Enriching Computer Support for Constructionism

The dominant emphasis in current e-learning practice is instructionist in character. This is surprising when we consider that the benefits of constructionism as a learning paradigm are so widely recognised. Moreover, though the constructionist philosophy can be seen as applying to activities that are not necessarily computer based (such as bricolage and concept mapping), its modern application in educational technology has been closely linked with computer use. In particular, Papert’s work on mathematical education through Logo programming has both informed the original concept of constructionism and been a major influence over subsequent computer-based constructionist developments. This chapter questions whether — despite these precedents — traditional computer programming paradigms are well suited for the constructionist educational agenda. It argues that other approaches to computer model building, such as those based on spreadsheet principles, are in fact much better aligned to the objectives of constructionism. Building on this basis, it proposes that more effective computer support for the constructionist perspective can potentially be offered by Empirical Modelling (EM). Adopting this approach demands a reappraisal of the relationship between the formal and the informal with relevance for education, mathematics, and computing.

Hypervideo and Cognition

This chapter discusses how advanced digital video technologies, such as hypervideo, can be used to broaden the spectrum of meaningful learning activities. Hypervideo is conceptualized as the true integration of video into nonlinear information structures by means of spatio-temporal links. Based on cognitive-psychological perspectives, the discussion focuses on the way cognitive and socio-cognitive processes relate to the specific characteristics of hyperlinked videos, and how they inform their design. Then, with regard to technology, two approaches are introduced, providing tools for knowledge building and interaction with nonlinear information structures based on dynamic video information. Case studies and research findings are presented and prospects for future research are outlined.

Toward Noninvasive Adaptation of Metaphores in Content

Web information systems (WIS) can be considered as media. These media implement a tool language that enables access to content. Accessing that content aids users in achieving their goals. The language in which that content is given nowadays is often natural language or very close to it. Consequently the content involves metaphors. As the Web is open to virtually everyone, the users of WIS are likely to differ from each other with respect to ethnicity, language, gender, age, culture, education, preferences, physical or mental handicaps, and so forth. Consequently users are likely to respond differently to the metaphors occurring in the content. This chapter, therefore, proposes an approach to adapting the employed metaphors to user types for improving the value that the WIS offers to its users. This is expected to result in both increased useracceptance and number of business transactions. Therefore, an increased return on investment for the WIS is expected as well. We propose a conceptual model for user type and context-aware mapping of concepts in a target domain to concepts in a source domain. The respective mapping is used for modeling metaphors. We formalize that mapping in terms of the Higher-Order Entity-Relationship Modeling (HERM) language and in the Web Ontology Language (OWL). The conceptual model we provide can be used as a basis for hot generation of content representation such that the metaphors occurring in the content are adapted to the types of the users interacting with the WIS. As a step toward implementation, we formulate a high-level architecture that enables us to noninvasively adapt the metaphors in the WIS content to the types of the users. We report our experiences regarding exploration of the feasibility of the architecture. These experiences result from implementing a prototype that shows how metaphors—in a noninvasive manner, that is, without changing its code or content—can be added to the contents of an already-existing WIS. The chapter is completed by presenting the results of formal user evaluation which demonstrate the user acceptance of the respective metaphors.

Cognitively Informed Systems

Cognitively informed systems as introduced by Alkhalifa (2005b) is a perspective that encourages system designers to consider the findings of cognitive science as informative to the design of their systems. This relies on an underlying assumption that the presentation, interaction abilities, as well as the system structure, are likely to achieve more efficient communication if the design is aligned with the expectations of the human cognitive machine. In other words, this perspective deals with issues such as how to best present materials for the perceptual system to isolate the required differences and focus on the correct points in the image, how to offer sufficient interaction to enhance learning, or how to elicit different levels of cognitive engagement with the system. This chapter offers a survey of the main areas of the field and examples are given of how these areas can inform particular aspects of future system design. A case study is also presented as support to this perspective. The main conclusion thatcan be drawn is that this new perspective is not only practical but also worthwhile.

Bridging the Gap between Human Communications and Distance-Learning Activities

This chapter introduces context-aware computer-mediated communication for distance learning systems. It argues that linking deeply communication to learning activities offers an interesting approach to develop the efficiency of systems in facilitating and increasing discussions between learners. To make this link, the author bases his work on various theories, such as communication theories, situated cognition theory, and activity theory. This theoretical study leads to research issues concerning a contextual forum model. The description of the computing implementation of this model aims at giving researchers some possible uses and recommendations in dealing with context-sensitive communication tools. Finally, the chapter mentions futures trends and suggests emerging research opportunities within the field of communication services that are able to adapt dynamically to the user’s activity.

The Effect of Technology on Student Science Achievement

Empirical data from a California secondary school was analyzed to determine the direct and indirect effects of technology on student science achievement. Prior research indicates that technology has had minimal effect on raising student achievement. Little empirical evidence exists examining the effects of technology as a tool to improve student achievement by developing higher-order thinking skills. Furthermore, previous studies also have not focused on the manner in which the technology is being used in the classroom to enhance teaching and learning. As a result, the variables analyzed in this study include student computer use, teacher use of computers for instruction, student attendance, teacher preparation, demographic factors, and final science course grades. The method of quantitative analysis includes a path analysis using final course grades as the ultimate endogenous variable. Key findings include evidence that while technology training for teachers increased their use of the computer for instruction, a student’s final science course grade did not improve.

Guidance in the Interface

How can we design technology that suits human cognitive needs? In this chapter, we review research on the effects of externalizing information on the interface versus requiring people to internalize it. We discuss the advantages and disadvantages of externalizing information. Further, we discuss some of our own research investigating how externalizing or not externalizing information in program interfaces influences problem-solving performance. In general, externalization provides information relevant to immediate task execution visibly or audibly in the interface. Thus, remembering certain task-related knowledge becomes unnecessary, which relieves working memory. Examples are visual feedback aids such as “graying out” nonapplicable menu items. On the contrary, when certain needed task-related information is not externalized on the interface, it needs to be internalized, stored in working memory and long-term memory. In many task situations, having the user acquire more knowledge of the structure of the task or its underlying rules is desirable. We examined the hypothesis that while externalization will yield better performance during initial learning, internalization will yield a better performance later. We furthermore expected internalization to result in better knowledge, and expected it to provoke less trial-and-error behavior. We conducted an experiment where we compared an interface with certain information externalized versus not externalizing it, and measured performance and knowledge. In a second session 8 months later, we investigated what was left of the participants’ knowledge and skills, and presented them with a transfer task. The results showed that requiring internalization can yield advantages over having all information immediately at hand. This shows that using cognitive findings to enhance the effectiveness of software (especially software with specific purposes) can make a valuable contribution to the field of human-computer interaction.

Impact of Individual Differences on Web Searching Performance

This chapter will encourage the consideration of the role of individual differences in determining Web behavior and performance, which could inform and improve the development of search engines. Currently, users of search engines may experience differences in their level of success in searching for information. This difference could be realized through search success or search strategies. However, there is currently no definitive explanation regarding the characteristics that influence differences in search engine use and behavior. This chapter will serve asan introduction to and explore the phenomena of online Web searching and the potential role of individual differences in investigating this situation. An overview of the literature will be detailed as well as issues regarding how individual differences can be incorporated into this type of research. This chapter will support the notion that individual usage and performance with Web search engines is influenced by a collection of factors, more specifically, individual differences.

Swarm-Based Wayfinding Support in Open and Distance Learning

Open and distance learning (ODL) gives learners freedom of time, place, and pace of study, putting learner self-direction centre stage. However, increased responsibility should not come at the price of overburdening or abandonment of learners as they progress along their learning journey. This paper introduces an approach to wayfinding support for distance learners based on self-organisation theory. It describes an architecture that supports the recording, processing, and presentation of collective learner behaviour designed to create a feedback loop informing learners of successful paths towards the attainment of learning goals. The approach is presented as an alternative to methods of achieving adaptation in hypermedia-based learning environments which involve learner modelling.

Using Bayesian Networks for Student Modeling

This chapter purveys an account of Bayesian networks-related technologies for modeling students in intelligent tutoring systems. Uncertainty exists ubiquitously when we infer students’ internal status, for example, learning needs and emotion, from their external behavior, for example, responses to test items and explorative actions. Bayesian networks offer a mathematically sound mechanism for representing and reasoning about students under uncertainty. This chapter consists of five sections, and commences with a brief overview of intelligent tutoring systems, emphasizing the needs for uncertain reasoning. A succinct survey of Bayesian networks for student modeling is provided in Bayesian Networks, and we go through an example of applying Bayesian networks and mutual