IB Geography HL Internal Assessment Example

The fieldwork question is clearly geographical and focused on a real coastal system. The variables named here are measurable and theory-linked, which gives the investigation a strong basis for hypothesis formation and a coherent line of enquiry.

How directly does this statement connect dune formation to the variables actually measured? The student may want to consider whether the research question should reflect direct dune indicators, such as height, width, or sediment deposition, to match the evidence more closely.

The geographic context is precise and practical. By naming Mambrui, giving regional reference points, and explaining access, the student shows that primary fieldwork is feasible at this scale.

This map caption contains a directional inaccuracy: Kenya’s coast is on the eastern side of the country, not the south-west. That weakens geographic accuracy and should be corrected because locational framing is part of the enquiry’s credibility.

Consider revising the national map caption and adding a brief explanation of how the map guides the enquiry. Clearer map conventions and a correct directional reference would strengthen the locational argument.

The local map establishes the study area, but it does not clearly locate the actual transect, sampling points, or the precise fieldwork corridor. Adding the route and sites would make the spatial context more fieldwork-specific and easier to interpret.

The sampling design is unclear here because the transect is described in a way that is hard to interpret, and the method later mixes systematic and random approaches without fully reconciling them. The student should state one coherent sampling strategy and explain exactly where each measurement is taken.

The student could strengthen this by explaining why systematic sampling is the best fit for a distance-based enquiry and how it reduces bias compared with a fully random design. That would make the method more clearly aligned with the research question.

The method relies on estimation, but it does not explain how the student reduced subjectivity or how cover was judged consistently. The student should describe a standardised cover-estimation procedure so the data are more reliable.

The procedures are clearly broken into steps, which makes the investigation easy to follow. This structure helps show that the student has considered how each method will be carried out in the field.

This is a strong methodological detail because it standardises wind measurement height. Keeping the instrument at the same level improves comparability between sites.

Wind direction is mentioned in the procedure, but it does not appear later in the data or analysis. The student should ask whether this variable is actually necessary, or whether it should be collected and analysed consistently.

This is too vague for a high-quality method because the duration of each reading is not specified. The student should state the exact time interval used at each site so the technique can be repeated and assessed properly.

Repeating the pH measurement and averaging the results is a good way to improve reliability. This shows awareness that individual readings can vary and should not be used in isolation.

The raw-data table is directly relevant to the hypothesis and is organised so the spatial pattern can be followed easily. Presenting repeated trials and averages makes the dataset more usable for later analysis.

The graph communicates the trend effectively, but it would be stronger if the student removed any unnecessary negative scale values and ensured the axes match the data range more cleanly. That would improve clarity and prevent confusion when reading the pattern.

The ranking table contains inconsistent values when compared with the raw averages, so the correlation result cannot be trusted fully. The student should recheck the ranks carefully and ensure tied values are handled consistently before interpreting the coefficient.

This table provides the essential raw measurements for testing the wind hypothesis. The inclusion of repeated readings and averages allows the student to show the trend with enough detail for analysis.

What additional presentation would help show whether the two wind readings at each site were consistent or variable? Including spread or repeated-reading information would make the data treatment more convincing.

The soil pH table is well chosen for the enquiry because it directly supports the third hypothesis. The repeated trials improve transparency and make the pattern easier to verify.

The table is useful in principle, but the ranking and calculation need closer checking because convention and accuracy errors appear elsewhere in the analysis. The student should verify the raw values, ranks, and final coefficient together so the statistical treatment is dependable.

The analysis goes beyond description by linking numerical evidence to the hypothesis and then explaining the pattern with process-based ideas. That combination of data, trend, and explanation is a real strength.

The anomaly is identified well, but the explanation is speculative and not strongly evidenced. The student should either support this claim with field observations or present it more cautiously as a possible explanation rather than a definite cause.

This explanation is plausible, but some of the causal language is too strong for the evidence collected. The student should distinguish carefully between what the data show and what is inferred from theory, especially where dune formation itself was not directly measured.

The student identifies the main pattern clearly and supports it with specific values. This is effective analysis because it shows the trend rather than simply stating that the hypothesis was right.

This statement is inaccurate because the highest recorded wind speed is at the shoreline site, not at 100 m. The student should correct the interpretation of the graph before building the wider argument from it.

A stronger analytical conclusion would avoid saying the hypothesis has been ‘proven’ and instead use language that reflects the evidence level, such as ‘supported by the data.’ That would make the written analysis more academically precise.

The student uses both the graph and correlation result to interpret the pattern, which strengthens the line of argument. The explanation also connects the result to coastal processes such as salt spray and organic matter input.

What direct evidence in the investigation shows dune formation itself, rather than just conditions associated with it? Reflecting on this would help the student separate correlation from direct measurement.

Does the evidence show that lower pH causes more vegetation cover, or only that the two variables change together? That distinction matters when deciding how strongly the conclusion can be phrased.

The conclusion is clearly written and returns to the enquiry focus. It summarises the direction of the main trends so the reader can see the overall outcome of the investigation.

The conclusion overstates the certainty of the findings. Because the investigation did not directly measure dune formation or stability, the student should frame the result as supported by the data rather than proven absolutely.

This is too broad for the evidence collected. The conclusion should be tied more tightly to the measured variables and should avoid implying direct proof of dune formation when only related indicators were sampled.

This is a good concluding link because it connects the soil trend to a coastal process and explains why the pattern may occur. The student shows awareness of how field evidence and theory can be combined.

The conclusion would be stronger if it briefly quantified the key trends, such as the change from the lowest to highest vegetation cover or the drop in wind speed and pH. Specific figures would make the final judgement more convincing.

This strength is relevant, but the evaluation remains incomplete because it does not consider the limitations of using only one transect or the small number of readings at each site. The student should evaluate these design limits as well, since they affect reliability and representativeness.

The evaluation identifies a relevant strength in the design: the enquiry did address each hypothesis. This shows the student is reflecting on the overall structure of the investigation rather than only listing problems.

The evaluation usefully identifies a real fieldwork constraint and links it to a specific anomaly in the vegetation data. That connection between limitation and outcome improves the quality of the evaluation.

This is a relevant limitation because the student explains how weather conditions could affect both the dependent measurements and the validity of the results. The comment is analytical rather than purely descriptive.

The explanation is useful, but it would be stronger if the student clarified whether rain affected the soil itself, the probe readings, or both. More precise evaluation would show a better understanding of validity and measurement error.

The improvement is relevant, but it could be more detailed by naming exactly how many days, what weather conditions, and which measurements should be repeated. Specificity would make the evaluation more actionable.

What other design change, besides GIS, could reduce the impact of inaccessible vegetation on the anomaly at 300 m? Considering an alternative field method would deepen the evaluation.

This is a strong idea, and it would be even better if the student explained how GIS would be combined with on-site sampling or repeated transects. That would show a more realistic and detailed improvement plan.