Course generation as a hierarchical task network planning problem

This thesis presents course generation based on Hierarchical Task Network planning (HTN planning). This course generation framework enables the formalization and application of complex and realistic pedagogical knowledge. Compared to previous course generation, this approach generates structured courses that are adapted to a variety of different learning goals and to the learners\u27; competencies. The thesis describes basic techniques for course generation, which are used to formalize seven different types of courses (for instance introducing the learner to previously unknown concepts and supporting him during rehearsal) and several elementary learning goals (e. g., selecting an appropriate example or exercise). The course generator developed in this thesis is service-oriented thus allowing the integration of learning supporting services into the generated course in a generic and pedagogically sensible way. Furthermore, learning environments can access the functionality of the course generator using a Web-service interface. Repositories are treated as services that can register at the course generator and make their content available for course generation. The registration is based on an ontology of instructional objects. Its classes allow categorizing learning objects according to their pedagogical purpose in a more precise way than existing metadata specifications; hence it can be used for intelligent pedagogical functionalities other than course generation. Course generation based on HTN planning is implemented in Paigos and was evaluated by technical, formative and summative evaluations. The technical evaluation primarily investigated the performance to Paigos; the formative and summative evaluations targeted the users\u27; acceptance of Paigos and of the generated courses.Diese Arbeit stellt Kursgenerierung vor, die auf Hierarchical Task Network Planung (HTN Planung) basiert. Der gewählte Rahmen erlaubt die Formalisierung von komplexem und realistischem pädagogischem Wissen und ermöglicht im Vergleich zu bisherigen Techniken die Generierung von strukturierten Kursen, die an eine Vielzahl von Lernzielen angepasst sind. Aufbauend auf allgemeinen Techniken zur Kursgenerierung wird das pädagogische Wissen für sieben verschiedene Kurstypen und für eine Reihe von elementaren Lernzielen formalisiert. Die in dieser Arbeit vorgestellte Kursgenerierung ist service-orientiert. Dadurch steht ein generischer Rahmen zu Verfügung, in dem externe Lernsysteme in die generierten Kurse eingebunden werden und dem Lernenden zur Verfügung gestellt werden können, wenn es pädagogisch sinnvoll ist. Weiterhin können andere Lernsysteme über eine Web-Service Schnittstelle auf die Funktionalitäten des Kursgenerators zugreifen: Datenbanken werden als Services betrachtet, die an dem Kursgenerator registriert werden können, und auf die während der Kurserstellung zugegriffen wird. Die Registrierung verwendet eine Ontologie, die verschiedene instruktionale Typen von Lernobjekten repräsentiert und es erlaubt, Lernobjekte nach ihrem pädagogischen Verwendungszweck zu klassifizieren. Sie geht dabei über existierende Metadatenspezifikationen hinaus und ermöglicht pädagogische komplexe Funktionalitäten, so wie beispielsweise Kursgenerierung und weitere. Die vorgestellte Kursgenerierung ist implementiert in Paigos und wurde durch technische, formative und summative Evaluationen untersucht. Die technische Evaluation analysierte in erster Linie die Performanz von Paigos; die formative und summative Evaluationen widmeten sich der Frage der Akzeptanz und Verständlichkeit der von Paigos erzeugten Kurse aus Benutzersicht

THE "POWER" OF TEXT PRODUCTION ACTIVITY IN COLLABORATIVE MODELING : NINE RECOMMENDATIONS TO MAKE A COMPUTER SUPPORTED SITUATION WORK

Language is not a direct translation of a speaker’s or writer’s knowledge or intentions. Various complex processes and strategies are involved in serving the needs of the audience: planning the message, describing some features of a model and not others, organizing an argument, adapting to the knowledge of the reader, meeting linguistic constraints, etc. As a consequence, when communicating about a model, or about knowledge, there is a complex interaction between knowledge and language. In this contribution, we address the question of the role of language in modeling, in the specific case of collaboration over a distance, via electronic exchange of written textual information. What are the problems/dimensions a language user has to deal with when communicating a (mental) model? What is the relationship between the nature of the knowledge to be communicated and linguistic production? What is the relationship between representations and produced text? In what sense can interactive learning systems serve as mediators or as obstacles to these processes

An Architectural Approach to Ensuring Consistency in Hierarchical Execution

Hierarchical task decomposition is a method used in many agent systems to organize agent knowledge. This work shows how the combination of a hierarchy and persistent assertions of knowledge can lead to difficulty in maintaining logical consistency in asserted knowledge. We explore the problematic consequences of persistent assumptions in the reasoning process and introduce novel potential solutions. Having implemented one of the possible solutions, Dynamic Hierarchical Justification, its effectiveness is demonstrated with an empirical analysis

An architecture for organisational decision support

The Decision Support (DS) topic of the Network Enabled Capability for Through Life Systems Engineering (NECTISE) project aims to provide organisational through-life decision support for the products and services that BAE Systems deliver. The topic consists of five streams that cover resource capability management, decision management, collaboration, change prediction and integration. A proposed architecture is presented for an Integrated Decision Support Environment (IDSE) that combines the streams to provide a structured approach to addressing a number of issues that have been identified by BAE Systems business units as being relevant to DS: uncertainty and risk, shared situational awareness, types of decision making, decision tempo, triggering of decisions, and support for autonomous decision making. The proposed architecture will identify how either individuals or groups of decision makers (including autonomous agents) would be utilised on the basis of their capability within the requirements of the scenario to collaboratively solve the decision problem. Features of the scenario such as time criticality, required experience level, the need for justification, and conflict management, will be addressed within the architecture to ensure that the most appropriate decision management support (system/naturalistic/hybrid) is provided. In addition to being reliant on a number of human factors issues, the decision making process is also reliant on a number of information issues: overload, consistency, completeness, uncertainty and evolution, which will be discussed within the context of the architecture

Learning and Production of Movement Sequences: Behavioral, Neurophysiological, and Modeling Perspectives

A growing wave of behavioral studies, using a wide variety of paradigms that were introduced or greatly refined in recent years, has generated a new wealth of parametric observations about serial order behavior. What was a mere trickle of neurophysiological studies has grown to a more steady stream of probes of neural sites and mechanisms underlying sequential behavior. Moreover, simulation models of serial behavior generation have begun to open a channel to link cellular dynamics with cognitive and behavioral dynamics. Here we summarize the major results from prominent sequence learning and performance tasks, namely immediate serial recall, typing, 2XN, discrete sequence production, and serial reaction time. These populate a continuum from higher to lower degrees of internal control of sequential organization. The main movement classes covered are speech and keypressing, both involving small amplitude movements that are very amenable to parametric study. A brief synopsis of classes of serial order models, vis-à-vis the detailing of major effects found in the behavioral data, leads to a focus on competitive queuing (CQ) models. Recently, the many behavioral predictive successes of CQ models have been joined by successful prediction of distinctively patterend electrophysiological recordings in prefrontal cortex, wherein parallel activation dynamics of multiple neural ensembles strikingly matches the parallel dynamics predicted by CQ theory. An extended CQ simulation model-the N-STREAMS neural network model-is then examined to highlight issues in ongoing attemptes to accomodate a broader range of behavioral and neurophysiological data within a CQ-consistent theory. Important contemporary issues such as the nature of working memory representations for sequential behavior, and the development and role of chunks in hierarchial control are prominent throughout.Defense Advanced Research Projects Agency/Office of Naval Research (N00014-95-1-0409); National Institute of Mental Health (R01 DC02852