Developing Students' Data Handling Skills for Future Job-skilling

1. Welcome to ‘Developing students’ data handling skills for future job-skilling (Levels 7–10)’ The webinar will start promptly at 3:45pm on 28 May 2019.

2. Developing students’ data handling skills for future job-skilling May 2019

3. Transferable skills (soft skills) Skills and abilities relevant and helpful at school, socially and professionally: • Team work: work effectively in a group or a team to achieve goals • Leadership: initiative and leadership abilities • Personal motivation, organisation and time management: manage and prioritise your workload and time effectively • Listening: active listening to understand other perspectives • Written communication: write accurately, clearly and concisely in a variety of styles • Verbal communication: speak clearly and dynamically in a variety of situations • Research and analytical skills: generate, collect, interpret and analyse information • Numeracy skills: accurately and effectively work with numbers • Information technology: effectively use computers and technology • Personal development: know yourself and find ways to develop

4. “In the new work order, young people will need excellent enterprise skills – digital literacy, critical thinking, creativity, financial savvy, flexibility, the ability to collaborate, self sufficiency – to survive and thrive in a radically altered economy.” The Age, 3 May 2016

5. Scientific literacy – different from science literacy “A scientifically and technologically literate person is one who can read and understand common media reports about science and technology, critically evaluate the information presented, and confidently engage in discussions and decision-making activities regarding issues that involve science and technology.” “Position Paper: The Nature of Science” 2006, Ontario Discussion: Which ‘transferable’ skills are included in scientific literacy?

6. Data handling skills: what can your students do? (An exercise to take into your classes tomorrow…or some time soon!)

7. Levels 7-10 data skills • Accurate observations • Accurate data generation and collection • Various data representations • From tables to graphs…and graphs to tables • Interpretation of data • Qualitative and quantitative data • Discrete and continuous data • Interpolation and extrapolation

8. Is foot size related to height? • How would your students respond to the following: • What answer would you predict, and why? • What type of graph could be drawn to help answer the question? • What other data would have been useful to collect about Persons A-J, and what other questions could then be asked? • Would calculating a mean or median of the shoe sizes or heights be useful? • How could the data’s accuracy be improved?

9. Activity: Is paw size related to height? • Discuss: • How would you investigate this question in level-appropriate ways? • What level of assistance will be provided for students? • How will data be collected in level-appropriate ways by students? • How will data be analysed and presented in level-appropriate ways? • How does the Science SIS strand relate to the Critical and Creative Thinking capability in terms of scientific inquiry? “Paws for thought”

10. Formative assessment and feedback • What do your students already know? • Where are your students on a ‘learning continuum’?

11. How do I know where my class of 23 students are on a learning continuum for understanding of ‘data representation’? KEY: = student Levels 7&8 Levels 9&10 Unit 1 VCE …and are they in the same place in the continuum for content other than data representations?

12. Science skills F-12 continuum

13. Graphs and tables in F-6 science

14. Data curriculum links levels 7-8

15. Data curriculum links levels 9-10

16. Feedback • What does the student work illustrate about their existing knowledge/ place in a learning continuum? • What do you need to do, as a teacher, to progress the student work further? • Is there some way in which parents/ outside bodies may be able to assist in the learning program? Feedback to a student should: • be about the particular qualities of his/her work, with advice on what he/she can do to improve • avoid comparisons with other students

17. Goldilocks and the three bears… …three years on What do you think? Should we enter this year’s Master PorridgeChef? Not sure – isn’t Goldilocks judging the competition again? Hardly worth while, really – we never get more than two words of feedback! Too hot! Too cold! Just right!

18. Using media articles to explore student understanding and skills… and to provide feedback

19. Media snippet: Passenger growth forecast Data representations: Can your students convert graphs into a table of data?

20. Media snippet: Federal government parliamentary seats Data representations: In what other ways can this data be represented? Which representation is the most effective way to communicate information?

21. Media snippet: Finance Why is scale important in representing data?

22. Evaluating data involving discrete variables

23. Does the frequency of each colour m&m in a fun size packet of 12 reflect its popularity? • m&m’s come in six colours: • blue • brown • green • orange • red • yellow • Tasks: • List m&m’s in your favourite order, then analyse the data for one fun size pack/ all packs to respond to the question. • Do the results for one fun pack apply to the whole bag of fun packs? • Can the results of your analysis be applied to other fun pack bags?

24. Somebody had to do the investigation! • Does the colour of each fun size pack relate to the colour of the m&m contents? • Do similar coloured fun size packs contain the same proportion of coloured m&m’s? You should have used a logbook!

25. Data generation and representation Levels 7&8:Construct and use a range of representations…to record and summarise data from students’ own investigations…and to represent patterns and relationships Levels 9&10: Construct and use a range of representations…to record and summarise data from students’ own investigations…to represent qualitative and quantitative patterns or relationships, and distinguish between discrete and continuous data

26. Bag 1: Brown

27. Bag 2: Green

28. Bag 8: Orange

29. Bag 4: Blue (miscount)

30. Data check Incorrect tally by experimenter – illustrates the importance of checking data and/or repeating measurements

31. Total bag colours in a fun size pack (N=12)

32. Total m&m colours in a fun size pack (N=180)

33. Problem solving: Client brief An organisation has these three signs above their recycling bins. Are they effective?

34. Problem solving: Do recycling bins work? Client brief: An organisation has asked that your students design and undertake an investigation to determine how well recycle bins work. Consider health and safety requirements, and when and how data will be generated and presented, to make recommendations to the client.

35. Would you eat a hamburger? Is there actually any ham in a hamburger?

36. Would you eat a doggieburger?

37. TheXoloitzcuintle is a hairless breed of dog, found in toy, miniature and standard sizes. It is also known as the Mexican hairless dogand has been used as a historical source of food for the Aztec Empire.

38. Would you eat a doggieburger? • French news sources from the late 19th century carried stories reporting lines of people buying dog meat, which was described as being "beautiful and light." • It is thought to have medicinal properties, and is especially popular in winter months in northern China, as it is believed to raise body temperature after consumption and promote warmth • It is legal to eat dogs in most States and Territories, except for South Australia. However, it is illegal to sell dog meat for human consumption in any Australian State or Territory.

39. Data: Would you eat a doggieburger?

40. Activity: Capabilities Opportunities for students to develop capabilities should be included in planning learning activities, such as in the example below. Plan: Choose a year level and discuss how the capabilities can be incorporated into questions about food choices. Ethical understanding Science curriculum context examples: ethics of eating meat; laboratory-grown versus organically-grown meat Critical and creative thinking Science curriculum context examples: nutritional analysis; species depletion Would you eat a doggieburger? Intercultural understanding Science curriculum context examples: cultural taboos; distribution of species globally; environmental conditions favouring animal survival Personal and social capability Science curriculum context examples: factors influencing decision-making and choice

41. Exploration of relationships between continuous variables

42. E(ggs)xactly! Accuracy and precision Class comparisons of measures of length, width, mass and volume can be used to discuss measurement precision in addition to exploring relationships between quantities Use Archimedes’ principle to measure the volume of an egg Do eggs with larger circumferences crack more easily? How do class measurements of an egg’s length and width compare? What is the degree of precision of different weighing machines?

43. Outliers, extrapolation and interpolation Student data provide good opportunities to discuss the treatment of outliers and to practise interpolation and extrapolation Formative feedback task: Provide students with a linear data set that includes one or two outliers. Ask them to plot the data and comment on trends/ relationships

44. Issues in science: using data to justify viewpoints and conclusions

45. Which nations contribute most to climate change? Data extracts from Netherlands Environment Agency, 2011; World Resources Institute

46. Drawing conclusions from data: climate change Levels 7-8 Science Understanding: Scientific knowledge and understanding of the world changes as new evidence becomes available Science Understanding: Some of Earth’s resources are renewable, but others are non-renewable Science Inquiry Skills: Reflect on the method used to investigate a question or solve a problem, including evaluating the quality of the data collected, and identify improvements to the method Levels 9-10 Science Understanding: Scientific understanding…is contestable and is refined over time through a process of review by the scientific community Science Understanding: Global systems, including the carbon cycle, rely on interactions involving the atmosphere, biosphere, hydrosphere and lithosphere Science Inquiry Skills: Analyse patterns and trends in data, including describing relationships between variables, identifying inconsistencies in data and sources of uncertainty

47. Further information: M&Ms data for a fun size pack of 12

48. Bag 1: Brown

49. Bag 2: Green

50. Bag 3: Yellow