Activity: Greenpeace
AOs |
Indicators |
Outcomes |
Snapshot |
Learning experiences |
Cross curricular
Extension ideas |
Assessment |
Spotlight |
Links |
Connections
Purpose
This activity is a possible starting point for a Level 8 Statistics course. This activity asks students to critically evaluate a briefing from Greenpeace about “The changing face of New Zealand farming”. Students will identify several claims made in the briefing and discuss the evidence used to support these claims. Investigating these claims further will allow the teaching and learning programme to be developed to include any of the achievement objectives for Level 8 statistics in a context which has been identified as being of interest.
The full article can be found at:
Achievement objectives
- S8-1: Carry out investigations of phenomena, using the statistical inquiry cycle:
- A. conducting surveys
- C. using informed contextual knowledge, exploratory data analysis, and statistical inference
- D. communicating findings and evaluating all stages of the cycle.
- S8-2: Make inferences from surveys and experiments:
- A. determining estimates and confidence intervals for means, proportions, and differences, recognising the relevance of the central limit theorem
- B. using methods such as resampling or randomisation to assess the strength of evidence.
- S8-3: Evaluate a wide range of statistically based reports, including surveys and polls, experiments, and observational studies:
- A. critiquing causal-relationship claims.
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Indicators
- A. Critiquing causal-relationship claims:
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Specific learning outcomes
Students will be able to:
- identify claims made in the Greenpeace briefing paper “The changing face of NZ farming”
- identify the evidence used to support these claims
- suggest ways of collecting further data to support or negate the claims.
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Diagnostic snapshot(s)
In pairs, students can be given a time limit to highlight a certain number of sentences that make a claim. Teachers are then able to gauge the level at which students identify claims made and also measure the access to the data in terms of literacy requirements. Pairs need to report back on their findings.
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Planned learning experiences
Prior knowledge is required about:
- Time series – comparison
- Bivariate graphs – causality versus correlation.
Students will need the definition of “Agriculture Emissions”:
- “Agricultural methane emissions are emissions from animals, animal waste, rice production, agricultural waste burning (non-energy, on-site), and savannah burning.”
Students may identify the following sentences:
- “New Zealand agricultural emissions have already increased by 15% since 1990. The dairy sector is responsible for this entire increase. Between 1990 and 2007 there was a 58% rise in dairy cow numbers from 3.39 million to 5.28 million. Government figures project that the number of dairy cows in New Zealand will increase dramatically by up to a further 21% in 2010 to 6.4 million dairy cows”.
This could lead to a time series investigation (data available from
Statistics NZ: Infoshare (industry sector > agriculture > Variable by Total New Zealand (Annual-Jun) > highlight all New Zealand, highlight Dairy cows in milk or in calf, highlight all time periods), for example, will show that the Greenpeace claims are not backed up by the government data in that there were only 4.2 million dairy cows in 2007).
There could also be a bivariate data investigation about the relationship between number of dairy cows and agricultural emissions. There is scope here to investigate other data sets, like the number of sheep and agriculture emissions.
On page 7 of the article, students could investigate further the claims made under the heading of “Social costs of boom and bust dairying”. In particular the impact of the job loss claims can be investigated.
Possible adaptations to the activity
- Could consider city dwellers moving to agricultural areas and the impact of vehicle emissions in these regions.
- Possible adaptations here include sourcing other articles that contain a reference to time series or bivariate data.
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Cross curricular links
- English – reading comprehension
- Agriculture/Horticulture
- Science – emissions
- Chemistry – make-up of the emissions
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Extension/enrichment ideas
- Consider investigating the individual effect on emissions from several sources, rather than the total effect from all sources combined.
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Planned assessment
This teaching and learning activity could lead towards assessment in the following achievement standards:
Options:
- One big assessment covering the three achievement standards.
- Give the students the option to choose one or more standards after covering the material.
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Spotlight on
Pedagogy
- Making connections to prior learning and experience:
- Checking prior knowledge using a variety of
diagnostic strategies.
- Providing real-life problems in which the context is relevant to the student.
Key competencies
- Key competency: Thinking
- Students make deductions, they justify and verify, interpret and synthesis and they create models.
- Students design investigations, explore and use patterns and relationships in data and they predict and envision outcomes.
Values
- Students will be encouraged to value:
- innovation, inquiry, and curiosity, by thinking critically, creatively, and reflectively
- ecological sustainability, which includes care for the environment.
Māori/Pasifika
Planning for content and language learning
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Links
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Connections
Last updated July 10, 2024
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