Computing KS1 Y1Y2 Convention

ScratchJr: First Animations

5 lessons

Subject
Computing
Key Stage
KS1
Year group
Y1, Y2
Statutory reference
create and debug simple programs
Source document
Computing (KS1/KS2) - National Curriculum Programme of Study
Estimated duration
5 lessons
Status
Convention
Coverage: 6/11 expected capabilities surfaced
Curriculum anchorConcept modelDifferentiation dataThinking lensLesson structureLearner scaffolding
Cross-curricular linksVocabulary definitionsSuccess criteriaPrior knowledge linksAccess and inclusion

Concepts

This study delivers 1 primary concept and 1 secondary concept.

Primary concept: Algorithms (CO-KS12-C001)

Type: Knowledge | Teaching weight: 1/6

An algorithm is a precise, unambiguous sequence of instructions for solving a problem or accomplishing a task. Algorithms are more general than programs: an algorithm describes what needs to be done, while a program is a specific implementation of an algorithm in a particular programming language. Algorithms must be correct (they produce the right output), precise (each step is unambiguous), and finite (they eventually reach a conclusion). At KS1 and KS2, pupils learn to write and recognise algorithms, understand their properties, and implement them as programs.

Teaching guidance: Begin with unplugged algorithm activities before moving to digital programming. Teach pupils to write step-by-step instructions for everyday tasks (making a sandwich, brushing teeth) to illustrate the precision required in algorithms. Use robot or turtle-following activities where pupils give instructions and observe the results of ambiguity or incorrect ordering. Discuss what happens when an algorithm has an error: the program does what we said, not what we meant. Introduce the idea of testing algorithms with different inputs to check they work in all cases. Key vocabulary: algorithm, instruction, sequence, step, precise, unambiguous, input, output, process, program, implement, order, correct, test, debug Common misconceptions: Pupils may think that an algorithm is the same as a program; clarifying that an algorithm is the idea or plan, while a program is the specific coded implementation, is important. Pupils may not appreciate that algorithms need to be unambiguous; activities where deliberately ambiguous instructions lead to unexpected results make this vivid. The idea that a computer does exactly what it is told, rather than what we intended, is a fundamental shift in thinking that needs explicit attention.

Differentiation

LevelWhat success looks likeExample taskCommon errors

EntryFollowing a set of step-by-step instructions (an algorithm) to complete a task, understanding that the order matters.Follow these instructions to draw a house: 1. Draw a square. 2. Draw a triangle on top. 3. Draw a rectangle door. 4. Draw two square windows. Did the order matter?Skipping steps or doing them out of order; Not understanding that algorithms must be followed precisely
DevelopingWriting simple algorithms as a sequence of instructions for a familiar task, identifying when instructions are ambiguous or incomplete.Write instructions for making a jam sandwich that a robot could follow exactly. Test your instructions with a partner.Writing instructions that are too vague for a computer to follow precisely; Assuming the reader knows things that are not explicitly stated
ExpectedDesigning algorithms to solve problems, comparing different approaches and evaluating their efficiency.Write two different algorithms for sorting five numbered cards into order. Which is more efficient?Writing algorithms that only work for the specific example, not the general case; Not being able to trace through an algorithm step by step to check it works
Greater DepthAnalysing the efficiency of algorithms, predicting how they perform with larger inputs, and explaining the concepts of algorithm design to others.If Algorithm 1 (checking pairs) takes 10 comparisons to sort 5 cards, roughly how many might it take for 10 cards? Why does it take longer?Assuming the time doubles when the input doubles (it often grows faster); Not connecting algorithm efficiency to real-world computing problems

Model response (Entry): I followed each step in order and drew the house. The order matters because I needed the square first to know where to put the triangle roof on top.
Model response (Developing): 1. Pick up the bread bag. 2. Take out two slices. 3. Put them on the plate. 4. Pick up the knife. 5. Open the jam jar. 6. Put the knife in the jam. 7. Spread jam on one slice. 8. Put the other slice on top. When my partner tested them, they asked 'which side up?' for step 3 — I needed to add 'flat side up'.
Model response (Expected): Algorithm 1 (Checking pairs): Compare each pair of cards from left to right. If they are in the wrong order, swap them. Repeat until no swaps are needed. Algorithm 2 (Finding smallest): Find the smallest card and put it first. Find the next smallest and put it second. Continue until all are sorted. Algorithm 2 requires fewer comparisons for this small set, making it more efficient. But Algorithm 1 is simpler to understand.
Model response (Greater Depth): For 10 cards, it could take many more comparisons — maybe around 45 or more — because each pass through the list compares 9 pairs, and you might need many passes. The number of comparisons grows much faster than the number of cards. This is why computer scientists care about efficiency — a slow algorithm that works fine for 5 items might be too slow for 1000 items.

Secondary concept: Debugging and Logical Reasoning (CO-KS12-C003)

Type: Skill | Teaching weight: 2/6

Debugging is the process of finding and fixing errors in programs. It requires logical reasoning: the ability to read code, mentally execute it step by step, identify where the actual behaviour diverges from the expected behaviour, and determine what change will fix the error. Logical reasoning about programs is also required to predict what a program will do before running it. At KS1 and KS2, developing debugging skills and the habit of logical, systematic thinking about code is as important as learning to write new code.

Differentiation

LevelWhat success looks likeCommon errors

EntryIdentifying that a program is not working as expected and finding an obvious error by comparing the output with the intended result.Rewriting the whole program instead of finding and fixing the specific error; Not being able to describe what the program should do versus what it actually does
DevelopingUsing logical reasoning to trace through a program step by step, predicting the output and identifying where it goes wrong.Not updating the variable value between iterations; Tracing through only the first iteration and guessing the rest
ExpectedSystematically debugging programs by testing, identifying errors, hypothesising causes, making changes and retesting.Fixing one bug without retesting to check it doesn't cause another; Changing multiple things at once and not knowing which change fixed the problem
Greater DepthApplying debugging strategies systematically, including using test data, print statements for tracing, and explaining why certain types of bugs are harder to find.Only testing with one type of input data; Not testing edge cases (empty lists, duplicates, already-sorted data)


Thinking lens: Cause and Effect (primary)

Key question: What caused this to happen, and how do we know? Why this lens fits: Debugging requires pupils to trace how each instruction in a program causes a specific change in output — identifying which instruction is the cause of the unexpected behaviour is the core cognitive act. Question stems for KS1:
  • What made that happen?
  • What will happen if...?
  • Why did it change?
  • Can you finish: it happened because...?
  • Secondary lens: Patterns — Logical reasoning about program behaviour involves recognising that the same sequence of instructions will always produce the same result, establishing the notion of predictable, repeatable computational patterns.

    Session structure: Practical Application

    Practical Application

    A hands-on sequence where pupils apply knowledge and skills to solve a practical problem or create a functional outcome. Begins with a real-world context, builds skills through rehearsal, guides design or planning, supports making or problem-solving, and concludes with evaluation against success criteria.

    contextskill_rehearsaldesignmake_or_solveevaluate Assessment: Practical outcome (solution, product, program) evaluated against defined success criteria, with written or verbal explanation of the process and decisions made.

    Computing focus

    Programming paradigm: Block Based Software/tool: ScratchJr Computational concepts: sequence, input output Abstraction level: Visual Themes: programming, animation, creative computing

    Why this study matters

    ScratchJr provides the first experience of on-screen programming using visual blocks. Pupils create simple animations by snapping together movement and appearance blocks. The block-based interface removes the barrier of typing, allowing pupils to focus on the logic of sequencing. Creating a character that moves and speaks is inherently motivating and produces a shareable outcome.


    Pitfalls to avoid

  • Blocks placed in wrong order -- demonstrate that sequence matters by swapping two blocks and observing the change
  • Too many characters at once -- start with one sprite and add complexity gradually
  • Not testing after each addition -- teach the test-frequently habit

  • Computational thinking skills (KS1)

    These disciplinary skills should be woven through teaching, not taught in isolation:

  • Algorithm design (KS3) — Design, implement and analyse algorithms for non-trivial computational problems including sorting and searching; understand that multiple algorithms can solve the same problem with different efficiency characteristics; use logical reasoning and formal comparison techniques to assess the relative utility of alternative algorithms; implement algorithms in at least two programming languages, at least one text-based.
  • Pattern recognition (KS2) — Identify patterns and regularities in data sets and program behaviours; use pattern recognition to select appropriate control structures (repetition for repeated actions, selection for conditional branching); generalise from specific examples to produce reusable solutions; recognise when an existing algorithm or program component can be reused to solve a new problem.
  • Abstraction (KS1) — Focus on the most important features of a problem or task while ignoring unnecessary detail; represent real-world actions as simple step-by-step instructions that capture the essential logic without irrelevant specifics.
  • Decomposition (KS2) — Decompose a complex programming problem or digital project into distinct, manageable sub-problems that can be developed and tested independently; plan program structure using top-down design before coding; use procedures and functions as the coded expression of decomposed sub-problems.
  • Decomposition (KS1) — Break a familiar task or problem into a sequence of smaller, ordered steps; understand that a complex instruction can be split into simpler sub-instructions that together achieve the same goal; apply this thinking when giving instructions to a programmable toy or creating a simple program.
  • Abstraction (KS3) — Design and evaluate computational abstractions that model the state and behaviour of real-world problems and physical systems; select appropriate levels of abstraction for a given problem context; use abstract data types, classes and interfaces to hide implementation detail; understand the layered abstractions present in computing systems from hardware to application.

  • Vocabulary word mat

    TermMeaning

    actual
    algorithm
    bug
    check
    correct
    debug
    error
    execute
    expected
    fix
    implement
    input
    instruction
    logic error
    order
    output
    precise
    predict
    process
    program
    reasoning
    sequence
    step
    syntax error
    test
    trace
    unambiguous
    block
    sprite
    animation
    event
    start
    move
    say

    Scaffolding and inclusion (Y1)

    GuidelineDetail

    Reading levelPre-reader / Emergent
    Text-to-speechRequired
    Max sentence length8 words
    VocabularyConcrete nouns and action verbs only. No abstract concepts without physical anchor. Examples: dog, apple, jump, big, one more.
    Scaffolding levelMaximum
    Hint tiers2 tiers
    Session length5–12 minutes
    Worked examplesRequired — Animated, narrated walkthrough with no text. Character models the thinking aloud.
    Feedback toneWarm Nurturing
    Normalize struggleYes
    Example correct feedbackThe frog jumped exactly four spaces — you counted perfectly!
    Example error feedbackOh, let us count again together! [animation demonstrates]


    Knowledge organiser

    Key terms:
  • program
  • block
  • sprite
  • sequence
  • animation
  • event
  • start
  • move
  • say
  • Core facts (expected standard):
  • Algorithms: Designing algorithms to solve problems, comparing different approaches and evaluating their efficiency.

  • Graph context

    Node type: ComputingTopicSuggestion | Study ID: TS-CO-KS1-003 Concept IDs:
  • CO-KS12-C001: Algorithms (primary)
  • CO-KS12-C003: Debugging and Logical Reasoning
  • Cypher query:

    ``cypher

    MATCH (ts:ComputingTopicSuggestion {suggestion_id: 'TS-CO-KS1-003'})

    -[:DELIVERS_VIA]->(c:Concept)

    -[:HAS_DIFFICULTY_LEVEL]->(dl)

    RETURN c.name, dl.label, dl.description

    ``


    Generated from the UK Curriculum Knowledge Graph — zero LLM generation.