From Sensory to Long-Term: A Classroom Reenactment of Memory Models

 From Sensory to Long-Term: A Classroom Reenactment of Memory Models

Bringing Psychology to Life at Philip M Russell Ltd – Hemel Private Tuition
#ALevelPsychology #MemoryModels #ActiveLearning #CognitivePsychology

This week, our A-Level Psychology students didn’t just learn about memory models—they acted them out. And it turns out, becoming the sensory register or long-term memory store yourself is one of the most effective ways to understand how memory works.

We brought the Multi-Store Model of Memory (Atkinson and Shiffrin, 1968) off the page and into the classroom—with students physically taking on the roles of key memory components. The result? Engagement, laughter, and most importantly—deep understanding.

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🧠 The Setup: Turning Theory into Theatre

We divided the room into three zones:

• Sensory Memory (SM) – flashcards quickly shown and hidden

• Short-Term Memory (STM) – a student acting as a temporary processing hub, holding a small number of items

• Long-Term Memory (LTM) – a seated student with a “file cabinet” (real or imagined)

Other students became:

• Rehearsal Mechanism – deciding which information is worth transferring

• Retrieval Pathways – passing notes or signals back from LTM to STM

• Distractions – students deliberately interfering with STM by adding noise or irrelevant information

Each participant had a role in demonstrating how information moves from fleeting sensory input to potentially lifelong storage—and how easily it can be disrupted or forgotten.

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🔄 What Students Learned:

• Capacity & Duration of each store: Sensory memory is fleeting; STM is limited; LTM can last a lifetime.

• Encoding Differences: We illustrated how information changes format between stores—e.g., iconic to acoustic.

• The Role of Rehearsal: Without rehearsal, STM information was quickly “lost” (or eaten by the classroom bin labelled “Decay”).

• Forgetting & Retrieval: Interference and retrieval failure were acted out in real time, with students misplacing "files" or returning the wrong memory.

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🧑‍🏫 Why This Works:

Active learning taps into deeper cognitive processing. Instead of memorising the model, students were living it.
They debated, defended, and discussed their roles—effectively rehearsing the information into their own long-term memory.

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Student Feedback:

🗨️ "I finally get why rehearsal is so important—it’s literally the difference between remembering and forgetting!"
🗨️ "Playing STM was stressful—I couldn’t hold more than 5 things before I dropped one. Just like in real life."

Exactly the point.

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Want to Try It?

All you need:

• Post-it notes or cards as "memories"

• Printed labels for each store and pathway

• A few willing students to act out retrieval, rehearsal, and interference

• A bit of imagination and a lot of participation

It’s a perfect activity before tackling exam-style questions on memory—especially AO1 and AO3 evaluation.

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Psychology isn’t just theory—it’s experience. Let’s make memory memorable.
#ALevelPsychology #MemoryModels #CognitivePsychology #PhilipMRussellLtd #HemelPrivateTuition #PsychologyClassroom #ActiveLearning

 

Building Simple Games to Learn Python Logic

Sometimes the best way to learn programming isn’t with dry exercises, but with games. Today’s student project was a classic: Rock–Paper–Scissors. Simple enough to code, but with plenty of opportunities to stretch logic, design, and even a little AI.

Why Games Work

Games are brilliant for learning because they combine:

• Clear rules – easy to translate into code.

• Instant feedback – the student can play against their program.

• Room to grow – simple structure, but endless improvements possible.

The Rock–Paper–Scissors Game

The student started by writing a Python program that asks the player to choose rock, paper, or scissors. The computer randomly chooses one too. Then, using simple logic (if–elif–else statements), the program decides who wins.

import random

choices = ["🪨 Rock", "📄 Paper", "✂️ Scissors"]

player = input("Choose Rock, Paper, or Scissors: ").lower()
computer = random.choice(choices)

print(f"You chose: {player}")
print(f"Computer chose: {computer}")

if player == "rock" and "Scissors" in computer:
    print("You win! 🎉")
elif player == "paper" and "Rock" in computer:
    print("You win! 🎉")
elif player == "scissors" and "Paper" in computer:
    print("You win! 🎉")
elif player in computer.lower():
    print("It’s a draw 🤝")
else:
    print("Computer wins! 💻")

The addition of emojis made it more fun to play — because who doesn’t prefer seeing ✂️ beat 📄 instead of plain text?

Adding a Simple AI

Once the basic game was working, the challenge was to make the computer a little “smarter.” Instead of always picking at random, the AI could:

• Track the player’s previous choices.

• Make a “guess” about what the player might choose next.

• Select the winning move accordingly.

Even with a basic approach — like assuming the player won’t pick the same move twice — the student was already learning about algorithms, prediction, and probability.

What They Learned

• Python syntax and structure – inputs, conditionals, loops.

• Debugging – why “scissor” isn’t the same as “scissors.”

• Logic design – turning human rules into code.

• AI basics – how programs can “adapt” instead of acting randomly.

Final Thought

Building a game may look like child’s play, but it’s one of the best ways to learn programming. Today’s rock–paper–scissors could easily grow into tomorrow’s full strategy game — and along the way, students discover that Python is less about code on a page and more about solving problems creatively.

At Hemel Private Tuition, we encourage students to build, test, and play with code — because learning works best when it’s fun.

 

When Pipes Leak and Chemistry Speaks

Recently, I had a plumbing problem. A leaky pipe appeared in the loft. At first, I assumed it was the joint — an easy fix. But no, it turned out to be the pipe itself. A tiny pinprick hole had eaten its way through the copper, leaving a little fountain in the loft tricking its way through the ceiling.

When we looked inside the pipe, we found a blue deposit clinging to the metal. And that’s where the chemist in me took over. Forget the plumber — this was a job for science!

Why Did the Copper Pipe Corrode?

Copper is normally quite resistant to corrosion, which is why we use it for pipes. But over time, water, dissolved oxygen, and other ions (like chlorides from salts or impurities) can attack it. The result is corrosion, producing copper compounds — often blue or green in colour.

So what was this mysterious blue substance? At GCSE and A-Level Chemistry, that’s exactly the kind of problem you learn how to solve.

Step 1: Make Observations

The colour gives our first clue. Blue suggests a copper(II) compound. Copper(I) salts tend to be white, while copper(II) salts are usually vivid blue or green.

Step 2: Dissolve and Test

Take a small sample (if this were in the lab, not your plumbing!) and dissolve it in water. If it dissolves to give a blue solution, you’re likely dealing with a copper(II) salt.

Step 3: Flame Test

A classic GCSE experiment: put a little on a flame test wire. Copper compounds burn with a beautiful blue-green flame — a strong confirmation.

Step 4: Precipitation Reactions

Add sodium hydroxide solution to your blue solution. A blue precipitate of copper(II) hydroxide should form. This is a classic test for copper(II) ions, taught in GCSE Chemistry.

Step 5: More Advanced A-Level Analysis

At A-Level, students would go further. They might use:

• Ligand tests – adding ammonia gives the deep blue tetraamminecopper(II) complex, a gorgeous colour change that students never forget.

• Spectroscopy – flame emission spectroscopy or even UV-Vis spectroscopy to identify the metal ions precisely.

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1. Test for Carbonates (CO₃²⁻)

• Method: Add dilute hydrochloric acid (HCl).

• Observation: If carbonate ions are present, you’ll see bubbling/fizzing as carbon dioxide gas is released.

• Confirm: Bubble the gas through limewater — it will turn cloudy.

• Equation:

  $$CuCO₃ (s) + 2HCl (aq) → CuCl₂ (aq) + H₂O (l) + CO₂ (g)$$

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2. Test for Sulfates (SO₄²⁻)

• Method: Add dilute hydrochloric acid (to remove interfering carbonates), then add barium chloride solution (BaCl₂).

• Observation: A white precipitate of barium sulfate forms if sulfate ions are present.

• Equation:

  $$Ba^{2+} (aq) + SO₄^{2-} (aq) → BaSO₄ (s)$$

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3. Test for Chlorides (Cl⁻)

• Method: Add dilute nitric acid (to remove carbonates), then add silver nitrate solution (AgNO₃).

• Observation: A white precipitate of silver chloride forms.

• Confirm: The precipitate dissolves in dilute ammonia solution.

• Equation:

  $$Ag^{+} (aq) + Cl^{-} (aq) → AgCl (s)$$

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Summary Table

Ion to Test	Reagent	Positive Result
Carbonate (CO₃²⁻)	Dilute HCl	Effervescence, CO₂ turns limewater cloudy
Sulfate (SO₄²⁻)	BaCl₂ + HCl	White precipitate of BaSO₄
Chloride (Cl⁻)	AgNO₃ + HNO₃	White precipitate of AgCl (soluble in ammonia)

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So, in your plumbing pipe story:

• If adding acid caused fizzing → carbonate.

• If barium chloride gave a white precipitate → sulfate.

• If silver nitrate gave a white precipitate → chloride.

The Verdict

Most likely, the blue substance in the pipe was basic copper carbonate, the familiar green-blue corrosion product also known as patina. But with a few tests, a student could identify whether it was carbonate, hydroxide, or another copper(II) salt.

So my plumbing problem became an impromptu chemistry case study. It just goes to show: the science in your textbooks isn’t locked in the lab — it’s happening in your pipes, your kitchen, and all around you.

At Hemel Private Tuition, we like to bring chemistry alive with real-life examples like this — whether it’s a corroded pipe or a colourful test tube. After all, the best way to learn is when the science drips (sometimes literally) into everyday life.

 

PASCO Experiment: How Colour Affects Heat Absorption

Aim

Measure and compare the rate and magnitude of temperature rise in liquids with different surface colours under the same illumination.

Big ideas (GCSE/A-Level links)

• Energy transfer by radiation; absorption vs reflection

• Specific heat capacity (controlled)

• Experimental design: variables, repeats, averages

• Data handling: gradient as a rate, curve comparison

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Equipment

• PASCO Wireless Temperature Sensors (PS-3201) × 3–5

• Optional: PASCO Wireless Light Sensor (PS-3213) or Weather/Light Meter (to log incident light)

• SPARKvue (iPad/Chromebook/PC) or PASCO Capstone

• Identical clear containers (100–250 mL beakers or PET cups) × number of colours

• Food dye (blue, red, black) or coloured card/film wraps (matte black, white, red, blue, silver)

• Water (same volume & start temp for all)

• Light source: full-spectrum LED panel or halogen lamp at fixed distance (or direct sun; see controls)

• Ruler/tape (to fix lamp distance), tripod/stands and sensor clamps

• Thermal/reflective mat (to reduce conduction from bench), stopwatch

• Safety: heat-resistant gloves for halogen lamps; cable management

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Variables

• Independent: Colour (of liquid or container surface)

• Dependent: Temperature (°C) vs time; optionally incident light (lux)

• Controls: Water volume, initial temperature, container shape/size, lamp distance/angle, exposure time, room airflow

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Preparation & Calibration (5–8 min)

1. Label containers: Black, White, Red, Blue, Silver (or Dyed Black/Blue/Red + Clear Control).

2. Equal volumes: 150 mL water each. Let all equilibrate to room temperature (±0.5 °C).

3. Sensor check:

   • Open SPARKvue → Add Sensor → connect all temp sensors; rename them by colour.

   • If using a Light Sensor: connect and zero in the experiment position without the lamp on; then start a 10-s baseline.

4. Geometry: Place containers on an insulating mat, in a straight line perpendicular to the lamp, with front faces aligned. Set lamp at a fixed distance (e.g., 40 cm) and height so each container is illuminated equally. Use a bookend/board behind to block backlight spill.

Tip: If using sunlight, run all colours simultaneously; log global illumination with the Light Sensor and note any cloud events. Avoid drafts.

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Method (student-friendly steps)

A. Baseline (2 min)

1. Insert each temperature probe mid-depth, not touching sides/bottom.

2. Start recording in SPARKvue (1 Hz sampling).

3. Log 60 seconds with the lamp OFF to capture starting temperatures.

B. Exposure (10–15 min)

4. Switch the lamp ON. Start a countdown timer (10 minutes typical).

5. Do not stir. Keep room conditions stable.

6. Watch the live graph; ensure no sensor has drifted or touched a wall. If necessary, pause and correct, then note the interruption time in your log.

C. Cooling (optional, 5–10 min)

7. Turn the lamp OFF and continue logging while samples cool to observe cooling curves (useful for modelling).

D. Replicates

8. Repeat the run at least twice (swap container positions between runs to remove positional bias).

9. For dyed-water version, ensure dye concentrations are consistent (e.g., 4 drops per 150 mL).

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Data capture settings (suggested)

• Sampling rate: 1 sample/s (higher gives noisier curves without benefit).

• Display Table + Graph (T vs t for each colour).

• If using Light Sensor: add lux vs t panel; keep within the lamp’s stable output.

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Analysis

1) Initial heating rate (gradient)

• In SPARKvue, use the slope tool over the first 180 s for each curve.

• Record dT/dt (°C·s⁻¹) → this is your absorption rate proxy.

2) Peak temperature

• Read T_max after fixed exposure time (e.g., 10 min).

3) Area under curve (optional)

• Integrate T(t) above baseline to compare total thermal gain.

4) Statistics

• Compute mean ± SD for dT/dt and T_max across replicates.

• Bar chart T_max and dT/dt by colour with error bars.

• If using sunlight, normalise by average lux during each run.

Expected trend

• Black (or very dark) absorbs the most → steepest slope, highest T_max.

• White/Silver reflect more → shallowest slope, lowest T_max.

• Blue/Red sit between, depending on lamp spectrum and dye absorbance.

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Example results table (template)

Colour	Run	dT/dt (°C·min⁻¹)	T_max (°C)	ΔT @10 min (°C)
Black	1	1.9	37.6	18.3
Black	2	2.0	37.9	18.7
White	1	0.9	31.2	11.9
White	2	1.0	31.4	12.1
…	…	…	…	…

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Validity & controls

• Position swap between runs to cancel hot-spot effects.

• Same volume & start temp for all samples.

• Matte surfaces absorb more consistently than glossy (specify finish).

• Avoid convection drafts (close doors/vents).

• Keep lamp output constant; warm-up LEDs/halogen for 2–3 min before baseline.

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Safety

• Lamps and housings can become hot; handle with care.

• Manage trip hazards from power leads.

• Use low-voltage LED if possible; if halogen, keep combustibles clear.

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Extensions (great for A-Level projects)

• Spectral angle: Add coloured filters between lamp and sample; discuss wavelength-dependent absorption.

• Surface vs volume: Compare coloured wraps on containers (surface effect) vs dyed liquids (volumetric absorption).

• Material albedo: Replace water with sand/soil trays wrapped in different colours (links to urban heat island).

• Model fitting: Fit heating curves to Newtonian heating with an added source term; estimate effective absorptivity constants.

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Conclusion prompt (for students)

• Rank colours by heating rate and peak temperature.

• Explain differences using absorption/reflection and electromagnetic spectrum.

• Evaluate uncertainties and improvements for future runs.

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How we teach this at Hemel Private Tuition

At Philip M Russell Ltd (Hemel Private Tuition) we run this practical live in our lab or through our multi-camera online studio so students see the curves build in real time in SPARKvue/Capstone. We pair it with discussion of radiation physics, experimental design, and data analysis skills needed for GCSE and A-Level success.

Vectors, Arrows and Angles – Getting Directional in Maths​

Vectors, Arrows and Angles – Getting Directional in Maths

Today’s maths lessons were all about vectors—those handy arrows that tell us not just how far, but which way. Whether we were introducing GCSE students to vector notation or helping A-Level students break down 3D vector problems, the theme was clear: direction matters.

We explored how to:

• Represent vectors using arrows and coordinates

• Add and subtract vectors geometrically and algebraically

• Find magnitudes and directions

• Use vectors in geometric proofs and navigation problems

Finding Magnitudes and Directions in A-Level Maths
Hemel Private Tuition – A-Level Focus

Today’s A-Level Maths topic: Vectors – Magnitude and Direction
This is where geometry meets algebra, and we learn to turn coordinates into meaningful measurements.

🔹 Magnitude – How Long Is That Vector?

The magnitude of a vector tells us its length — essentially the distance between the start and end points.

For a 2D vector a = (x, y), the magnitude is:

$$|\mathbf{a}| = \sqrt{x^2 + y^2}$$In 3D, for a = (x, y, z):$$|\mathbf{a}| = \sqrt{x^2 + y^2 + z^2}$$

It’s just an application of Pythagoras – but in multiple dimensions!

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🔹 Direction – Where’s It Pointing?

To find the direction (angle θ) of a vector in 2D, we use trigonometry:

If a = (x, y), then

$$\theta = \tan^{-1} \left(\frac{y}{x}\right)$$

Make sure to consider the quadrant the vector lies in — the inverse tangent only gives angles from –90° to +90°, so adjust accordingly for vectors in the second or third quadrant.

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🧠 Why It Matters

Whether it’s physics, navigation, or mechanics, vectors give us control over motion and force. Knowing both how far (magnitude) and where (direction) a vector is pointing is essential in solving real-world problems — and plenty of exam ones too.

Today, our students practised:

• Converting between coordinate form and magnitude/direction

• Resolving vectors into components

• Applying vector direction to projectile motion and forces

For our A-Level students, we even took a deep dive into scalar products and solving vector equations—perfect preparation for mechanics modules.

There’s something satisfying about seeing a messy problem turn into a clean arrow pointing exactly where it should. And with plenty of diagrams, animations, and real-world examples (including sailing and drone paths!), it all started to make sense.

A projectile is fired upwards at 60 degrees to the horizontal at 45 m/s. Using vectors, resolve the velocity into its vertical and horizontal vectors, and then determine the maximum height it can achieve.

Given:

• Initial speed $u = 45\, \text{m/s}$

• Angle $\theta = 60^\circ$

• Acceleration due to gravity $g = 9.8\, \text{m/s}^2$

We’re going to:

1. Resolve the initial velocity into horizontal and vertical components

2. Use kinematic equations to calculate the maximum height reached

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Step 1: Resolve the velocity into components

Using vector resolution:

• Horizontal velocity:

  $$u_x = u \cos \theta = 45 \cos 60^\circ = 45 \times 0.5 = 22.5\, \text{m/s}$$

• Vertical velocity:

  $$u_y = u \sin \theta = 45 \sin 60^\circ = 45 \times 0.866 = 38.97\, \text{m/s}$$

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Step 2: Calculate Maximum Height

At maximum height, the vertical velocity becomes 0.

Use the kinematic equation:

$$v^2 = u^2 + 2as$$

Let’s solve for $s = h$, the maximum height, with:

• Final vertical velocity $v = 0$

• Initial vertical velocity $u = 38.97\, \text{m/s}$

• Acceleration $a = -9.8\, \text{m/s}^2$ (negative because it acts downward)

$$0 = (38.97)^2 + 2(-9.8)h$$ $$0 = 1517.2 - 19.6h$$ $$19.6h = 1517.2$$ $$h = \frac{1517.2}{19.6} \approx 77.4\, \text{m}$$

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✅ Final Answer:

• Horizontal velocity: $22.5\, \text{m/s}$

• Vertical velocity: $38.97\, \text{m/s}$

• Maximum height: $\boxed{77.4\, \text{m}}$

If your child is struggling with direction (literally or mathematically), we’re here to help.

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Book a 1:1 lesson—online or in our classroom.
📍 Hemel Hempstead | GCSE & A-Level Tuition
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