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Book part
Publication date: 20 September 2018

Intelligent Tutoring for Team Training: Lessons Learned from US Military Research

Jared Freeman and Wayne Zachary

Technology for training military teams has evolved through a convergence of advances in simulation technology for individual and collective training, methods for analyzing…

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Abstract

Technology for training military teams has evolved through a convergence of advances in simulation technology for individual and collective training, methods for analyzing teamwork and designing training solutions, and intelligent tutoring technologies that adapt training to the student, to accelerate learning. A number of factors have slowed this evolution toward intelligent team tutoring systems (ITTS), including the challenges of processing communications data, which are the currency of teamwork, and the paucity of automated and generalizable measures of team work. Several systems fulfill a subset of the features required of an ITTS, namely the use of team training objectives, teamwork models, measures of teamwork, diagnostic capability, instructional strategies, and adaptation of training to team needs. We describe these systems: the Advanced Embedded Training System (AETS), Synthetic Cognition for Operational Team Training (SCOTT), the AWO Trainer, the Benchmarked Experiential System for Training (BEST), and the Cross-Platform Mission Visualization Tool. We close this chapter with recommendations for future research.

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
DOI: https://doi.org/10.1108/S1534-085620180000019013
ISBN: 978-1-78754-474-1

Keywords

  • Team
  • simulation
  • training
  • education
  • mission rehearsal
  • intelligent team tutoring system

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Book part
Publication date: 20 September 2018

Five Lenses on Team Tutor Challenges: A Multidisciplinary Approach

Stephen B. Gilbert, Michael C. Dorneich, Jamiahus Walton and Eliot Winer

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…

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Abstract

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.

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
DOI: https://doi.org/10.1108/S1534-085620180000019014
ISBN: 978-1-78754-474-1

Keywords

  • Intelligent team tutoring system
  • intelligent tutoring system
  • training
  • teamwork
  • task work
  • interdisciplinary research

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Article
Publication date: 31 August 2004

Using a virtual student model for testing intelligent tutoring systems

Mircea Gh. Negoita and David Pritchard

Education is increasingly using Intelligent Tutoring Systems (ITS), both for modelling instructional and teaching strategies and for enhancing educational programs. The…

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Abstract

Education is increasingly using Intelligent Tutoring Systems (ITS), both for modelling instructional and teaching strategies and for enhancing educational programs. The first part of the paper introduces the basic structure of an ITS as well as common problems being experienced within the ITS community. The second part describes WITNeSS ‐ an original hybrid intelligent system using Fuzzy‐Neural‐GA techniques for optimising the presentation of learning material to a student. The original work in this paper is related to the concept of a “virtual student”. This student model, modelled using fuzzy technologies, will be useful for any ITS, providing it with an optimal learning strategy for fitting the ITS itself to the unique needs of each individual student. In the third part, experiments focus on problems developing a “virtual student” model, which simulates, in a rudimentary way, human learning behaviour. Part four finishes with concluding remarks.

Details

Interactive Technology and Smart Education, vol. 1 no. 3
Type: Research Article
DOI: https://doi.org/10.1108/17415650480000023
ISSN: 1741-5659

Keywords

  • Intelligent Tutoring Systems
  • hybrid intelligent systems
  • fuzzy systems
  • neural networks
  • Intelligent learning systems

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Book part
Publication date: 20 September 2018

Considerations in the Design of a Team Tutor

Anne M. Sinatra and Robert Sottilare

This chapter considers the essential elements and processes in designing and building a computer-based tutor to instruct teams. In this chapter, the choices of authoring…

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Abstract

This chapter considers the essential elements and processes in designing and building a computer-based tutor to instruct teams. In this chapter, the choices of authoring tools, the instructional context, the goal of the instruction, and the characteristics of the domain were evaluated in terms of their influence on the Intelligent Tutoring System (ITS) design in support of team learning and performance. While each team tutor may be unique in terms of its learning objectives, measures, selections of learning strategies and tutor interventions, there are some identified design decisions that need to be made. Considering the best decision for the specific tutor's design is intended to ease the authoring burden and make computer-based team tutoring more ubiquitous.

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
DOI: https://doi.org/10.1108/S1534-085620180000019016
ISBN: 978-1-78754-474-1

Keywords

  • Team intelligent tutoring systems
  • intelligent tutoring systems
  • collaborative learning
  • collaborative problem-solving
  • training design
  • team measurement

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Book part
Publication date: 20 September 2018

Building Intelligent Conversational Tutors and Mentors for Team Collaborative Problem Solving: Guidance from the 2015 Program for International Student Assessment

Arthur C. Graesser, Nia Dowell, Andrew J. Hampton, Anne M. Lippert, Haiying Li and David Williamson Shaffer

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a…

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Abstract

This chapter describes how conversational computer agents have been used in collaborative problem-solving environments. These agent-based systems are designed to (a) assess the students’ knowledge, skills, actions, and various other psychological states on the basis of the students’ actions and the conversational interactions, (b) generate discourse moves that are sensitive to the psychological states and the problem states, and (c) advance a solution to the problem. We describe how this was accomplished in the Programme for International Student Assessment (PISA) for Collaborative Problem Solving (CPS) in 2015. In the PISA CPS 2015 assessment, a single human test taker (15-year-old student) interacts with one, two, or three agents that stage a series of assessment episodes. This chapter proposes that this PISA framework could be extended to accommodate more open-ended natural language interaction for those languages that have developed technologies for automated computational linguistics and discourse. Two examples support this suggestion, with associated relevant empirical support. First, there is AutoTutor, an agent that collaboratively helps the student answer difficult questions and solve problems. Second, there is CPS in the context of a multi-party simulation called Land Science in which the system tracks progress and knowledge states of small groups of 3–4 students. Human mentors or computer agents prompt them to perform actions and exchange open-ended chat in a collaborative learning and problem-solving environment.

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
DOI: https://doi.org/10.1108/S1534-085620180000019012
ISBN: 978-1-78754-474-1

Keywords

  • AutoTutor
  • collaboration
  • collaborative problem solving
  • conversational agents
  • PISA
  • problem solving

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Book part
Publication date: 20 September 2018

Index

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Abstract

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
DOI: https://doi.org/10.1108/S1534-085620180000019002
ISBN: 978-1-78754-474-1

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Book part
Publication date: 20 September 2018

Examining Challenges and Approaches to Building Intelligent Tutoring Systems for Teams

Robert Sottilare and Eduardo Salas

This chapter examines some of the challenges and emerging strategies for authoring, distributing, managing, and evaluating Intelligent Tutoring Systems (ITSs) to support…

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Abstract

This chapter examines some of the challenges and emerging strategies for authoring, distributing, managing, and evaluating Intelligent Tutoring Systems (ITSs) to support computer-based adaptive instruction for teams of learners. Several concepts related to team tutoring are defined along with team processes, and fundamental tutoring concepts are provided including a description of the learning effect model (LEM), an exemplar describing interaction between learners and ITSs with the goal of realizing optimal tutor decisions. The challenges noted herein are closely related to the LEM and range from acquisition of learner data, synthesis of individual learner and team state models based on available data, and tutor decisions which center on optimizing strategies (recommendations) and tactics (actions) given the state of the learner, the team, and the conditions under which they are being instructed, the environment. Finally, we end this chapter with recommendations on how to use this book to understand and design effective ITSs for teams.

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
DOI: https://doi.org/10.1108/S1534-085620180000019001
ISBN: 978-1-78754-474-1

Keywords

  • Adaptive instruction
  • intelligent tutoring systems
  • learning effect model
  • team tutors
  • learning
  • performance

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Book part
Publication date: 20 September 2018

Modeling Dynamic Team Interactions for Intelligent Tutoring

Pravin Chopade, Michael Yudelson, Benjamin Deonovic and Alina A. von Davier

This chapter focuses on the state-of-the-art modeling approaches used in Intelligent Tutoring Systems (ITSs) and the frameworks for researching and operationalizing…

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Abstract

This chapter focuses on the state-of-the-art modeling approaches used in Intelligent Tutoring Systems (ITSs) and the frameworks for researching and operationalizing individual and group models of performance, knowledge, and interaction. We adapt several ITS methodologies to model team performance as well as individuals’ performance of the team members. We briefly describe the point processes proposed by von Davier and Halpin (2013), and we also introduce the Competency Architecture for Learning in teaMs (CALM) framework, an extension of the Generalized Intelligent Framework for Tutoring (GIFT) (Sottilare, Brawner, Goldberg, & Holden, 2012) to be used for team settings.

Details

Building Intelligent Tutoring Systems for Teams
Type: Book
DOI: https://doi.org/10.1108/S1534-085620180000019010
ISBN: 978-1-78754-474-1

Keywords

  • Intelligent tutoring systems
  • team interactions
  • computational psychometrics
  • collaborative problem solving
  • learning and assessments system
  • machine learning
  • competency
  • artificial intelligence

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Article
Publication date: 29 June 2010

Tutoring the elderly on the use of recommending systems

Anastasios Savvopoulos and Maria Virvou

The elderly are often unfamiliar with computer technology and can encounter great difficulties. Moreover, the terms used in such systems may prove to be a challenge for…

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Abstract

Purpose

The elderly are often unfamiliar with computer technology and can encounter great difficulties. Moreover, the terms used in such systems may prove to be a challenge for these users. The aim of this research is to tutor the elderly on using an adaptive e‐shop system in order to buy products easily.

Design/methodology/approach

In view of the above, the paper creates an intelligent tutoring component for the elderly. It incorporated this component into an e‐shop application for interactive TV in order to evaluate it. The component created is both medium‐ and domain‐independent.

Findings

The independent tutoring component that provided combined product recommendations and adaptive help actions had a positive influence on the elderly and created a friendlier shopping environment for them.

Originality/value

The research proposes a novel component for the elderly that uniquely combines product recommendations and adaptive help reactions. This component can be used in a large variety of recommendation applications as it is medium‐ and domain‐independent.

Details

Campus-Wide Information Systems, vol. 27 no. 3
Type: Research Article
DOI: https://doi.org/10.1108/10650741011054465
ISSN: 1065-0741

Keywords

  • Computer based learning
  • Elderly people
  • Electronic commerce

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Article
Publication date: 1 April 1986

COGNITIVE MODELS IN INFORMATION RETRIEVAL — AN EVALUATIVE REVIEW

P J. DANIELS

Selected current and recent work in the area of cognitive modelling is reviewed. Particular attention is paid to user models (that is, the model held by a system of a…

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Abstract

Selected current and recent work in the area of cognitive modelling is reviewed. Particular attention is paid to user models (that is, the model held by a system of a user). The relevance of this work to information retrieval is assessed and some attempts to include user models in IR systems are discussed. Implications are drawn for future work in IR.

Details

Journal of Documentation, vol. 42 no. 4
Type: Research Article
DOI: https://doi.org/10.1108/eb026797
ISSN: 0022-0418

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