Irregular Warfare (IW), with its emphasis on social and cognitive phenomena such as population sentiment, is a major new focus of the Department of Defense (DoD). One of the most important classes of IW action is Information Operations (IO), the use of information to influence sentiment. With the DoD’s new focus on IW comes the new need to analyze and forecast the effects of IO actions on population sentiment. Analysts at the DoD traditionally use Modeling and Simulation to analyze and forecast the effects of conventional warfare’s actions on the outcome of wars, but IW and IO in particular are far more complex than conventional physics-based simulations. DoD analysts are in the early stages of looking for scientifically rigorous methods in the Modeling and Simulation of IO’s complex effects. This paper presents the state of IO modeling and simulation in the DoD, using examples from several computer models now being used, in these early stages of IW analysis. It discusses how the ideas of Complex Adaptive Systems (CAS) and threshold events in particular may be incorporated into IO modeling in order to increase its scientific rigor, fidelity, and validity.

International law is a singular exception to the top-down systems of law within nations. It presents the puzzle of how the law can be created or changed in the absence of authoritative rule-making institutions. The present paper is part of a work in progress that locates the law-making apparatus of international law in a complex adaptive system. Herein the focus is on thresholds. The first and most detailed threshold describes the emergence of the complex adaptive system. The second threshold consists of the transformation of international law from the voluntary to the automatic. The third threshold is here but has not yet been crossed: actualizing human rights as enforceable claims by individuals against States.

To help realize the potential of complex systems models we need new measures appropriate for capturing processes that exhibit feedback, nonlinearity, heterogeneity, and emergence. As part of a larger research project encompassing several categories of dynamical properties this paper provides formal and general definitions of tipping point-related phenomena. For each tipping concept this paper provides a probabilistic definition derived from a Markov model representation. We start with the basic features of Markov models and definitions of the foundational concepts of system dynamics. Then several tipping point-related concepts are described, defined, measured, and illustrated with a simplified graphical example. The paper finishes with several branches of future work involving new measures for complex systems and the fusion of research domains.

Issues concerning students involved with online learning paths, that need to be faced by e-Tutors on their day-to-day activity, most often than not fall into known pedagogical patterns - that are problems and difficulties already occurred in the past and dealt with. These pedagogical patterns belong to e-Tutors' know-how and experience and their resolution are frequently a matter of activating routine processes or giving  pre-factored answers; nevertheless statistical data indicates that these issues consume a considerable slice of tutors' time. While a portion of the scientific community is still devoting much effort in developing artificial tutoring systems - by deploying AI/MAS-enabled technologies - the solution being investigated by our team focuses on enhancing already-available, open source LMS by implementing a general-purpose tracking and monitoring toolkit able to support e-Tutors in recognizing and dealing with pedagogical patterns stored into a decentralised Knowledge Base. The system architecture is designed to house multiple platforms (only one adapter interface needs to be written for each LMS) and is able to perform real-time, as well as scheduled, data collection by means of Jade-based agents and schedulers.  Information obtained from the processed data is then returned to the platform via web services and specific interfaces (instant messaging chatbot). The first deployed prototype is currently being experimented in adult higher education learning paths and is able to track student activity, forum readings and writings and offers a basic chat-based help interface. Our aim is to turn a standard LMS into a knowledge aggregator where information about its users, its contents and interactions between the two can be mined via Knowledge Services; resulting data could then be used to refine users' and groups' profiles, to monitor learners' deviance from expected learning path, and ultimately to adjust the applied pedagogical model.

This paper presents a methodology to acquire new knowledge in TutorJ using external information sources. TutorJ is an ITS whose architecture is inspired to the HIPM cognitive model, while meta-cognition principles have been used to design the knowledge acquisition process. The system behavior is intended to increase its own knowledge as a consequence of the interaction with users. The implemented methodology uses external links and services to capture new knowledge from contents related to discussion topics and transforms these contents into structured knowledge that is stored inside an ontology. The purpose of the proposed methodology is to lower the effort of system scaffolding creation and to increase the level of interaction with users. The focus is on self-regulated learners while meta-cognitive strategies have to bee defined to adapt and to increase the effectiveness of tutoring actions.

Narrative is emerging as an effective medium for contextualizing learning. Narrative-centered learning environments provide engaging, story-centric virtual spaces that offer guided learning and problem-solving opportunities. Students’ ability to self-regulate learning can significantly impact performance in these environments, and, in general, is critical for academic achievement. This paper explores the relationship between narrative-centered learning and self-regulation. Connections are drawn between the salient characteristics of narrative-centered learning environments and principles for promoting and enhancing self-regulated learning in science education. These relationships are further explicated through an examination of the CRYSTAL ISLAND learning environment. The paper explores the hypothesis that narrative-centered learning environments are particularly well suited for simultaneously promoting learning, engagement, and self-regulation.

This paper presents GnuTutor, an open source intelligent tutoring system (ITS) inspired by the AutoTutor ITS. The goal of GnuTutor is to create a freely available, open source ITS platform that can be used by schools and researchers alike. To achieve this goal, significant departures from AutoTutor's current design were made so that GnuTutor would use a smaller, non-proprietary code base but have the major functionality of AutoTutor, including mixed-initiative dialogue, an animated agent, speech act classification, and natural language understanding using latent semantic analysis. This paper describes the GnuTutor system, its components, and the major differences between GnuTutor and AutoTutor.

We are working on a temporal reasoning web tool for 7-11 olds. The acquisition of temporal relations and reasoning with them depends on age and experience, as well as linguistic factors. We conducted a preliminary evaluation with 6–8 olds in order to assess whether and how they would visually represent “while” temporal relations of a story. In this paper, we present and discuss our experimental evaluation, which paves the way for the visual representation of such relations in our e-tool.

This paper describes the DynaLearn project, which seeks to address contemporary problems in science education by integrating well established, but currently independent technological developments, and utilize the added value that emerges. Specifically, diagrammatic representations are used for learners to articulate, analyse and communicate ideas, and thereby construct their conceptual knowledge. Ontology mapping is used to find and match co-learners working on similar ideas to provide individualised and mutually benefiting learning opportunities. Virtual characters are used to make the interaction engaging and motivating. The development of the workbench is tuned to fit key topics from environmental science curricula, and evaluated and further improved in the context of existing curricula using case studies. Through this approach, the DynaLearn project will deliver an individualised and engaging cognitive tool for acquiring conceptual knowledge that fits the true nature of this expertise.

The goal of this paper is to present four key assumptions regarding the measurement of cognitive and metacognitive regulatory processes used during learning with hypermedia. First, we assume it is possible to detect, trace, model, and foster SRL processes during learning with hypermedia. Second, understanding the complex nature of the regulatory processes during learning with hypermedia is critical in determining why certain processes are used throughout a learning task. Third, it is assumed that the use of SRL processes can dynamically change over time and that they are cyclical in nature (influenced by internal and external conditions and feedback mechanisms). Fourth, capturing, identifying, and classifying SRL processes used during learning with hypermedia is a rather challenging task.