Jay Maxwell is Year 2 Coordinator at Assumption College English Programme, one of the longest-standing international/English language programmes in Thailand. In this post he describes an approach to deep learning he used in a Year 2 science lesson. The approach, which relies, among other things, on dialogic between students, means finding ways to facilitate conversation about complex phenomena among learners of English. Jay’s experience suggests that, with a little careful planning, this can be a reality in the EAL classroom.
This article will look at a current phenomenon in education known as deep learning. This is the relationship between rote learning and analytical skills or surface and deep learning. The hypothesis to be tested is that, by using strategies that enhance accessibility and professional development experience, it’s possible to apply deep learning to a science lesson in the EAL lower primary context. I have since used it in the classroom and have reflected on it.
‘Deep learning’ involves more than reciting facts and practising skills, characteristics of ‘Surface learning’ approaches. Deep learning refers to the higher order skills needed to engage with complex ideas and problems: analysis, critical thinking, reflection, and the capacity to make connections and transfer knowledge and skills to new contexts.
Deep learning first requires recall and use of surface knowledge and skills. Traditional approaches like memorisation and rehearsal can equip students with these foundations, but the problem is that teaching and learning often stops here.
The additional challenge in an EAL setting is to make this approach accessible to learners who may have limited English skills. Educators can do this by using the ‘little languages’ approach. Students using phonics based or high frequency word based literacy programs will have a 200, 400, 600 or 800 word little language in which they can, if allowed, articulate critical thinking on complex issues. Year 2 students are at between 200 to 400 words. If you learn only 800 of the most frequently-used lemmas in English, you’ll be able to understand 75% of the language as it is spoken in normal life.
For those that may wonder what a deep learning activity looks like in this context, the lesson was a Year 2 (7 year olds) science lesson. The topic was ‘Light and Shadows Science Experiment’.
The introduction was to recap the previous lessons on light and how the Earth’s rotation causes day and night. Students made a mind map on smartboard (facilitates active learning).
The hook (key element) was to watch the video “Lights and Shadow” and elicit ‘wonderings’ from learners. Introduce ‘shadow’ to the class, cover prior knowledge, use open ended questioning to surface learn properties of objects in relation to shadows such as transparent, opaque and translucent. Using little languages we discuss transparent as “a thing we can see through. Opaque are things we cannot see through” during a hands on demonstration with classroom objects.
The think pair and share used regression of terms. A dialogue where we question what we mean by our definitions. We collectively defined shadows, light, predict, observe and evaluate (in child friendly language), then predicted what will change about shadows over the course of the day. This is called using accountable talk. The theory underpinning this is social constructivism, or students making breakthroughs in learning together. While this looks daunting, it can be as simple as discussing and agreeing a shadow is where there is no light (using little languages).
The lesson main was to, in the morning, ask some children to identify shadows on the playground (alternative learning environments). Children then identify their own shadows and in pairs trace these with a piece of chalk. Return to the same spot in the playground at lunch (voluntary) and trace in pairs again (intrinsic motivation). Finally return to stand in the same spots and trace shadow around 3 pm. Pairs discuss what they have observed over the course of the day re shadows (guided dialogic discourse). The teacher used open ended questioning to have learners consider the role of the Earth’s rotation. Pairs evaluated their observations (feedback) using exploratory talk. They then joined with another pair to give further peer feedback on their predictions, observations and evaluations. Learners make connections: What was good and what wasn’t? What was a surprise? What would you do differently? (autonomous student learning).
The conclusion was to draw the threads of the lesson together on three large pieces of paper, one each for prediction, observation and evaluation (in little languages, What did you think will happen, What did you see happen and What did you learn?) of today’s light and shadow experiment. Based on what has been learned and peer feedback given, I had children create mind maps/flow charts with labels, observations and illustrations (allowing learner agency) for each part (of the above child friendly) scientific process regarding the experiment (collaboration). I asked learners to reflect on a verbal rubric. Does it fit? Is it true? Is there a better way to show it? Do we have evidence (show how you know)? This may seem abstract and complex, however, as we are discussing an ongoing hands on concrete learning experience the children have done in pairs they can surprise you with their insights. This is as long as the activity is held in an accessible way, as with little languages.
Overall, the lesson produced a few positives. Key features of deep learning were present in the lesson, however, as with some content driven lessons, it felt rushed.
The introduction did establish and review prior learning, surface learning and engage visual learners. From there the hook did elicit wonderings from some of the children and got the majority interested in the topic. This is crucial as it’s one of the key features of deep learning. Think, pair and share was a positive. The regression of terms is academic speak for asking why until everything being discussed has been scrutinised. As the facilitator, the teacher sets those parameters with accountable talk. Other parts of the lesson included key elements of deep learning such as alternative learning environments (heightens engagement) and intrinsic motivation (learning being its own reward).
Further to this there was a defined time for guided dialogic discourse, which lead to feedback, exploratory talk and importantly, making connections, which is autonomous student learning.
Overall, we have a small amount of anecdotal evidence that key features of deep learning are possible in the lower primary EAL context. With the right preparation and accessible teaching strategies like using little languages, an age appropriate version the deep learning approach can be successfully implemented.
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