IB Geography HL Internal Assessment Example

Consider tightening “climates” to “microclimates” in the main question. That would better match the spatial scale of the fieldwork and make the question even more precise for hypothesis formation.

The location description is specific enough to justify primary data collection and to explain why the two sites should differ microclimatically. Referencing vegetation, water bodies, and the built environment helps the comparison feel geographically meaningful rather than generic.

What specific microclimatic mechanisms are expected to dominate in Downtown here: reduced evapotranspiration, altered ventilation, heat storage in materials, or anthropogenic heat? Naming the mechanism more precisely would strengthen the geographic logic of the comparison.

The student frames a clear geographical investigation with a focused place comparison. Naming both the broader urban contrast and the exact Singapore locations makes the enquiry feasible for primary fieldwork and gives the reader a strong sense of scale and context.

This is a strong theoretical setup because the hypotheses are explicitly derived from urban heat-island theory. That makes the enquiry testable and shows the student understands how geographical theory can generate measurable predictions.

How well does rotation alone control weather change if the weather itself is changing during the fieldwork window? The student could reflect on whether repeated measurements at matched times or randomisation would better isolate location effects.

It would be stronger to add the instrument range and accuracy here. Including measurement precision would help the reader judge whether the method was sufficiently sensitive for small microclimatic differences.

The student explains a clear sampling strategy and links it to the comparison being made. Purposive selection is appropriate here because the study needs contrasting urban microclimates, and the rationale for representation is clearly stated.

This is methodologically unclear because wind direction is presented as secondary data but also appears to be collected on site with the compass app. The student should clarify exactly how it was obtained and how it was intended to influence the investigation.

The explanation of qualitative data is too vague. The student should specify what was observed, how observations were recorded, and how those observations would be used in analysis; otherwise, the method reads as descriptive rather than operational.

This is a useful standardisation step because it reduces random variation caused by immediate sensor adjustment. Clear timing instructions improve consistency across zones and make the measurements more comparable.

Including a pilot study is a strong methodological choice because it shows the student tested the procedure before full data collection. That usually improves confidence in the technique and helps standardise how the equipment is used across groups.

The box plot is an appropriate presentation choice because it shows median, spread, and potential outliers for each zone. That makes it well suited to comparing microclimatic variation across multiple sampling sites.

The photographs are useful because they connect the numerical data to visible site characteristics. That helps the reader understand why particular zones might differ in temperature or humidity and improves the relevance of the qualitative evidence.

The statistical result is reported, but the analysis omits key details such as sample size, test assumptions, and what exactly was compared. The student should state these explicitly so the reader can judge whether the inference is robust.

Consider adding the actual values or a small summary table beside the graph. That would make the comparison easier to verify and would strengthen the link between the visual trend and the written interpretation.

Again, the box plot is a strong choice because humidity differences are being compared across six zones rather than a single pair of values. The student uses a presentation method that supports pattern detection and later statistical discussion.

This presentation is inconsistent with the data being discussed, because the surrounding text refers to humidity rather than surface temperature. The student should correct the caption and ensure each figure title matches the variable actually plotted.

The humidity result is reported accurately, but the treatment of the data is only partial because the non-significant outcome is still interpreted quite strongly later. The student should use this p-value to be more cautious about claims of a real difference.

The student correctly uses the p-value to support the interpretation of the air-temperature result. This shows appropriate statistical awareness and helps the argument move beyond simple description.

This claim is plausible, but it is too general on its own. The student should support it with the specific measured evidence from wind, shade, or surface data rather than relying mainly on theoretical explanation.

This is a speculative explanation unless the fieldwork collected evidence on the cooling system’s local effect. The student should distinguish clearly between what the data show and what is being inferred from background research.

This is a strong analytical move because it compares the measured pattern with broader urban heat-island theory. The student does not just describe the result; they interpret it in relation to time of day and thermal storage.

If rainfall affected both areas, why would it raise humidity more in Downtown than in Gardens by the Bay? The student could test whether surface type, drainage, or ventilation provides a stronger explanation than rainfall alone.

This explanation shows an attempt to connect the observed humidity pattern to a named geographical process. Linking the data to urban-canyon theory gives the analysis more depth than a simple statement of higher or lower values.

The student introduces a useful counter-explanation, but the reasoning is not developed enough. This would be stronger if it were tied to measured observations or to a clearer explanation of how shade and evapotranspiration interact.

Consider ending with a more balanced summary that states which hypothesis is supported, which is not, and how strong the evidence is for each. That would make the conclusion more precise and easier to verify against the analysis.

If the humidity difference is not statistically significant, what exactly can the conclusion claim with confidence about Hypothesis 2? This is worth revisiting so the final judgement matches the strength of the evidence.

This is a thoughtful concluding insight because it generalises the findings beyond the specific sites. The student recognises that urban microclimates are shaped by interacting factors rather than a single explanation.

The conclusion is directly tied to the original hypothesis and uses the analysis evidence to make a clear judgement. That is exactly what a supported conclusion should do.

This conclusion is stronger than the evidence allows, because the humidity test was not statistically significant and the results do not clearly support a decisive rejection. The student should qualify the judgement more carefully and acknowledge the weak evidence.

The conclusion would be stronger if it addressed the missing surface-temperature evidence from Hypothesis 1. Because surface temperature was part of the original claim, it should be explicitly included in the final judgement.

The student demonstrates real critical reflection by identifying observer bias in the qualitative evidence. That is a mature evaluation point because it addresses how the research process itself may have shaped the findings.

The improvement is relevant and could be made even stronger by explaining the likely effect on the data. For example, a fixed mount would improve height consistency and reduce between-group variation in temperature or humidity readings.

This is a strong limitation because the student explains its impact on representativeness rather than just naming it. Linking single-day sampling to short-term weather variation shows clear evaluative thinking.

Consider naming the specific secondary datasets that would be most useful, such as hourly weather records or land-surface information. Concrete examples would make the evaluation more actionable and show exactly how the study could be improved.

The limitation is valid, but the evaluation stops short of explaining how much this may have skewed the results. The student should link the limited sampling density to specific risks such as missing hotspots, shaded pockets, or unusually windy areas.

This is a useful point, but it is presented more as a correction than an evaluation of impact. The student should explain how the vagueness affected data selection, analysis, or conclusion quality to make the evaluation more analytical.

The student evaluates the wording of the question directly and recognises why it was broadly workable. This is a relevant evaluation point because it shows awareness that question design shapes the quality of the investigation.