IB Geography HL Internal Assessment Example

Excellent location specificity here. The coordinate-based description, county reference, and coastal setting make the fieldwork site precise enough for primary investigation and show a strong understanding of geographic context.

The fieldwork question is clearly geographical and focused on a specific coastal landform system. By naming Mambrui, wind speed, vegetation cover and soil pH, the student creates a question that can be investigated through primary data and linked to coastal-process theory.

The inclusion of a national locational map is a useful contextual anchor. It helps the reader place Mambrui within Kenya before moving to the more detailed local map, which supports spatial understanding.

The scale line appears inconsistent and difficult to interpret. The student should replace it with a clearly labelled scale bar or map ratio so the map conventions are unambiguous and easier to read.

Although this map is relevant, it does not actually show the exact transect or sampling points used in the fieldwork. Adding the transect line and sample locations would make the geographical context much more directly tied to the investigation method.

The local map label contains a clear spelling error, and the place name is inconsistent with the rest of the investigation. The student should verify all map labels carefully, because inaccurate naming weakens precision and can confuse the spatial context.

The student has made a good attempt to increase reliability through repeat trials. Repeating measurements at each point is methodologically sound because it reduces the influence of random error and allows averages to be calculated.

This is a strong justification of sampling design because it explains why random selection improves fairness at the starting point. The method is also clearly linked to the transect structure, which makes the investigation easier to replicate.

The method lists the measured variables, but it does not directly collect data on sand-dune formation itself. The student should add a method for dune morphology or sediment movement, such as dune height, profile, or deposition, so the investigation matches the research question more fully.

The measurement procedure is too vague because the height and exposure of the anemometer are not standardised. The student should state a fixed measurement height and consistent placement conditions, since wind speed changes with height and this affects comparability.

The student could strengthen this method by explaining the estimation rule used for vegetation cover, for example percentage interception or grid-square counting. That would make the data collection more transparent and reduce subjectivity in the visual estimate.

What controlled the depth of the probe at each site? If the insertion depth varied, could that have altered the pH readings and affected the reliability of the distance–pH pattern?

The raw data table is relevant and well structured, with repeated trials and calculated means. This gives the reader enough information to inspect consistency and follow the subsequent analysis.

Using repeat readings and an average is a sound approach here because it reduces the impact of one-off fluctuations. This improves the usefulness of the wind-speed dataset for later graphing and correlation analysis.

A line graph is not the most effective representation for two measured variables that are being compared across points. A scatter graph would be more appropriate here because it would show correlation without implying a continuous sequence between unequal vegetation values.

The statistical treatment here is unclear and appears inconsistent with the stated Spearman method. The student should separate the calculator output for Pearson correlation from any rank-correlation calculation and show the Spearman process transparently, especially where tied values are present.

The student could improve data quality by reporting the uncertainty or variability of these trials, not just the mean. Including spread measures such as range would make the treatment of the information more rigorous and easier to evaluate.

A bar graph is a less effective choice for a continuous variable such as distance from the shoreline. A scatter plot or line graph would show the directional trend more clearly and better match the nature of the data.

This is a strong piece of analysis because it identifies a trend, quantifies it, and begins to explain it using coastal-process ideas. The student also acknowledges the anomaly at 400 m, which shows analytical maturity rather than simply describing the graph.

The interpretation of $R^2$ is clear and mathematically meaningful. Linking the statistic back to Bagnold’s theory strengthens the analysis because it shows the student using evidence to support geographical explanation rather than only reporting numbers.

The outlier is identified well, but the explanation is still tentative. The student should support anomaly claims with field evidence, such as vegetation type, exposure, or micro-topography at that site, rather than relying mainly on speculation.

The trend is interpreted clearly, but the wording edges toward causation. The student should be more careful to distinguish correlation from cause, especially because the investigation does not isolate pH from other variables such as distance, moisture, and dune position.

The student could make this analysis much stronger by presenting the rank table with a short worked example and the final substitution into the formula. That would let the reader verify the calculation rather than only accepting the final coefficient.

How far can the conclusion claim influence on dune formation when no direct measurement of dune height, profile, or sediment deposition was taken? A more precise distinction between measured relationships and inferred dune development would make the argument more convincing.

The conclusion is clear, directly answers the question, and is rooted in the investigation’s results. The student also recognises relative importance between variables, which shows an attempt to synthesise the evidence rather than simply restating findings.

A stronger conclusion would briefly restate the most important numerical evidence for each hypothesis before making the overall judgement. That would make the final answer more clearly anchored in the analysis.

This goes beyond the evidence because the investigation did not directly measure dune height, profile, or sediment accumulation. The student should qualify this claim so the conclusion reflects what was actually measured, not only what is inferred from the proxies.

The evaluation identifies a specific strength and explains why it matters for representativeness. This is effective because it connects the sampling design to the quality of the data rather than just listing a positive feature.

This is a strong limitation because it is specific, geographically meaningful, and linked to a clear effect on validity. The student shows an understanding that sampling location can distort measured relationships, not just that it is inconvenient.

The student could make the evaluation even stronger by explaining how the longer transect would affect the outcome data, not just the context. For example, adding where additional measurements would be taken and what patterns are expected would make the improvement more operational.

The improvement is well chosen because it directly targets two real limitations: probe consistency and moisture-related bias. The explanation of impact is especially strong because it shows why the change would improve reliability and comparability.

This strength is overstated because the statistical treatment still contains weaknesses, particularly in the Spearman procedure and the limited sample size. The student should evaluate those limitations more cautiously rather than presenting the statistics as fully validating the conclusions.