Article Summary: Maton (2014) Legitimation Code Theory and Semantic Waves


To explain how semantic waves (based on the semantics dimension of Legitimation Code Theory) can help students to build upon prior knowledge, particularly everyday, practical knowledge, to develop new knowledge, particularly technical, disciplinary knowledge.

**I used ChatGPT to help write this summary. The text in italics is from ChatGPT with light editing. A description of my experience can be found in this post.

Legitimation Code Theory and Semantics

Legitimation Code Theory (LCT) is a framework used to understand and analyze knowledge practices in various fields. In a way, it’s similar to Cognitive Load Theory because it aims to unpack the knowledge that is often tacitly assumed in instruction. As a theory from sociology, however, it takes a holistic view of knowledge, curriculum, and pedagogy rather than an individualistic view of cognition, so it is better suited to describing instruction than learning. While LCT has five dimensions, this paper focuses on the semantics dimension. In LCT, semantics has two primary dimensions: semantic gravity and semantic density.

Semantic gravity refers to the degree to which knowledge is tied to a specific context. In other words, it’s about how abstract a piece of information is. Semantic gravity can be described as stronger (SG+) or weaker (SG-). When semantic gravity is stronger (SG+), knowledge is highly dependent on the context. For example, describing algorithms in the context of programming and with examples of sorting algorithms, search algorithms, or machine learning algorithms has a high semantic gravity. On the other hand, when semantic gravity is weaker (SG-), knowledge is more abstract and can be applied across different situations. For instance, understanding algorithms as a set of instructions or sequence of steps to perform a task, including brushing your teeth or following a recipe, has a low semantic gravity.

Semantic density refers to the complexity and degree of integration of meaning within a piece of knowledge. It can be described as higher (SD+) or lower (SD-). Semantic density reminds me a lot of element interactivity in Cognitive Load Theory. Higher semantic density (SD+) occurs when a concept or piece of knowledge carries a lot of meaning, integrates multiple ideas, and can be applied to various situations. For example, the theory of relativity in physics is considered to have high semantic density because it involves complex relationships between space, time, and matter, and can be applied to a wide range of scenarios. Lower semantic density (SD-) means that the knowledge or concept has less complexity and integration of meaning. An example of low semantic density would be a simple equation like “distance = speed × time.” While useful in specific circumstances, this equation does not convey the same level of complexity as the theory of relativity.

Semantic gravity and semantic density can be used as orthogonal dimensions to create a 2-by-2 grid that describes types of instruction (see figure below adapted from Shay, 2013). When semantic gravity is low (i.e., concepts are abstract), low semantic density provides generic instruction while high semantic density provides theoretical instruction. When semantic gravity is high (i.e., concepts are contextualized), low semantic density provides practical instruction while high semantic density provides instruction for skilled professionals.

Semantic Waves

Of course, instruction is not limited to only one of these quadrants. Maton argues that instruction should weave together different types of instruction, allowing people to build new knowledge by using what they already know as a foundation for learning more complex concepts. This oscillation between low and high semantic gravity and density forms a semantic wave.

The idea of a semantic wave is that in order to fully understand a concept, it is necessary to engage with both specific details and abstract ideas. The typical semantic wave starts with high semantic density and low semantic gravity (i.e. the theoretical quadrant), moves to the opposite, practical quadrant, and back. This movement can be visualized as a wave-like pattern with the following key stages:

  • Unpacking (descending wave): In this stage, abstract concepts or ideas are broken down into more concrete, understandable, and context-specific examples. This helps learners to connect new information to their existing knowledge and experiences.
  • Exploring (trough): At this point, learners engage with concrete examples, exploring the relationships between the concepts and the specific contexts. This deepens their understanding of the topic and helps them to see how the abstract concepts can be applied in real-world situations.
  • Repacking (ascending wave): Once learners have explored the concrete examples, they are encouraged to synthesize their understanding and repackage the concepts into more generalized, abstract terms. This helps them to grasp the overarching principles and make connections between different areas of knowledge.
  • Consolidating (peak): Finally, learners consolidate their understanding by relating the newly acquired knowledge to other abstract concepts in their mental schema. This enables them to see the broader relevance of the topic and strengthens their ability to transfer the learning to new contexts.

However, another type of semantic wave might start with high low semantic density and low high semantic gravity to help learners build a foundation of knowledge based on concrete everyday examples and specific details. This can then be followed by a shift to low high semantic density and high low semantic gravity, where learners engage with more abstract technical and generalized concepts.

By oscillating between these two modes of thinking and learning, learners can develop a more nuanced and comprehensive understanding of the subject matter. Semantic waves enable learners to navigate between the abstract and concrete, facilitating the development of both conceptual and contextual knowledge. This approach can also help learners develop the critical thinking skills necessary to navigate complex ideas and make connections between different concepts.

Why this is important

I particularly like semantic waves as a framework for instruction with novices to meet learners where they are and connect rigorous instruction to prior knowledge. I also think it is relevant to culturally relevant/sustaining pedagogy as a way to incorporate prior knowledge and bridge the low semantic gravity of diverse, lived experiences with the high semantic gravity required for conceptual knowledge. In computing education, it could be a useful framework for connecting computing to other disciplines or unplugged activities, as Paul Curzon, Jane Waite, Karl Maton, and James Donohue have explored. While this paper advocates for semantic waves, it also cautions against misappropriate uses based on features of the wave.

  • semantic range – while cycling between high and low semantic gravity and density helps weave together different types of knowledge, it is possible to reach too high early in the learning process. Instead, the semantic range (i.e., the difference between high and low semantic gravity and density) should gradually increase through the curriculum (Georgiou, 2014).
  • semantic threshold – much like instruction can reach too high on the semantic scale early in the learning process, it can also reach too high early in learners’ developmental process. Elementary students aren’t going to understand the theory of relativity no matter how many semantic waves they experience.
  • semantic flow – semantic flow describes the connectedness between points along the wave. Steep leaps or cliffs in the wave are unlikely to facilitate connections among different types of knowledge.

Though this paper is from 2014, LCT has exploded in popularity in the past 10 years. In the past few months, I’ve reviewed 3 papers in CSEd that use LCT and semantic waves, despite never seeing it before. And it’s easy to understand why it’s popular. Semantic waves have been applied across various disciplines and educational levels, as it provides a clear framework for designing effective teaching and learning experiences.

Maton, K. (2014). Building Powerful Knowledge: The Significance of Semantic Waves. In: Barrett, B., Rata, E. (eds) Knowledge and the Future of the Curriculum. Palgrave Studies in Excellence and Equity in Global Education. Palgrave Macmillan, London. Retrieved from

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2 thoughts on “Article Summary: Maton (2014) Legitimation Code Theory and Semantic Waves

  1. Pingback: Experience Co-Writing a Blog Post with ChatGPT | Lauren Margulieux

  2. Pingback: Article Summaries: Series Introduction | Lauren Margulieux

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