Teacher CPD · Secondary Schools

Teaching Leaving Certificate Computer Science

This professional development course equips educators to deliver Leaving Certificate Computer Science effectively. Participants explore the specification's strands, core concepts, and assessment structure; master programming pedagogy, including computational thinking, PRIMM, and debugging; teach topics like data representation, algorithms, networks, and AI; design applied learning tasks and supervise coursework projects; and develop year plans with exam strategies and reflection on self-efficacy.
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Enrol now — course content opens on 1 Jul 2026. We'll email you that morning with access details.
€79
Enrolment per teacher
What's included
  • Self paced
  • Online course
  • Step-by-step lessons
  • Certificate from Coding Ireland

Explore the Course

Two lessons that establish three things: an honest map of where you are starting from (the confidence audit); a working understanding of what the LCCS specification requires of students (and therefore of you); and a productive mindset for delivering LCCS without pretending to be a domain expert.

Welcome, Confidence Audit and the Lead Learner Mindset Beginner
The LCCS Specification — Three Strands, Two Components, and the Anticipated Tranche 3 Update Beginner

Replaces 'watch the teacher code, then write your own' with computational thinking as named classroom moves, PRIMM and worked examples, explicit instruction in debugging, and a sequenced two-year Python progression with programming-specific assessment.

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

Subject-knowledge confidence-building for non-specialists across LCCS Strand 2: data representation, algorithms, computer systems, networks and the web, and AI / machine learning / computers in society. Aim is teaching confidence at LCCS level, not a CS degree.

Data Representation — Binary, Encoding, Images and Sound for Non-specialists Beginner
Algorithms and Algorithmic Thinking for Non-specialists Beginner
Computer Systems — Hardware, Software, Operating System for the Written Paper Beginner
Networks and the Web — the Topic Most Out-of-field Teachers Find Hardest Beginner
AI, Machine Learning and Computers in Society Beginner

The four prescribed team-based ALTs — Interactive Information Systems, Analytics, Modelling and Simulation, Embedded Systems — as the heart of Strand 3 and the principal way students develop the design, build and reflection muscles they will need for the Coursework Project. Includes frank engagement with the calendar-pressure reality some teachers face.

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

The Coursework Project carries 30% of the LCCS grade and is the largest single source of teacher anxiety. Three lessons cover reading the brief and marking scheme like an examiner, coaching topic selection and supervising the ~10-week SEC window, and applying the marking scheme to a sample submission.

Reading the Coursework Project Brief and Marking Scheme Like an Examiner Beginner
Coaching Topic Selection and Supervising the Year-long Project Beginner
Applying the Marking Scheme to a Sample Submission Beginner

The written paper carries 70% of the grade and is the component most often neglected in favour of the louder Coursework Project. Four lessons cover paper architecture, chief-examiner-driven exam prep, year-round exam technique, and finally the year plan, recruitment pipeline and final reflection.

The Written Paper — Structure, Question Architecture and Marking Patterns Beginner
Chief Examiner Insights — Highest-frequency Errors and Teaching Responses Beginner
Exam Technique Across the Year — Question Parsing, Code-question Strategy and Time Management Beginner
Your Year Plan, Recruitment Pipeline and Final Reflection Beginner

Two lessons that establish three things: an honest map of where you are starting from (the confidence audit); a working understanding of what the LCCS specification requires of students (and therefore of you); and a productive mindset for delivering LCCS without pretending to be a domain expert.

Welcome, Confidence Audit and the Lead Learner Mindset Beginner
The LCCS Specification — Three Strands, Two Components, and the Anticipated Tranche 3 Update Beginner

Replaces 'watch the teacher code, then write your own' with computational thinking as named classroom moves, PRIMM and worked examples, explicit instruction in debugging, and a sequenced two-year Python progression with programming-specific assessment.

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

Subject-knowledge confidence-building for non-specialists across LCCS Strand 2: data representation, algorithms, computer systems, networks and the web, and AI / machine learning / computers in society. Aim is teaching confidence at LCCS level, not a CS degree.

Data Representation — Binary, Encoding, Images and Sound for Non-specialists Beginner
Algorithms and Algorithmic Thinking for Non-specialists Beginner
Computer Systems — Hardware, Software, Operating System for the Written Paper Beginner
Networks and the Web — the Topic Most Out-of-field Teachers Find Hardest Beginner
AI, Machine Learning and Computers in Society Beginner

The four prescribed team-based ALTs — Interactive Information Systems, Analytics, Modelling and Simulation, Embedded Systems — as the heart of Strand 3 and the principal way students develop the design, build and reflection muscles they will need for the Coursework Project. Includes frank engagement with the calendar-pressure reality some teachers face.

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

The Coursework Project carries 30% of the LCCS grade and is the largest single source of teacher anxiety. Three lessons cover reading the brief and marking scheme like an examiner, coaching topic selection and supervising the ~10-week SEC window, and applying the marking scheme to a sample submission.

Reading the Coursework Project Brief and Marking Scheme Like an Examiner Beginner
Coaching Topic Selection and Supervising the Year-long Project Beginner
Applying the Marking Scheme to a Sample Submission Beginner

The written paper carries 70% of the grade and is the component most often neglected in favour of the louder Coursework Project. Four lessons cover paper architecture, chief-examiner-driven exam prep, year-round exam technique, and finally the year plan, recruitment pipeline and final reflection.

The Written Paper — Structure, Question Architecture and Marking Patterns Beginner
Chief Examiner Insights — Highest-frequency Errors and Teaching Responses Beginner
Exam Technique Across the Year — Question Parsing, Code-question Strategy and Time Management Beginner
Your Year Plan, Recruitment Pipeline and Final Reflection Beginner

What You'll Learn

Learning Goals

  1. Develop a lead learner mindset to foster confidence and effective use of AI coding assistants in teaching computer science.
  2. Master the LCCS specification, including strands, core concepts, assessment structure, and anticipated updates.
  3. Apply computational thinking and evidence-based pedagogies to design engaging programming lessons for non-specialist students.
  4. Teach core computer science concepts—data representation, algorithms, systems, networks, and societal impacts—using accessible mental models.
  5. Plan and supervise applied learning tasks and coursework projects to build student skills while adhering to assessment guidelines.
  6. Prepare students for the written examination through analysis of question patterns, error mitigation, and exam technique strategies.

Learning Outcomes

  1. Assess personal self-efficacy in the five LCCS competence areas and apply the Lead Learner mindset to integrate AI coding assistants into classroom practice.
  2. Design Python programming lessons incorporating PRIMM progressions, faded worked examples, and diagnostic questions targeting specific misconceptions.
  3. Teach core LCCS concepts—data representation, algorithms, computer systems, networks, and AI/society—using mental-model-first approaches tailored for non-specialist students.
  4. Plan and facilitate Applied Learning Tasks (ALTs) as team-based cycles, adapting to curriculum constraints while preparing students for the Coursework Project.
  5. Develop a comprehensive two-year LCCS year plan, including exam techniques, marking scheme application, and recruitment strategies, informed by chief examiner insights.

Ready to start this course?

Enrol today and learn at your own pace.

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