITS’s more effective

Towards Empowering Educators to Create their own Smart Personal Assistants

Despite a growing body of research about the design and use of Smart Personal Assistants such as Amazon’s Alexa or Google’s Assistant, little is known about their ability to help educators offering individual support in large-scale learning environments. Smart Personal Assistant ecosystems empower educators to develop their own agents without deep technological knowledge. The objective of this paper is to design and validate a method that helps educators to create Smart Personal Assistants as learning tutors. Using a design science research approach, we first gather requirements from students and educators as well as from information systems and education theory. Next, we create an alpha version of our method and evaluate it with a focus group before we instantiate our artifact in an everyday learning environment. The findings indicate that our method is able to empower educators to design Smart Personal Assistants that significantly improve students’ learning success