How has climate change and the rise of global temperatures affected the number of wildfires across Australia.SL
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5
Official IB Result
20/30
General feedback
20/30
0
15
30
No overall summary is available for this report.
3.1·Strength
Page 1
The overall topic is presented in a clear, direct sentence that immediately tells the reader what the investigation is about. This helps the report feel purposeful from the start.
3.2·Weakness
Page 2
The wording is sometimes imprecise, especially when it moves between “frequency,” “intensity,” “wildfire occurrence,” and “hectares burned.” The student should use one consistent term set so the reader can follow the argument without having to infer the intended variable.
3.3·Weakness
Page 3
The temperature variable is still not defined clearly enough. Using “global temperature increase rate” without a baseline, unit explanation, or justification makes the method harder to replicate and weakens the validity of later analysis.
3.4·Weakness
Page 3
A single source is not enough to justify the full dataset selection. The student should explain why this source was chosen, how the data were extracted, and whether any limitations in the source affected consistency across the years used.
3.5·Weakness
Page 3
No ethical or risk considerations are discussed in the planning section. Even for secondary data, the student should acknowledge issues such as responsible reporting of disaster impacts and the limitations of relying on public datasets.
3.6·Weakness
Page 3
The sampling strategy is only implied by the year range and is not justified. The student should state why 2013–2023 was selected and whether this period is representative, rather than simply convenient or data-available.
3.7·Weakness
Page 3
The temperature table is difficult to interpret because the unit and meaning of the percentage are not explained. The student should label whether this is an anomaly, a rate, or a relative change, because that affects how the rest of the analysis should be read.
3.8·Weakness
Page 3
The method is repeatable, but it is not yet specific enough to guarantee relevant data collection. The student should explain exactly how wildfire data were selected, whether totals were national or regional, and how the 2019–2020 season was handled across calendar years.
3.9·Strength
Page 4
The tables present the yearly data clearly enough to support later graphing, and the student keeps the dataset consistent across the investigation. This makes the progression from raw values to analysis easier to follow.
3.10·Suggestion
Page 4
The student should document the calculator settings and any preprocessing choices used to generate the graph and regression. Including these details would make the method transparent and allow another student to reproduce the same output.
3.11·Suggestion
Page 5
A brief linking sentence after each graph would improve flow by explaining exactly how the visual evidence supports the next stage of analysis. That would make the report feel more connected and easier to read.
3.12·Weakness
Page 5
The wildfire dataset is described as “approximate,” but the basis for those approximations is not explained. The student should clarify how the estimates were derived and why the uncertainty margins are appropriate, since this affects reliability.
3.13·Strength
Page 5
The scatter plot is an appropriate choice for testing association between two continuous variables. Showing the data visually helps the reader see the weak upward pattern before the correlation coefficient is introduced.
3.14·Weakness
Page 6
The hectares table includes uncertainty values, which is a good idea, but the table does not show how these uncertainties were used later. The student should either incorporate them into the analysis or explain why they are reported but not analysed further.
3.15·Weakness
Page 7
The regression graphic is useful, but the x-axis is still framed in terms of years rather than the actual explanatory variable. The student should make the axis label match the research question so the graph directly tests temperature against burned area.
3.16·Weakness
Page 7
The correlation of 0.2331 is reported for years and wildfire increase, but that is not the same relationship as the final research question. The student should avoid mixing proxy variables with the core investigation unless the proxy is clearly justified and analysed separately.
3.17·Weakness
Page 9
The analysis correctly identifies a trend, but it stops short of deeper statistical testing. The student should add significance information such as a p-value or confidence interval to show whether the weak correlation is likely to be meaningful rather than incidental.
3.18·Weakness
Page 9
The analysis would be easier to follow if each statistical result were introduced with a short explanation of what it tests and why it matters. At present, some equations and coefficients appear with limited signposting, which weakens coherence.
3.19·Weakness
Page 9
The anomaly is mentioned, but its effect is still only described qualitatively. The student should quantify how the 2019–2020 values influence the correlation or regression, for example by comparing results with and without those years.
3.20·Strength
Page 9
The final correlation coefficient is clearly stated and the student uses it to support the interpretation of a weak positive relationship. This is a solid analytical move because it moves beyond description and quantifies the pattern.
3.21·Question
Page 10
If the 2019–2020 outlier years were removed, would the overall relationship still look weakly positive? Testing that would help determine whether the result is robust or mostly driven by one anomalous period.
3.22·Strength
Page 12
The student does make a plausible application by linking the finding to mitigation and wildfire preparedness. This is a sensible transfer from analysis to real-world planning, which is important for an ESS investigation.
3.23·Weakness
Page 12
The reference list shows several entries, but the formatting is not fully consistent and some sources are repeated or abbreviated in a way that reduces clarity. The student should standardize citation style and give each source complete bibliographic details.
3.24·Weakness
Page 12
The application is only weakly justified because the result is a weak correlation, not strong predictive power. The student should explain what the finding can realistically support, such as multi-factor planning, rather than implying the trend alone can drive action.
3.25·Suggestion
Page 12
The conclusion would be stronger if it explicitly restated the numerical result and then interpreted what a weak correlation means for the original hypothesis. That would make the reasoning more precise and easier to evaluate.
3.26·Weakness
Page 12
The conclusion is broadly consistent with the results, but it still shifts between “global temperatures,” “wildfires,” and implied severity in a way that blurs the dependent variable. The student should keep the conclusion tightly tied to hectares burned if that is the analysed measure.
3.27·Weakness
Page 12
The solution evaluation is minimal. The student should consider strengths and weaknesses such as feasibility, cost, effectiveness during extreme seasons, and possible trade-offs, instead of only naming broad benefits.
3.28·Weakness
Page 13
Several bibliography items are listed only as titles in brackets, which makes it difficult to verify them. Full references would improve academic credibility and make the research trail clearer.
Criteria A: Identifying the Context (CXT)
5/6
0
3
6
Criteria Strands
A.1Research question formulation
Good
A.2Environmental issue context
Excellent
A.3Connection between issue and research
Good
Criteria Feedback
- You choose a relevant environmental problem and explain why it matters in a real-world Australian context.
- You clearly connect climate change mechanisms to wildfire risk, including warming, evaporation, drought, and major fire seasons.
- You use a concrete example such as Black Summer to strengthen the background and show the significance of the issue.
- Your research question is not fully precise because the dependent variable and the wording of the question do not always match.
- The temperature variable is not defined tightly enough, which makes the focus less coherent than it could be.
- Some important links between the environmental issue and the chosen variables are mentioned but not fully integrated into the rationale.
1.1·Suggestion
Page 1
The hypothesis would be stronger if it named the exact variables and direction of change, for example specifying whether global temperature anomaly, annual temperature increase rate, or Australian temperature is being used. That precision would make the investigation easier to test and interpret.
1.2·Weakness
Page 1
The wording is not fully focused because it asks about the “number of wildfires,” while the later data actually measure hectares burned. The student should align the dependent variable in the question with the variable used in the analysis, and define the temperature metric more precisely.
1.3·Strength
Page 1
The research focus is clearly relevant to ESS and immediately signals an environmental investigation. Linking climate change to wildfire patterns in Australia gives the reader a strong sense of direction and ensures the question is grounded in a real-world issue.
1.4·Strength
Page 2
The connection between the issue and the investigation is made explicit here: the student explains why understanding the climate–wildfire relationship matters for adaptation and loss reduction. This is effective because it shows the purpose of the study rather than only its topic.
1.5·Question
Page 2
What would change in the argument if the investigation used Australian temperature anomalies instead of global temperature trends? That comparison would help test whether the chosen independent variable is the best match for the environmental issue.
1.6·Strength
Page 2
The student strengthens the context by explaining ecological and human impacts, including carbon dioxide release, biodiversity loss, and health effects. Those consequences make the issue more substantial than a simple trend description.
1.7·Strength
Page 2
The background gives useful context by describing recent severe fire seasons in Australia rather than treating the issue abstractly. This helps show why the investigation matters and connects the research to a recognisable environmental problem.
Criteria B: Planning (PLA)
3/6
0
3
6
Criteria Strands
B.1Method design
Good
B.2Sampling strategy
Poor
B.3Risk and ethical considerations
Poor
Criteria Feedback
- You have a repeatable secondary-data approach that can produce relevant information.
- You use correlation and regression in a way that is appropriate for exploring the relationship between the variables.
- You identify a sampling window and a clear broad topic for the dataset.
- The method is not described in enough detail to show exactly how all data were selected, processed, and handled.
- Your sampling choice is not justified clearly, so the representativeness of the period used is uncertain.
- Risk and ethical considerations are missing, including limitations linked to secondary data and data handling.
Criteria C: Results, Analysis and Conclusion (RAC)
4/6
0
3
6
Criteria Strands
C.1Data presentation
Excellent
C.2Data analysis
Good
C.3Conclusion drawing
Good
Criteria Feedback
- You present appropriate tables and graphs for the data you are using.
- You correctly identify the overall relationship as weakly positive and make use of the regression and correlation results.
- You recognise that unusual years and other factors influence the pattern, which shows some interpretive awareness.
- Some visual presentation details are unclear, such as variable labels and the exact meaning of the temperature measure.
- Your analysis is correct but incomplete because you do not test the strength of the relationship beyond the basic statistics.
- The conclusion shifts between different wildfire measures, which reduces the precision of the overall argument.
Criteria D: Discussion and Evaluation (DEV)
3/6
0
3
6
Criteria Strands
D.1Conclusion evaluation
Good
D.2Method evaluation
Good
D.3Improvements and extensions
Poor
Criteria Feedback
- You show awareness that the result cannot be explained by one factor alone and that rainfall, drought, and other conditions also matter.
- You make some attempt to evaluate the conclusion in the wider environmental context rather than treating the result in isolation.
- You identify several strengths and limitations of the investigation and show that correlation does not equal causation.
- Your evaluation is more descriptive than analytical, because you mention limitations without weighing which ones matter most.
- Your suggestions for improvement are fairly general and do not always target the main weaknesses in the investigation.
- Some claims go beyond what your data directly measured, especially when discussing broader climate feedbacks and policy implications.
2.1·Strength
Page 10
The student shows awareness of method limitations by discussing moisture loss in soil and vegetation as a pathway, rather than treating temperature as the only cause. This makes the interpretation more environmentally realistic.
2.2·Weakness
Page 10
The evaluation remains descriptive because it identifies factors that matter, but does not weigh which ones matter most. The student should compare the relative influence of temperature, rainfall, ignition sources, and land management on wildfire extent.
2.3·Strength
Page 11
The discussion usefully broadens the explanation to rainfall variability and prolonged drought, which helps explain why the temperature relationship is weak. This is stronger than a single-factor explanation and improves the quality of evaluation.
2.4·Weakness
Page 11
The positive feedback loop is mentioned, but the argument would be stronger if the student explained whether the investigation actually measured this loop or only inferred it. As written, the claim goes beyond the evidence collected.
2.5·Weakness
Page 11
Black Summer is used well as contextual evidence, but the evaluation still does not fully integrate how this extreme event affects the validity of the dataset. The student should explain whether the anomaly strengthens the argument, distorts the trend, or does both.
2.6·Suggestion
Page 12
A stronger extension would be to test additional variables that directly affect burned area, such as rainfall, wind speed, or drought indices. That would address the main weakness of the current design and make the evaluation more method-focused.
2.7·Suggestion
Page 12
The application section would be more convincing if each proposed action was tied to a specific finding, such as using the weak correlation to justify multi-factor fire planning rather than temperature-only policy. That would turn general recommendations into evidence-based ones.
2.8·Weakness
Page 12
The recommendations are still broader policy ideas rather than improvements to the investigation itself. The student should propose methodological refinements, not just management responses, if the goal is to strengthen the IA’s evaluative depth.
Criteria E: Applications (APP)
2/3
0
2
3
Criteria Strands
E.1Application proposal
Good
E.2Solution evaluation
Poor
Criteria Feedback
- You suggest practical responses such as early warning, resilience measures, and improved fire management.
- You show that the findings can be connected to real-world decision-making.
- You indicate that the issue has relevance beyond one location.
- Your applications are broad and only loosely tied to the specific findings from your investigation.
- You do not fully evaluate the strengths and weaknesses of the proposed solutions.
- The application section would be stronger if it were more evidence-based and more closely linked to the actual pattern you found.
Criteria F: Communication (COM)
3/3
0
2
3
Criteria Strands
F.1Structure and organization
Good
F.2Terminology and concision
Good
F.3Logic and coherence
Good
Criteria Feedback
- Your report has a clear overall structure with a recognizable progression from background to method, results, discussion, and conclusion.
- You use relevant environmental terminology appropriately in many places.
- Your argument is generally logical and easy to follow overall.
- The structure is not always sustained cleanly, especially where sections could be separated and signposted more clearly.
- Some wording is repetitive or imprecise, which affects concision and clarity.
- Inconsistent use of variable names and measures makes parts of the report harder to follow.