chi-yan.net: SCIENCE EDUCATION HOME
SCIENCE EDUCATION
Introduction Science Education
for All
Conceptual Change Learning Learning with Multiple Representations
Frontiers of Sciences:
Charles K. Kao and
the Information Age

Information gently but relentlessly drizzles down on us in an invisible, impalpable electric rain...just plug in a modem and watch a flood of information from the world's uncounted electronic memories come pouring out into your laptop... For better or worse the world is awash with information. ( von Baeyer, 2003, pp. 3-4)

The main insight highlighted the cognitive bottleneck of the human mind: while information is no longer scarce, attention is. (Kings et al., 2008, p.20)

 


 

Introduction to Learning with Multiple Representations

The metaphor of the electric rain (von Bayer, 2003) brings home to us the awesome reality of the information age in which digital signals representing information of all kinds are ubiquitous without our notice of them. However, while information is no longer scarce, we just have enough time to pay attention to some of it (Kings et al, 2008). Further, information is not knowledge until the learner organises the bits and pieces of the formation into some meaningful entities with relations, extensions and abstractions in the learning process to construct deep understanding from multiple representations of knowledge (Ainsworth, 1999).

As learning always involves some ways of representing information, science teachers have long been using different representational techniques in the classroom to communicate ideas to students by voice, writing, images, gestures, and so on. Representations are simply the ways we communicate ideas or concepts to others by representing them either externally—taking the form of spoken language (verbal), written symbols (textual), pictures, physical objects, or a combination of these forms—or internally when we think about these ideas (Hiebert & Carpenter, 1992).

Conceptions can be regarded as the learner’s internal representations constructed from the external representations of entities constructed by other people, such as teachers or software designers. Conceptions are also described as learners’ mental models of an object or an event. From the conceptual-change learning perspective, representability is essential for making difficult concepts more intelligible (Thorley, 1990). As a complement to the many empirical and theoretical studies of analogies and metaphors in science education, the increasingly sophisticated learning technologies have necessitated new perspectives for doing better analyses and interpretations of the unprecedented new opportunities and challenges the new technologies have brought to teachers and researchers (e.g., see Jacobson & Kozma, 2000).

Recently, researchers in the cognitive–computational sciences have investigated the pedagogical functions of using more than one form of computer-based representation in educational software or multiple external representations (van Someren, Reimann, Boshuizen, & de Jong, 1998). Accordingly, there are three reasons for using more than one representation in computer-based learning environments. First, specific information can best be conveyed in a specific representation. A combination of several representations is therefore necessary to display learning material that contains a variety of information. Second, expertise in problem solving depends very much on having a large repertoire of multiple representations of the same domain, switching between them, and selecting the most appropriate ones for use in problem solving. Third, a specified sequence of learning material is beneficial for the learning process.

These multiple representations, as some researchers claimed, can support learning by providing/ supporting complementary information and/or cognitive processes, by constraining interpretations or misinterpretations of phenomena, and by promoting the construction of a deeper understanding of concepts through: abstraction, such as detecting and extracting a subset of relevant elements from a representation; extension or extending knowledge learned in one representation to new situations with other representations; and relations, such as translating between two or more unfamiliar representations (Ainsworth, 1999). Unfortunately, learning with multiple representations may not always be useful because of the new costs and challenges for the learners.

Ainsworth (2006) recently proposed, in the DeFT (Design, Function, Tasks) framework, that, to understand the effectiveness of learning with multiple representations, considerations must be given to three aspects: design parameters unique to learning with multiple representations; the functions of multiple representations that support the learning; and the cognitive tasks undertaken by a learner interacting with multiple representations. Multiple representations appear to be a promising construct for improving learning of complex concepts in science (Tsui & Treagust, 2003). Indeed, some studies have shown that the notion of multiple representations, when used in normal classroom teaching, can also be useful for analyzing and solving problems in physics and mathematics (e.g., see Dufresne, Gerace, & Leonard, 1997).

In synthesizing the studies over the past decade in using MERs in biology education, Treagust and Tsui (2013) co-edited a new book entitled Multiple Representations in Biological Education published by Springer.

References

Ainsworth, S. (1999). The functions of multiple representations. Computers & Education, 33(2/3), 131-152.

Ainsworth, S. (2006). DeFT: A conceptual framework for considering learning with multiple representations. Learning and Instruction, 16, 183-198.

Dufresne, R. J., Gerace, W. J., & Leonard, W. J. (1997). Solving physics problems with multiple representations. The Physics Teacher, 35, 270-275.

Hiebert, J., & Carpenter, T. P. (1992). Learning and teaching with understanding. In D. A. Grouws (Ed.), Handbook of Research in Mathematics Teaching and Learning (pp. 65-97). New York: Macmillan.

Jacobson, M. J., & Kozma, R. B. (Eds.). (2000). Innovations in science and mathematics education: Advanced designs for technologies of learning. Mahwah, NJ: Lawrence Erlbaum Associates.

Kings, N. J., Davies, J., Verrill, D., Aral, S. Brynjolfsson, E., & van Alstyne, M.(2008). Social networks, social computing and knowledge management. In P. Warren, J. Davies, & D. Brown (Ed.), ICT futures: Delivering pervasive, real-time and secure services (pp.17-26). West Sussex (England): John Wiley & Sons

Thorley, N. R. (1990). The role of the conceptual change model in the interpretation of classroom interactions. Unpublished doctoral dissertation, University of Wisconsin-Madison, Wisconsin, USA.

Tsui, C.-Y., & Treagust, D. F. (2003). Genetics reasoning with multiple external representations. Research in Science Education, 33(1), 111-135.

van Someren, M. W., Reimann, P., Boshuizen, H. P. A., & de Jong, T. (Eds.). (1998). Learning with multiple representations. London: Pergamon.

von Baeyer, H. C. (2003). Information: The new language of science. Cambridge, MA: Harvard University Press.


 

 

 

©2007-2017 CY Tsui Home