Intelligent Tutoring System Authoring Tools for Non-Programmers

An intelligent tutoring system (ITS) is a software application that tries to replicate the performance of a human tutor by supporting the theory of learning by doing . ITSs have been shown to improve the performance of a student in wide range of domains. Despite their benefits, ITSs have not seen widespread use due to the complexity involved in their development. Developing an ITS from scratch requires expertise in several fields including computer science, cognitive psychology and artificial intelligence. In order to decrease the skill threshold required to build ITSs, several authoring tools have been developed. In this thesis, I document several contributions to the field of intelligent tutoring in the form of extensions to an existing ITS authoring tool, research studies on authoring tool paradigms and the design of authoring tools for non-programmers in two complex domains - natural language processing and 3D game environments. The Extensible Problem Specific Tutor (xPST) is an authoring tool that helps rapidly develop model-tracing like tutors on existing interfaces such as webpages. xPST\u27s language was made more expressive with the introduction of new checktypes required for answer checking in problems belonging to domains such as geometry and statistics. A web-based authoring (WAT) tool was developed for the purpose of tutor management and deployment and to promote non-programmer authoring of ITSs. The WAT was used in a comparison study between two authoring tool paradigms - GUI based and text based, in two different problem domains - statistics and geometry. User-programming of natural language processing (NLP) in ITSs is not common with authoring toolkits. Existing NLP techniques do not offer sufficient power to non-programmers and the NLP is left to expert developers or machine learning algorithms. We attempted to address this challenge by developing a domain-independent authoring tool, ConceptGrid that is intended to help non-programmers develop ITSs that perform natural language processing. ConceptGrid has been integrated into xPST. When templates created using ConceptGrid were tested, they approached the accuracy of human instructors in scoring student responses. 3D game environments belong to another domain for which authoring tools are uncommon. Authoring game-based tutors is challenging due to the inherent domain complexity and dynamic nature of the environment. We attempt to address this challenge through the design of authoring tool that is intended to help non-programmers develop game-based ITSs

Intelligent tutoring in virtual reality for highly dynamic pedestrian safety training

This thesis presents the design, implementation, and evaluation of an Intelligent Tutoring System (ITS) with a Virtual Reality (VR) interface for child pedestrian safety training. This system enables children to train practical skills in a safe and realistic virtual environment without the time and space dependencies of traditional roadside training. This system also employs Domain and Student Modelling techniques to analyze user data during training automatically and to provide appropriate instructions and feedback. Thus, the traditional requirement of constant monitoring from teaching personnel is greatly reduced. Compared to previous work, especially the second aspect is a principal novelty for this domain. To achieve this, a novel Domain and Student Modeling method was developed in addition to a modular and extensible virtual environment for the target domain. While the Domain and Student Modeling framework is designed to handle the highly dynamic nature of training in traffic and the ill-defined characteristics of pedestrian tasks, the modular virtual environment supports different interaction methods and a simple and efficient way to create and adapt exercises. The thesis is complemented by two user studies with elementary school children. These studies testify great overall user acceptance and the system’s potential for improving key pedestrian skills through autonomous learning. Last but not least, the thesis presents experiments with different forms of VR input and provides directions for future work.Diese Arbeit behandelt den Entwurf, die Implementierung sowie die Evaluierung eines intelligenten Tutorensystems (ITS) mit einer Virtual Reality (VR) basierten Benutzeroberfläche zum Zwecke von Verkehrssicherheitstraining für Kinder. Dieses System ermöglicht es Kindern praktische Fähigkeiten in einer sicheren und realistischen Umgebung zu trainieren, ohne den örtlichen und zeitlichen Abhängigkeiten des traditionellen, straßenseitigen Trainings unterworfen zu sein. Dieses System macht außerdem von Domain und Student Modelling Techniken gebrauch, um Nutzerdaten während des Trainings zu analysieren und daraufhin automatisiert geeignete Instruktionen und Rückmeldung zu generieren. Dadurch kann die bisher erforderliche, ständige Überwachung durch Lehrpersonal drastisch reduziert werden. Verglichen mit bisherigen Lösungen ist insbesondere der zweite Aspekt eine grundlegende Neuheit für diesen Bereich. Um dies zu erreichen wurde ein neuartiges Framework für Domain und Student Modelling entwickelt, sowie eine modulare und erweiterbare virtuelle Umgebung für diese Art von Training. Während das Domain und Student Modelling Framework so entworfen wurde, um mit der hohen Dynamik des Straßenverkehrs sowie den vage definierten Fußgängeraufgaben zurecht zu kommen, unterstützt die modulare Umgebung unterschiedliche Eingabeformen sowie eine unkomplizierte und effiziente Methode, um Übungen zu erstellen und anzupassen. Die Arbeit beinhaltet außerdem zwei Nutzerstudien mit Grundschulkindern. Diese Studien belegen dem System eine hohe Benutzerakzeptanz und stellt das Potenzial des Systems heraus, wichtige Fähigkeiten für Fußgängersicherheit durch autodidaktisches Training zu verbessern. Nicht zuletzt beschreibt die Arbeit Experimente mit verschiedenen Formen von VR Eingaben und zeigt die Richtung für zukünftige Arbeit auf

Exploring Fog of War Concepts in Wargame Scenarios

This thesis explores fog of war concepts through three submitted journal articles. The Department of Defense and U.S. Air Force are attempting to analyze war scenarios to aid the decision-making process; fog modeling improves realism in these wargame scenarios. The first article Navigating an Enemy Contested Area with a Parallel Search Algorithm [1] investigates a parallel algorithm\u27s speedup, compared to the sequential implementation, with varying map configurations in a tile-based wargame. The parallel speedup tends to exceed 50 but in certain situations. The sequential algorithm outperforms it depending on the configuration of enemy location and amount on the map. The second article Modeling Fog of War Effects in AFSIM [2] introduces the FAT for the AFSIM to introduce and manipulate fog in wargame scenarios. FAT integrates into AFSIM version 2.7.0 and scenario results verify the tool\u27s fog effects for positioning error, hits, and probability affect the success rate. The third article Applying Fog Analysis Tool to AFSIM Multi-Domain CLASS scenarios [3] furthers the verification of FAT to introduce fog across all war fighting domains using a set of CLASS scenarios. The success rate trends with fog impact for each domain scenario support FAT\u27s effectiveness in disrupting the decision-making process for multi-domain operations. The three articles demonstrate fog can affect search, tasking, and decision-making processes for various types of wargame scenarios. The capabilities introduced in this thesis support wargame analysts to improve decision-making in AFSIM military scenarios

Explicit Feedback Within Game-based Training: Examining The Influence Of Source Modality Effects On Interaction

This research aims to enhance Simulation-Based Training (SBT) applications to support training events in the absence of live instruction. The overarching purpose is to explore available tools for integrating intelligent tutoring communications in game-based learning platforms and to examine theory-based techniques for delivering explicit feedback in such environments. The primary tool influencing the design of this research was the Generalized Intelligent Framework for Tutoring (GIFT), a modular domain-independent architecture that provides the tools and methods to author, deliver, and evaluate intelligent tutoring technologies within any training platform. Influenced by research surrounding Social Cognitive Theory and Cognitive Load Theory, the resulting experiment tested varying approaches for utilizing an Embodied Pedagogical Agent (EPA) to function as a tutor during interaction in a game-based environment. Conditions were authored to assess the tradeoffs between embedding an EPA directly in a game, embedding an EPA in GIFT’s browser-based Tutor-User Interface (TUI), or using audio prompts alone with no social grounding. The resulting data supports the application of using an EPA embedded in GIFT’s TUI to provide explicit feedback during a game-based learning event. Analyses revealed conditions with an EPA situated in the TUI to be as effective as embedding the agent directly in the game environment. This inference is based on evidence showing reliable differences across conditions on the metrics of performance and self-reported mental demand and feedback usefulness items. This research provides source modality tradeoffs linked to tactics for relaying training relevant explicit information to a user based on real-time performance in a game

Cognitive Architectures for Serious Games

This dissertation summarises a research path aimed at fostering the use of Cognitive Architectures in Serious Games research field. Cognitive Architectures are an embodiment of scientific hypotheses and theories aimed at capturing the mechanisms of cognition that are considered consistent over time and independent of specific tasks or domains. The theoretical approaches provided by the research in computational cognitive modelling have been used to formalise a methodological framework to guide researchers and experts in the game-based education sector in designing, implementing, and evaluating Serious Games. The investigation of cognitive processes involved during the game experience represents the fundamental step of the pro- posed approach. Two different case studies are described to discuss the possible use of the suggested framework. In the first case study, the aim was to design a modified version of the Tetris game with the intention of making the game more effective in training the visual-spatial skill called mental rotation. In the second scenario, the frame- work was used as a basis for creating an innovative persuasive game. This case study provides an example of adopting cognitive architectures for implementing a non-player character with human-like behaviour developed using targeted cognitive theories

Expressing Motivations By Facilitating Other’s Inverse Reinforcement Learning

It is often necessary to understand each other’s motivations in order to cooperate. Reaching such a mutual understanding requires two abilities: to build models of other’s motivations in order to understand them, and to build a model of “my” motivations perceived by others in order to be understood. Having a self-image seen by others requires two recursive orders of modeling, known in psychology as the first and second orders of theory of mind. In this paper, we present a second-order theory of mind cognitive architecture that aims to facilitate mutual understanding in multi-agent scenarios. We study different conditions of empathy and gratitude leading to irrational cooperation in iterated prisoner’s dilemma

Simulation Game Concept For AI-Enhanced Teaching Of Advanced Value Stream Analysis and Design

Value stream analysis and design is employed globally by improvement teams within industrial settings to maximize value creation and eliminate waste. For ending methodical time-centricity, research expanded the methodology to incorporate diverse facets like material flow cost accounting, information logistics, and external influence factors. These enhancements, along with increasing data volumes, are prompting a re-evaluation of how professional improvement teams should think and operate. Consequently, a transformation of the pedagogical approach used for educating students and professionals necessitates novel solutions. Conventional teaching methods such as expository lectures are widely considered inadequate in promoting knowledge retention and engagement. So far, existing research has not yet resulted in a solution that can effectively impart the methodological complexity of advanced value stream analysis and design in a motivating and vivid fashion. To address this gap, this paper applies a tailored CRISP gamification framework to develop a simulation game concept. These concept enables AI-enhanced teaching of advanced value stream analysis and design focusing on identification of multi-stage resource-efficient optimization strategies. Through integration of game-based learning with AI a trained reinforcement learning agent can act either competitively or cooperatively, creating a unique form of teaching accounting the aspects personalization, adaptive feedback, content creation, and analysis and assessment