Chemistry IA Exemplar: Banana Ripening Factors and Glucose… | RevisionDojo

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IB Chemistry HL Internal Assessment Example

Investigating, how time of ripening (0, 1, 2, 3, 4 days) and external factors (SATP surrounding, wrapping stems in aluminum foil, putting objects in closed paper bag) affect the glucose concentration in bananas (Musa acuminata), using refractometry as a methodHL

Official IB Result

17/24

General feedback

17/24

No overall summary is available for this report.

3.1·Strength

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The research question is clearly framed around a specific biological system, time range, and intervention set. By naming the banana species, the 0–4 day window, and the three storage treatments, the student gives the investigation a focused scope that is appropriate for a chemistry-based study.

3.2·Strength

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The student justifies the chosen observation window by explaining that the design captures early ripening and the transition where treatments are likely to diverge. That rationale strengthens the methodological logic because it shows the data collection period was selected to match the biological process being studied.

3.3·Strength

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The controlled-variables table is a real strength because it does more than list variables: it links each control to a reason for protecting validity. This shows the student understands why temperature, sample mass, and timing matter rather than treating them as checklist items.

3.4·Weakness

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The controlled-variables table would be stronger if it included the actual recorded temperature and humidity values rather than only the intention to control them. Without logged values, the reader cannot judge how stable the environment really was or how much it may have influenced ripening.

3.5·Weakness

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The equipment list still leaves some uncertainty about the intervention materials, especially because the write-up later switches between foil types. Since gas permeability affects ripening, the student should specify exactly which material was used for stem wrapping and whether it was standardized across all samples.

3.6·Question

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What would happen to the validity of the comparison if the bananas assigned to one treatment started at noticeably different ripeness levels from the others?

3.7·Strength

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The sample-preparation steps are detailed enough to make the investigation broadly reproducible, especially the fixed 50.00 g mass and the repeated blending before measurement. That consistency matters because it reduces variation introduced by sample handling.

3.8·Weakness

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The instruction to place bananas in “tightly closed” bags is not standardized enough for full reproducibility. The student should state how closure tightness, bag size, and airflow were controlled, because small differences in ventilation could change ripening rate.

3.9·Weakness

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The method still does not justify the use of only one banana per treatment per day. This is a major design limitation because it means one fruit can determine the result for an entire group, so the student should explain replication strategy or acknowledge that biological power is limited.

3.10·Suggestion

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Consider adding a brief note on randomization, such as assigning bananas to treatments randomly from each bunch. That would reduce selection bias and make the treatment comparison more convincing.

3.11·Strength

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The treatment setup is clearly described for the three groups, so the reader can see how the external factors were applied. This makes the design easier to follow and helps distinguish the independent variable from the measurement procedure.

3.12·Weakness

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The evaluation identifies weaknesses, but it does not rank them by impact. The most serious issue is the lack of biological replicates and randomization, because that affects the entire comparison, so the student should explain which limitation matters most and why.

3.13·Strength

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The weakness table is constructive because it names concrete limitations such as prism cleanliness and air bubbles rather than using generic statements. That specificity makes the evaluation more scientifically useful because each weakness can be linked to a particular source of error.

3.14·Weakness

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The strengths table names valid positives, but it does not explain how each strength affected the final reliability of the investigation. The student should move from listing strengths to showing their actual significance for the quality of the data.

3.15·Weakness

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The improvement column is helpful, but some suggestions remain too general to show full evaluation. For example, the student should explain how a given change would reduce systematic error, random error, or bias, not just state that it would be better.

3.16·Strength

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The explanation of bubble interference is well linked to the measurement mechanism, which makes the improvement meaningful rather than decorative. This is a good example of connecting a limitation to how the instrument actually works.

3.17·Strength

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The evaluation goes beyond simple listing by identifying that bananas from the same bunch may still differ in glucose content. This shows awareness that biological variability can affect reliability even when the method is otherwise controlled.

3.18·Question

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Which limitation most threatens the overall conclusion: instrument error, biological variation between bananas, or the use of a single fruit per treatment? Why?

3.19·Suggestion

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Consider restructuring the evaluation so each weakness is followed immediately by its likely effect on validity or reliability and then by the improvement. That format would make the relative importance of each issue much clearer.

3.20·Weakness

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The improvement discussion would be stronger if it connected each change to a specific weakness already identified in the table. At the moment, the advice is relevant, but the chain from problem to fix to expected improvement is only partly explicit.

Criteria A: Research Design

5/6

Criteria Strands

A.1Research question context

Excellent

A.2Methodological considerations

Good

A.3Methodology description

Good

Criteria Feedback

You frame the investigation in a clear biological and chemical context that makes the research question purposeful and specific.
You describe a mostly reproducible procedure with a coherent sequence of steps and relevant controls.
You show awareness of controlling variables such as timing, mass, calibration, and environmental exposure.

Some important design limits are not fully developed, especially the lack of biological replicates and randomization.
The justification for why the data collected are sufficient is incomplete.
A few procedural details remain ambiguous, such as the exact wrapping/storage conditions and some environmental settings.

Criteria B: Data Analysis

4/6

Criteria Strands

B.1Communication of data recording and processing

Good

B.2Consideration of uncertainties

Good

B.3Data processing quality

Good

Criteria Feedback

You record and process the data in a generally clear and structured way, with tables of repeated trials, means, and processed values.
You make good use of graphs, trendlines, and R² values to show patterns in the data.
You attempt to move beyond raw readings by converting values into comparable processed quantities and calculating change over time.

Some labels and uncertainty statements are inconsistent, which reduces precision.
Your uncertainty handling is present but not fully or consistently propagated through the derived values.
The assumption that the refractometer reading represents glucose alone is not sufficiently justified, which weakens the interpretation of the processed data.

1.1·Weakness

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The table header reports the readings as

\pm 0.01%

, but the equipment list later gives the refractometer as

\pm 0.1%

. This inconsistency reduces precision because the reader cannot tell which uncertainty was actually used in analysis.

1.2·Strength

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The raw-data table presents the three trials for each group in a structured way, which makes the dataset easy to inspect and compare. Including repeated measurements at each time point is a useful step toward transparent data recording.

1.3·Strength

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The attempt to convert percentage readings into a mass-based value and then calculate percentage rise shows good ambition in the processing. It moves the analysis beyond raw instrument output and creates a basis for comparing change over time.

1.4·Weakness

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The third processed table column is mislabeled as Group 2 while containing Group 3 values. Even a single label error weakens communication because it creates avoidable ambiguity about which dataset is being analyzed.

1.5·Weakness

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The mean uncertainty is written as

\pm 0.1%

without showing how that uncertainty was derived from the three trials. The student should either propagate uncertainty from repeated measurements or explain why the instrument uncertainty is being carried through unchanged.

1.6·Question

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If the refractometer measures total soluble solids rather than glucose alone, how does that affect the interpretation of the mass values calculated here?

1.7·Strength

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The processed-data table improves precision by summarizing the means for each group across time. This is effective because it turns a large raw dataset into values that can be directly compared in graphs and conclusions.

1.8·Weakness

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The percentage-rise equation is ambiguous because it gives a ratio-based expression without clearly defining whether the result is a fraction or a percentage. The student should state the formula explicitly with units so the processing cannot be misread.

1.9·Suggestion

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It would improve clarity to add one worked example that shows the uncertainty of the mean alongside the mean itself. That would make the processing chain easier to follow and would strengthen the connection between raw data and conclusions.

1.10·Weakness

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The uncertainty expression here mixes mass and percentage units in a way that is not mathematically clear. The student should rewrite the propagation step with consistent units so the reader can see how the uncertainty in grams was obtained.

1.11·Strength

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The sample calculation for the mean is clearly shown, which helps the reader verify how the processed values were obtained. That transparency is good scientific communication because it allows the processing steps to be checked rather than just accepted.

1.12·Weakness

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The reported uncertainty for percentage rise is not shown as a properly propagated result from the two mass values. Because derived quantities depend on earlier uncertainties, the student should demonstrate the propagation rather than presenting a single rounded estimate.

1.13·Suggestion

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Consider adding error bars with a clear explanation of whether they represent instrumental uncertainty, spread between trials, or both. That distinction matters because each type of uncertainty tells the reader something different about the quality of the data.

1.14·Strength

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The graphing is effective because the student includes trendlines and R² values, which helps communicate whether the relationship with time is strong or weak. This supports the analysis by turning a table of numbers into a visible pattern.

Criteria C: Conclusion

4/6

Criteria Strands

C.1Conclusion relevance and support

Excellent

C.2Scientific context comparison

Good

Criteria Feedback

You make a relevant scientific link between ripening, ethylene, and the observed changes in the fruit.
Your conclusion stays connected to the evidence and uses the processed results to compare the treatments.
You acknowledge the irregular pattern in one group instead of ignoring it, which strengthens the credibility of your overall response.

The comparison to scientific context is present but not fully developed with quantitative support from literature.
Some explanation goes beyond what the data can directly justify, especially where refractometer readings are treated as direct glucose evidence.
The significance of the anomaly in one treatment is identified but not fully interpreted in terms of confidence in the result.

2.1·Strength

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The conclusion is well supported when it refers back to specific graph trends rather than making a vague summary. Linking the claim to Graphs 4 and 5 shows that the student is using processed evidence to justify the statement.

2.2·Suggestion

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A stronger conclusion would briefly compare the observed effect sizes with expected ripening behaviour in bananas. That would help show whether the pattern is merely consistent with theory or actually notable in magnitude.

2.3·Strength

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The relative comparison between the three treatments is clearly stated, and the student uses the percentage-rise figures to support the ranking of effects. That is a strong way to make the conclusion specific and evidence-based.

2.4·Weakness

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The anomaly in Group 3 is identified, but the conclusion does not explain why it matters for confidence in the result. The student should state whether it suggests measurement noise, biological variation, or a treatment effect, because that changes the strength of the conclusion.

2.5·Weakness

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The statement that the data have “quite high quality” is too strong because the submission does not actually present standard deviations or a statistical test here. The student should avoid claiming high quality unless the analysis visibly supports that judgment.

2.6·Weakness

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The conclusion should distinguish between what the data show and what is inferred from the mechanism. The student currently treats rising Brix as direct evidence of rising glucose, but that inference is only valid if the measurement limitation is justified.

2.7·Strength

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It is good that the student acknowledges the irregular pattern in Group 3 instead of smoothing it over. Recognizing an anomaly improves credibility because it shows the conclusion is based on the actual data, not only the expected pattern.

2.8·Strength

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The final conclusion stays aligned with the investigation aim and does not drift into unrelated claims. This coherence helps the reader see that the research question has been answered directly from the analysis.

2.9·Question

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How would the conclusion change if the investigation had used a method that measured glucose specifically rather than total soluble solids?

Criteria D: Evaluation

4/6

Criteria Strands

D.1Methodological weaknesses

Good

D.2Suggested improvements

Good

Criteria Feedback

You identify specific weaknesses in the method rather than making only vague comments.
Your suggested improvements are realistic and connected to the issues you noticed in the investigation.
You show awareness that measurement quality can be affected by sample handling and biological variation.

The improvements are not always explained in terms of how much they would improve the reliability of the results.
The relative seriousness of the different weaknesses is not fully developed.
The biggest design limitation, the lack of biological replicates, is not addressed strongly enough in the evaluation.

Loading scores...

Official IB Result

17/24

General feedback

17/24

No overall summary is available for this report.

3.1·Strength

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3.2·Strength

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3.3·Strength

Page 4• Click to view

3.4·Weakness

Page 4• Click to view

3.5·Weakness

Page 5• Click to view

3.6·Question

Page 5• Click to view

What would happen to the validity of the comparison if the bananas assigned to one treatment started at noticeably different ripeness levels from the others?

3.7·Strength

Page 5• Click to view

3.8·Weakness

Page 5• Click to view

3.9·Weakness

Page 5• Click to view

3.10·Suggestion

Page 5• Click to view

Consider adding a brief note on randomization, such as assigning bananas to treatments randomly from each bunch. That would reduce selection bias and make the treatment comparison more convincing.

3.11·Strength

Page 5• Click to view

3.12·Weakness

Page 11• Click to view

3.13·Strength

Page 11• Click to view

3.14·Weakness

Page 11• Click to view

3.15·Weakness

Page 12• Click to view

3.16·Strength

Page 12• Click to view

3.17·Strength

Page 12• Click to view

3.18·Question

Page 12• Click to view

Which limitation most threatens the overall conclusion: instrument error, biological variation between bananas, or the use of a single fruit per treatment? Why?

3.19·Suggestion

Page 12• Click to view

3.20·Weakness

Page 12• Click to view

Criteria A: Research Design

5/6

Criteria Strands

A.1Research question context

Excellent

A.2Methodological considerations

Good

A.3Methodology description

Good

Criteria Feedback

You frame the investigation in a clear biological and chemical context that makes the research question purposeful and specific.
You describe a mostly reproducible procedure with a coherent sequence of steps and relevant controls.
You show awareness of controlling variables such as timing, mass, calibration, and environmental exposure.

Some important design limits are not fully developed, especially the lack of biological replicates and randomization.
The justification for why the data collected are sufficient is incomplete.
A few procedural details remain ambiguous, such as the exact wrapping/storage conditions and some environmental settings.

Criteria B: Data Analysis

4/6

Criteria Strands

B.1Communication of data recording and processing

Good

B.2Consideration of uncertainties

Good

B.3Data processing quality

Good

Criteria Feedback

You record and process the data in a generally clear and structured way, with tables of repeated trials, means, and processed values.
You make good use of graphs, trendlines, and R² values to show patterns in the data.
You attempt to move beyond raw readings by converting values into comparable processed quantities and calculating change over time.

Some labels and uncertainty statements are inconsistent, which reduces precision.
Your uncertainty handling is present but not fully or consistently propagated through the derived values.
The assumption that the refractometer reading represents glucose alone is not sufficiently justified, which weakens the interpretation of the processed data.

1.1·Weakness

Page 6• Click to view

The table header reports the readings as

\pm 0.01%

, but the equipment list later gives the refractometer as

\pm 0.1%

. This inconsistency reduces precision because the reader cannot tell which uncertainty was actually used in analysis.

1.2·Strength

Page 6• Click to view

1.3·Strength

Page 7• Click to view

1.4·Weakness

Page 7• Click to view

1.5·Weakness

Page 7• Click to view

The mean uncertainty is written as

\pm 0.1%

1.6·Question

Page 7• Click to view

If the refractometer measures total soluble solids rather than glucose alone, how does that affect the interpretation of the mass values calculated here?

1.7·Strength

Page 7• Click to view

1.8·Weakness

Page 7• Click to view

1.9·Suggestion

Page 7• Click to view

1.10·Weakness

Page 7• Click to view

1.11·Strength

Page 7• Click to view

1.12·Weakness

Page 8• Click to view

1.13·Suggestion

Page 8• Click to view

1.14·Strength

Page 8• Click to view

Criteria C: Conclusion

4/6

Criteria Strands

C.1Conclusion relevance and support

Excellent

C.2Scientific context comparison

Good

Criteria Feedback

You make a relevant scientific link between ripening, ethylene, and the observed changes in the fruit.
Your conclusion stays connected to the evidence and uses the processed results to compare the treatments.
You acknowledge the irregular pattern in one group instead of ignoring it, which strengthens the credibility of your overall response.

The comparison to scientific context is present but not fully developed with quantitative support from literature.
Some explanation goes beyond what the data can directly justify, especially where refractometer readings are treated as direct glucose evidence.
The significance of the anomaly in one treatment is identified but not fully interpreted in terms of confidence in the result.

2.1·Strength

Page 10• Click to view

2.2·Suggestion

Page 10• Click to view

2.3·Strength

Page 10• Click to view

2.4·Weakness

Page 10• Click to view

2.5·Weakness

Page 10• Click to view

2.6·Weakness

Page 10• Click to view

2.7·Strength

Page 10• Click to view

2.8·Strength

Page 11• Click to view

2.9·Question

Page 11• Click to view

How would the conclusion change if the investigation had used a method that measured glucose specifically rather than total soluble solids?

Criteria D: Evaluation

4/6

Criteria Strands

D.1Methodological weaknesses

Good

D.2Suggested improvements

Good

Criteria Feedback

You identify specific weaknesses in the method rather than making only vague comments.
Your suggested improvements are realistic and connected to the issues you noticed in the investigation.
You show awareness that measurement quality can be affected by sample handling and biological variation.

The improvements are not always explained in terms of how much they would improve the reliability of the results.
The relative seriousness of the different weaknesses is not fully developed.
The biggest design limitation, the lack of biological replicates, is not addressed strongly enough in the evaluation.