Tools for understanding teaching

Have you ever delivered a fantastic learning experience, and still your students gape at you blankly? You are not alone. It happens to the best of lecturers.  How can one make sense of why students are not making sense? Hanelie Adendorff discusses legitimation code theory as an analytical framework which gives scientists entry into the discourse of scholarly teaching and learning. Het jy al ooit ‘n fantastiese leerervaring aangebied, en jou studente staar jou net stil aan? Jy’s nie die enigste een nie. Dit gebeur met die beste dosente oobest nfl uniforms custom nfl jersey big and tall nfl jerseys custom jerseys jordan store best human hair wigs nike air max 270 women’s sale nike air jordan retro 4 jordan 4 cheap online wig store nike women’s air max 270 white shoes stores nike air jordan women best lesbian sex toys cheap nfl jerseys cheap nfl jerseys k. Hoe kan ons sin maak van ‘n les wat nie vir ons studente sin maak nie? Hanelie Adendorff bespreek die analitiese raamwerk wat legitimasie-kode-teorie bied om wetenskaplikes in te wy in die akademiese diskoers van leer en onderrig.

by Hanelie Adendorff

Have you ever designed and delivered a noteworthy learning experience, appeasing the most demanding of “how-to” lists, and still your students gape at you blankly?

If so, you are not alone. It happens to the best of us. Just ask well-known physicist Eric Mazur, author of confessions of a converted lecturer. 

Now imagine it is 1997 and you have run out of words, ways and images to explain chemical equilibrium to students. You decide to do what scientists do. You sit down at your department-issued Pentium PC and Google … no wait, Google is still just a twinkle in the eyes of Page and Brin. So, you open Netscape Navigator and search “learning theory”, expecting something like this:

“In chemistry and physics, atomic theory is a scientific theory of the nature of matter, which states that matter is composed of discrete units called atoms.” (Wikipedia: https://en.wikipedia.org/wiki/Atomic_theory)

But instead, you get a page with about a hundred uncategorised links to different learning theories (infographics is not yet a thing either). You randomly click through some (also no Google analytics yet) and stumble across words like habitus and agency. Words that seem like you should know them, being an academic, and yet you have the profound sense that you are not “getting” them. You reach out to someone in this field, but they talk a language even more foreign, in which research is by nature subjective, data is rich (or poor?) and the analytical tool of choice is something called “coding”. Sadly, no-one can quite explain to you how it works (much like you and your students and chemical equilibrium).

Tales that Echo

This tale from my own journey echoes just about every story I heard in a recent research project on why scientists find it so hard to foray into the field of teaching and learning research. It highlights some of the key challenges faced by scientists who try to foray into the world of teaching and learning research: the accessibility of the theories and the multitudes of worldviews. Incidentally, figuring out how to help scientists – and myself – navigate this strange new world has been my passion and academic project ever since (Adendorff, 2011).

Now fast forward to 2014. You are introduced to a theory with quadrants and equations which promises to help you make sense of why your students are not making sense. Your first response is a little like when you first saw Schrödinger’s equation in third-year physics. But, like with Schrödinger’s equation you have a sense that there is something here. You persevere; you read and give up and read some more. You read what others have said and you watch and re-watch every Karl Maton talk on YouTube, and eventually it starts to coalesce into a picture. You get it. And you have the sense that it can help you help your students get it too. Enter Legitimation Code Theory, more affectionately known as LCT. LCT builds on and extends the work of Pierre Bourdieu and Basil Bernstein. Through its focus on realist and relational thinking it offers a way to bring “theory and data into genuine dialogue” (Maton et al, 2015: 8). It is neither a meta-theory, nor substantive theory, but a “practical theory” or “explanatory framework” that can help us operationalize meta-theories and derive substantive theories.

Over the last decade, LCT has sparked the imagination of science lecturers in unprecedented ways, bringing with it a new energy and a boom in teaching and learning research published by STEM academics. See for example the work of Mouton, (2019a and 2019b), Steenkamp (2019),  Rootman-le Grange and Blackie (2018) and Blackie (2014) at Stellenbosch University. Key to its success is the idea that scientists seem to experience it as ‘less foreign’. But why?

Building Bridges

Many science lecturers are passionate about their teaching. They want to help their students grasp difficult and dense concepts. However, for most of them there is a significant barrier to accessing the ideas and data presented in the education literature. When we ask them to engage with social science theory, we are not only asking them to engage with a different worldview (Crotty, 1998), we are also asking them to engage with a knowledge practice that is underpinned by vastly different rules.

What LCT offers these lecturers is an analytical framework that can build a bridge between the worlds of the social sciences and that of science.

What LCT brings is what science understands: explanatory power. After all, scientific theories are usually adopted or discarded based on explanatory power or, in the language of LCT, based on the strength of their epistemic relations.

Traversing Fields

LCT’s Specialization dimension, which is interested in the basis of legitimacy of knowers and knowledge claims in different knowledge practices, also helps us explain the difficulty of scientists accessing the world of teaching and learning research. It suggests that we can distinguish knowledge practices in terms of how they relate to their

• knowers (social relations) and
• the knowledge claims they make (epistemic relations).

Practices typically valorise one or the other, and occasionally neither or both.

• In practices that valorise knowers (or social relations), such as the humanities, what matters is “who you are” (Maton, 2014), your attributes and values. This does not mean that knowledge is not important in these practices, or that anything goes, but rather than knowledge here serves the purpose of developing the knower’s attributes, values, positioning and ways of being. In these practices, we find hierarchically arranged knowers, with a flatter knowledge structure. Again, there will be hierarchy within different topics, but generally the order in which topics are introduced does not matter too much.
• In practices that valorise knowledge (or epistemic relations), such as the STEM environments, what you know – the specialized knowledge and skills you bring – is more important than who you are. This is not to say that knowers do not matter, but that they matter less than their contribution. Here knowers serve the purpose of building the body of knowledge. We might say that the STEM environments can be a little knower-blind. In these fields, we usually find a flatter knower structure and a hierarchical knowledge structure, in which one element builds on the other.

Now we can start to see the difficulty in traversing these fields, as all scholarly teachers in STEM fields need to do, and the appeal of a framework like LCT. Like science, LCT does not ask who you are, but what you know and what you can do with that knowledge. It thus offers scientists a more accessible way to start exploring the world of teaching and learning research. Assuming a depth ontology, it also brings social science research a little closer to the worldview of science, as described in Bhaskar’s Realist Theory of Science (Bhaskar, 2008).

Strengthening Epistemic Relations

Work on respectful collaboration (Winberg et al, 2018) between scientists and the academic development environment, suggests that such collaboration in STEM contexts entails a readiness to take the knowledge practice of science into account, amongst others, by  strengthening the epistemic relations of work in the teaching and learning environment, and this is exactly what LCT does (Adendorff, Le Grange and Rewitzky, in press). Many scientists venture into teaching and learning research with the sole purpose of finding better ways to help students make sense of their teaching, and to understand why they sometimes don’t. With its focus on making the invisible visible, and its ability to bring social science research into the STEM fold, LCT offers a useful, and accessible, framework for doing this, without sacrificing rigour. LCT also helps us understand that gaining legitimacy in this new world of scholarly teaching, called knower building in LCT language, happens through a slow process of immersion into the agreed upon canon as a way of learning the appropriate (or accepted) ways of being and doing in the field of teaching scholarship. Using LCT does not circumvent this, but it does offer a slightly more understandable guide to accompany scientists along the first few uncertain steps.  

In the strange new land of teaching and learning research, many scientists have found themselves needing an “interpreter”. It is in this moment of “getting lost in translation” that LCT seems to offer something unique to STEM environments: a solid analytical tool that understands the meaning making practices in science whilst being firmly rooted in social science.

References

Adendorff, H. (2011). Strangers in a strange land–on becoming scholars of teaching. London Review of Education, 9(3), 305-315.

Blackie, M. A. (2014). Creating semantic waves: Using Legitimation Code Theory as a tool to aid the teaching of chemistry. Chemistry Education Research and Practice, 15(4), 462-469.

Maton, K. (2013). Knowledge and knowers: Towards a realist sociology of education. Routledge.

Maton, K., Hood, S., & Shay, S. (Eds.). (2015). Knowledge-building: educational studies in legitimation code theory. Routledge.

Mouton, M., & Archer, E. (2019a). Legitimation code theory to facilitate transition from high school to first-year biology. Journal of Biological Education, 53(1), 2-20.

Mouton, M. (2019b). A case for project based learning to enact semantic waves: towards cumulative knowledge building. Journal of Biological Education, 1-18.

Rootman-le Grange, I., & Retief, L. (2018). Action Research: Integrating Chemistry and Scientific Communication To Foster Cumulative Knowledge Building and Scientific Communication Skills. Journal of Chemical Education, 95(8), 1284-1290.

Steenkamp, C. M., Rootman-le Grange, I., & Müller-Nedebock, K. K. (2019). Analysing assessments in introductory physics using semantic gravity: refocussing on core concepts and context-dependence. Teaching in Higher Education, 1-16.

Winberg, C., Wright, J., Wolff, K. E., Pallitt, N., Bozalek, V. G., & Conana, H. (2018). Critical interdisciplinary dialogues: Towards a pedagogy of well-being in stem disciplines and fields. South African Journal of Higher Education, 32(6), 270-287.

About the Author

Dr Hanelie Adendorff obtained her Masters and Ph D degrees in Chemistry.  While lecturing Chemistry at Stellenbosch University she researched factors influencing students’ learning.   In 2005 she took a transdisciplinary step to join the SU Centre for Teaching and Learning (CTL) where she still represents the Sciences as Senior Advisor on the enhancing of teaching and learning.  She has published extensively on LCT and other scholarly approaches, facilitated a special interest group on decolonising science teaching and learning, and took a leadership role in the emergency remote teaching solutions at the university during Covid-19 lockdown.  She enjoys game-based learning, infographics, storyboards and other creative waves.

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