Teacher CPD · Secondary Schools

Teaching Leaving Certificate Computer Science

This CPD equips teachers to deliver Leaving Certificate Computer Science with confidence. It integrates computational thinking and PRIMM pedagogy, practical approaches to core concepts from data representation through to AI and society, and structured guidance on Applied Learning Tasks, the Coursework Project and the written examination. Teachers develop a sustainable Lead Learner mindset, gain ready-to-use classroom strategies, and leave with a complete fifth-year teaching plan and recruitment pipeline.
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Enrol now — course content opens on 1 Jul 2026. We'll email you that morning with access details.
€39
Enrolment per teacher
What's included
  • Self paced
  • Online course
  • Step-by-step lessons
  • Certificate from Coding Ireland

Explore the Course

Establishes an honest baseline of where you start (a confidence audit), a working understanding of what the current LCCS specification requires, and the Lead Learner mindset that lets out-of-field teachers deliver LCCS confidently without performing domain expertise.

Welcome, Confidence Audit and the Lead Learner Mindset
The LCCS Specification: Three Strands, Two Components, and What 2027 Changes

Replaces 'watch me code, now you try' with evidence-based programming pedagogy: computational thinking as named classroom moves, PRIMM and worked examples, explicit debugging instruction, and a sequenced two-year Python progression with programming-specific assessment.

Computational Thinking as Classroom Practice
PRIMM, Worked Examples and Misconception Diagnostics
Teaching Debugging Explicitly
Scaffolding Python Across Two Years and Assessing Programming Progress

Builds subject-knowledge confidence across Strand 2 (Core Concepts) for non-specialists, covering data representation, algorithms, computer systems, networks and the web, and AI/machine learning/computers in society at the level the spec and the SEC written paper actually require.

Teaching Data Representation: Mental Models Before Conversion (for Non-specialists)
Algorithms and Algorithmic Thinking for Non-specialists
Computer Systems, Hardware, Software, and the Operating System for the Written Paper
Networks and the Web: Teaching It Without Drowning Students in Acronyms
AI, Machine Learning and Computers in Society

Builds capability to design and run the four prescribed team-based Applied Learning Tasks across the two-year course, addresses the realistic 3-versus-4 calendar decision honestly, and frames the ALT sequence as deliberate preparation for the individual Coursework Project.

Designing and Running Alts in a Crowded Senior Cycle Calendar
Using Alts as Coursework Project Preparation

Builds Coursework Project supervision capability, the longest assessment-focused module because the Coursework is where good teaching most directly affects outcomes: reading the brief and marking scheme like an examiner, coaching topic selection within the SEC brief, supervising the concentrated eight-to-ten-week window, and applying the six quality descriptors to a sample submission.

Reading the Coursework Project Brief and Marking Scheme Like an Examiner
Coaching Topic Selection and Supervising the Concentrated Coursework Window
Applying the Marking Scheme to a Sample Submission

Gives the 70% written paper its proper share of attention: paper architecture, marking-scheme insights into where students lose marks, year-round exam technique, and a final lesson synthesising the whole course into a Year 5 LCCS plan with a JC-to-LCCS recruitment pipeline and a closing reflection.

The Written Paper, Structure, Question Architecture and Marking Patterns
Marking-scheme Insights, Where Students Lose Marks and Teaching Responses
Exam Technique Across the Year, Question Parsing, Code-question Strategy and Time Management
Your Year Plan, Recruitment Pipeline and Final Reflection

Establishes an honest baseline of where you start (a confidence audit), a working understanding of what the current LCCS specification requires, and the Lead Learner mindset that lets out-of-field teachers deliver LCCS confidently without performing domain expertise.

Welcome, Confidence Audit and the Lead Learner Mindset
The LCCS Specification: Three Strands, Two Components, and What 2027 Changes

Replaces 'watch me code, now you try' with evidence-based programming pedagogy: computational thinking as named classroom moves, PRIMM and worked examples, explicit debugging instruction, and a sequenced two-year Python progression with programming-specific assessment.

Computational Thinking as Classroom Practice
PRIMM, Worked Examples and Misconception Diagnostics
Teaching Debugging Explicitly
Scaffolding Python Across Two Years and Assessing Programming Progress

Builds subject-knowledge confidence across Strand 2 (Core Concepts) for non-specialists, covering data representation, algorithms, computer systems, networks and the web, and AI/machine learning/computers in society at the level the spec and the SEC written paper actually require.

Teaching Data Representation: Mental Models Before Conversion (for Non-specialists)
Algorithms and Algorithmic Thinking for Non-specialists
Computer Systems, Hardware, Software, and the Operating System for the Written Paper
Networks and the Web: Teaching It Without Drowning Students in Acronyms
AI, Machine Learning and Computers in Society

Builds capability to design and run the four prescribed team-based Applied Learning Tasks across the two-year course, addresses the realistic 3-versus-4 calendar decision honestly, and frames the ALT sequence as deliberate preparation for the individual Coursework Project.

Designing and Running Alts in a Crowded Senior Cycle Calendar
Using Alts as Coursework Project Preparation

Builds Coursework Project supervision capability, the longest assessment-focused module because the Coursework is where good teaching most directly affects outcomes: reading the brief and marking scheme like an examiner, coaching topic selection within the SEC brief, supervising the concentrated eight-to-ten-week window, and applying the six quality descriptors to a sample submission.

Reading the Coursework Project Brief and Marking Scheme Like an Examiner
Coaching Topic Selection and Supervising the Concentrated Coursework Window
Applying the Marking Scheme to a Sample Submission

Gives the 70% written paper its proper share of attention: paper architecture, marking-scheme insights into where students lose marks, year-round exam technique, and a final lesson synthesising the whole course into a Year 5 LCCS plan with a JC-to-LCCS recruitment pipeline and a closing reflection.

The Written Paper, Structure, Question Architecture and Marking Patterns
Marking-scheme Insights, Where Students Lose Marks and Teaching Responses
Exam Technique Across the Year, Question Parsing, Code-question Strategy and Time Management
Your Year Plan, Recruitment Pipeline and Final Reflection

What You'll Learn

Learning Goals

  1. Adopt the Lead Learner mindset to teach Leaving Certificate Computer Science with confidence and sustainability
  2. Embed computational thinking and PRIMM pedagogy into classroom practice to develop strong programming skills
  3. Teach core computer science concepts through mental models and structured approaches accessible to non-specialist teachers
  4. Design, sequence and assess Applied Learning Tasks that effectively prepare students for the Coursework Project
  5. Apply examiner-level understanding of the marking schemes to support students in both the Coursework Project and the Written Paper

Learning Outcomes

  1. Conduct a personal confidence audit and adopt the Lead Learner mindset to sustain long-term LCCS delivery
  2. Map the LCCS specification’s three strands, two assessment components, and 2027 changes to classroom planning
  3. Implement PRIMM, worked examples, and explicit debugging routines in Python lessons across two years
  4. Design and sequence four Applied Learning Tasks that directly scaffold the Coursework Project
  5. Apply SEC marking schemes to both the Coursework Project and Written Paper using on-balance judgement and examiner-style analysis

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