IB Environmental systems and societies (ESS) - Old SL Internal Assessment Example

The context remains quite general and does not yet explain why Madrid and The Hague are the right comparison. The student should add location-specific context such as local waste policies, school recycling infrastructure, or curriculum differences to make the environmental issue directly relevant to the research question.

What exactly would count as being more “proactive” in the data: doing more behaviors, doing them more often, or reporting stronger school influence? Clarifying this would make the dependent variable much more precise.

The student states a clear comparison between two education systems and defines the target group as 17–18-year-olds. That gives the investigation a genuine focus and makes the question more relevant than a broad topic on waste management alone.

It would strengthen the investigation to rewrite the question in measurable terms, for example by specifying one behavior and one frequency scale. A more operational question makes it easier to collect comparable data and draw a conclusion that actually matches the variable measured.

The comparison between Madrid and The Hague introduces location as a second variable alongside education system. This weakens the focus because differences in city policy, infrastructure, and culture may also affect habits. The student should explain how these factors are controlled, or reframe the question to avoid conflating system and place.

The student uses secondary-source support to show that recycling and reuse can reduce environmental harm. That helps build context with evidence instead of relying only on general opinion.

The research question is still too broad because it combines several different behaviors such as single-use plastic, composting, and buying second-hand clothes into one measure of “proactiveness.” The student should narrow this to one clearly operationalized waste-management behavior, or define a single composite index before collecting data.

The introduction successfully establishes why waste management matters environmentally by linking it to climate change. This gives the research question a real-world purpose rather than treating it as a purely school-based comparison.

The student begins to connect education with behavior by suggesting that classroom exposure and environmental awareness can shape habits. This is a useful line of reasoning because it starts to explain why the comparison may matter.

Consider linking the environmental issue to a concrete school-level mechanism, such as exposure to environmental education, access to bins, or school recycling programs. That would help show why education system differences might realistically influence waste behavior.

The connection is described mostly as a general idea rather than a clear mechanism. The student should explain how education leads to behavior change, for example through knowledge, norms, repeated practice, or school facilities, so the link becomes more than self-report perception.

A stronger method would define each behavioral category in advance and keep the same response scale for every question. That would improve consistency and make the results easier to compare across both education systems.

How would the student know whether a habit was caused by school learning rather than family habits, peer influence, or local waste systems? Addressing this would improve the method’s validity.

The method relies on asking whether habits happen “because of what you learned at school,” which is difficult for participants to judge reliably. This creates response bias and weakens validity; a stronger design would measure behavior directly and then separately ask about perceived influences.

The method is laid out in a step-by-step way that another researcher could broadly repeat, which is a strength. Using a Google Form and describing how it was sent to participants gives the procedure some clarity and transparency.

The dependent variable is not yet measured in a way that matches the research question. If “proactiveness” is the intended outcome, the student needs a consistent measurement scale before data collection, rather than mixing yes/no/maybe responses with later frequency categories.

The sampling explanation confuses the Google Form with the sampling strategy itself. A data-collection tool is not the same as a sampling method, so the student should distinguish between how participants were recruited and how the questionnaire was delivered.

The student clearly states the intended sample split between the two groups. That shows awareness that the comparison needs data from both education systems rather than only one side.

Although the student names stratified sampling, the description does not justify it convincingly. Recruiting people “that I know” suggests convenience sampling within each group, which can introduce bias and make the sample less representative.

The student should explain why stratifying by education system improves fairness of comparison, and then show how participants were selected within each stratum. That would make the sampling strategy more credible and easier to evaluate.

The risk assessment would be stronger if the student explicitly listed low-level survey risks and corresponding safeguards, such as anonymising responses and allowing withdrawal at any time. That would show a more comprehensive evaluation of ethics.

Saying there is “no risk involved” is too absolute for survey research. Even minimal-risk studies should consider confidentiality, data storage, participant pressure, and the fact that respondents are 17–18 years old.

The ethical considerations are relevant but incomplete because they do not mention the right to withdraw or how data will be stored and protected. Those details matter for demonstrating that participant consent was informed and not just assumed.

The raw data tables include both groups in one place, which makes the comparison accessible. This is useful because it allows the reader to see the source data before moving to graphs and interpretation.

The processed data do not include all relevant survey items, so the presentation is incomplete. The student should graph the key behaviors consistently across both groups so the reader can compare the full pattern of waste-management habits rather than a selected subset.

Figure 1 directly compares the two education systems on whether environmental issues are taught, which is a relevant processed representation of the raw data. The figure supports later analysis by making the contrast easy to see.

This figure is only shown for one education system, which limits comparison with the other group. For a valid investigation, the same variable should be processed for both groups so trends can be evaluated side by side.

The graph shows an important behavior, but it is hard to use analytically unless the response categories match the dependent variable consistently. The student should standardize the measure across all figures so each graph contributes to one clear interpretation of proactiveness.

If the students in both systems had different access to recycling facilities or school programs, would the same conclusion still hold? Considering that possibility would make the interpretation more rigorous.

It would strengthen the analysis to summarise the key percentage differences in one place before concluding. A concise comparison of the strongest and weakest contrasts would make the final judgment more evidence-based.

The conclusion implies that the education system itself caused the difference, but the investigation does not control for city, policy, or infrastructure differences. The student should frame the claim more cautiously as an association unless those confounding factors are addressed.

The conclusion clearly states the overall direction of the findings and refers back to the processed graphs. That makes the final claim easier to trace to the evidence presented earlier in the report.

The conclusion is broader than the evidence warrants because it generalizes to “waste management habits” even though only selected behaviors were analysed. The student should restrict the conclusion to the exact data collected, or justify how the analyzed items represent the full construct.

The evaluation does not address major methodological threats such as convenience sampling, social desirability bias, or the confounding of education system with city context. These omissions matter because they directly affect how trustworthy the conclusion is.

The student recognises that the response scale was created too late, which is an important limitation. However, the evaluation should also explain that changing units after data collection weakens comparability and may distort the interpretation of the dependent variable.

A stronger evaluation would separate strengths, weaknesses, and limitations of the method into distinct points. That structure would make it easier to show that the student has considered the investigation from multiple angles, not only the measurement issue.

The evaluation correctly identifies a major problem with the questionnaire design, but it stays focused on measurement format rather than the broader implications of the conclusion. The student should explain how the methodological issue affects the reliability of the final claim.

Time constraints are mentioned, but the limitation is not fully developed. The student should explain what effect this had on validity or sample quality, rather than simply noting that a better version could not be resent.

The limitation about economic disparities is sensible, but it is only one constraint. The student should also consider feasibility, teacher training, curriculum time, and cultural acceptance to evaluate the application more fully.

The application is relevant, but the discussion is still quite general. The student should connect the proposed educational response more explicitly to the investigation’s findings and explain why those results justify this application.

It would be stronger to suggest a specific extension, such as comparing schools with different environmental programs or adding a waste audit alongside the survey. That would address major weaknesses in the current method and deepen the evaluation.

The proposed solution is only weakly justified by the investigation itself because the data do not isolate education system from other influences. The student should qualify the application as a tentative implication rather than a firm recommendation based on causation.

The evaluation of the solution is too narrow because it only mentions economic disparities. A fuller evaluation should consider practical strengths and weaknesses such as affordability, curriculum integration, teacher readiness, and whether the intervention would actually change behavior over time.

The student could strengthen this section by linking the application to one specific behavior from the results, such as composting or reducing single-use plastics. A targeted application would make the recommendation more convincing and easier to justify.

The proposed application is directly connected to the topic of the investigation: using education to improve waste-management habits. That relevance is important because it shows the student is not offering a generic solution detached from the data.

The report is clearly organised into logical sections, which helps the reader move from question to method to results and evaluation without getting lost. That structure supports comprehension throughout the investigation.

Some terms, especially “proactiveness,” are not defined consistently enough for the reader to follow the logic smoothly. The student should define key terms once and then use the same meaning throughout the report.

The sampling section is clearly signposted, and the headings make the report easy to navigate. This is an effective communication choice because it helps the reader locate key methodological information quickly.

The phrase “the chosen sampling method is a Google form” is imprecise terminology. The student should distinguish between the survey instrument and the sampling strategy to communicate methodology more accurately.

Could a reader understand the full argument without relying on the conclusion to reinterpret the results? If not, the student should make the analytical links clearer earlier in the report.

A short summary sentence at the end of each major section would improve coherence by showing how each part leads to the next. That would make the report’s argument easier to follow, especially in the results and evaluation sections.

The discussion is understandable, but its logic is weakened by the shift from multiple-choice responses to frequency units after the fact. The student should make the measurement language consistent from the start so the argument reads more coherently.