IB Geography SL Internal Assessment Example

The research question is clearly geographical and focused on a measurable river process. The phrase “how and why” gives the investigation both a descriptive and explanatory direction, which is helpful for later hypothesis formation and analysis.

This gives a precise fieldwork setting and shows the investigation was feasible for primary data collection. Naming the river, region, and the measured variables helps establish a realistic geographic context.

The maps are relevant, but the cartographic conventions are incomplete. A clear scale, north arrow, key/legend, and cleaner site labelling are needed so the reader can immediately interpret distance, direction, and sample-site distribution.

The student should tighten this contextual statement by naming the exact confluence point and clarifying its significance to the downstream discharge pattern. That would make the geographic context more analytical, not just descriptive.

The Bradshaw model is used appropriately to ground the inquiry in geographical theory. This theoretical link strengthens Criterion A because it shows the student is not just describing a river, but testing a recognised downstream pattern.

This description is too general for a method section. It should specify who measured what, in what order, and with what exact procedure at each site, because vague method descriptions make it difficult to judge whether the data collection was consistent.

This is a strong procedural choice because repetitions can reduce random error and make the velocity data more reliable. The student could later build on this by stating exactly how the repeated trials would be averaged and how anomalous trials would be handled.

This method is well matched to the research question because it explains how channel shape data are collected in a way that can later be used to calculate discharge. The use of five depth points is a sensible attempt to improve representativeness across the channel.

The float method is described clearly, but the student should acknowledge that an orange measures surface velocity rather than mean channel velocity. Without a correction factor or another device, the calculated discharge may be systematically biased.

The uncertainty table would be stronger if each uncertainty were linked to its effect on discharge. For example, the student could explain whether timing error, tape error, or depth estimation has the largest influence on the final $Q$ value.

This uncertainty is only useful if it is applied consistently to the measured dimensions and carried through the discharge calculation. The student should show how the percentage uncertainty was propagated, otherwise the table remains descriptive rather than analytical.

This label is problematic because the values shown, such as 400 and 660, are not plausible as metres for a small river site. The student should correct the unit system and ensure the same unit is used consistently throughout the raw and processed tables.

The raw data are organised in a way that directly supports the investigation. Presenting distance, width, depth, and velocity together makes it easy to trace how the independent variable relates to the measured channel properties.

This heading is misleading because the values listed are distances from source, not geographic coordinates. Clear, accurate labels matter because they affect how the reader interprets the data and how the graph is later read.

The correlation coefficient is relevant, but the student should explain what this value means in context, not just report it. The analysis would be stronger if the interpretation of $r$ was paired with a note on strength, direction, and limitations of the sample.

This is labelled as a line graph, but the data are better treated as a scatterplot because the x-values are unevenly spaced. The student should correct the terminology so the presentation matches the nature of the data.

The student chose an appropriate visualisation for a continuous downstream trend. A graph with plotted data and a trendline is a sensible way to reveal whether discharge changes systematically with distance from the source.

This explanation is plausible, but it is not yet fully supported by site-specific evidence in the report. The student should connect the conclusion more explicitly to measured or mapped tributary inputs rather than presenting a likely cause as if it were established.

The word “proving” is too absolute for fieldwork data that contain anomalies and measurement limitations. The conclusion should use more cautious language such as “supports” or “suggests” unless the evidence is both comprehensive and statistically robust.

The conclusion is clearly tied to the analysed data and directly answers the research question. The student also uses the correlation result to justify the claim, which shows an attempt to base the conclusion on evidence rather than opinion.

If there was “one anomaly,” how confident can the student be that the relationship was the “exact” one expected? A more nuanced conclusion would weigh the anomaly against the overall trend and the quality of the measurements.

This is a strong evaluation point because it identifies a real methodological strength and explains why it matters. Collecting data simultaneously reduces one important source of variation, so the student shows awareness of how reliability can be improved.

This is a significant limitation and should be discussed more explicitly in the evaluation. A single reading at each site reduces reliability, especially when the method itself proposes repeated trials, so the student should explain how this affects confidence in the final correlation.

The improvement is relevant, but it would be stronger if the student explained the impact on the results. For example, more evenly spaced sites would make the downstream pattern easier to compare and would reduce bias caused by clustered sampling.

The evaluation identifies an important issue, but the explanation is inaccurate because the graph’s x-axis is distance from source, not a simple count of coordinates. The student should correct this and explain more precisely how irregular spacing affects interpretation and correlation strength.

This improvement would be stronger if it named specific instruments and linked them to a specific uncertainty reduction. For example, a flow meter or digital depth probe would improve measurement precision and make the discharge values more defensible.