scholarly journals A Distributed Augmented Reality Training Architecture For Distributed Cognitive Intelligent Tutoring Paradigms

2021 ◽  
Author(s):  
Bradley Herbert ◽  
Nilufar Baghaei ◽  
Mark Billinghurst ◽  
Grant Wigley
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
Agent Based ◽  
3 Dimensional ◽  
Virtual Objects ◽  
3D Space ◽  
Educational Theories

Modern training typically incorporates real-world training applications. Augmented Reality (AR) technologies support this by overlaying virtual objects in real-world 3-Dimensional (3D) space. However, integrating instruction into AR is challenging because of technological and educational considerations. One reason is a lack of architecture for supporting Intelligent Tutoring Systems (ITSs) in AR training domains. We present a novel modular agent-based Distributed Augmented Reality Training (DART) architecture for ITSs to address two key AR challenges: (1) a decoupling of the display and tracking components and (2) support for modularity. Modular agents communicate with each other over a network, allowing them to be easily swapped out and replaced to support differing needs. Our motivation is driven by the fact that AR technologies are vary considerably and an ITS architecture would need to be flexible enough to support these requirements. Finally, we believe that our novel architecture will appeal to practical designers of ITSs and to the more theoretical educators who wish to use such systems to simulate and broaden research in the distributed cognitive educational theories.

scholarly journals A Distributed Augmented Reality Training Architecture For Distributed Cognitive Intelligent Tutoring Paradigms

2021 ◽  
Author(s):  
Bradley Herbert ◽  
Nilufar Baghaei ◽  
Mark Billinghurst ◽  
Grant Wigley
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
Agent Based ◽  
3 Dimensional ◽  
Virtual Objects ◽  
3D Space ◽  
Educational Theories

Modern training typically incorporates real-world training applications. Augmented Reality (AR) technologies support this by overlaying virtual objects in real-world 3-Dimensional (3D) space. However, integrating instruction into AR is challenging because of technological and educational considerations. One reason is a lack of architecture for supporting Intelligent Tutoring Systems (ITSs) in AR training domains. We present a novel modular agent-based Distributed Augmented Reality Training (DART) architecture for ITSs to address two key AR challenges: (1) a decoupling of the display and tracking components and (2) support for modularity. Modular agents communicate with each other over a network, allowing them to be easily swapped out and replaced to support differing needs. Our motivation is driven by the fact that AR technologies are vary considerably and an ITS architecture would need to be flexible enough to support these requirements. Finally, we believe that our novel architecture will appeal to practical designers of ITSs and to the more theoretical educators who wish to use such systems to simulate and broaden research in the distributed cognitive educational theories.

An agent-based intelligent tutoring systems review

2019 ◽  
Vol 14 (2) ◽  
pp. 125
Author(s):  
Ines Šarić Grgić ◽  
Ani Grubišić ◽  
Slavomir Stankov ◽  
Maja Štula
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Tutoring Systems ◽  
Agent Based

Toward a Feature-Driven Understanding of Students' Emotions during Interactions with Agent-Based Learning Environments

Gamification ◽  
2015 ◽  
pp. 2148-2166
Author(s):  
Jason M. Harley ◽  
Roger Azevedo
Keyword(s):  
Learning Environments ◽  
Intelligent Tutoring Systems ◽  
Affective Computing ◽  
Intelligent Tutoring ◽  
Relative Effectiveness ◽  
Specific System ◽  
Tutoring Systems ◽  
Agent Based ◽  
Selective Review

This selective review synthesizes and draws recommendations from the fields of affective computing, intelligent tutoring systems, and psychology to describe and discuss the emotions that learners report experiencing while interacting with agent-based learning environments (ABLEs). Theoretically driven explanations are provided that describe the relative effectiveness and ineffectiveness of different ABLE features to foster adaptive emotions (e.g., engagement, curiosity) vs. non-adaptive emotions (e.g., frustration, boredom) in six different environments. This review provides an analytical lens to evaluate and improve upon research with ABLEs by identifying specific system features and their relationship with learners' appraisals and emotions.

scholarly journals Design of Multi-Agent Based Intelligent Tutoring Systems

2009 ◽  
Vol 38 (38) ◽  
pp. 48-59 ◽  
Author(s):  
Egons Lavendelis ◽  
Janis Grundspenkis
Keyword(s):  
Internal Structure ◽  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Design Approach ◽  
Design Stage ◽  
Design Phase ◽  
Tutoring Systems ◽  
Agent Based ◽  
Agent Oriented Software Engineering ◽  
Multi Agent

Design of Multi-Agent Based Intelligent Tutoring SystemsResearch of two fields, namely agent oriented software engineering and intelligent tutoring systems, have to be taken into consideration, during the design of multi-agent based intelligent tutoring systems (ITS). Thus there is a need for specific approaches for agent based ITS design, which take into consideration main ideas from both fields. In this paper we propose a top down design approach for multi-agent based ITSs. The proposed design approach consists of the two main stages: external design and internal design of agents. During the external design phase the behaviour of agents and interactions among them are designed. The following steps are done: task modelling and task allocation to agents, use case map creation, agent interaction design, ontology creation and holon design. During the external design phase agents and holons are defined according to the holonic multi-agent architecture for ITS development. During the internal design stage the internal structure of agents is specified. The internal structure of each agent is represented in the specific diagram, called internal view of the agent, consisting of agent's actions and interactions among them, rules for incoming message and perception processing, incoming and outgoing messages, and beliefs of the agent. The proposed approach is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The approach is developed using the same concepts as JADE agent platform and is suitable for agent code generation from the design diagrams.

Toward a Feature-Driven Understanding of Students' Emotions during Interactions with Agent-Based Learning Environments

2014 ◽  
Vol 6 (3) ◽  
pp. 17-34 ◽  
Author(s):  
Jason M. Harley ◽  
Roger Azevedo
Keyword(s):  
Learning Environments ◽  
Intelligent Tutoring Systems ◽  
Affective Computing ◽  
Intelligent Tutoring ◽  
Relative Effectiveness ◽  
Specific System ◽  
Tutoring Systems ◽  
Agent Based ◽  
Selective Review

This selective review synthesizes and draws recommendations from the fields of affective computing, intelligent tutoring systems, and psychology to describe and discuss the emotions that learners report experiencing while interacting with agent-based learning environments (ABLEs). Theoretically driven explanations are provided that describe the relative effectiveness and ineffectiveness of different ABLE features to foster adaptive emotions (e.g., engagement, curiosity) vs. non-adaptive emotions (e.g., frustration, boredom) in six different environments. This review provides an analytical lens to evaluate and improve upon research with ABLEs by identifying specific system features and their relationship with learners' appraisals and emotions.

scholarly journals Requirements analysis of Multi-Agent Based Intelligent Tutoring Systems

2009 ◽  
Vol 38 (38) ◽  
pp. 37-47 ◽  
Author(s):  
Egons Lavendelis ◽  
Janis Grundspenkis
Keyword(s):  
Intelligent Tutoring Systems ◽  
Intelligent Tutoring ◽  
Requirements Analysis ◽  
Use Case ◽  
Tutoring Systems ◽  
Agent Based ◽  
Agent Oriented Software Engineering ◽  
Goal Modelling ◽  
Goal Hierarchy ◽  
Multi Agent

Requirements analysis of Multi-Agent Based Intelligent Tutoring SystemsThe agent oriented software engineering research proposes general assumptions for agent oriented software development, while intelligent tutoring system (ITS) research proposes specific ITS architecture and other specific knowledge for ITS development. Both of these views should be taken into consideration while developing multi-agent based ITSs. Thus there is a need for specific approaches for all phases of agent based ITS development which take into consideration main ideas from both agent oriented software engineering and ITS research. In this paper we propose a requirements analysis approach for multi-agent based ITSs. A case study of a simple ITS is included, too. Requirements analysis in the proposed approach consist of two main steps, namely goal modelling and use case modelling. During the goal modelling the main goals of the system are identified and a goal hierarchy for the system is created. During the use case modelling use cases needed to achieve each lower level goal and their descriptions are created. The proposed approach of the requirements analysis is intended to be a part of the full life cycle methodology for multi-agent based ITS development. The developed use case model (especially use case scenarios) is used during the agent interaction design and task definition. Goal hierarchy during the design phase is mainly used for checking, if the results of design achieve all system's goals.

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