Interactivity of Information Representations in e-Learning Environments

Introduction

Interactive e-learning environments have the potential to facilitate learning; their effectiveness, however, has not lived up to original optimistic expectations (Kulik & Kulik, 1991; Jonassen & Reeves, 1996; Alessi & Trollip, 2001). In this chapter we use the term e-learning environment (ELE) to refer to any interactive computer-based software that mediates and supports learners’ engagement with information. Examples of ELEs include interactive mathematical software, interactive physics simulations, interactive biology animations, and interactive geovisualizations. These ELEs facilitate learning through activities such as reasoning about the growth of fractal patterns, analyzing the workings of a cell, exploring weather patterns and ocean currents, investigating the structure of chemical bonds, and so on. The main characteristic of ELEs is that they display and allow interaction with information in order to facilitate learning. As such, the framework proposed in this chapter treats ELEs as technology-independent; that is, they may be implemented with desktop computers, tabletop computers, tablets, and so on, all of which may or may not be networked to other sources of information such as the internet.

When ELEs are designed well they can support information processing functions of their learners to carry out cognitive activities (Brey, 2005). In the past, many ELEs have played a secondary role in cognition and learning; that is, they served simply as aids or amplifiers of cognitive abilities (Dror, 2007). This is especially the case when ELEs are thought of as vehicles for passively maintaining and displaying information, rather than as technologies that engage learners in reflective, critical thinking (Jonassen & Reeves, 1996; Jonassen, Peck, & Wilson, 1998). When designed with their interactive features at the forefront of consideration, ELEs not only can amplify learners’ cognitive abilities, but also can become partners in cognition (Salomon & Perkins, 2005; Dror, 2007; Bruner, 2005). In this manner, ELEs have the potential to reorganize learners’ thoughts and to facilitate deeper understanding of information (Pea, 1985). ELEs, by distributing the information-processing load of the visual information, can allow learners to think in partnership with the represented information (Sedig & Sumner, 2006). The theory of distributed cognition states that the internal mental processes of the learner, combined with external representations of information, form a system with which the learner's cognition is distributed across. Thus learning cannot be analyzed from the perspective of an individual in isolation, since it requires an interplay between the mind of an individual and things in the external world. In other words, learning is an emergent property of interactions among internal and external representations of information (Karasavvidis, 2002). This is true of a learner solving a problem using an abacus, a slide rule, or a computer. In order to perform high-level learning activities, such as reasoning and decision making, one often combines and processes information from both internal and external representations, in an integrative and dynamic manner (Zhang, 2000). In the case of ELEs, representations such as text, maps, and images comprise the interface, and the learner’s cognition is distributed across their mental representations and the representations of the ELE. Thus the careful design of appropriate representations is of critical importance for supporting learners’ thought processes.