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Assessments, even summative, are part of the learning journey not just for the student, but for the instructors as well. In competency-based education, performance assessments (PAs) are regarded as a highly authentic method of measurement, but their complexity makes them vulnerable to construct-irrelevant variance such as group bias. A differential item functioning (DIF) study was conducted to detect potential bias in a series of information technology PA tasks in which task scenarios were identified (by SMEs) as neutral or potentially controversial, where the latter was hypothesized as more likely to trigger DIF for certain demographics. Given the variety of DIF methods available and their relative strengths and weaknesses, three common statistical methods – Mantel-Haenszel (MH), logistic regression (LR), and Lord's chi-square (LC) - were used followed by a substantive review of DIF items. Hypotheses were largely supported by the analysis and review. Discussion centers on the implication of findings for assessment strategies in education.

The purpose of this chapter is to provide a comprehensive overview of context-rich learning, including its theoretical foundations, alignment with various instructional practices, and potential benefits and limitations. The chapter begins with a discussion of the theoretical underpinnings of context-rich learning, exploring its connections to constructivism, experiential learning, problem-based learning, and situated learning. Each section provides specific examples of how context-rich learning aligns with these theories and highlights the benefits of incorporating authentic, real-world experiences into the learning process.

With the changes in societies and economies, new formats and packaging of educational products have been emerging as alternatives to the traditional degrees and certificates. Most of these offerings emerge outside higher education institutions and aim to alleviate the gap between the supply of skills and the needs of industries which had a big impact on the educational space. The authors studied approximately four hundred thousand tweets discussing educational offerings. They used a combination of topic modeling and network analysis to group topics into wider themes over the topic network. They also used word embeddings to measure semantic similarity of words related to specific educational packagings and further understand the discussion carried out on Twitter. The results of this study show how public opinion on Twitter discussed formal and non-formal educational offerings in ways that stress economic and professional advancement. Finally, the results from the word embeddings analysis revealed a need for common and clear taxonomy that differentiates between educational formats.

Programs in an online competency-based higher education (OCBHE) institute will focus on a set of skills and competencies that form a theme throughout multiple courses, where one course builds upon another in terms of increasing the strength or depth of competency. Thus, for students within a given program or major, it is ideal for scores from course assessments with overlapping content to correlate and indicate higher-order skills or competencies. The purpose of this study was to use factor analysis to test the internal and structural validity of course-level performance assessment scores for a group of courses taken as part of a data analytics program in an OCBHE institution. Moreover, the presence of program-level competencies was investigated using hierarchical factor analysis for two groups: a faster, shorter course track and a slow, longer course track. Results supported validity at the course level as well as the presence of a higher-order factor (program-level competency) for the fast course track but not the slow track.

The gap between higher education and industry is often discussed, but mitigating solutions lag behind and contribute to its widening. This chapter explores the root causes of this gap and examines the establishment of common frameworks based on skills and approaches to assessing those skills as the path forward. The perspective and needs of the industry, the learners, and higher education are discussed. Data silos to inform the educational product on skills in need by industry exist. Tools to support communicating skills in various technology solutions in the spirit of a holistic learning and employment record are emerging. Skills and competencies that populate those records must be relevant, appropriately validated, and communicated using an agreed-upon language. Selected examples of current and emerging approaches in the skills-first approach to establishing common frameworks for communication and assessment are provided to illustrate possibilities.

Fuzzy algebraic structures are a useful and flexible tool for modeling cognitive agents and their societies. In this article we propose a fuzzy algebraic framework where the valuating sets are other than the unit interval (lattices, partially ordered sets or relational structures) . This provides for a flexible organization of the information gathered by the agent (via interactions with the environment and/or other agents) and enables its selected use when different drives are active. Agents (Petitagé, ANNA, POPSICLE and Izbushka), which are instantiations of our model, are also given in order to illustrate the use of this framework, as well as its possible extensions.

Salmonella enterica serotype Enteritidis is known as one of the most common pathogenic bacteria causing salmonellosis in humans. Raw materials of animal origin (eggs, chicken meat) are frequent vectors that transmit this bacterium. Since eggs are used for the production of pasta, due to insufficient thermal treatment during pasta drying, they can be a potential risk to consumer health. Different essential oils of herbs can be used to reduce present pathogenic microorganisms. This paper compares a decrease in the number of Salmonella enterica serotype Enteritidis (D) ATCC 13076 and Salmonella enterica serotype Enteritidis isolated from outbreaks of salmonellosis in egg-based pasta under the influence of thyme and sweet basil essential oils. The results indicate that the utilized oils were more effective against the epidemic strain than the ATCC strain. In addition, thyme oil caused a more significant inhibition of Salmonella enterica serotype Enteritidis during the production process.

This paper overviews the interactivist model of representation and its applications in artificial intelligence (AI) and intelligent robotics. Selected examples from approaches in AI and robotics are contrasted with the generic interactivist architecture in order to illustrate specific features of it. Petitage, an artificial agent that instantiates our interactivist-expectative theory of agency and learning (IETAL), is discussed in detail from the interactivist perspective.

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.

The paper introduces lattice-valued fuzzy lattices. Two types of L-fuzzy lattices are defined, and their connection is observed. The first type of fuzzy lattices is obtained via fuzzification of the membership to the carrier, and the second via fuzzification of the ordering relation in a crisp lattice. It is proved that the two approaches are equivalent, similarly as in the crisp lattice theory.

“Emergence” is itself emergent; although originating in the context of the “sciences of complexity” — i.e., life sciences, cybernetics, multiagent systems research, and artificial life research — “emergent thinking” has spread to other parts of the academy, including the social sciences and business. Utilizing examples drawn from popular culture, this chapter looks to the ways IT has proven influential in other cultural contexts, but not without a price. The second part of the chapter interrogates the transportation of emergent thinking into these other discourses, taking them to task for not embracing the promises inherent in emergence and, in fact, merely reproducing the old under the sign of the emergent new. Finally, by borrowing notions of “surprise” from robotics and multiagent systems, I suggest new possibilities for emergence to lead to genuine paradigm shifts in the ways we think.

Based on the material presented in Appendix A and B, in this chapter we give alternatives to the definition of agent using fuzzy algebraic structures. The notion of intrinsic representation of the environment is formally defined as a fuzzy relation valued by the lattice of drives of the agent.

This chapter concludes the book by stating open questions and directions for further research. Some of the topics that we focus on here are the interaction between the human and nonhuman agents, interactions of the human in heterogeneous environments, and alternative representations of environments. We also present Izbushka, a context-0 interactive environment, and discuss our immediate future research expectations from its implementation, as well as its implication to multiple application areas.

The existing theories of concept formation involve categorization based upon the physical features that differentiate the concept. Physical features do not provide the understanding of objects, entities, events, or words, and so cannot be used to form a concept. We have come to believe that the affect the object, entity, event, or word has on the environment is what needs to be evaluated for true concept formation. Following our argument for a change in the direction of research, our views on some of the other aspects of concept formation are presented.

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.

In this chapter we overview our simulation environment (Multi-Agent Systems Simulations [MASim]) that we developed, with the intention of studying behaviors in smaller societies of agents. We will give a gallery of selected recorded behaviors and brief comments on each.

This chapter overviews a robotic platform developed at our Cognitive Agency and Robotics Laboratory (CARoL, n.d.) for the purpose of carrying out Interactivist-Expectative Theory of Agency and Learning (IETAL) and Multi-Agent Simulated Interactive Virtual Environments (MASIVE)-like experiments in a realistic environment. Performing IETAL and MASIVE-like experiments with a robotic agent(s) requires specialized agent(s) in a specialized environment. The solution overviewed here is done on a shoestring budget and is easy to replicate and modify.

This chapter presents an empirical investigation executed with the purpose of calibrating parameters of the Interactivist-Expectative Theory of Agency and Learning (IETAL) and Multi-Agent Simulated Interactive Virtual Environments (MASIVE) simulations. The experiment aims to understand the use of inborn schemes in humans, their use of context, as well as imitation in two-person teams. Variations were performed in order to implement observations from previous versions of the experiment.

Fuzzy algebraic structures are a useful and flexible tool for modeling cognitive agents and their societies. In this article we propose a fuzzy algebraic framework where the valuating sets are other than the unit interval (lattices, partially ordered sets or relational structures) . This provides for a flexible organization of the information gathered by the agent (via interactions with the environment and/or other agents) and enables its selected use when different drives are active. Agents (Petitagé, ANNA, POPSICLE and Izbushka), which are instantiations of our model, are also given in order to illustrate the use of this framework, as well as its possible extensions.

In this chapter, we give an overview of the results of a Human-Robot Interaction experiment, in a near zerocontext environment. We stimulate the formation of a network joining together human agents and non-human agents, in order to examine emergent conditions and social actions. Human subjects, in teams of three to four, are presented with a task–to coax a robot (by any means) from one side of a table to the other–not knowing with what sensory and motor abilities the robotic structure is equipped. On the one hand, the “goal” of the exercise is to “move” the robot through any linguistic or paralinguistic means. But, from the perspective of the investigators, the goal is both broader and more nebulous–to stimulate any emergent interactions whatsoever between agents, human or non-human. Here we discuss emergent social phenomena in this assemblage of human and machine, in particular, turn-taking and discourse, suggesting (counter-intuitively) that the “transparency” of non-human agents may not be the most effective way to generate multi-agent sociality.

This chapter presents an innovative approach to the field of information fusion. Fuzzy mediation differentiates itself from other algorithms, as this approach is dynamic in nature. The experiments reported in this work analyze the interaction of two distinct controllers as they try to maneuver an artificial agent through a path. Fuzzy mediation functions as a fusion engine to integrate the two inputs to produce a single output. Results show that fuzzy mediation is a valid method to mediate between two distinct controllers. The work reported in this chapter lays the foundation for the creation of an effective tool that uses positive feedback systems instead of negative ones to train human and nonhuman agents in the performance of control tasks.

In this chapter we explain how Interactivist-Expectative Theory of Agency and Learning (IETAL) agents learn their environment and how they build their intrinsic, internal representation of it, which they then use to build their expectations when on quest to satisfy their active drives.

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.

What motivates people? Why do they behave as they do? For that matter, why do people do anything at all? Questions like these have persisted over 100 years of psychology despite decades upon decades of research to answer them. Waves of academic thinking have addressed these issues, with each new school of thought providing different answers, at least in form if not in function. In our brief overview of motivation theory, we will emphasize commonalities in theory rather than their differences. We will then discuss evolutionary theory as an overarching, modern model of motivation — specifically, the motivations to promote one’s survival and to be reproductively fit in a Darwinian sense. This discussion of evolutionary theory will be applied to human motivation and to the many ways in which people — such as ourselves — adapt in a complex world.

The focus of this chapter is the explanation of a method for allowing languages to emerge within a multi-agent system. The need for such a method tends to be in larger multi-agent systems that focus either on large domains or span across multiple domains. This method can also be adapted for interfacing multi-agent systems with humans through natural languages. Also addressed in this chapter are the necessary requirements for a multi-agent system to utilize an evolving communication system. A specification of an evolving vocabulary is presented along with an explanation of results from an experiment that contains an implementation of these specifications.

In this chapter, we present the Interactionist-Expectative Theory on Agency and Learning (IETAL). Our intention is to provide remedies for the observed deficiencies in the existing approaches to the problem of autonomous agents design. A brief critical overview of the relevant agent-oriented research is given first.

The first part of this appendix presents three approaches in defining the fuzzy version (generalization) of the mathematical graph structure: graphs with fuzzy vertices, graphs with fuzzy edges, and graphs with fuzzy vertices and edges. Their advantages and shortcomings are discussed briefly. Fuzzy graphs are observed in the light of fuzzy relations theory, and as a generalization of the notion of random graph. In the second part, we generalize some fuzzy algebraic structures towards not only [0, 1] valued, but lattice, poset, and relational structured valued structures. It is exciting to see how powerful a modeling tool they are, and also to see how classical results continue to hold as but a special case of the new results.

In this chapter we propose a model for a multi-agent society, based on expectancy and interaction. Based on results on imitations, we proposed a multi-agent version of the Interactivist-Expectative Theory of Agency and Learning (IETAL) theory—Multi-agent Systems in Interactive Virtual Environments (MASIVE).

In this chapter we give a brief overview of the history and the key developments of the concepts of agents and multi-agent systems that are relevant to the book, as preliminaries to the presentation of the theories in the subsequent chapters of the first section of the book.

In this chapter we briefly overview the connections between drives, motivations, and actions in an agent. Drives take a central place in our agents and influence significantly how they use their internalization of the environment. Appendix B focuses more elaborately on these phenomena in humans.

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.

In this chapter we offer a flexible training environment and strategies for diversity infusion in the Information Technology curriculum. The chapter overviews “My First Diversity Workbook,” and the ways in which it may be used in diversity training for faculty. The major part of such workshops consists of four parts. In the first part, the trainer talks about his or her positive experiences of diversity infusion in the curriculum, and serves as a motivational component of the training. The second and third components explain how to get inspired for micro and macro infusion of topics in the curriculum from the outside and the inside. By using external examples and facts, or internal experiences and introspections, the instructors may successfully diversify a unit lesson or the whole curriculum. In the fourth component, the trainer talks about continuing to share classroom experiences after the workshop is done—usually online, within the framework of an e-group. We describe fitting these four components into two different contexts, and outline in detail the schedule and experiences of participants from those two workshops, custom-tailored to the needs of the institution that the training was designed for. These workshop patterns are fully replicable. The chapter not only describes the author’s strategies in covering the topics, but also provides a selection of sources that the trainer and the participants may use when replicating or modifying these trainings.

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.”

Introducing diversity topics in the natural, mathematical and computer sciences is a hard task, since these disciplines are traditionally labeled as “diversity-unfriendly,” due to their primary foci of study. In this chapter we illustrate how tessellations may be used as a tool for infusion of multicultural topics, as well as a framework, designed after the Towson University course “Computers and Creativity,” where these concepts have been successfully implemented.

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.

Some of the algorithms developed within the artificial neural-networks tradition can be easily adopted to wireless sensor network platforms and in the same time they can meet the requirements for sensor networks like: simple parallel distributed computation, distributed storage, data robustness and auto-classification of sensor readings. Dimensionality reduction, obtained simply from the outputs of the neural-networks clustering algorithms, leads to lower communication costs and energy savings. Two different data aggregation architectures are presented, with algorithms which use wavelets for initial data-processing of the sensory inputs and artificial neural-networks which use unsupervised learning methods for categorization of the sensory inputs. They are analyzed on a data obtained from a set of several motes, equipped with several sensors each. Results from deliberately simulated malfunctioning sensors show the data robustness of these architectures.