In the previous post we looked at the transfer of learning from block based coding to text based languages. Semantic waves offer a theory that help us to structure our lessons to support transfer of learning (Maton, Waite et al). When we present concrete examples in single contexts transfer of learning is going to be weak. We need to present multiple examples in a range of context. This allows us to abstract out the underlaying features. This idea of moving along a continuum between the abstract and concrete is given by the term semantic gravity. For instance, if we talk about an algorithm in abstract terms we might say that it is a sequence of steps to solve a problem. At this stage we have presented it as an abstract idea so has low semantic garvity. In a lesson we might then go on and write algorithms for drawing squares. This represents a concrete episode with high semantic gravity. In a good lesson we might also want to give multiple examples of algorithm in different context like following a recipe, solving a Rubik’s cube, getting up in the morning and solving a maths problem to aid with transfer.
Alongside semantic gravity we also need to consider semantic
density. Semantic density refers to language
that we use. Language with low semantic density consist of terms that are more
familiar in day-to-day use and easier to understand but may be analogous. Using
technical language that is more precise would have higher semantic density. Of course for students to understand we need
to unpack semantically dense language and use language in context that are
already familiar to pupils. For instance
when discussion bitmap images we might say that they go blurry or blocky when
we zoom in. Students will understand because
they are likely to have experienced this for themselves. But the language is imprecise and we need to
ensure are students using the correct technical language. So we might also say of bitmap images that
they are not scalable and image quality is affected by its resolution among
other factors. This is considered semantically
dense.
Curzon et al introduce the idea of semantic profiling where
the sematic gravity and density vary throughout the lesson. Ideally we want to follow a semantic wave,
starting out with low semantic gravity and high density which we unpacks to low
density and high gravity using concrete examples and simple language as an
analogue. But we cannot leave it at that;
we need to repack the learning and return to abstract concepts with high
semantic density. If we stick to complex language and abstract concepts
throughout the lesson there is a danger that students will not be able to
access the learning. On the other hand,
if we only use simple language with concrete examples not much learning will
occur. For instance, let us go back to our example of an algorithm. If we say that an algorithm is like a recipe,
then students will go away with misconceptions about what an algorithm is and have
an incomplete understanding. A third
type of profile exists if we start out with complex language and abstract
concepts, move towards simpler language with analogous concrete examples and
then leave it at that. In such a situation
students find it difficult to transfer into other contexts, because it will
miss the abstract repackaging. By reflecting
on which position we are on the semantic wave in a lesson this will help us to
ensure the learning of the students is transferrable.
References
Paul Curzon, Jane Waite, Karl Maton, and James Donohue. 2020. Using Semantic Waves to Analyse the Effectiveness of Unplugged Computing Activities. In WiPSCE ’20: The 15th Workshop in Primary and Secondary Computing Education, October 28–30, 2010, Online. ACM, New York, NY, USA, 10 pages
Karl Maton, 2013, Making semantic waves: A key to cumulative
knowledge-building, Linguistics and Education,Volume 24, Issue 1, Pages 8-22, https://doi.org/10.1016/j.linged.2012.11.005.
Jane Waite, Karl Maton, Paul Curzon, and Lucinda Tuttiett.
2019. Unplugged Computing and Semantic Waves: Analysing Crazy Characters. In
Proceedings of UKICER2019 Conference.ACM, New York, NY, USA, 7 pages. https:
//doi.org/10.1145/1122445.1122456
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