We study the dynamics and patterns of adoption of two competing technologies as well as the effectiveness and optimal- ity of viral pricing strategies by a technology seller. Our model considers two incompatible technologies of differing quality and a market in which user valuations are heterogeneous and subject to network effects. Taking the perspec- tive of a seller of the higher quality technology with imperfect information about user preferences, we investigate the problem of predicting market equilibrium outcomes. We provide partial characterization results about the structure and robustness of equilibria and give conditions under which the higher quality technology purveyor can make significant inroads into the competitor’s market share. We then show that myopic best-response dynamics in our setting are monotonic and convergent, and propose two pricing mechanisms that use this insight to help the entrant technology seller tip the market in its favor. Comparable implementations of both mechanisms reveals that the nondiscriminatory strategy, based on a calculated public price subsidy, is less costly and just as effective as a discriminatory policy. Additionally, we study discriminatory and nondiscriminatory price mechanisms in the context of profit maximization and show that problem is NP-Hard under uncertainty for both regimes. Finally, we use simulations to analyze a game in which the pricing decisions of both competing sellers are endogenous and now show, in contrast to our analytical results with exogenous prices, that a higher quality technology consistently holds a competitive advantage over the lower quality competitor, irrespective of its market share.

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 hypothesize that enhancing computer tutors to respond to student uncertainty over and above correctness is one method for increasing both student learning and self-monitoring abilities. We explore this hypothesis using data from an experiment with a wizarded spoken tutorial dialogue system, where tutor responses to uncertain and/or incorrect student answers were manipulated. Our results suggest that monitoring and responding to student uncertainty has the potential to improve both cognitive and metacognitive student abilities.

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.

We have developed computer environments that support learning by teaching and the use of self regulated learning (SRL) skills through interactions with virtual agents. More specifically, students teach a computer agent, Betty, and can monitor her progress by asking her questions and getting her to take quizzes. The system provides SRL support via dialog-embedded prompts by Betty, the teachable agent, and Mr. Davis, the mentor agent. Our primary goals have been to support learning in complex science domains and facilitate development of metacognitive skills. More recently, we have also employed sequence analysis schemes and hidden Markov model (HMM) methods for assigning context to and deriving aggregated student behavior sequences from activity data. These techniques allow us to go beyond analyses of individual behaviors, instead examining how these behaviors cohere in larger patterns. We discuss the information derived from these models, and draw inferences on students’ use of self-regulated learning strategies.