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Learning with Multiple Representations

Shaaron Ainsworth

Nicolas Van Labeke

Gareth Peevers

Aims

The aim of this research is to consider the ways in which multiple external representations (MERs) are used to support cognitive processes in learning. The DeFT framework describes how, by manipulating the design of learning environments, we can effectively achieve the various functions of multi-representational systems without overwhelming learners with complex cognitive tasks.

The aims of this research are three fold:

• to examine how different combinations of representations influence learning
• to identify a set of design principles for successful multi-representational software
• to develop a model of how people learn with multiple representations

To date, three learning environments have been built and evaluated to look at subjects ranging from primary mathematics to undergraduate biology: CENTS , COPPER and DEMIST.

We are also investigating the use of muliple representataion when learning complex tasks such as an Alchemist's Factory.

The DeFT Framework

The DeFT (Design, Functions, Tasks) framework for multi-representational learning environments sets out a number of important questions that designers and teachers should ask in order to provide effective learning experiences for their students. It provides an account of the different pedagogical functions that multiple representations can play, the design parameters that are unique to learning with more than one representation and the cognitive tasks that must be undertaken by a learner when interacting with MERs.

DeFT can be used to classify existing systems (see examples and contribute your system here), to guide empirical research, to consider how to support learners working with multiple representations and to create design guidelines.

Functions of MERs

There are three key functions of MERs: to complement, constrain and construct. MERs can complement each other by supporting complementary processes or by containing complementary information. When two representations constrain each other, one supports interpretation of the other. Finally, MERs can support the construction of deeper understanding when learners abstract over representations to identify what are the shared invariant features of a domain and what are the properties of a representation (see Ainsworth, 1999).

Cognitive Tasks

• Learners must come to understand a representation (i.e. what are the format and operators of a representation).
• Learners must understand the relation between the representations and the domain
• Learners must translate between representations
• If designing their own representations, learners need to select and construct an appropriate representation.

Design Parameters

1. Redundancy: In multi-representational environments, designers can chose how information is distributed over representations. This influences the complexity of each representation and the redundancy of information across the representational system;
2. Form: The computational properties of the representational system (e.g. does it combine text and graphics);
3. Translation: The degree of support provided for mapping between two representations. This can range from no support through to highlighting and on to full dyna-linking where behaviour on one representation is reflected onto another;
4. Sequence: Many systems present only a subset of their representations at one time, consequently two further decisions must be made - in what order to present the representations and when to change the representations that are displayed.
5. Number: The number of representations supported by the system.

DEMIST

DEMIST (Design Environment for Multi-representational Instructional Simulation Technology) is a Multi-Representational Instructional Simulation environment that allow systematic exploration of the key parameters of DeFT. We can quickly author a range of multi-representational scenarios that differ in terms of informational redundancy, form, automatic translation, sequence of presented representation and number visible at any one time. Authors can create systems that include representations of many different forms (multiple modalities, levels of abstraction, etc).

There is no theoretical limit to the number of representations that can be presented at any given time, although we can set this limit in authoring mode. Representations can be co-present, can be switched between under learner control or learners can be given a predetermined sequence of representations. Finally and, uniquely amongst simulation environments to our knowledge, translation between representations can be varied from full dyna-linking through to complete independence.

Selected Publications

Ainsworth, S.E. , (1999) A functional taxonomy of multiple representations. Computers and Education, 33(2/3), 131-152. ISSN 0360-1315
Van Labeke, N. and Ainsworth, S.E. , (2001 ). Applying the DeFT Framework to the Design of Multi-Representational Instructional Simulations . AIED'2001 Artificial Intelligence in Education, San Antonio, TX, May 21-23, 314-321.
Ainsworth, S.E. , Bibby, P.A. , & Wood, D.J (1998) Analysing the Costs and Benefits of Multi-Representational Learning Environments in Spada, H., Reimann, P. Bozhimen, & T. de Jong (Eds) Learning with Multiple Representations, Elsevier Science, pp 120-134.
Ainsworth, S.E. , Bibby, P.A. , & Wood, D.J . (2002). Examining the effects of different multiple representational systems in learning primary mathematics. Journal of the Learning Sciences. 11(1), 25-62. ISSN 1050-8406.
Van Labeke, N. and Ainsworth, S.E. , (2002). Representational decisions when learning population dynamics with an instructional simulation. In S. Cerri & G. Gouardères & F. Paraguaçu (Eds.), Intelligent Tutoring Systems (pp. 831-840). Berlin: Springer-Verlag. ISBN 3-540-43750-9
Ainsworth, S.E. and Van Labeke, N.(2002) Using a multi-representational design framework to develop and evaluate a dynamic simulation environment. Paper presented at Dynamic Information and Visualisation Workshop, Tuebingen, July, 2002.