More Food, Not More Fertiliser: How Smarter Land Management Can Increase UK Crop Yields

For many students, the relationship between fertiliser and crop yield appears straightforward:

More fertiliser means more mineral ions.
More mineral ions mean more plant growth.
More plant growth means more food.

Unfortunately, biology is rarely that simple.

Adding fertiliser can certainly increase crop yields when a mineral nutrient is limiting growth. However, once the crop has enough of that nutrient, adding more may produce little additional yield. In some cases, excessive fertiliser can damage plants, waste money, pollute rivers and reduce the long-term productivity of the soil.

The real challenge is therefore not simply to use less fertiliser. It is to use nutrients more intelligently while managing the soil, water, crop rotation, pests and biodiversity as one connected biological system.

This matters far beyond the A Level Biology examination. It raises an important national question:

How can we produce enough food for the UK population without exhausting the land on which future food production depends?

The Mistake of Treating Soil Like an Empty Plant Pot

When students first study plant nutrition, soil can appear to be little more than material that holds a plant upright.

In reality, productive agricultural soil is a complex living ecosystem containing:

• mineral particles;

• water;

• air spaces;

• bacteria;

• fungi;

• earthworms;

• decomposers;

• organic matter;

• plant roots;

• dissolved mineral ions.

A healthy soil does several jobs at once. It anchors plants, stores water, supplies mineral ions, allows gas exchange around roots and supports the organisms responsible for decomposition and nutrient cycling.

This is why two fields receiving the same quantity of fertiliser may produce very different yields.

One field may have good drainage, a suitable pH, plenty of organic matter and an open soil structure through which roots can grow easily. The other may be compacted, waterlogged, acidic or badly eroded. Adding extra fertiliser to the second field will not necessarily solve its problems.

The fertiliser may be present, but the plants may be unable to use it effectively.

Crop Yield Is Controlled by the Limiting Factor

An important biological principle is that growth is restricted by whichever essential factor is in shortest supply.

This is sometimes described as the law of the minimum.

A wheat crop may have access to plenty of nitrate ions, but its growth could still be restricted by:

• a shortage of phosphate;

• insufficient potassium;

• lack of water;

• low light intensity;

• unsuitable temperature;

• poor soil aeration;

• an incorrect soil pH;

• disease;

• pest damage;

• competition from weeds;

• root restriction caused by compaction.

Adding more nitrate fertiliser will not correct a phosphate deficiency or repair compacted soil.

This is similar to asking a student to improve an examination result simply by doing more revision, without first identifying whether the real problem is subject knowledge, mathematical skill, reading the question or managing time. More of the same intervention is not always the answer.

Effective farming begins with diagnosis.

Why Nitrogen Is Important — and Why Too Much Becomes a Problem

Plants require nitrogen to produce amino acids, proteins, nucleic acids and chlorophyll. A nitrogen-deficient crop may show poor growth and yellowing leaves because it cannot produce enough chlorophyll.

When nitrogen is genuinely limiting, applying nitrate- or ammonium-containing fertiliser can produce a considerable increase in biomass and yield.

However, the response does not continue indefinitely.

At first, each additional unit of fertiliser may produce a useful increase in yield. Eventually, the increase becomes smaller. The crop approaches its biological maximum, and another application may cost more than the extra harvested crop is worth.

Defra’s current nutrient guidance emphasises this point: the aim is not simply to reduce fertiliser, but to match nutrient applications to the needs of the crop and the nutrients already available in the soil. Applying more fertiliser does not automatically produce greater profit.

Excessive nitrogen can also:

• encourage soft, rapid growth that is more vulnerable to lodging or disease;

• remain unused in the soil after harvest;

• be washed into groundwater;

• enter rivers through drainage and runoff;

• contribute to eutrophication;

• be released as nitrous oxide, a powerful greenhouse gas;

• increase unnecessary financial costs for the farmer.

The objective should therefore be maximum nutrient-use efficiency rather than maximum nutrient application.

Overfertilisation and Eutrophication

The environmental consequences of excess fertiliser provide a direct link with the A Level Biology topic of eutrophication.

If nitrate or phosphate reaches a lake or slow-moving river, it may stimulate rapid algal growth. An algal bloom can block light from reaching submerged plants, reducing photosynthesis.

When the algae and aquatic plants die, decomposing microorganisms feed on the dead organic material. Their rate of aerobic respiration increases, removing dissolved oxygen from the water.

As oxygen concentration falls, fish and many aquatic invertebrates may die.

The problem does not mean that all farmers are using fertiliser irresponsibly. Nutrients can reach water through several routes, including soil erosion, runoff from saturated land, poorly timed applications and the movement of nitrate through soil.

Nevertheless, the scale of the issue is significant. The Environment Agency reported in June 2026 that agriculture is the leading source of nitrogen and phosphorus pollution in English waterways and contributes around 40% of water pollution nationally.

Protecting crop yield and protecting rivers are therefore not opposing objectives. Better nutrient efficiency can support both.

Test the Soil Before Treating the Soil

One of the simplest principles of effective land management is to measure before applying.

A farmer needs to know:

• the soil pH;

• the existing concentrations of important nutrients;

• the soil type;

• the amount of organic matter;

• the previous crop;

• whether manure or compost has recently been applied;

• the expected nutrient demand of the next crop;

• the likely yield;

• recent rainfall and soil moisture conditions.

A field that already contains sufficient phosphate does not benefit from having more phosphate added merely because it is included in a standard compound fertiliser.

Similarly, a soil may contain nutrients that are chemically present but unavailable to plants because the pH is unsuitable.

Regular soil testing allows each field to be managed according to its actual condition rather than according to a general assumption.

Modern nutrient-planning systems combine soil analysis, expected yield, cropping history and established guidance to recommend fertiliser, manure and lime applications. This allows nutrients already present in the soil to be included in the calculation rather than ignored.

The Four Rights of Fertiliser Use

Effective nutrient management can be summarised through four questions.

Is It the Right Nutrient?

Plants need a balanced supply of mineral ions.

Nitrogen supports proteins, nucleic acids and chlorophyll. Phosphorus is important in ATP, nucleic acids and cell membranes. Potassium is involved in enzyme activity, osmoregulation and the control of stomata.

Adding nitrogen will not solve a potassium deficiency. Adding a general fertiliser without testing may supply nutrients that the soil already contains while failing to correct the true deficiency.

Is It the Right Amount?

Too little may restrict yield, but too much produces diminishing returns and increases the risk of nutrient loss.

The optimum amount is not necessarily the amount that produces the greatest possible biological yield. It may be the amount that produces the best economic return while keeping environmental losses acceptably low.

Is It the Right Time?

A crop cannot absorb a large quantity of nitrogen before it has developed an extensive root system.

Applying nutrients long before the period of rapid growth increases the time during which they can be lost.

Dividing the total fertiliser requirement into several smaller applications can sometimes match nutrient availability more closely to crop demand.

Timing should also take account of weather. Applying fertiliser shortly before heavy rain on saturated ground creates a much greater pollution risk than applying it when growing conditions allow rapid plant uptake.

Is It in the Right Place?

Fertiliser spread unevenly across a field may leave some areas deficient and others overfertilised.

Placing nutrients where developing roots can reach them can improve uptake. However, placing concentrated fertiliser too close to seeds or young roots can cause damage.

These principles are frequently described as using the right source, at the right rate, at the right time and in the right place.

Why Crop Rotation Can Increase Yield

Growing the same crop repeatedly may appear efficient. The farmer can use the same equipment, follow a familiar routine and sell a consistent product.

Biologically, however, continuous cropping can create serious problems.

A single crop may repeatedly remove the same mineral nutrients from the soil. Its pests and pathogens may also survive between seasons because their preferred host returns every year.

Rotating crops can interrupt these cycles.

A rotation may include:

• a cereal crop such as wheat;

• a legume such as peas, beans or clover;

• a broad-leaved break crop;

• a spring-sown crop;

• a temporary grass or herbal ley;

• a cover crop between harvested crops.

Each crop affects the soil differently. Their roots grow to different depths, they remove different proportions of mineral ions, and they support different communities of soil organisms.

Rotation is therefore not simply about changing what is planted. It is a planned biological method of managing nutrients, pests, weeds, diseases and soil structure.

Legumes: Using Bacteria Instead of a Bag of Nitrogen

Legumes provide one of the clearest links between crop rotation and the nitrogen cycle.

Peas, beans and clover can form root nodules containing nitrogen-fixing bacteria, commonly associated with the genus Rhizobium.

The bacteria convert atmospheric nitrogen gas into nitrogen-containing compounds that can eventually contribute to plant nutrition. In return, the plant supplies the bacteria with carbohydrates produced through photosynthesis.

This is a mutualistic relationship: both organisms benefit.

A legume crop does not simply pour nitrate directly into the soil while it is growing. Much of the fixed nitrogen becomes incorporated into plant proteins and other organic compounds. However, nitrogen may become available to later crops through root turnover, fallen plant material, residues and decomposition.

Grain legumes can therefore reduce the nitrogen fertiliser requirement of the following crop. AHDB reports that cereals grown after grain legumes may require 23–31 kilograms less nitrogen fertiliser per hectare and may produce higher yields than cereals following another cereal crop, although results depend on crop, soil and management conditions.

Legumes also act as break crops, disrupting some cereal pest, weed and disease cycles.

This does not mean they can be grown repeatedly without consequences. Legumes have their own diseases and rotational restrictions. Effective rotation depends on diversity, not simply replacing continuous wheat with continuous beans.

A Possible Four-Year Rotation

A simplified arable rotation might look like this:

Year One: Winter Wheat

Wheat provides a valuable cereal crop but has a relatively high demand for nitrogen. Soil tests and expected yield are used to calculate nutrient applications.

Year Two: Field Beans

Beans provide a break from cereal production and form root nodules containing nitrogen-fixing bacteria. They also produce a protein-rich crop for human or animal consumption.

Year Three: Winter Wheat

The wheat may benefit from the rotational effects of the previous bean crop, including residual nitrogen, improved soil conditions and the disruption of some cereal disease cycles.

Year Four: Spring Barley with an Overwinter Cover Crop Before Sowing

A cover crop protects the soil after harvest, captures remaining nutrients and reduces the amount of bare ground exposed to erosion.

The exact rotation would need to be adapted to the soil, climate, local pests, available machinery and market demand. A rotation that works well on one farm may be inappropriate on another.

The important biological idea is planned variation.

Cover Crops: Keeping Living Roots in the Soil

After a crop has been harvested, leaving a field bare for months can create several problems.

Rain may break down soil aggregates and wash particles away. Nitrate remaining in the soil may leach beyond the reach of the next crop. Weeds may colonise the land, and soil organisms lose the supply of carbohydrates associated with living roots.

A cover crop is grown primarily to protect or improve the soil rather than to provide the main harvested product.

Different cover crops perform different functions.

Legumes can fix nitrogen and add organic material. Grasses and cereals develop extensive root systems, absorb residual nutrients and help suppress weeds. Brassicas may provide rapid ground cover and deep rooting. Mixed cover crops can combine several functions.

Cover crops may:

• reduce erosion;

• absorb nitrate that might otherwise be leached;

• increase organic matter;

• improve soil aggregation;

• support soil microorganisms;

• create root channels;

• suppress some weeds;

• provide habitats and food for wildlife.

However, cover crops are not automatically beneficial in every situation. They use water, cost money to establish and may harbour pests if poorly selected. Their destruction must also be timed correctly so that the nutrients in their biomass become available when the next crop needs them.

Good land management is based on evidence and adaptation, not slogans.

Soil Structure Can Be as Important as Soil Chemistry

A soil test may show that the correct mineral ions are present, yet a crop may still perform poorly because the soil structure has been damaged.

Repeated movement of heavy machinery, particularly when soil is wet, can compress the soil particles together. This reduces the size and number of air spaces.

Compaction can:

• restrict root growth;

• reduce oxygen availability for root respiration;

• slow water infiltration;

• increase surface runoff;

• create waterlogging;

• reduce the activity of some soil organisms;

• make nutrients less accessible.

Roots require ATP for active transport of mineral ions. If waterlogged soil contains little oxygen, aerobic respiration in root cells is restricted, reducing the energy available for active transport.

This creates an important examination link: a plant can be surrounded by mineral ions but still fail to absorb them effectively if root respiration is limited.

Preventing compaction may involve reducing unnecessary machinery passes, avoiding travel on waterlogged ground, using suitable tyres, establishing deep-rooting crops and increasing organic matter.

Healthy soil structure is now recognised as central to long-term UK food production. The government’s 2026 Farming Roadmap links sustainable soil management with greater yields, water retention, lower erosion and reduced reliance on artificial fertilisers. It includes a commitment to bring at least 60% of agricultural soil in England into sustainable management by 2030.

Organic Matter Is Not Just “Natural Fertiliser”

Farmyard manure, compost, crop residues and green manures can return nutrients to the soil, but their value extends beyond their mineral content.

Organic matter can:

• increase water-holding capacity;

• improve soil structure;

• support decomposers;

• increase cation exchange capacity;

• reduce erosion;

• help soil resist compaction;

• supply nutrients gradually through mineralisation.

This gradual release can improve nutrient cycling, but it also makes nutrient supply less immediately predictable than applying a soluble fertiliser.

Organic materials must still be managed carefully. Manure applied in excessive quantities or at the wrong time can also cause nitrate and phosphate pollution.

“Organic” does not mean “unlimited” or “risk-free”. The amount of nutrient supplied by manure, slurry or compost must be included in the field’s nutrient budget.

Matching Management to Different Soils

There is no single fertiliser plan suitable for every British field.

Sandy Soils

Sandy soils drain rapidly and often contain less organic matter. Nitrate can be lost relatively easily through leaching.

Smaller, carefully timed applications may be more effective than one large application. Cover crops and organic matter can help retain nutrients and water.

Clay Soils

Clay soils can retain nutrients well but may suffer from compaction, poor drainage and waterlogging.

The priority may be improving structure and avoiding heavy machinery when wet rather than applying more fertiliser.

Acidic Soils

Low pH can reduce the availability of some mineral ions and affect the activity of soil organisms.

Applying lime may sometimes improve nutrient availability more effectively than adding additional fertiliser.

Chalky or Alkaline Soils

High pH can reduce the availability of certain micronutrients. A plant may show deficiency symptoms even though the element is present in the soil.

The correct response is diagnosis and targeted treatment, not an indiscriminate increase in all fertilisers.

Precision Agriculture: Treating a Field as Many Different Areas

A field may look uniform from the road but contain considerable variation.

One part may have deeper soil. Another may drain badly. A third may have a history of manure application, while a sloping section may have lost topsoil through erosion.

Applying the same amount of fertiliser across the whole field may overfeed some areas and underfeed others.

Precision agriculture uses technologies such as:

• GPS-guided machinery;

• yield mapping;

• satellite images;

• drone surveys;

• soil conductivity measurements;

• crop canopy sensors;

• variable-rate fertiliser spreaders;

• digital nutrient records.

A combine harvester can record yield at different points across a field. These data can be compared with soil test results and previous applications.

The farmer can then investigate why particular areas perform poorly.

The answer may be additional nutrient, but it might instead be drainage, compaction, pH, pest damage or loss of topsoil.

Technology is most useful when it improves biological decision-making. A colourful map is not valuable unless it leads to a better diagnosis.

Integrated Pest Management Protects Yield Without Depending on One Solution

Increasing food production is not only about helping crops grow. It is also about preventing avoidable losses.

Integrated pest management combines several approaches:

• crop rotation;

• resistant varieties;

• monitoring pest populations;

• protecting natural predators;

• changing sowing dates;

• mechanical weed control;

• targeted pesticide use when necessary.

The aim is not necessarily to eliminate every pest organism. That may be impossible and ecologically damaging.

Instead, pest populations are kept below the level at which they cause unacceptable economic damage.

Crop rotation can remove the host on which a pest or pathogen depends. Hedgerows and field margins may support predatory insects and birds. Resistant crop varieties may reduce the need for chemical control.

As with fertiliser, the principle is intelligent targeting rather than maximum input.

A Practical A Level Investigation

Students can model the relationship between fertiliser concentration and plant growth using fast-growing plants such as radish, wheat or cress.

Several groups of genetically similar seedlings could receive nutrient solutions containing different nitrate concentrations:

• no added nitrate;

• a low concentration;

• a medium concentration;

• a high concentration;

• a very high concentration.

Important control variables would include:

• plant species and variety;

• number of seeds;

• soil or growth medium;

• volume of solution;

• light intensity;

• temperature;

• watering;

• length of the investigation;

• pot size.

Growth could be measured using shoot height, leaf number, leaf area, fresh mass or, preferably, dry mass.

Students should not assume that the highest nitrate concentration will produce the greatest biomass. A likely pattern is an initial increase followed by a plateau, with very high concentrations potentially reducing growth.

The investigation could then be extended by asking:

• At what point does nitrate stop being the limiting factor?

• What other variables might limit growth?

• Why is dry mass more reliable than fresh mass?

• How could the investigation be made more representative of a field?

• What environmental risks arise when nitrate is supplied beyond plant demand?

• How could crop rotation be incorporated into a longer-term investigation?

This turns a simple plant-growth experiment into a discussion about agriculture, economics, ecosystems and food security.

Can Better Land Management Feed the UK?

It is tempting to reduce food security to a single target: produce everything within the UK.

In practice, food security depends on both strong domestic production and resilient trade. The official UK Food Security Report describes security as having diverse supply sources without relying on a single point of failure.

In 2023, UK production was equivalent to 62% of the country’s total food supply by value and 75% of foods that can be grown domestically. The proportions vary greatly between products: the UK produces a high proportion of its cereals but a much smaller proportion of its fresh fruit.

Increasing sustainable domestic production can make the country more resilient, but crop yield is only one part of the answer.

The UK also needs to consider:

• reducing food waste;

• protecting high-quality agricultural land;

• improving storage and distribution;

• developing crop varieties suited to changing climates;

• increasing fruit and vegetable production where practical;

• securing water supplies;

• supporting pollinators;

• reducing dependence on vulnerable imported inputs;

• maintaining a skilled farming workforce;

• balancing domestic production with diverse international trade.

There is little value in producing a very high yield for a few years if the method causes erosion, destroys soil structure or pollutes the water needed for future agriculture.

The goal must be reliable production over decades.

A Better Definition of Maximum Yield

“Maximum yield” should not mean forcing the greatest possible harvest from every hectare in a single season.

A more useful definition would be:

The greatest reliable yield that can be maintained without degrading the soil, water, biodiversity and biological processes on which future production depends.

That may involve applying fertiliser, because nutrients removed in harvested crops must often be replaced.

It may also involve deciding not to apply fertiliser where the soil already contains enough.

It means using legumes to contribute nitrogen, cover crops to retain nutrients, organic matter to improve structure, rotations to interrupt disease cycles and technology to target interventions accurately.

Conclusion: Feed the Crop, but Protect the System

The central lesson is not that fertilisers are bad.

Modern crop production would be extremely difficult without replacing the mineral nutrients removed from fields at harvest. Fertiliser has helped farmers produce more food from a limited area of land.

The problem begins when fertiliser is treated as the only answer.

A plant does not grow in a bag of chemicals. It grows within a biological system involving roots, microorganisms, soil particles, water, air, decomposers, competitors, predators and climate.

Successful land management therefore requires more than adding nutrients. It requires understanding which factor is limiting growth, measuring the condition of the soil, rotating crops, protecting soil structure, retaining organic matter and matching every intervention to a genuine biological need.

As I often remind students, biology becomes much more interesting when we stop looking at each topic separately.

The nitrogen cycle, active transport, respiration, decomposition, mutualism, succession, biodiversity and eutrophication all meet in the same field.

The challenge of feeding the UK is not simply to make crops grow faster.

It is to build an agricultural system in which healthy crops, healthy soils and healthy ecosystems can continue producing food long into the future.

A Level Psychology and the Difficult Question of Abnormality: Could a Sane Person Prove They Were Sane?

 

Rosenhan (1973): Could a Sane Person Prove They Were Sane?

A Level Psychology and the Difficult Question of Abnormality

Imagine entering a psychiatric hospital knowing that there is nothing mentally wrong with you.

You have reported hearing a voice, but after admission you behave normally. You speak sensibly, cooperate with the staff, explain that the voice has disappeared and quietly record what happens around you.

How long would it take before someone recognised that you were not mentally ill?

A few hours?

Perhaps a day?

Surely an experienced psychiatrist would soon realise that a mistake had been made.

David Rosenhan’s famous 1973 study, On Being Sane in Insane Places, suggested that the answer might be much more worrying. His research raised the possibility that once a person had been given a psychiatric label, almost everything they did could be interpreted through that label.

However, Rosenhan’s study raises an even larger question:

What do we actually mean by mental abnormality?

Is abnormality something statistically unusual? Is it behaviour that breaks society’s rules? Is it an inability to manage everyday life? Or is it simply a failure to meet an ideal picture of mental wellbeing?

These are not merely examination questions. The answers can affect whether a person receives treatment, loses their independence, experiences stigma or is taken seriously when asking for help.

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Why Did Rosenhan Conduct the Study?

Rosenhan was interested in the validity of psychiatric diagnosis.

Validity concerns whether a diagnosis is accurate: does the label genuinely identify the condition it claims to identify?

He was also interested in reliability. Would different clinicians looking at the same person reach similar conclusions?

Physical illnesses can often be investigated using blood tests, scans, biopsies and other measurements. Mental health diagnoses depend much more heavily on interviews, descriptions of experiences, observed behaviour and professional judgement.

That does not mean mental illness is not real. Depression, psychosis, anxiety and other forms of psychological suffering can be severe and disabling.

The problem is deciding how clinicians distinguish between:

• an unusual experience and a psychiatric symptom;

• temporary distress and a lasting disorder;

• eccentric behaviour and harmful dysfunction;

• culturally acceptable behaviour and behaviour considered abnormal;

• someone who is mentally unwell and someone who only appears to be.

Rosenhan wanted to discover whether trained professionals could reliably distinguish a person who was experiencing mental illness from someone who was not.

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The First Part of the Study: Eight “Pseudopatients”

Rosenhan organised a form of covert participant observation.

Eight mentally healthy people, including Rosenhan himself, attempted to gain admission to 12 psychiatric hospitals in the United States. Rosenhan called them pseudopatients.

The hospitals varied considerably. They included public and private institutions, hospitals with different levels of funding and facilities located in different parts of the country.

Each pseudopatient contacted a hospital and reported hearing a voice. The voice was described as unclear but appeared to say words such as “empty”, “hollow” or “thud”.

Apart from this reported hallucination and changes to identifying information, the pseudopatients were instructed to tell the truth about their lives.

All eight were admitted. Seven received a diagnosis of schizophrenia, while one was diagnosed with manic-depressive psychosis, the historical terminology used at the time. After admission, they stopped reporting symptoms and behaved normally. Their hospital stays lasted from 7 to 52 days, with an average of 19 days. None was identified by hospital staff as a pseudopatient.

This result is often presented very simply:

Eight sane people entered psychiatric hospitals and the psychiatrists failed to recognise that they were sane.

However, the situation is more complicated than that.

The pseudopatients had deliberately reported a symptom associated with serious mental illness. A clinician assessing someone who claims to hear voices cannot simply assume that the person is lying. Admitting a patient for further observation could be viewed as a cautious response rather than obvious incompetence.

The more troubling part of the study was what happened after the pseudopatients began behaving normally.

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Once the Label Was Applied, Everything Looked Like a Symptom

The pseudopatients openly wrote notes about their experiences.

Rather than treating this as normal note-taking, staff sometimes interpreted it as part of the supposed illness. One record referred to “writing behaviour”, as though the act of writing itself had become clinically significant.

Ordinary details from the pseudopatients’ lives were also interpreted in ways that appeared to support the diagnosis.

This demonstrates the possible effect of confirmation bias.

Confirmation bias occurs when people pay greater attention to information that supports an existing belief while overlooking evidence that challenges it.

Once the staff believed that a person had schizophrenia, normal behaviour could be reinterpreted as evidence of schizophrenia:

• Writing notes became “writing behaviour”.

• Waiting for lunch could be interpreted as an abnormal preoccupation with food.

• Asking when they would be released could appear demanding or symptomatic.

• Nervousness could be seen as evidence of illness rather than a reasonable reaction to being confined in a psychiatric hospital.

• Calm behaviour might be interpreted as a temporary improvement rather than evidence that the original diagnosis was wrong.

The label did not simply describe the person. It influenced how other people perceived the person.

That is one reason Rosenhan remains useful when teaching labelling theory, institutionalisation, observer bias and the social construction of abnormality.

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The Patients Sometimes Saw What the Professionals Missed

Another striking feature was that some of the genuine patients suspected that the pseudopatients were not mentally ill.

Some suggested that they might be journalists or researchers investigating the hospital.

This presents an uncomfortable contrast. Patients who lacked professional qualifications sometimes appeared more willing than staff to question the original label.

One possible explanation is that patients spent more time with one another. They saw each other across a wide range of situations rather than through brief formal interviews or medical records.

Staff members were also working within an institution. They had procedures to follow, limited time and responsibilities for many patients. Their observations were shaped by the hospital environment and by the information already written in the patient’s notes.

This does not necessarily mean that individual staff members were uncaring or incompetent. It suggests that the system itself may have encouraged particular interpretations.

That is an important lesson for psychology students: behaviour does not occur in isolation. We need to consider both the person and the situation.

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Could the Pseudopatients Simply Announce the Truth?

One might imagine that the pseudopatients could have said:

“I am not mentally ill. This is an experiment.”

The difficulty is that this statement might itself have been dismissed.

Once someone has been classified as mentally ill, protesting against the diagnosis may be interpreted as a lack of insight into their condition. The attempt to disprove the label can therefore be used to reinforce it.

This creates a disturbing circular argument:

1. The person is diagnosed as mentally ill.

2. The person denies being mentally ill.

3. The denial is interpreted as evidence that the person lacks insight.

4. The diagnosis is therefore maintained.

The pseudopatients were eventually discharged, but most left with a diagnosis of schizophrenia “in remission” rather than with the admission that no disorder had been present.

In other words, the label was modified but not completely removed.

This illustrates the potential stickiness of psychiatric labels. Once applied, they can influence future expectations, treatment decisions and how behaviour is interpreted.

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The Second Part: Looking for Pseudopatients Who Did Not Exist

The second part of Rosenhan’s research reversed the problem.

A research and teaching hospital had reportedly argued that errors like those in the first phase would not happen there. Staff were told that Rosenhan might send one or more pseudopatients during the following months.

Staff therefore assessed incoming patients and judged whether they might be impostors.

Among 193 patients, 41 were judged highly likely to be pseudopatients by at least one staff member. Twenty-three were suspected by at least one psychiatrist, and 19 were suspected by both a psychiatrist and at least one other staff member.

Rosenhan had sent no pseudopatients.

It would be misleading to say simply that “mentally ill people were declared sane”. The staff were not necessarily declaring that these people had no difficulties. They were judging that some might be pretending to have symptoms.

Nevertheless, the result demonstrated the power of expectation.

During the first phase, staff expected to see mental illness and therefore saw evidence of illness in normal behaviour.

During the second phase, staff expected to encounter impostors and therefore became suspicious of genuine patients.

The direction of the error changed because the expectation changed.

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False Positives and False Negatives

The study can be understood through two types of diagnostic error.

A false positive

A false positive occurs when a person who does not have a disorder is diagnosed as having one.

The pseudopatients in the first phase represented alleged false positives.

A false negative

A false negative occurs when a genuine condition is missed or dismissed.

The second phase created the risk that genuine patients might be treated as impostors.

Neither error is harmless.

A false positive may expose someone to:

• inappropriate medication;

• unnecessary hospital admission;

• stigma;

• loss of independence;

• disruption to education, employment and family life;

• a medical record that influences future decisions.

A false negative may mean that someone who desperately needs help is not believed or treated.

Psychological diagnosis must therefore balance two serious risks: diagnosing a disorder that is not present and failing to recognise one that is.

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What Is Mental Abnormality?

Modern A Level Psychology courses commonly examine four definitions in the field of mental health:

1. deviation from ideal mental health;

2. deviation from social or cultural norms;

3. failure to function adequately;

4. statistical infrequency.

Each definition captures something useful, but none provides a complete answer.

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1. Statistical Infrequency

Under this definition, a behaviour or characteristic may be considered abnormal when it is statistically rare.

For example, an extremely low IQ score is unusual within the population and may be associated with an intellectual disability when accompanied by difficulties in adaptive functioning.

This approach appears objective because it uses numerical data.

However, rarity does not automatically mean illness.

An exceptionally high IQ is statistically unusual but is not normally regarded as a disorder. Exceptional musical ability, extraordinary memory and elite athletic performance are also rare.

The opposite problem occurs when an undesirable experience is common. Anxiety, stress and periods of low mood may affect large numbers of people. Their frequency does not make severe suffering unimportant.

Statistical infrequency can tell us that someone is unusual. It cannot, by itself, tell us that the person is unwell.

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2. Deviation from Social or Cultural Norms

Every society has expectations about acceptable behaviour.

These include formal rules, such as laws, and informal expectations concerning clothing, communication, personal space, relationships and emotional expression.

A person who seriously violates these expectations may be judged abnormal.

The problem is that social norms are not fixed.

They vary:

• between cultures;

• between generations;

• between social groups;

• according to the situation;

• across historical periods.

Talking loudly to oneself might attract concern in a library but seem entirely normal during a theatre rehearsal. Removing one’s clothes would usually be unacceptable in a supermarket but expected in a changing room.

Even the same behaviour can be judged differently depending on who performs it.

Social norms can also be used to control people who challenge authority. Political protest, religious practice, sexuality and gender expression have all been judged differently across cultures and historical periods.

Deviation from a social norm may tell us that society disapproves of a behaviour. It does not automatically prove the presence of mental illness.

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3. Failure to Function Adequately

This definition focuses on whether a person can manage everyday life.

Possible indicators include difficulty:

• caring for oneself;

• maintaining relationships;

• attending school or work;

• communicating effectively;

• managing personal safety;

• coping with ordinary responsibilities;

• experiencing life without overwhelming distress.

This approach can be more humane because it considers the effect of a condition on the individual rather than merely asking whether the behaviour looks unusual.

For example, repeatedly checking that a door is locked might appear relatively harmless. If the checking takes several hours, causes extreme anxiety and prevents the person from leaving home, it has become seriously maladaptive.

However, functioning is also difficult to judge.

Some people continue working and caring for others while experiencing severe psychological distress. Outward achievement does not necessarily mean that someone is well.

Conversely, a person may temporarily struggle to function after bereavement, illness, unemployment or another major life event. That does not automatically mean that they have a psychiatric disorder.

There is also the question of who decides what “adequate” functioning looks like.

---

4. Deviation from Ideal Mental Health

Instead of defining illness, this approach begins by describing positive psychological wellbeing.

Marie Jahoda suggested that ideal mental health might involve characteristics such as:

• a positive attitude towards oneself;

• personal growth and self-actualisation;

• independence;

• resistance to stress;

• an accurate perception of reality;

• successful adaptation to the environment.

Someone who falls substantially below these ideals might be considered psychologically abnormal.

This definition has a positive focus. It encourages us to think of mental health as more than the absence of a diagnosed disorder.

However, the criteria may be too demanding.

Most people occasionally doubt themselves, misunderstand situations, depend on other people or fail to cope well with stress. If perfect psychological health is the standard, almost everyone becomes abnormal.

Some criteria may also reflect Western ideas about independence, personal achievement and self-development. Other cultures may place greater value on family duty, interdependence and community.

The definition offers a useful goal, but it may not provide a fair diagnostic boundary.

---

What Rosenhan Shows About These Definitions

Rosenhan’s pseudopatients were statistically ordinary in many respects, functioned effectively outside the hospitals and did not display continuing symptoms after admission.

Nevertheless, they had reported an experience that was both unusual and associated with deviation from ordinary expectations: hearing a voice that other people could not hear.

That single reported symptom was enough to place them within a powerful diagnostic context.

The study suggests that definitions of abnormality are not applied mechanically. Human judgement remains involved.

Clinicians must decide:

• how unusual a behaviour is;

• whether it is culturally appropriate;

• whether it causes distress;

• whether it affects functioning;

• how long it has lasted;

• whether another explanation is more likely;

• how much risk is involved;

• whether the person’s account is reliable.

The diagnosis is therefore influenced not only by behaviour but also by context, interpretation and expectations.

---

A Useful Classroom Activity: How Much Does a Label Change Our Judgement?

One effective way to explore Rosenhan is to give two groups of students an identical description of a person.

For example:

Alex has recently moved to a new city. Alex spends a great deal of time alone, keeps the curtains closed, writes extensively in notebooks and sometimes smiles without an obvious reason.

Tell the first group that Alex is a university student preparing a novel.

Tell the second group that Alex has recently been discharged from a psychiatric hospital.

Then ask both groups to explain the behaviour.

The first group may decide that Alex is creative, private and absorbed in writing.

The second may interpret the closed curtains as withdrawal, the notebooks as obsessive behaviour and the smiling as evidence of responding to an unseen stimulus.

The behaviour has not changed.

Only the label has changed.

This does not prove that diagnosis is always wrong. It demonstrates how prior information can alter interpretation.

---

Another Activity: Does Context Change Abnormality?

Students can examine the same behaviour in different settings:

Speaking to someone who is not visibly present

• In 1973, this might have appeared highly unusual.

• Today, the person may be using a small wireless headset.

• In a religious setting, the person might be praying.

• In a drama lesson, the person may be rehearsing.

• In another case, the person may genuinely be experiencing an auditory hallucination.

The observable behaviour is similar, but its meaning changes with context.

Students should therefore learn to ask:

What else would I need to know before reaching a conclusion?

That is a much more scientific response than immediately applying a label.

---

Evaluating Rosenhan’s Study

Strength: High Ecological Validity

The research took place in real psychiatric hospitals.

The pseudopatients encountered genuine admission procedures, clinicians, institutional rules and ward environments. This gives the study a realism that would be difficult to reproduce in a laboratory.

The consequences were also real. Participants experienced admission, diagnosis and the difficulty of securing discharge.

---

Strength: It Revealed the Possible Power of Labels

Rosenhan demonstrated how a diagnostic label might influence the interpretation of later behaviour.

This has applications beyond psychiatry.

Teachers, employers, doctors and even family members can begin to interpret everything through an existing label:

• “lazy”;

• “gifted”;

• “troublesome”;

• “anxious”;

• “aggressive”;

• “attention-seeking”.

Once attached, a label can become a lens through which all later behaviour is viewed.

---

Strength: It Generated an Important Debate

The study forced psychology and psychiatry to confront questions about reliability, validity, institutional treatment and the dignity of patients.

Later diagnostic manuals introduced more explicit, operationalised criteria intended to improve consistency. Research suggests that structured criteria improved reliability in some research settings, although disagreement and uncertainty were not eliminated.

The wider lesson is that criticism can improve a discipline when it leads to better methods rather than simple rejection.

---

Limitation: The Pseudopatients Did Report a Serious Symptom

The pseudopatients were not simply healthy people who walked into hospitals while behaving normally.

They reported hearing voices.

From a clinician’s perspective, this could justify further assessment, particularly when failing to admit someone experiencing psychosis might place that person at risk.

Psychiatrist Robert Spitzer argued that Rosenhan’s conclusions went beyond what the research demonstrated. Failure to detect someone who is deliberately presenting a convincing symptom is not necessarily the same as being unable to recognise sanity.

This is a valuable evaluation point because it prevents students from accepting a dramatic conclusion without questioning the method.

---

Limitation: The Sample Was Very Small

Only eight pseudopatients took part.

Although they attended different hospitals, this remains a limited sample from one country and one historical period.

Psychiatric hospitals, staff training, diagnostic manuals and attitudes towards patients have changed since the early 1970s.

We should be cautious about assuming that exactly the same results would occur in every modern mental health service.

---

Limitation: Ethical Problems

The hospital staff did not give informed consent to participate in the study.

They were deceived and could not withdraw because they did not know that research was taking place.

Genuine patients were also observed without being asked for consent.

The pseudopatients themselves faced psychological and physical risks. They entered institutions without knowing how long they would remain or how they would be treated.

The research therefore created serious tensions between the value of the findings and the rights of participants.

---

Limitation: The Research Is Difficult to Replicate

A precise replication would be ethically and practically difficult.

Modern researchers could not easily arrange for healthy participants to deceive psychiatric services, occupy hospital places and receive unnecessary treatment.

This makes it difficult to test the reliability of Rosenhan’s findings using the same procedure.

---

A More Recent Controversy

Rosenhan’s study is often presented in textbooks as a clear and settled piece of evidence.

It is not.

Later investigations have questioned the completeness of Rosenhan’s records, the identities and experiences of the reported pseudopatients and whether the published account accurately represented everything that happened.

Contemporary commentators have therefore argued that the study should be taught critically rather than accepted as an unquestionable historical fact.

This does not make the questions raised by Rosenhan unimportant.

It means the study itself must be subjected to the same careful examination that it demanded of psychiatry.

That is how science should work.

---

What Should A Level Students Conclude?

The weakest conclusion would be:

“Rosenhan proved that psychiatrists cannot identify mental illness.”

That is too broad.

A stronger conclusion would be:

“Rosenhan demonstrated how expectations, diagnostic labels and institutional contexts may influence the interpretation of behaviour.”

An even better conclusion would add:

“However, the pseudopatients deliberately reported a serious symptom, the sample was small, the procedure was ethically problematic and later researchers have questioned aspects of the original account.”

That type of answer demonstrates knowledge, application, analysis and evaluation.

It also avoids treating a complex study as a simple story in which the researchers were clever and the hospital staff were foolish.

---

A Personal Reflection: This Study Should Create Humility, Not Cynicism

When I teach Rosenhan, students are often fascinated by the apparent absurdity of the situation.

They imagine that they would immediately recognise the pseudopatients. They are confident that they would not be influenced by a label.

The classroom activities usually weaken that confidence.

Once students receive information suggesting that a person has a disorder, they often begin to interpret ambiguous behaviour as evidence of that disorder. They are not deliberately being unfair. They are doing what human beings naturally do: using prior information to make sense of uncertainty.

That is why the most important lesson from Rosenhan is not that mental health professionals are untrustworthy.

It is that all human judgement is vulnerable to bias.

Expertise should reduce that risk, but expertise does not remove it completely.

Good diagnosis therefore requires:

• clear criteria;

• sufficient time;

• careful listening;

• evidence from more than one source;

• awareness of culture and context;

• consideration of alternative explanations;

• willingness to revise an earlier judgement;

• respect for the individual behind the label.

A diagnosis may help someone understand their experiences and access effective treatment. It should not become the person’s entire identity.

---

Conclusion: Who Decides What Is Normal?

Rosenhan’s study remains disturbing because it challenges our confidence in a simple dividing line between sanity and insanity.

Mental health is not usually a switch that is either on or off. It is often a continuum involving distress, functioning, duration, context, risk and culture.

Statistical rarity is not enough.

Breaking a social norm is not enough.

Struggling to function is important but not always proof of a disorder.

Failing to achieve perfect mental health would classify almost everyone as abnormal.

No single definition solves the problem.

Rosenhan’s study should not be used to claim that mental illness is imaginary or that diagnosis has no value. Psychological suffering is real, and accurate diagnosis can lead to support, understanding and treatment.

The study offers a warning instead:

Never allow a label to become more important than the person being observed.

A scientific and humane mental health system must be capable of making careful judgements—but it must also be capable of questioning them.

Perhaps the most important sign of a reliable professional is not absolute certainty.

It is the willingness to ask:

“What evidence would make me reconsider my conclusion?”

 

Building an A Level Platform Game Project — Part 4: Adding Platforms and Collision Detection

In Part 1, we planned the platform game and set realistic success criteria.

In Part 2, we created the game window and added basic left and right movement.

In Part 3, we added gravity and jumping, so the player could rise, fall and land on the ground.

Now we reach one of the most important stages in the whole project: platforms and collision detection.

This is where the game stops being a character jumping on a single ground line and starts to become a proper platform game world.

It is also where many students discover that game programming is not quite as simple as it first appears.

A platform looks simple. It is just a rectangle on the screen.

But the program has to answer some awkward questions:

• Has the player landed on top of the platform?

• Has the player hit the side of the platform?

• Has the player jumped into the underside of the platform?

• Should the player stand on the platform or fall through it?

• What happens if the player is moving quickly?

• How does the program know which platform the player is touching?

This is why collision detection is such a good A Level Computer Science topic. It takes a simple visual idea and turns it into a proper programming problem.

Why Platforms Matter

A platform game needs a world for the player to interact with.

So far, our player can move, jump and land, but only on the bottom of the screen. That is useful for testing, but it is not enough for a game.

Platforms allow us to create:

• different routes through the level

• jumps of different difficulty

• collectables placed in interesting positions

• hazards that must be avoided

• areas that require planning and timing

• a proper start and finish point

Once platforms work, we can begin to design levels.

That is why this article is so important. Collision detection is the bridge between movement and level design.

The Aim for Part 4

The aim of this stage is:

Add rectangular platforms to the game and allow the player to land on them without falling through.

By the end of this stage, the game should include:

• several visible platforms

• a player affected by gravity

• collision detection between the player and platforms

• the ability to land on top of platforms

• prevention of repeated jumping while in the air

• testing evidence showing that platforms work correctly

This is still a prototype, but it is now much closer to a real game.

Representing Platforms

The simplest platform can be represented as a rectangle.

In Pygame-style code, a platform might be written as:

platform = pygame.Rect(200, 450, 200, 20)

This creates a rectangle with:

• x-position: 200

• y-position: 450

• width: 200

• height: 20

Instead of one platform, we can store several platforms in a list:

platforms = [
    pygame.Rect(0, 580, 800, 20),
    pygame.Rect(150, 480, 200, 20),
    pygame.Rect(450, 380, 200, 20),
    pygame.Rect(250, 280, 180, 20)
]

This gives us a basic level layout:

• a ground platform at the bottom

• one platform slightly higher

• another platform further across

• a higher platform above

This simple list is already important.

It means the level is not just drawn manually. It is stored as data.

That is a key idea for future articles, because later we can develop this into proper level design.

Drawing the Platforms

Once the platforms are stored in a list, they can be drawn using a loop:

for platform in platforms:
    pygame.draw.rect(screen, (0, 0, 0), platform)

This is better than writing a separate drawing command for every platform.

It also makes the project easier to extend.

If we want to add another platform, we add another rectangle to the list. The drawing loop does not need to change.

This is a good point for students to mention in their project documentation:

I stored the platforms in a list so that the program could process them using a loop. This made it easier to add, remove or change platforms without rewriting the drawing code.

That shows good programming thinking.

Representing the Player as a Rectangle

In earlier versions, the player used separate variables such as:

player_x
player_y
player_width
player_height

For collision detection, it is useful to create a rectangle for the player as well:

player_rect = pygame.Rect(player_x, player_y, player_width, player_height)

A rectangle makes it easier to check whether the player overlaps a platform.

For example:

if player_rect.colliderect(platform):
    print("Collision detected")

This is the basic idea behind rectangle collision detection.

It is not perfect, but it is ideal for a first platform game.

What Is Collision Detection?

Collision detection means checking whether two objects are touching or overlapping.

In this project, we need to know when the player touches:

• the ground

• a platform

• a wall

• a hazard

• a collectable

• a finish point

For now, we will focus only on platforms.

The simplest approach is rectangle collision detection.

If the player rectangle overlaps a platform rectangle, a collision has happened.

That sounds easy.

The difficult part is deciding what to do after the collision.

Why Collision Response Is Harder Than Collision Detection

Detecting a collision simply tells us that two rectangles overlap.

It does not automatically tell us where the collision happened.

The player might have:

• landed on top of the platform

• hit the platform from below

• run into the side

• touched a corner

The response should be different in each case.

If the player lands on top, they should stand on the platform.

If the player hits the underside, they should stop moving upwards.

If the player hits the side, they should not pass through the platform.

For this stage, we will keep things simple and focus on landing on top of platforms.

Side collisions can be developed later.

This is a sensible project decision because it controls the scope.

A Simple Landing Algorithm

To land on a platform, the program needs to check whether the player is falling and whether the bottom of the player has reached the top of the platform.

The player is falling when:

player_y_velocity > 0

The bottom of the player is:

player_rect.bottom

The top of the platform is:

platform.top

If the player is falling and collides with a platform, we can place the player on top of the platform:

player_rect.bottom = platform.top
player_y_velocity = 0
on_ground = True

This means:

• the player is no longer falling

• the player is positioned exactly on top of the platform

• the player is allowed to jump again

That is the basic idea.

Updating the Player Position

One important issue is that the player’s rectangle must be updated as the player moves.

A sensible structure is:

1. Check input.

2. Move horizontally.

3. Apply gravity.

4. Move vertically.

5. Check collisions with platforms.

6. Draw everything.

The order matters.

If the order is wrong, collisions may behave strangely.

For example, if the program checks collisions before the player moves, it may be using old position data.

Example Code for Platforms and Landing

Here is a simplified version of the Part 4 prototype:

import pygame

pygame.init()

SCREEN_WIDTH = 800
SCREEN_HEIGHT = 600

screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))
pygame.display.set_caption("Escape the Platforms")

clock = pygame.time.Clock()

player_rect = pygame.Rect(100, 500, 40, 60)
player_speed = 5
player_y_velocity = 0

gravity = 0.5
jump_strength = -12
on_ground = False

platforms = [
    pygame.Rect(0, 580, 800, 20),
    pygame.Rect(150, 480, 200, 20),
    pygame.Rect(450, 380, 200, 20),
    pygame.Rect(250, 280, 180, 20)
]

running = True

while running:
    clock.tick(60)

    for event in pygame.event.get():
        if event.type == pygame.QUIT:
            running = False

    keys = pygame.key.get_pressed()

    # Horizontal movement
    if keys[pygame.K_LEFT]:
        player_rect.x -= player_speed

    if keys[pygame.K_RIGHT]:
        player_rect.x += player_speed

    # Screen boundary checks
    if player_rect.left < 0:
        player_rect.left = 0

    if player_rect.right > SCREEN_WIDTH:
        player_rect.right = SCREEN_WIDTH

    # Jumping
    if keys[pygame.K_SPACE] and on_ground:
        player_y_velocity = jump_strength
        on_ground = False

    # Apply gravity
    player_y_velocity += gravity
    player_rect.y += player_y_velocity

    # Assume the player is not on the ground until a platform proves otherwise
    on_ground = False

    # Platform collision detection
    for platform in platforms:
        if player_rect.colliderect(platform) and player_y_velocity > 0:
            player_rect.bottom = platform.top
            player_y_velocity = 0
            on_ground = True

    # Draw everything
    screen.fill((255, 255, 255))

    for platform in platforms:
        pygame.draw.rect(screen, (0, 0, 0), platform)

    pygame.draw.rect(screen, (0, 0, 255), player_rect)

    pygame.display.update()

pygame.quit()

This is a major step forward.

The player now interacts with platforms.

The player can jump, fall and land on different surfaces.

The level is still basic, but it is becoming a real platform game.

Why on_ground = False Is Reset Each Frame

This line is important:

on_ground = False

It appears before checking platform collisions.

At first, this may look strange.

Why set on_ground to False when the player might be on a platform?

The reason is that each frame, the program should check the current situation again.

The player is assumed to be in the air unless a collision with a platform proves they are standing on something.

If the player is touching a platform from above, the collision code sets:

on_ground = True

This keeps the jumping logic accurate.

Without this, the game might incorrectly think the player is still on the ground after walking off the edge of a platform.

That is an excellent bug to discuss in the project write-up.

The Walk-Off-the-Platform Problem

One of the most important tests is what happens when the player walks off a platform.

The expected result is simple:

The player should fall.

However, if the on_ground variable is not updated correctly, the player may be able to jump in mid-air after walking off the edge.

That would be a bug.

The solution is to reset on_ground each frame and only set it to True when a platform collision confirms that the player is standing on something.

This is a good example of state management.

The program must keep track of whether the player is grounded, but that state must be checked and updated continuously.

Common Collision Detection Bugs

This stage is likely to produce bugs. That is not a failure. It is exactly why this makes a good A Level project.

Bug 1: The Player Falls Through Platforms

This can happen if the player is moving too fast or if the collision check is in the wrong place.

Possible fixes include:

• checking collisions after vertical movement

• reducing gravity

• limiting the maximum falling speed

• checking whether the player was above the platform in the previous frame

Bug 2: The Player Gets Stuck Inside a Platform

This often happens when the player overlaps a platform but is not moved back to a safe position.

A simple fix is:

player_rect.bottom = platform.top

This places the player exactly on top of the platform.

Bug 3: The Player Can Jump After Walking Off a Platform

This usually happens because on_ground remains True after the player leaves the platform.

Resetting on_ground each frame helps solve this.

Bug 4: The Player Lands on the Side of a Platform

If the collision detection is too simple, the game may treat a side collision as a landing.

This is one reason why more advanced collision detection often separates horizontal and vertical movement.

For now, we are focusing mainly on landing from above. Later, students may improve the algorithm to handle side collisions more accurately.

Separating Horizontal and Vertical Collision

A more advanced approach is to deal with horizontal and vertical movement separately.

The program can:

1. Move the player horizontally.

2. Check for side collisions.

3. Move the player vertically.

4. Check for floor or ceiling collisions.

This is more complex, but it gives better control.

For example, if the player moves horizontally into a wall, the program can stop sideways movement without affecting vertical movement.

If the player falls onto a platform, the program can stop vertical movement without affecting horizontal movement.

This is something students could add as an extension once the basic version works.

It would also make a strong discussion point in the evaluation.

Using Platform Data for Future Levels

At the moment, our platforms are stored like this:

platforms = [
    pygame.Rect(0, 580, 800, 20),
    pygame.Rect(150, 480, 200, 20),
    pygame.Rect(450, 380, 200, 20),
    pygame.Rect(250, 280, 180, 20)
]

This is already a simple form of level design.

If we change the numbers, we change the level.

For example, moving a platform higher makes the jump harder.
Making a platform narrower makes landing more difficult.
Placing platforms further apart changes the route.
Adding a platform creates a new possible path.

This is where students can begin to see the connection between code and game design.

The level is not just decoration. It is data.

In the next part of the series, we can develop this further by creating proper levels, perhaps storing them as lists, dictionaries or external files.

Designing a First Test Level

A good first test level should not be too difficult.

The aim is to test the mechanics, not frustrate the player.

A sensible first level might include:

• a wide ground platform

• one low platform that is easy to jump onto

• a second platform slightly higher

• a third platform further away

• a finish point that will be added later

For now, the goal is simply to check that the player can land on each platform.

Students should avoid making the platforms too small too early.

Difficult levels should come after reliable mechanics.

Testing Platforms and Collision Detection

Testing is essential at this stage.

Students should create a test table that checks normal movement and awkward cases.

Test Number	Test	Expected Result	Actual Result	Pass/Fail
1	Run the program	Player appears and platforms are visible	Player and platforms appear	Pass
2	Player falls onto ground platform	Player lands and stops falling	Player lands correctly	Pass
3	Jump onto first raised platform	Player lands on top of platform	Player lands correctly	Pass
4	Walk off a raised platform	Player falls downwards	Player falls correctly	Pass
5	Press jump while standing on platform	Player jumps upwards	Player jumps correctly	Pass
6	Press jump after walking off platform	Player should not jump again in mid-air	Player cannot jump in mid-air	Pass
7	Land on second platform	Player lands and can jump again	Player lands correctly	Pass
8	Hit side of platform	Player should not behave unpredictably	Needs improvement	Fail/Partial
9	Fall from top platform to ground	Player lands on lower surface	Player lands correctly	Pass
10	Move to screen edge	Player remains inside screen	Player remains inside screen	Pass

Notice that one test may not fully pass.

That is acceptable if it is recorded honestly.

A project that identifies limitations and suggests improvements is often stronger than one that pretends everything is perfect.

Linking Back to Success Criteria

This stage supports several success criteria from the planning article:

• The game contains several platforms.

• The player can stand on the top of each platform.

• The player falls when not standing on a platform.

• The player can jump from a platform.

• The player cannot repeatedly jump while in the air.

• The player can move left and right while jumping.

• The player remains within the screen boundaries.

Students should keep referring back to the original criteria.

This makes the project feel coherent rather than random.

A good development log entry might say:

This stage met the success criteria relating to platforms, jumping and landing. The player can now land on several rectangular platforms. Testing showed that walking off a platform causes the player to fall, which fixed an earlier problem where the player could still jump after leaving the platform.

That is strong project evidence.

Evidence Students Should Collect

For this stage, useful evidence might include:

• screenshot of the platform layout

• screenshot of the player standing on a platform

• screenshot of the player falling between platforms

• code showing the platform list

• code showing collision detection

• test table for landing and falling

• notes about bugs and fixes

• short video showing the player jumping between platforms

The most important thing is to collect evidence while the work is happening.

Trying to recreate evidence at the end of the project is much harder.

Personal Reflection: This Is Where Students Start to Understand Games Differently

This is one of my favourite stages when teaching programming projects.

At the beginning, students often think of games mainly in terms of graphics.

They talk about characters, backgrounds and visual style.

But when they add platforms and collision detection, they begin to see that a game is really a system of rules.

A platform is not just a rectangle.

It is something the player can stand on, fall from, jump from and interact with.

The program has to decide what touching means.

That is a powerful lesson.

Students begin to understand that programming is not simply making something appear on screen. It is defining behaviour.

That is why a simple retro platform game can be such a good project.

It looks small, but it contains real computational thinking.

Practical Task for Students

Before moving on to level design, students should complete this task.

Part 4 Student Task

Add platforms and collision detection to your platform game.

Your program should include:

1. At least four platforms, including the ground.

2. Platforms stored in a list.

3. A loop to draw all platforms.

4. A player rectangle used for collision detection.

5. Gravity applied each frame.

6. Collision detection between the player and platforms.

7. A landing response that places the player on top of the platform.

8. An on_ground variable that updates correctly.

9. A test table for platform collisions.

10. Screenshots or video evidence of the player landing on platforms.

Extension Task

Improve the platform system by adding one of the following:

• side collision detection

• ceiling collision detection

• moving platforms

• one-way platforms

• different platform types

• platforms stored in a separate level data structure

• a simple finish point

• a debug mode showing collision rectangles

Students should only attempt extensions once the basic collision detection is reliable.

Development Log Example

A good development log entry might look like this:

Development Stage

Adding platforms and collision detection.

Aim

To allow the player to land on raised platforms instead of only landing on the bottom of the screen.

What Was Added

• platform list

• platform drawing loop

• player rectangle for collision detection

• collision detection using rectangle overlap

• landing response when falling onto a platform

• updated on_ground logic

Problems Found

• The player could initially jump after walking off a platform.

• The player sometimes overlapped slightly with a platform before being corrected.

• Side collisions were not handled accurately in the first version.

Changes Made

• Reset on_ground to False each frame.

• Set on_ground to True only when landing on a platform.

• Set the bottom of the player rectangle to the top of the platform after collision.

• Recorded side collision as an area for later improvement.

Evidence Collected

• screenshots of the player on platforms

• code showing the platform list

• code showing collision detection

• test table

• notes explaining the walk-off-platform bug

This sort of development record is exactly what students need for a strong A Level project.

Preparing for Levels

Once platforms work, we are ready for the next major step: level design.

A level is more than a random collection of platforms.

A good level has:

• a start point

• a route

• increasing challenge

• safe areas

• risk areas

• a finish point

• opportunities for scoring

• suitable difficulty for the target user

At the moment, our platforms are hard-coded into one list.

That is fine for the prototype.

But as the game grows, we can improve this by storing levels as separate data structures.

For example, we might eventually have:

level_1_platforms = [...]
level_2_platforms = [...]
level_3_platforms = [...]

Or we might store level data in dictionaries:

level_1 = {
    "platforms": [...],
    "player_start": (100, 500),
    "finish": (700, 520)
}

This opens the door to multiple levels.

It also creates excellent A Level project material because the student can explain how the game data is organised.

Final Thoughts: Collision Detection Turns Movement Into a Game

Adding platforms and collision detection is a major step in the project.

The player is no longer just moving around a blank screen.

The player is now interacting with a world.

They can jump onto platforms, fall from them, land on them and begin to move through a level.

This stage also creates some of the best learning moments in the whole project. The bugs are real. The problems are interesting. The solutions require thought.

The player may fall through platforms.
They may get stuck.
They may jump when they should not.
They may collide from the side in unexpected ways.

All of that is valuable.

A good A Level project is not one where everything works perfectly first time. It is one where the student can show how they found problems, tested them, improved the program and explained the decisions they made.

With platforms now working, the project is ready to move from mechanics to design.

In the next article, we will look at how to turn these platforms into proper levels, with routes, difficulty, start points, finish points, collectables and hazards.