Designing Multimedia to Trace Goal Setting in Studying

Designing Multimedia to Trace Goal Setting in Studying

Mingming Zhou
Copyright: © 2009 |Pages: 24
DOI: 10.4018/978-1-60566-158-2.ch015
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We suggest that multimedia environments can benefit from learning as well as offer significant capacity to serve as research purposes. Because motivational processes can support or inhibit complex learning, we first review current hypermedia learning models by specifically focusing on how they integrate motivational elements into their frameworks. Following our observation of a gap in the way motivational constructs (e.g., achievement goal orientation) are operationally defined, we suggest alternative methods, called traces, which make these latent constructs visible and measurable. The goal-tracing methodology we describe draws on achievement goal theory and extensive empirical studies in various settings. Using it, we treat learners’ use of cognitive tools as traces that express their goal orientations. By applying data mining techniques to these data, we show how it is possible to identify goal patterns together with study tactic patterns. We propose that future research can benefit substantially by merging trace methodologies with other methods for gathering data about motivation and learning.
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New technologies are rapidly being introduced into schools and other learning settings, and multimedia is an increasingly common format for learning. This offers new possibilities to structure, represent, adapt and integrate various learning content and materials as multimedia learning environments implement the latest technological features. Rarely, however, have these implementations been grounded in research-based principles (Kozma, 1991; Moore, Burton, & Myers, 1996). Further, a great opportunity has gone unfulfilled because too little attention has been paid to significant and almost cost-free capabilities to log extensive, detailed data about learning processes without much intrusion into learners’ activities (Winne, 2006a). However, before capitalizing on this opportunity, it is first necessary to know what data should be gathered to describe learning as a process. Guidance on this front comes from models and theories.

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