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A zoo can show that an educational game changes visitor behavior only by measuring a specific action, not by counting plays or minutes on screen. Define the target behavior, record whether visitors were exposed to and used the game, observe or ask about the outcome with a consistent instrument, and compare results with a suitable baseline or control group. Add a delayed follow-up before claiming that any change lasts.
Define the change before measuring it
Start with a testable outcome sentence. For example: “After playing the game, visitors will be more likely to put recyclable drink containers in the correct bin,” or “Visitors will be less likely to tap the exhibit glass.” The sentence should name the action, the opportunity to perform it and the time window. “Raise awareness” is not an outcome until the zoo specifies what awareness means and how it will be measured.
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Separate learning from conduct
A knowledge outcome might be a correct answer to a question about an animal or conservation action. A behavior outcome is an observable act, such as using a bin correctly, keeping a safe distance or refraining from touching glass. An attitude or intention outcome is different again: it captures what visitors report believing or planning to do. A game may improve one of these and leave the others unchanged, so do not combine them into one vague impact score.
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For conduct outcomes, specify exactly what observers record. Define the behavior, qualifying examples, exclusions, the start and end of an observation and how to code ambiguous cases. For instance, “negative behavior” could require a child to strike or repeatedly tap the glass, while a single accidental touch is excluded. Record the opportunity for the behavior as well as the behavior itself; a visitor cannot be credited with choosing the correct recycling bin if no bin was available.
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Track exposure separately from impact
Measure the path from availability to use: whether a visitor or group passed the game, noticed it, approached within arm’s reach while oriented toward it, played it, watched someone else play or completed a particular level. Duration can describe use, but it is not a learning measure. In a 2014 evaluation, interaction included playing or watching, approaching meant pausing within arm’s reach while facing the screen, and researchers used a stopwatch to record dwell time. The paper warns that interest indicators such as dwell time alone do not establish learning or attitudes. Read the full evaluation.
What the touchscreen study actually found
Whitehouse and colleagues studied 1,084 participants at two UK zoo-based primate research centres. At the Edinburgh site, 24% of 82 observed visitors played the games; that is a site-specific observation, not a target or universal play rate. Among participating visitors, 47.0% of those aged 16 or younger interacted with the touchscreen, compared with 35.2% of older visitors. Average game-play time was 2.31 minutes.
These figures tell a zoo who encountered and used the installation. They do not show that the people who stayed longest learned most, nor that players changed their conduct. The sites differed in layout and device access, and visitor flow and peak periods could affect who had a chance to play. Treat engagement as an exposure variable that must be paired with an outcome.
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Choose a design that supports attribution
The design determines what a result can legitimately mean. Use the strongest practical design and report its limitations rather than presenting a single post-game survey as proof of causality.
| Design | What it can answer | Main limitation |
|---|---|---|
| Post-use feedback only | Whether users report enjoyment, understanding or intention immediately after play | No baseline or comparison; respondents may differ from non-respondents |
| Pre/post measurement | Whether the same people change on a knowledge, attitude or intention measure | Repeated testing, maturation and self-selection can explain some change |
| Contemporaneous comparison group | Whether change among game-exposed visitors exceeds change among comparable visitors without access | Groups may differ in age, interest, visit purpose or exhibit conditions |
| Within-participant comparison | Whether the same participant responds differently after exposure or across matched conditions | Order effects and carryover can bias results; it is not automatically causal |
| Delayed follow-up | Whether knowledge or reported action persists after the visit | Attrition and self-report reduce reach; direct real-world conduct may remain unmeasured |
A 2024 meta-analysis of 56 zoo-led intervention studies found a small-to-medium overall effect on conservation-related outcomes (standardized d+ = 0.40, 95% CI 0.28–0.51), with substantial heterogeneity. Effects were larger in within-participant than between-participant designs. The finding supports transparent reporting of design and uncertainty; it does not establish that any particular game will produce that effect. See the meta-analysis.
Record context that can change access
For every observation or questionnaire, record age band, group size, exhibit, date, time of day, crowding, weather when relevant, whether the game was working and whether the visitor had access to it. Installation location and queue patterns can change both exposure and behavior. A comparison group should be measured during comparable periods, not on a quiet weekday if game users were observed during a crowded holiday.
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Observe behavior directly when behavior is the claim
Self-reported intention is easier to collect than conduct, but it is not a substitute for conduct. Position observers so they can record the target action without coaching visitors. Use fixed observation windows, such as the first five minutes after a child reaches an exhibit, and sample visitors using a rule that does not depend on whether they appear enthusiastic.
Make observations reproducible
- Train at least two observers with the same codebook and practice on shared examples.
- Record the number of opportunities for the target action, not just the number of actions.
- Use anonymous group or visitor identifiers so observations can be linked to game exposure without collecting unnecessary personal data.
- Periodically double-code the same observation and calculate agreement; investigate disagreements by revising definitions rather than silently averaging them.
- Keep observers blind to the study hypothesis where feasible, so expectations do not influence borderline coding.
In a controlled educational intervention at three animal exhibits, researchers recorded both child and animal behavior. Children in treatment groups showed statistically significant reductions in negative behavior at each exhibit, while animal behavior generally did not show a corresponding change. This demonstrates that visitor behavior and animal response are separate endpoints and should not be treated as interchangeable. See the exhibit-behavior study.
Measure knowledge, attitudes and behavior as separate outcomes
Use game-specific knowledge questions
Choose a small set of factual questions tied directly to information presented in the game. Avoid trivia that visitors could answer from general knowledge, and keep wording understandable for the youngest intended audience. In the touchscreen evaluation, game-specific knowledge questions improved in the expected direction after play, while the conservation-opinion questions used in that study did not change. Interest in science also varied by which game visitors played. Report these outcomes separately rather than calling all of them “learning.”
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Use validated or piloted attitude scales
If the game targets connection to nature, species-conservation interest, environmental concern or attitudes toward biodiversity loss, use a scale designed for that construct. A 2024 study across nine zoos reported adjusted instruments, including a six-item species-conservation-interest construct and a five-item biodiversity-loss-attitude scale. It reported 674 pretest questionnaires and 619 posttest questionnaires. Pilot every item with the visitor age groups who will answer it, and keep opinion scales distinct from right-or-wrong knowledge questions. Review the zoo scale study.
A practical evaluation protocol
- State one primary outcome. Write the action or learning result, its opportunity and the observation or testing window. List secondary outcomes separately.
- Map the visitor pathway. Decide how you will identify passers-by, approaches, players, watchers, completion and duration. Log game outages and blocked access.
- Pilot the instrument. Test question comprehension, observation categories, timing and data-entry burden with the intended age groups and several crowd levels. Remove items that visitors consistently misunderstand.
- Collect a baseline. Observe the target behavior or administer questions before installation, or measure a comparable exhibit or visitor group without the game. Keep the sampling rule constant.
- Run the intervention period. Record exposure and outcome data together, plus context such as age band, exhibit, crowding and time. Do not recruit only the people who volunteer to play.
- Repeat the outcome measure. Use the same behavioral definitions and question wording at post-test. If testing the same people, plan for practice effects and record whether they saw the questions before.
- Add a delayed check when permanence matters. Contact consenting participants later or use a return-visit sample. Document how many were invited, responded and dropped out.
- Analyze by outcome. Compare the proportion or score for the primary behavior, then report knowledge, attitude and animal-response measures separately. Adjust or stratify for major context differences rather than hiding them in an overall average.
- Report uncertainty and reach. Give sample sizes, missing data, confidence intervals or equivalent uncertainty estimates, exposure rates and the exact comparison used. State whether the design was randomized, quasi-experimental or observational.
Interpret the evidence without overclaiming
Immediate learning is not lasting behavior
The touchscreen study was quasi-experimental, using independent groups before and after installation at two specific UK primate settings. Visitors who chose to play could differ from those who did not, and the devices and layouts were not comparable across sites. Its results support the possibility of immediate, game-specific knowledge learning in a zoo context, not a universal effect size or proof of durable conduct change. The authors noted that further research is needed to determine whether knowledge is retained over time.
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Do not import results from a different setting
An in-school repeat-engagement program with 445 students across seven schools reported a 60.5% increase in aggregate conservation understanding and a 24% increase in knowledge of pro-conservation behaviors. Those figures describe a school program, not a zoo visitor game, so they should not be used as the expected result for an exhibit installation. See the school-program evaluation.
Analyze and report results a visitor can trust
Begin with reach: the share of eligible visitors who passed, approached, played or watched. Then show the primary outcome for exposed and comparison groups, including the number of opportunities observed or questionnaires completed. For a behavior such as correct disposal, report both opportunities and correct actions; a percentage without its denominator is difficult to interpret.
Present absolute differences as well as relative changes, and include uncertainty intervals where possible. Explain missing responses, exclusions, repeat visitors and any changes to the game during data collection. If several exhibits or age groups were studied, show results by those strata before presenting a pooled estimate. A null attitude result alongside improved game-specific knowledge is an informative finding, not a failed evaluation.
When using a meta-analysis or comparing studies, note that intervention types, outcomes and designs differ. The pooled d+ = 0.40 from 56 studies is not a forecast for a particular touchscreen, and heterogeneity means effects varied substantially. Design-specific results should therefore be described as evidence with boundaries, not as a guarantee.
Common mistakes and fixes
- Mistake: treating dwell time as learning. Fix: pair duration with a factual question or direct behavior observation.
- Mistake: surveying only enthusiastic players. Fix: sample eligible passers-by and record non-use as an exposure category.
- Mistake: measuring an attitude when the goal is conduct. Fix: observe the defined action during a real opportunity.
- Mistake: changing the codebook mid-study. Fix: pilot first, version the definitions and document any necessary amendment.
- Mistake: claiming persistence from an exit survey. Fix: schedule a delayed follow-up and report attrition.
- Mistake: treating animal and visitor behavior as one outcome. Fix: code each separately and analyze the relationship only if the design supports it.
- Mistake: generalizing a site-specific play rate. Fix: label the site, sample and date, and collect local exposure data before setting targets.
A decision rule for the zoo team
Before launch, agree on the minimum evidence required for a positive claim. For example, the game might need a higher correct-recycling rate than the comparison group during matched observation windows, with no material decline in access for younger visitors. A weaker result—more play but no knowledge change, or knowledge change without observed conduct change—should lead to a narrower statement and a design revision, not a claim that the game “worked.”
The most defensible conclusion combines three layers: who encountered and used the game, what changed on the intended outcome, and how the comparison or repeated measurement supports attribution. Only a later measurement can support a statement that the change lasted.
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