Academic journal article Visible Language

Audience/online Information Interactions: New Research in Learning Preferences

Academic journal article Visible Language

Audience/online Information Interactions: New Research in Learning Preferences

Article excerpt

Abstract

This investigation proposes the need for a paradigmatic shift in the production of formal and behavioral online information to accommodate the differing learning preferences of its audiences. Developments in the presentation of information itself and the management of its complexity have not progressed at the same rate as the technology that produces it. Psychologist David Kolb (1974) found that the combinations created by an individual's perception and processing techniques form a unique learning style, which becomes the most preferred and comfortable way to process information for that individual. This project poses the question: In what ways can the redesign of online information presentations, formal and behavioral, support the different learning preferences of complex audiences? As a response I share my work-in-progress research into audience/online information interactions. This research emphasizes the need to acknowledge that information must be flexible and customized to enhance meaningful experience for different learners.

Introduction

This research project proposes that there is potential to turn the web, which is currently an information acquisition tool, into a cognitive tool that encourages meaningful learning for its users. It recommends a shift in the production of formal and behavioral characteristics of online information in order to accommodate the differing learning preferences of its audiences. It seeks to exploit the affordances of online interfaces by suggesting that the web not only promotes easy surface learning but also deep learning, revising search engines away from acquisition to meaning-making.

This paper poses the question: How can learning theories inform designers of online experiences as they provide search engine users with conditions for meaningful learning that turn the latter from online collectors to deep learners?

In order to investigate ways in which learning theories can inform meaningful user/information interactions, this paper will discuss learning in terms of technology, information, usability, design and learning styles. The investigation will explicate the current state of online information and delineate the problem. It will then offer an analysis of the existing taxonomy of research into user/ information interactions, an existing taxonomy of technology that attempts to promote deep online learning, define variables and terms used in the research and share visual examples of learners sketching their desired interactions with information. Finally, it will provide visual suggestions of ways in which learning style theories could inform the design of conditions for meaningful online user/ information experiences.

Project statement

Leung (2009) wrote, "Part of the service offered by experience designers is the process of making information meaningful for the user, but it is more difficult to ensure that users will turn such information to knowledge" (Leung, 2009, 17).

One of the misconceptions associated with access to information (online or offline) is that access to information equals access to knowledge. It does not. Wurman (2001) quotes Shedroff who described the continuum from data to wisdom in Information Anxiety 2. Data can be obvious or subtle. Data does not teach. Data is only data until it is designed, presented and organized for an audience when it then becomes information. Information, in turn, is different from knowledge. Access to information does not make one knowledgeable. "What most differentiates knowledge from information is the complexity of the experience used to communicate it... By necessity, knowledge can only be gained by experiencing the same set of data in different ways and therefore seeing it from different perspectives" (Shedroff, 2001, 28).

Wisdom, according to Shedroff is the ultimate level of understanding that allows us to find patterns and meta-patterns that we can use in unexpected ways (29). …

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