Enhancing Individuals' Cognition, Intelligence and Sharing Digital/Web-Based Knowledge Using Virtual Reality and Information Visualization Techniques and Tools within K-12 Education and its Impact on

This chapter addresses an ongoing work strategy for developing and sharing knowledge related to digital/ Web-based technology and multimedia tools, information visualization, computer graphics, desktop virtual reality techniques in combination with art/education. It includes a large body of research about advanced and contemporary technologies and their use for stimulating individuals’ education. These interactive processes of researching, developing and sharing knowledge have been carried out through interdisciplinary and collaborative learning and teaching experiences in the context of k-12 education in a primary public school and its surrounding community. The learning and direct manipulation of advanced and contemporary technologies have improved individuals’ technical skills, stimulated cooperative and collaborative work and innovations in the way of developing school’s curriculum content as well as supported ones’ independent learning. Furthermore, there have been changes on individuals’ mental models, behavior and cultural changes related to reflecting about diverse possibilities of using information and communication technology within collaborative formal and informal sustainable lifelong learning and teaching actions.

Individual Differences and Web-Based Learning

This chapter considers a range of individual difference variables that have potential relevance to specifically designed Web-based learning packages. These include: cognitive style, working memory efficiency, anxiety, gender, and current knowledge. It discusses, in general terms, the conditions under which the variables are important, and the potential interaction between them in affecting learning performance. The roles of the variables within the context of Web-based learning are then examined. It is argued that technical developments in computer technology that allow materials that can accommodate learning preferences by responding to the student’s choices and learning performance combined with a better psychological understanding of individual differences in learning should result in improved educational effectiveness.

Individual Differences in Adaptive Educational Hypermedia

The purpose of this chapter is to experimentally explore the effect of individual differences in an adaptive educational hypermedia application. To that direction, the constructs of cognitive style (Cognitive Style Analysis) and visual working memory (visuo-spatial subsystem of Baddeley’s model) were employed as personalization parameters, thus rendering possible the provision of personalized learning environments according to users’ intrinsic characteristics. Two distinct experiments were conducted, with a total sample of 347 university students, seeking out to ground the hypothesis that matching the instructional style to learners’ preferences would increase their performance. Both experiments demonstrated that users in the personalized condition generally outperformed those that were instructed in a condition mismatched to their cognitive style or visual working memory ability.

Affective Issues in Adaptive Educational Environments

Research in computer science recently began to take emotions into account because their influence in perception, reasoning, decision-making and learning is considered catalytic. In learning the appropriate sentimental background constitutes a significant requirement in order to be effective. However, many designers of adaptive learning systems develop their systems without taking into consideration the emotional factors that are related to the mood and the personality of the student. This omission deprives the learning process from a very important pedagogical dimension. In this chapter, the focus is on affective factors that are involved in the learning process and can be considered in designing adaptive learning environments. We present first the basic theories and models for affective computer. We deal with methods of affective elicitation and representation of affective knowledge. We then present affective educational applications. Finally, we discuss issues and future trends of affective computing in relation to the learning.

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The path to the study of cognition has to take into account working memory, as it is a key process of thinking operations in the human cognitive system. Naturally, this also holds for cognitive operations in the Web. The chapter introduces readers to current trends regarding models of working memory. The major models proposed in the literature are discussed here: Baddeley and Hitch’s multi-component model, Daneman and Carpenter’s account, Cowan’s embedded-process model, Kane and Engle’s executive attention model and long-term working memory model by Ericsson and Kintsch. The chapter focuses on the Baddeley and Hitch model, and the author argues that this specific model offers a more theoretically sound account of working memory operations. Unresolved issues and inefficiencies are also discussed and research directions are proposed.

The Effect of Communication Styles on Computer-Supported Collaborative Learning

This chapter investigates the relationships between communication styles, social networks, and learning in a Computer-Supported Collaborative Learning (CSCL) community. Using Social Network Analysis (SNA) and longitudinal survey data, the authors analyzed how 31 distributed learners developed collaborative learning social networks, when they had work together on the design of aerospace systems using online collaboration tools. The results showed that both learner’s personality characteristics (communication styles) and structural factors (a pre-existing friendship network) significantly affected the way the learners developed collaborative learning social networks. More specifically, learners who possessed high Willingness to Communicate (WTC) or occupied initially peripheral network positions were more likely to explore new network linkages in a distributed learning environment. The authors propose that the addition of personality theory (operationalized here as communication styles) to structural analysis (SNA) contributes to an enhanced picture of how distributed learners build their social and intellectual capital in the context of CSCL.

Understanding Learner Trait, Test and Computer Anxiety in the Context of Computer-Based Testing

Testing is an integral part of the learning process that aims to estimate the learner’s abilities as accurately and efficiently as possible. This estimation frequently is influenced by factors such as the learner’s emotional state and traits. This chapter looks into the area of Computer-based Testing (CBT), visiting the relevant literature on the subject, and then investigates the particular emotional states of learner trait, test and computer anxiety in that context. A study was carried out and revealed that although both trait and test anxiety as variables do not significantly affect learner performance, computer anxiety does. Finally, future research trends in this area are outlined.

Employing Innovative Learning Strategies Using an E-Learning Platform

Web-based learning environments have become an integral part of learning. The way that they are employed in the learning process, or in other words the learning strategy followed in that respect, is an important issue that has to be carefully thought of, deciding upon topics such as suitable pedagogical approaches and appropriate assessment techniques for a given context. The chapter deals with this exact issue by visiting the relevant literature on the subject, describing selected learning strategies that have been employed in the use of an innovative eLearning platform in schools in Europe and finally outlining and comparing two real case studies from two European countries.

Collaborative Learning in a Web-Based Environment

Collaborative learning has long been proven to be an effective approach in the traditional classroom setting. Despite the discussion of the benefits and potential of collaborative learning in a Web-based learning environment, there has been a lack of empirical studies showing whether and how distance learning students may benefit from this learning experience, particularly in comparison to their oncampus peers and from their own perspectives. This chapter reports on a study that uses a comparative approach to evaluate the effectiveness of collaborative learning and related teaching and learning outcomes in both distance learning and on-campus settings. The major findings of this study suggest that distance learning students tend to have more positive perceptions of collaborative learning than their peers in the traditional classroom setting. In addition, distance learning students tend to embrace collaborative learning readily and early compared to their on-campus peers. In terms of student class performance, this study shows that distance learning students can achieve essentially the same learning goals as their on-campus peers. However, there are individual differences in student performance. An analysis of factors contributing to the individual performance differences suggests that engagement is closely correlated to student class performance. This study also shows that, overall, both distance learning and on-campus students provide similar course and instructor evaluations for teaching effectiveness for classes with collaborative learning. Finally, the implications of this study and suggestions for future research are discussed.

Integrating Knowledge of Cognitive System and E-Learning Applications

This chapter outlines key findings of cognitive and developmental psychology which could be used as a theoretical framework to guide the design and research of e-learning applications. The chapter consists of two main sections. The first section presents the basic cognitive mechanisms and their development, while the second part discusses how our knowledge of the cognitive system can guide the design of computer-based learning environments. It is proposed that the human mind is organized in three levels: two of them are general-purpose mechanisms and processes and one consists of domainspecific structures of knowledge representation and problem solving processes. These three levels are associated with an effective learning framework. The suggested framework describes the basic points that any designer of e-learning environments must consider. The second section of the chapter discusses e-learning, connecting it to knowledge about the human cognitive system. In this section, we first present the conceptual bounds of the e-learning construct and discuss how cognitive theories of learning should guide the design of technologically-enhanced learning environments. It is proposed that a synergy between the study of human cognition and the design of e-learning applications is required for effectively understanding both fields.