Shining light on our ignorance: Data science for complex futures

Cedric Le Bescont Learning is exciting because of the paradoxical nature of knowledge: “the more we know, the more we realise the extent of our ignorance” (Grayling, 2021). As a teacher, the question about which I am passionate is how to teach ignorance? How to safely guide students at the frontier of their own knowledge? How to empower them to explore what they don’t know yet?

The scientific method is arguably the most prolific way of creating knowledge. The first principle and, at the same time, the real discovery of any modern scientific inquiry, is ignorance. Science produces questions worth investigating. Galileo was the first to use mathematics to model the variability of firsthand observations. In return, technology was developed to record ever more observations to validate this model. This creative loop was responsible for the unprecedented development of technology in the past three hundred years. Today, we face a deluge of data that is challenging our capacity to make sense of the world. It is important, in my view, that students learn and apply the scientific method to big data sets so they can face the complexity of our world with confidence.

APPRENTICE SCIENTISTS

At school, Science is often perceived as a definitive explanation of the complexity of the world when it is an incomplete and often incoherent set of models. Even though scientific theories are proven to be very powerful in rationally gauging our ignorance, the field of investigation will remain infinite. Science can make accurate predictions about simple systems but when it comes to more complex understanding, the so-called scientific knowledge showcased in textbooks is, in fact, hypothetical. Apprentice scientists should be enticed to investigate scientific knowledge and its inherent uncertainty.

In the classroom, concepts perceived as trivial, and the inquiry opportunities they represent, are sometimes overlooked. Complexity can be introduced too early, with students expected to recall explanations before being given the opportunity to observe, measure, name, classify or describe. Models are transferred as knowledge, rather than as limited and testable frameworks to develop scientific thinking. The main scientific models - the particle model, the Solar system model, the force model, the cell model and the energy model – should be applied and challenged by students from Year 7.

As a case in point, consider the Science Stage 4 syllabus point, “explain that predictable phenomena on the Earth, including day and night, seasons and eclipses are caused by the relative positions of the Sun, the Earth and the Moon” (National Education Standards Authority, 2021). Defining Day and Night and identifying observables is not as trivial as it seems. At Pymble, the Beyond Earth Project encourages students to become apprentice scientists. By using a wild range of tools, they collect data and analyse patterns that they then link to a physical or simulated model of the Solar system. Consequently, students take responsibility for their learning rather than collating knowledge. And what they discover is that learning is messy. Mistakes are the strongest evidence of learning.

In Biology, students use the microscope to observe tissues before being introduced to the concept of the cell. They are guided to develop their observational skills and abstract the concept of a tissue made of cells. Only then, the hypothesis that all living things are made of cells is proposed and tested and its power to guide the study of life explored. Technology has always been used to discover new focus points for inquiry.

Similarly, a great way to start the Year 7 program is through the following activity. Students observe African wildlife in silence for a significant amount of time by watching a live stream from Tembe Elephant Park, South Africa, projected on the board. It is important to also include the audio. The discussion that follows is full of wonder, with students naturally and instinctively naming, classifying and questioning what they see. Facing the unknown always unleashes creative and collaborative learning.

In Chemistry, molecular models are introduced gradually. Students observe boiling water and apply the particle model to construct explanations about water particles becoming capable of freely moving away from each other. Later on, students decompose water by electrolysis and again observe bubbles forming but, this time, at room temperature and of different substances, namely oxygen and hydrogen. The particle model then morphs into the molecular model with the introduction of the atom as a constituent of the molecule. There is no need to break the atom into a nucleus and electrons at this stage. Applying logic to develop models which are then challenged by new observations is how scientists learn.

DATA SCIENCE: THE NEW FRONTIER

Acknowledging that we, the classmates, don’t know is necessary to engage with scientific learning. As a teacher, my expertise does not lie in my ability to transfer knowledge, but rather in my capacity to guide learners. While my students are contemplating most of the concepts for the first time, I have walked this learnscape many times. And technology is pushing me to explore new horizons.

Last year, our Principal, Dr Kate Hadwen, shared with me the Introduction to Data Science Curriculum from the University of California, Los Angeles (UCLA). It made me conscious of the need to expand my expertise and my understanding of machine learning. Through regular conversations with our Deputy Principal – Academic, Justin Raymond, it was decided that Pymble would lead the development of a unique Data Science curriculum in NSW. While collaborating with our Head of Science, Dr Kristie Spence, on teaching the new Science Extension course in Year 12, it became clear that the Data Curriculum would prepare students well for the Science Extension course. Our Director of Research and Development, Dr Sarah Loch, guided me in applying for one of the College’s Professional Learning Grants, and I thank the Pymble Parents’ Association whose generosity granted me the resources to give me the confidence to lead this project.

Scientific progress has allowed for the development of technology that contains an outstanding ability to produce data.

In 2018, the total amount of data created, captured, copied and consumed in the world was 33 zettabytes (ZB) – the equivalent of 33 trillion gigabytes. This grew to 59ZB in 2020 and is predicted to reach a mindboggling 175ZB by 2025. One zettabyte is 8,000,000,000,000,000,000,000 bits ...

(VOPSON, 2021)

This data, revealed by our scientific understanding of the world, reflects our ignorance. There is an urgent need for a new set of skills and mental habits, identified in the NSW Curriculum Review Report, Nurturing Wonder and Inspiring Passion (2020), as well as in the HSC Science Extension syllabus (2017), to equip students in this digitalised unknown. This is the reason why Pymble Ladies’ College will be proposing a new Data Science Elective course in Years 9, 10 and 11 which will be available to our students as soon as 2022.

The Data Science course will be an opportunity to learn the R programming language and apply statistics to visualise, simulate, analyse and model datasets relevant to real-world complexity. It will welcome machines as artificial learners in the classroom, enticing both students and teachers to transform their classroom habits. Collaborating with machines will empower students to become self-efficient researchers, designers and communicators. Finally, as pedagogues, students represent the edge of our knowledge. As a learning leader, I focus on designing tools able to produce reliable and accurate data that can inform me, the teacher, of the individual learning paths being walked by my students. Each of our students’ experiences shines some light on our ignorance.

References

Explore Annenberg LLC, (2021). Tembe Elephant Park. Accessed July 8, 2021. https://explore.org/livecams/african-wildlife/ tembe-elephant-park Grayling, A.C. (2021). The frontiers of knowledge: What we know about science, history and the mind. United Kingdom: Viking. National Education Standards Authority. (2017). Science Extension Stage 6 Syllabus. Accessed August 23, 2021. https://educationstandards. nsw.edu.au/wps/wcm/connect/41ea5fc5ab2c-4a8d-95ad-95529413a7ee/ science-extension-stage-6-syllabus-2017. pdf?MOD=AJPERES&CVID=. National Education Standards Authority. (2020). Nurturing wonder and igniting passion: Designs for a new school curriculum. Accessed August 23, 2021. https:// nswcurriculumreform.nesa.nsw.edu.au/ pdfs/phase-3/final-report/NSW_Curriculum_ Review_Final_Report.pdf. National Education Standards Authority. (2021). Science 7-10, Earth and Space. Accessed August 23, 2021. https://educationstandards. nsw.edu.au/wps/portal/nesa/k-10/learningareas/science/science-7-10-2018/content/983

Vopson, M.M. (2021). The world’s data explained: How much we’re producing and where it’s all stored. The Conversation. May 5, 2021. Accessed August 23, 2021. http://theconversation.com/the-worlds-dataexplained-how-much-were-producing-andwhere-its-all-stored-159964.