A 3-Tier Planning Architecture for Managing Tutorial Dialogue

Managing tutorial dialogue is an intrinsically complex task that is only partially covered by current models of dialogue processing

Adapting Collaborative Chat for Massive Open Online Courses: Lessons Learned

Abstract. In this paper we explore how to import intelligent support for group learning that has been demonstrated as effective in classroom instruction into a Massive Open Online Course (MOOC) context. The Bazaar agent architecture paired with an innovative Lobby tool to enable coordination for synchronous reflection exercises provides a technical foundation for our work. We describe lessons learned, directions for future work, and offer pointers to resources for other researchers interested in computer supported collaborative learning in MOOCs

Persuasive and adaptive tutorial dialogues for a medical diagnosis tutoring system

The objective of this thesis is to address a key problem in the development of an intelligent tutoring system, that is, the implementation of the verbal exchange (a dialogue) that takes place between a student and the system. Here we consider TeachMed, a medical diagnosis tutoring system that teaches the students to diagnose clinical problems. However, approaches that are presented could also fit other tutoring systems. In such a system, a dialogue must be implemented that determines when and how pedagogic aid is provided to the student, that is, what to say to her, in what circumstances, and how to say it. Finite state machines and automated planning systems are so far the two most common approaches for implementing tutoring dialogues in intelligent tutoring systems. In the former approach, finite state machines of dialogues are manually designed and hard coded in intelligent tutoring systems. This is a straightforward but very time consuming approach. Furthermore, any change or extension to the hard coded finite state machines is very difficult as it requires reprogramming the system. On the other hand, automated planning has long been presented as a promising technique for automatic dialogue generating. However, in existing approaches, the requirement for the system to persuade the student is not formally acknowledged. Moreover, current dialogue planning approaches are not able to reason on uncertainties about the student's knowledge. This thesis presents two approaches for generating more effective tutorial dialogues.The first approach describes an argumentation framework for implementing persuasive tutoring dialogues. In this approach the entire interaction between the student and the tutoring system is seen as argumentation.The tutoring system and the student can settle conflicts arising during their argumentation by accepting, challenging, or questioning each other's arguments or withdrawing their own arguments. Pedagogic strategies guide the tutoring system by selecting arguments aimed at convincing the student.The second approach presents a non-deterministic planning technique which models the dialogue generation problem as one of planning with incomplete knowledge and sensing. This approach takes into account incomplete information about a particular fact of the student's knowledge by creating conditional branches in a dialogue plan such that each branch represents an adaptation of the dialogue plan with respect to a particular state of the student's knowledge or belief concerning the desired fact. In order to find out the real state of the student's knowledge and to choose the right branch at execution time, the planner includes some queries in the dialogue plan so that the tutoring system can ask the student to gather missing information. One contribution in this thesis is improving the quality of tutoring dialogues by engaging students in argumentative interactions and/or adapting the dialogues with respect to the student's knowledge. Another one is facilitating the design and implementation of tutoring by turning to automatically generated dialogues as opposed to manually generated ones

Élaboration, implémentation et intégration d'un module de gestion du dialogue tutoriel en langage naturel dans le cadre d'un agent cognitif

Les systèmes tutoriels intelligents (STI) sont un grand pas vers une réforme dans l'éducation. Ces systèmes offrent une souplesse d'enseignement que les autres aides pédagogiques informatiques n'ont pas. De ce fait, ils pourraient, s'ils sont bien intégrés dans un programme éducatif, décharger les professeurs pour qu'ils consacrent une attention particulière aux étudiants plus faibles. Les systèmes tutoriels intelligents atteignent cette souplesse grâce à la combinaison de sous-systèmes; l'un d'entre eux est la communication. Plusieurs recherches ont été effectuées dans ce sens notamment pour la communication en langage naturel. Cette communication peut être divisée en trois parties soit la compréhension du langage naturel, la génération de texte en langage naturel et la planification des dialogues. Cette dernière représente la base de ce type de communication. CTS (Cognitive Tutoring System) est un moteur de système tutoriel intelligent basé sur la conscience d'accès développée par le GDAC. CTS a été intégré à Canadarm Tutor pour son développement. Ce mémoire traite de l'ajout d'un système de planification du dialogue basé sur les travaux effectués sur Beetle. Dans un premier temps, plusieurs correctifs seront apportés au fonctionnement du Réseau des Actes pour tenter de stabiliser son comportement; d'autres amèneront le système plus près de ses fondements notamment l'ajout de la délibération. L'ajout du planificateur tel que décrit dans le STI de Beetle s'effectuera dans un second temps et utilisera l'architecture unique de CTS pour le faire. Cette combinaison d'architecture apportera plusieurs avantages et donnera un système de planification de dialogue générique et augmentable.\ud _____________________________________________________________________________

Diretrizes para a construção de mediadores sócio-construtivistas em sistemas de aprendizagem colaborativa apoiada por computador

Tese (doutorado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Engenharia de Produção.Este trabalho situa-se na área de Informática na Educação trazendo contribuições específicas às áreas de Sistemas Tutores Inteligentes (STI) e de Sistemas de Aprendizagem Colaborativa Apoiada por Computador. Esta última, mais conhecida por Computer Supported Collaborative Learning (CSCL), constitui-se em um dos enfoques mais relevantes de pesquisa em Informática na Educação no momento atual. Para tanto, este trabalho busca, através das técnicas e recursos de informática utilizados por estes sistemas (STI e CSCL), e por meio de uma abordagem apoiada pela teoria sócio-construtivista, define Diretrizes para a Construção de um Mediador Computadorizado embasado pela Teoria Sócio-Construtivista. O papel do mediador é inspirado no comportamento de um professor em sala de aula que segue a abordagem sócio-construtivista. Nesta tese, o termo sócio-construtivismo adotado faz referência aos trabalhos de Vygotsky e de Piaget com influência dos Pós-Piagetianos. Para caracterizar tal perspectiva, é importante ressaltar que ela considera a aprendizagem como resultado de uma atividade interativa, do indivíduo com os objetos e com os outros (relação interpessoal), e que o amadurecimento de determinados conceitos não é igual para todos os indivíduos e está relacionado às oportunidades que o meio cultural lhes oferece. O professor, dentro desta perspectiva, pode ser visto como um membro mais amadurecido deste grupo de aprendizagem que media o processo interativo

Students´ language in computer-assisted tutoring of mathematical proofs

Truth and proof are central to mathematics. Proving (or disproving) seemingly simple statements often turns out to be one of the hardest mathematical tasks. Yet, doing proofs is rarely taught in the classroom. Studies on cognitive difficulties in learning to do proofs have shown that pupils and students not only often do not understand or cannot apply basic formal reasoning techniques and do not know how to use formal mathematical language, but, at a far more fundamental level, they also do not understand what it means to prove a statement or even do not see the purpose of proof at all. Since insight into the importance of proof and doing proofs as such cannot be learnt other than by practice, learning support through individualised tutoring is in demand. This volume presents a part of an interdisciplinary project, set at the intersection of pedagogical science, artificial intelligence, and (computational) linguistics, which investigated issues involved in provisioning computer-based tutoring of mathematical proofs through dialogue in natural language. The ultimate goal in this context, addressing the above-mentioned need for learning support, is to build intelligent automated tutoring systems for mathematical proofs. The research presented here has been focused on the language that students use while interacting with such a system: its linguistic propeties and computational modelling. Contribution is made at three levels: first, an analysis of language phenomena found in students´ input to a (simulated) proof tutoring system is conducted and the variety of students´ verbalisations is quantitatively assessed, second, a general computational processing strategy for informal mathematical language and methods of modelling prominent language phenomena are proposed, and third, the prospects for natural language as an input modality for proof tutoring systems is evaluated based on collected corpora

MENON : automating a Socratic teaching model for mathematical proofs

This thesis presents an approach to adaptive pedagogical feedback for arbitrary domains as an alternative to resource-intensive pre-compiled feedback, which represents the state-of-the-art in intelligent tutoring systems today. A consequence of automatic adaptive feedback is that the number of tasks with pedagogical feedback that can be offered to the student increases, and with it the opportunity for practice. We focus on automating different aspects of teaching that together are primarily responsible for learning and can be integrated in a unified natural-language output. The automatic production and natural-language generation of feedback enables its personalisation both at the pedagogical and the natural-language dialogue level. We propose a method for automating the production of domain-independent adaptive feedback. The proof- of-concept implementation of the tutorial manager Menon is carried out for the domain of set-theory proofs. More specifically, we define a pedagogical model that abides by schema and cognitive load theory, and by the synergistic approach to learning. We implement this model in a Socratic teaching strategy whose basic units of feedback are dialogue moves. We use empirical data from two domains to derive a taxonomy of tutorial-dialogue moves, and define the most central and sophisticated move hint. The formalisation of the cognitive content of hints is inspired by schema theory and is facilitated by a domain ontology.Die vorliegende Arbeit präsentiert eine Annäherung an adaptives pädagogisches Feedback für beliebige Domäne. Diese Herangehensweise bietet eine Alternative zu ressource-intensivem, vorübersetztem Feedback, dass das heutige "state-of-the-art'; in intelligenten tutoriellen Systemen ist. Als Folge können zahlreiche Aufgaben mit pädagogischem Feedback für die Praxis angeboten werden. Der Schwerpunkt der Arbeit liegt auf der Automatisierung verschiedener Aspekte des Lehrprozesses, die in ihrer Gesamtheit wesentlich den Lernprozess beeinflussen, und in einer einheitlichen Systemausgabe Natürlicher Sprache integriert werden können. Die automatische Produktion und die Systemgenerierung von Feedback in Natürlicher Sprache ermöglichen eine Individualisierung des Feedback auf zwei Ebenen: einer pädagogischen und einer dialogischen Ebene. Dazu schlagen wir eine Methode vor, durch die adaptives Feedback automatisiert werden kann, und implementieren den tutoriellen Manager Menon als "proof-of-concept'; beispielhaft für die Domäne von Beweisen in der Mengentheorie. Konkret definieren wir ein pädagogisches Modell, das sich auf Schema- und Kognitionstheorie sowie auf die synergetische Herangehensweise an Lernen stützt. Dieses Modell wird in einer Sokratischen Lehrmethode implementiert, deren basale Feedback-Elemente aus Dialogakten bestehen. Zur Bestimmung einer Taxonomie Tutorielle-Dialogakte sowie des zentralen und komplexen Dialogakts hint (Hinweis) wenden wir empirische Daten aus zwei Domänen an. Die Formalisierung des kognitiven Inhaltes von Hinweisen folgt der Schematheorie und basiert auf einer Domänenontologie