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In this paper we take an interactivist perspective on the computer-mediated classroom, and explore the autopoietic teaching approach in the context of emergence of diversity in the classroom. The POPSICLE (Patterns in Orientation: Pattern-Aided Simulated Interaction Context-Learning Experiment) is presented and discussed, as well as its multiple consequences. POPSICLE is a study of learning in human subjects, as well as of interaction and transfer of knowledge between subjects in a collaborative environment.

In this chapter, we formalize the Interactivist-Expectative Theory of Agency and Learning (IETAL) agent in an algebraic framework and focus on issues of learnability based on context.

Pattern-Aided Simulated Interaction Context Learning Experiment (POPSICLE) Agent Simulator began as a sample project in object-oriented agent programming, but quickly grew into a complete framework for the simulation of agent behavior based upon an associative memory model. The system began its implementation as a Java 2 (J2SE 1.4) application, but was later migrated to a Java 5 application (J2SE 1.5) to utilize the type-safe collections and enumerated types that became available in the latest Java version. Various design patterns were employed in the development; the most predominate being the Model/Controller/View (MCV) architecture. As we will see later, the system also relies heavily on delegation and observers.

The development of wireless sensor networks is accompanied by several algorithms for data processing which are modified regression techniques from the field of multidimensional data series analysis in other scientific fields, with examples like nearest neighbor search, principal component analysis and multidimensional scaling (Guestrin, C. et al., Proc. IPSN'04, 2004). We argue that some algorithms, well developed within the neural-networks tradition for over 40 years, are well suited to fit into the requirements imposed by sensor networks: simple parallel distributed computation; distributed storage; data robustness; auto-classification of sensor readings. As a result of the dimensionality reduction obtained easily from the outputs of neural-network clustering algorithms, lower communication costs, and thus bigger energy savings, can be obtained. We present two possible applications of the ART and FuzzyART algorithms, which are unsupervised learning methods for clustering or categorization of the sensory inputs, applied on data obtained from a set of 5 Smart-It units (sensor nodes or motes) equipped with 6 sensors each. Results from simulations of purposefully faulty sensors show that these architectures are data robust to errors

Many institutions in higher education are offering at least some of their curriculum online and use online course management systems to support these learning environments. Successful participation in online learning depends on many factors, and may especially be influenced by the degree of computer competency users bring with them to the learning environment. The purpose of this study is to evaluate the effects of several areas of computer competency on student learning experiences and learning environment usability in online learning environments. Subject of evaluation was a multi-section course consisting of eight sections taught in hybrid format; approximately 50% of course work was conducted using the World Wide Web. Results of the study have direct implications in online course design and development.

Many in IT education—following on more than twenty years of multicultural critique and theory—have integrated “diversity” into their curricula. But while this is certainly laudable, there is an irony to the course “multiculturalism” has taken in the sciences in general. By submitting to a canon originating in the humanities and social sciences—no matter how progressive or well-intentioned—much of the transgressive and revolutionary character of multicultural pedagogies is lost in translation, and the insights of radical theorists become, simply, one more module to graft onto existing curricula or, at the very least, another source of authority joining or supplanting existing canons. In this essay, we feel that introducing diversity into IT means generating this body of creative critique from within IT itself, in the same way multiculturalism originated in the critical, transgressive spaces between literature, cultural studies, anthropology and pedagogy. The following traces our efforts to develop isomorphic critiques from recent insights into multi-agent systems using a JAVA-based, software agent we’ve developed called “Izbushka.”

In the past several years, the work within the framework of the interactivist-expectative theory on agency and learning (IETAL), we have concentrated on exploring the concept of learning environment partitions in an autonomous agent, and the problems encountered during its stay in the environment. The key concepts in the uniagent version of the theory are the concepts of expectancy and learning through interactions with the environment, while building an intrinsic model of it. Depending on the set of active drives and their hierarchy (not necessarily ordered with a partial ordering, but rather a general relational structure), the agent uses its intrinsic model to navigate its quest to satisfy the active drive(s). In this paper, we start with the existing results of the theory and generalize it to a theory of multiple agent systems, via introducing imitation-based interaction between homogenous agents. While sensing each other, the agents exchange their contingency tables, built during the interaction with the environment. This approach is inspired by results from both neurophysiology and psychology on the phenomenon of imitation. The formalization of the agent, as well as of the environment is necessary for setting up a successful experimental and simulation environment for exploring the new paradigms. Our approach relies on new modeling strategies and structures from the domain of lattice (L-fuzzy), posets (P-fuzzy) and general relational-structure-valued (R-fuzzy) algebraic structures.

The field of data mining is dedicated to the analysis of data to find underlying connections and the discovery of new patterns. This research targets the analysis of imprecise temporal associations through the modification of a standard market basket analysis approach by means of fuzzy set relations to classify the associations among different sources of data. The domain that is taken into consideration in this work is the one of medicine. We used data recorded within an Intensive Care Unit from a 8 month old infant that suffers from Acute Respiratory Distress Syndrome. In particular, we analyzed the response of the partial pressure of oxygen within the bloodstream to the application of a respirator. The results of this research show that it is possible to investigate such relations with the help of fuzzy set classification for temporal associations, and the result of such exploration is as easily understandable as the standard Market Basket algorithm. The findings support the physiological response, suggesting that this approach is worthy of notice. We are confident that such an algorithm will show its capabilities when applied to the clinical trials part of drug testing, given the results outlined in this article.

The field of data mining is dedicated to the analysis of data in order to find underlying connections and the discovery of new patterns. Since the volume of data to be analyzed is sometimes quite significant, there is the need for efficient data mining algorithms to be implemented. The market-basket algorithm can represent a breakthrough in data mining techniques. As the associations that are to be analyzed grow more and more abstract, the market-basket approach is unable to deal with imprecise temporal associations, leaving a big area uncharted. This research is dedicated to the analysis of temporal imprecise associations through the modification of a standard a-priori approach by means of fuzzy set relations to classify the associations relating different sources of data. The results of this research show that it is possible to investigate such relations with the help of fuzzy set classification for temporal associations, and the result of such exploration is as easily understandable as the standard a-priori algorithm.