IB Physics SL Internal Assessment Example

The research question is clearly focused on a specific relationship and uses measurable variables. This is a strong starting point because it gives the investigation a clear direction and makes the dependent and independent variables easy to identify.

This personal motivation is engaging, but it does not yet frame the investigation in a specific experimental context. The student should tighten the introduction by identifying the exact wire, temperature range, and why this material-choice matters scientifically.

The student gives useful theoretical context before the method begins, which helps justify why the investigation matters. Linking resistivity to electrical behaviour shows awareness of the scientific basis for the research question.

The method is only described in general terms here. To make the design reproducible, the student should state the exact wire material, dimensions, temperature interval, and how the temperature was changed and measured in practice.

The variables are identified clearly and in a directly usable form. This helps the reader immediately see what is controlled and what is measured, which is essential for a well-scoped investigation.

This control is sensible, but it is incomplete because the investigation later specifies copper wire. The student should align the stated context with the actual apparatus so the design is clearly specific rather than referring generically to 'metal wire'.

This is an important strength because it identifies the wire, but the surrounding method still does not explain how the wire was arranged or how the active measuring length was fixed. The student should specify the probe spacing and immersed length, since those directly affect resistance.

Consider naming the exact range and calibration check used. That level of detail would make the data-collection method easier to reproduce and would strengthen confidence in the resistance readings.

This is a key methodological assumption, but it is not verified. The student should explain how thermal equilibrium was confirmed rather than assuming five minutes was sufficient, because the reliability of the temperature readings depends on that step.

Repeating the full procedure five times is a good design choice because it supports repeatability and allows random error to be estimated. The student could strengthen this further by stating whether the same wire was used each time and whether the apparatus was fully reset between trials.

The uncertainty here is stated, but the numerical precision is questionable because a standard ruler is unlikely to support a $\pm 0.001\text{m}$ reading without further justification. The student should justify the instrument resolution or use a more realistic uncertainty based on the actual measuring device.

The student processes the repeated measurements into a mean value, which is an appropriate first step for reducing random variation. This supports clearer comparison across temperatures and shows that the raw data has been systematically organised.

The processed table is well chosen because it gathers mean values and uncertainty information in one place. That makes the trend easier to follow and shows an attempt to communicate the reliability of the data alongside the measurements.

This interpretation is too vague for analysis. The student should explain why the uncertainty changes with temperature and support the claim with evidence from the table, rather than simply stating that a pattern is visible.

The student states the intended model, but the analysis does not yet demonstrate it. A stronger approach would be to compare the data to the model quantitatively, using the graph gradient or residuals rather than relying only on a visual trend.

This resistivity value is far too large for copper and suggests a serious systematic error in the processing. The student should investigate likely causes such as lead/contact resistance or a mismatch between the measured wire and the stated material before trusting the calculated values.

The student shows the processing formula before applying it, which improves transparency. This is useful because it allows the reader to follow how the raw resistance data is turned into resistivity values.

The graph is useful, but the method for constructing the best, maximum, and minimum fit lines is not explained clearly enough. The student should state how these lines were chosen and why the quoted gradients are valid, otherwise the uncertainty on the trend is hard to trust.

If the gradient represents the temperature coefficient, why is the coefficient itself not calculated and compared with an accepted value for copper? Doing so would make the analysis much more meaningful and would test whether the data match scientific expectations.

This is a worthwhile attempt, but the uncertainty treatment is incomplete because temperature uncertainty and multimeter accuracy are missing. The student should include all dominant sources of uncertainty, otherwise the final error bars will underestimate the true spread in the data.

This calculation is incorrect. Because $A\propto d^2$, the percentage uncertainty in area should be doubled, not squared. The student should revisit the propagation rule and recalculate the area and resistivity uncertainties accordingly.

How would the conclusion change if the contact resistance were subtracted or measured separately? Thinking about that would help the student judge whether the apparent linearity reflects the wire itself or the measurement setup.

This is partly supported, but it does not acknowledge the strongest limitation of the investigation: the processed resistivity magnitude is implausible for copper. The conclusion should reflect that discrepancy instead of implying the method was fully valid.

The conclusion directly answers the research question and refers back to accepted theory. That is effective because it links the processed trend to the wider scientific model instead of restating the raw result in isolation.

Consider adding the calculated gradient or temperature coefficient here and comparing it to an accepted copper value. That comparison would make the conclusion scientifically stronger and would show whether the trend is merely qualitative or quantitatively accurate.

This conclusion overstates the evidence. The student should be more cautious because the uncertainties are not small enough to justify 'minimal deviation,' and the measured resistivity values are not consistent with accepted copper values.

The final conclusion is broadly correct, but it still needs to be tied to the full evidence base. The student should acknowledge the large discrepancy between measured and accepted copper resistivity values before claiming the hypothesis was confirmed without qualification.

It is good that the student does not present the investigation as flawless and does recognise limitations. This balanced tone is important in scientific writing because it shows reflection rather than overclaiming certainty.

How would the results change if the next trial started before the system fully returned to room temperature? Reflecting on that would help the student explain why residual heat could create a systematic upward shift in later readings.

This is a specific limitation linked to the data, not just a generic statement. The student explains how the issue affects scatter in the graph, which is exactly the kind of cause-and-effect evaluation expected here.

This is a realistic improvement, and it would be stronger if the student explained the expected benefit more explicitly. For example, higher-resolution temperature measurement would reduce scatter in the temperature axis and make the gradient more reliable.

This limitation is relevant, but the evaluation stops short of explaining how large its effect is compared with the other errors. The student should prioritise likely dominant systematic errors, especially because the measured resistivity is far above the accepted value for copper.

This is a sensible improvement, but the student could make it more evaluative by noting that temperature gradients would particularly affect the assumption that the wire reached the same temperature as the oil. That would show deeper understanding of why the method is vulnerable.

This limitation is mentioned, but it is probably less important than contact resistance or temperature equilibration. The student should rank limitations by impact so the evaluation shows judgment rather than simply listing many possible errors.

The improvement is clear and linked to a specific limitation. This works well because it explains the expected benefit instead of only naming a better tool or procedure.

The explanation here is not fully convincing scientifically. If coils touch, the main concern is not increased current density but changes to the effective resistance path and possible contact effects. The student should revise the mechanism and explain the error more accurately.

This is a strong idea for further research, but it would be even better if the student tied it to the present data. A broader temperature range matters because the current dataset only supports a limited linear trend and does not test whether the model holds more generally.