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Creating a physics teaching sequence with cognitive load theory in mind

Written by: Fabio Di Salvo
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4 min read
Fabio Di Salvo, Science Department, Nexus International School Singapore, Singapore

When deciding on the order in which to teach a physics curriculum, it is important to consider both the sequencing of content knowledge and the sequencing of mathematical skills. This article examines how our regular departmental discussions have allowed us to consider ways of optimising intrinsic cognitive load by specifically developing our curriculum sequencing across all secondary year groups.

Cognitive load theory (CLT) (Sweller, 1998) discusses working memory and how this affects learning. This can easily be overloaded, so it is important for teachers to be aware of the demands placed on students. CLT has been a source of fascinating discussions within our physics department, where we have created a teaching sequence that places an appreciation of these demands at the forefront.

Our departmental process follows the order of creating a sequence, implementing it across our classes, reflecting on our sequence and making modifications where needed. Before the start of the new academic year, our department discusses upcoming courses. We dissect the specification and decide on a sequence that builds the story of physics that we want to teach our students. Discussions are recorded on an online document, which is regularly annotated with our own reflections during the course, prior to meeting as a department for discussion.

For both content knowledge and mathematical skills, we follow the part–whole approach to sequencing. The sequencing of content knowledge is such that we ‘present material that aligns with the prior knowledge of the learner’ (de Jong, 2010, p. 126), with links to previous learning made explicit. For both our upper- and lower-year groups, we start by examining mechanics, as our students have had exposure to this in their everyday life. There are few unfamiliar terms and concepts in mechanics, with examples such as distance and speed already in their vocabulary, thus allowing us to decrease the load placed on our students’ working memory. With regard to skills, this is an ideal topic to start with as we can build in opportunities to develop their graphing skills. Distance–time and speed–time graphs can be made simple to begin with but then can increase in complexity and build in the use of speed and acceleration formulae. The sequencing of mathematical skills makes use of the skill hierarchy technique outlined by Lovell (2021), where the skills are structured in a way that ensures that they gradually progress in difficulty, with sub-skills being taught and practised first. A specific example of this is that we leave the more complicated mechanics formulae for when the students have had more exposure to algebraic manipulation. We cannot expect students to use more complex formulae such as v2 = u2 + 2as until they can use simpler three-term formulae such as F = ma. Both formulae appear early on in most GCSE exam board specifications; however, we do not teach the former until later in the course because of the higher level of skill involved. By considering both the sequencing of content and mathematical skills, we can tell our students the story of physics the way in which we want to, and not simply in the order outlined in an exam specification.

Sometimes as a department we do not agree on a specific sequence; however, any differences are minimal and we embrace these opportunities to try out slightly different sequences with our classes, allowing us to feed back to each other in our reflections. An example is whether to teach forces or motion first. We can teach velocity and acceleration first and then explain why objects accelerate using forces, or we can teach forces first and then describe acceleration as being one of the effects. Both of these sequences gradually build up in difficulty, so either sequence has an appreciation of the cognitive load on our students. As a department, we must end up at the same point in the overall sequence within a given time frame to ensure consistency with regard to assessment and student class changes, but we are able to take slightly different routes.

In our sequencing, we also consider the spacing of the content, so students have regular opportunities to revisit prior learning. For example, we split the mechanics topic into smaller sub-topics, allowing us to teach other topics in between. This is often referred to as a spiral-based approach to curriculum design, as outlined by Mountstevens and Astolfi (2019). We start with forces and motion, then move on to introductory waves. During the waves topic, we can link back to prior knowledge when we talk about the speed of a wave. Later on, we will return to the mechanics topic by looking at force and extension of a spring, which will give us the opportunity to then link new knowledge with energy stores. During our sequencing discussions, we are constantly asking ourselves whether we are effectively optimising the intrinsic cognitive load placed on the students. We ensure that new pieces of information are presented in small enough chunks, but not so small that the learning becomes fragmented.

Once we have started teaching our sequence of lessons, we regularly feed back to the department and review our sequence so far. Reflections centre on whether there were any areas where our students struggled and whether this was a direct consequence of our sequencing – perhaps we overloaded our students’ working memory during the lesson and this is able to be modified for next time. If we taught slightly different sequences, we reflect on the benefits and drawbacks of each within our lessons and potentially decide on an agreed order for next time.

While there is no best physics teaching sequence, by putting the optimisation of intrinsic load at the forefront of our lesson sequencing discussions, our department can create as best a sequence as we can for our own teachers and students. This sequence is one that has considered the build-up in complexity of both content and mathematical skills in order to present our story of physics in an accessible and effective manner.

References
  • de Jong T (2010) Cognitive load theory, educational research, and instructional design: Some food for thought. Instructional Science 38: 105–134
  • Lovell O (2021) Sweller’s Cognitive Load Theory in Action. Suffolk: John Catt Educational Ltd.
  • Mountstevens E and Astolfi C (2019) The spiral curriculum approach. Impact. 6: 74–76.
  • Sweller J (1998) Cognitive load during problem solving: Effects on learning. Cognitive Science 12: 257–285.
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