IB Geography HL Internal Assessment Example

The research question is focused, geographical, and clearly tied to dune processes. By naming the specific variables and place, the student creates a question that can realistically be tested through fieldwork and linked to coastal theory.

This is a strong justification of location choice. The student explains feasibility, accessibility and why Mambrui is a better site for observing natural dune processes than a heavily developed coast, which strengthens the geographic context.

The map sequence is well chosen because it narrows from the national scale toward the study area. This helps the reader understand where Mambrui sits within Kenya before zooming in to the local dune system.

The map would be even more useful if the exact transect line and sampling points were marked. That would make the spatial frame of the investigation more precise and help the reader see how the primary data were collected.

The method description is clearly tied to the investigation aim. The student does not just list techniques, but explains why the transect, quadrat and pH measurements are relevant to dune succession and soil change.

The sampling description needs to be internally consistent with the results. The methodology says 10 m intervals, but the data tables present 50 m steps and only 11 locations. The student should clarify the actual sampling interval and number of points used.

This is a good example of methodological care because the student recognises wind variability and uses repeated trials to improve reliability. Averaging repeated readings is appropriate for a variable environmental measure like wind speed.

A 10-second wind measurement is very brief for a highly variable condition, especially in rainy weather. The student should explain why this duration was sufficient or extend the sampling time so the wind data are more dependable.

The raw data table is relevant and well structured because it includes three trials and an average for each site. This gives enough information to check consistency and identify broader spatial trends across the transect.

The statistical output is labelled as Spearman’s rank, but the displayed values are linear-regression statistics such as $r$, $r^2$ and a regression equation. The student should correct the test label or show an actual Spearman calculation to avoid confusing the analysis.

The presentation would be stronger if the student used a scatter graph with the paired observation point clearly linked to inland distance or sampling site. That would make it easier to compare the graph directly with the transect data and identify anomalies.

The graph choice is appropriate because it compares two continuous variables and makes the overall relationship easy to see. This is a sensible presentation method for showing how vegetation cover changes with soil acidity.

This table title does not match the variables shown in the columns, which are vegetation height and wind speed. The student should rename the table accurately so the reader can interpret the dataset without ambiguity.

The interpretation goes beyond description by explaining the mechanism behind the trend. The student links the pattern to dune stabilisation, which is exactly the kind of reasoning that strengthens written analysis.

This conclusion is too absolute for correlational fieldwork. The evidence supports an association, but the student should avoid implying direct causation unless the method can isolate other variables more convincingly.

What evidence in the field notes or photo observations could help distinguish a true microclimatic anomaly from measurement error? Considering that would make the interpretation of the outlier much stronger.

The student clearly identifies the overall pattern and supports it with examples from the data. This shows effective use of graphical evidence rather than simple repetition of the table values.

This result is used well, but the analysis should still make clear that correlation does not by itself prove that vegetation height causes dune height. The student should keep the interpretation tied to association unless the method demonstrates causation more directly.

The conclusion overstates what the data can prove. The analysis mainly shows linked patterns along one transect, so the student should phrase the final claim more cautiously and distinguish correlation from direct influence.

The conclusion is concise and clearly answers the research question. It also draws together the main variables from the investigation, so the reader can see the overall direction of the findings immediately.

This evaluation identifies a genuine methodological strength and explains why it matters. Linking systematic sampling to reduced bias shows an understanding of reliability, not just a list of positives.

The improvement is well explained because it names both the change and its likely effect on the conclusion. That cause-and-effect link is what makes an evaluation convincing at HL level.

The limitation is relevant, but the explanation would be stronger if the student connected it more directly to how the results may have been skewed by site-specific conditions. The impact should be stated in terms of representativeness and confidence in the conclusion.

This suggestion is unfinished, so it weakens the evaluation’s professionalism and clarity. The student should complete the sentence and specify exactly when and why data collection would be better, so the improvement is actionable.