Welcome to the reading group

Meetings on the 3rd Tuesday of each month, 12.30 – 1.30 LKL small seminar room
For copy of the reading please email Elizabeth Andrew.

General information

A broad focus for each session will be to explore the notion of representation with reference to technology and learning. Papers and themes will be suggested by participants of the reading group. Key questions and themes generated in the first session that will inform our discussions include:

• How can the right representation for a learning outcome be identified?
• The design dimensions of representations for learning
• The relationship between cognitive load and cognitive effort
• Designing cognitive or computational constraints
• Designing thinking (problem/search) spaces that learners can benefit from
• The role of abstraction
• The authorship and ownership of representations (the relationship between personal experience and new experiences
• Different forms and modes of representation
• Processes of production of representations

Format

Introduction of the paper to include why chosen, what area related to, what question the authors trying to address. This is followed by informal open discussion

Reading group blog
(you are there) To post notes on each paper, continue the dialogue as needed; to maintain a record of the group; invite the authors whose work we discuss to contribute.

Discussions will be audio recorded.

Session 1 (20 March 2007)
Paper: Donald Peterson (1994) Re-representation and emergent information in three cases of problem solving, in T.Dartnall (ed.) Artificial Intelligence and Creativity, 81-92, Netherlands: Kluwer Academic Publishers.

Presented by Kevin Walker

The chapter appears in a book on creativity and AI. It contains three case studies, in which the author re-represents problems in order to add emergent information which helps solve the problems. A re-representation is not a mere re-description of a problem, he says, unless it produces such emergent information.

The three examples are mathematical puzzles. In each case the author removes unneeded data, puts data into a table then a new representation, in each case 'mathematising' or 'computationalising' the problem. This is important because in the context, the goal is for computers, not humans, to solve the problems. Thus the representations tend to be linear, logical, efficient and mathematical.

Key points from the discussion

The point of the paper was discussed for some time – was it suggesting a way of learning or a way of understanding how the solution process can be expressed as a logical set of rules for computation. This raised questions about the process of producing representations in solving problems: Should these be generated by learners themselves, or is moving through the process of how solutions are generated enough? The question of effective learning was discussed – how much is effective learning the solution of the problem posed, or the ability to generate new problems?

This relates to the level of activity of the learner: Much of our research at LKL centres on learners actively constructing knowledge (at the least) or things (real or virtual) as a means of building knowledge.

The relationship between internal representations (mental models) and external representations was raised. The extent to which abstraction is useful was discussed – with focus on the chess example in the paper it was pointed out that the removal of the example from the context of chess playing did not acknowledge the way in which grandmasters work with such problems – that is, they don’t think like computers but rather their expertise comes from seeing so many board configurations over time; in other words their expertise is tied to the representation of the chess board (see Sweller et al 1998).

Related to this point is the personalised nature of representations. Different learners may find alternative representations useful, but when it comes to sharing knowledge, or participating in a community with particular definitions, common representations (e.g. mathematical equations) are necessary. Language, therefore, is an important bridge between individual and shared representations. All of the examples in the paper are described using narrative means, with supporting illustrations.

What kinds of spaces do the different representations and re-presentations present for the learner was another thread of discussion. The need to reduce the ‘search space’ was discussed. The ways in which microworlds and other forms of constraint are needed to give freedom to explore through constraint.

This led to discussion of what kind of constraints are productive, when and where. Which led onto the notion of cognitive load, overload, off-loading and the relationship between these and learning. When and what is it useful to off-load and what role does/might technology have in this process (e.g. calculators). The question of what is actually being off-loaded and the assumption that this means learning is happening was questioned. Maximum cognitive offload = minimum cognitive effort (= learning?)

The work of other authors brought into the discussion: Scaife and Rogers, Cox,
Green and Blackwell, Petrie and Green, Simon Holland.

References

Sweller, J., van Merriënboer, J.J.G. and Paas, F. (1998) Cognitive architecture and instructional design. Educational Psychology Review 10(3), pp. 251-295.