EU CODE WEEK 2026

STEM CodeLab
CODE • DESIGN • SOLVE!

Aligned with the Türkiye Yüzyılı Maarif Modeli
Two-Week School Coding Camp and Workshop Plan

12 October 2026 – 23 October 2026 / 23:59
Grades 6, 8, 10 and 12

CODE → DESIGN → SOLVE → CREATE → SHARE

PREPARED BY: Gülümser ŞENTÜRK – Technology and Design Teacher

1. Activity Profile

Heading

Description

Activity Title

STEM CodeLab: Code, Design, Solve!

Scope

EU Code Week 2026 – school-based coding, STEM, robotics and artificial intelligence camp

Dates

12 October 2026 – 23 October 2026

End Time

23 October 2026, 23:59

Target Group

Students in Grades 6, 8, 10 and 12

Implementation Setting

Classroom, computer laboratory, robotics/STEM laboratory, workshop area and Coding@Home

Implementation Format

Seminar + workshop + group work + project development + home activity + exhibition/presentation

Main Theme

STEM, coding, computational thinking, physical computing, web/software development, game design, artificial intelligence, digital art and inclusion

2. Rationale

STEM CodeLab is designed to enable students to experience coding not merely as a technical process performed on a screen, but as a tool for identifying real-life problems, generating solutions, designing, prototyping, testing and improving products. The camp progresses in a spiral structure according to the readiness levels of different age groups. While computational thinking and block-based programming are foregrounded at lower grade levels, physical computing, web/software development, machine learning and AI-supported problem solving are explored in greater depth at upper grade levels.

3. General Aim

The activity aims to enable students to identify problems encountered in daily life and the school environment through a STEM approach; develop algorithms; and create functional or demonstrable digital/physical products by using coding, robotics, artificial intelligence and digital production tools in a safe, ethical, creative and collaborative manner.

4. Specific Objectives / Expected Learning Outcomes

· Defines a problem in terms of needs, users and constraints.

· Decomposes a problem into sub-problems and formulates appropriate solution steps as an algorithm.

· Uses fundamental programming structures such as sequencing, conditionals, loops, events and variables at an appropriate level.

· Develops an interactive product or game in block-based programming environments.

· Creates a physical computing prototype by explaining the sensor–input–process–output relationship.

· Safely constructs and tests an electronic circuit in simulation environments such as Tinkercad.

· Designs a simple interface or application appropriate to user needs in web/software development.

· Distinguishes the concepts of data, label, training, testing and prediction in machine learning.

· Discusses the capabilities and limitations of generative/agentic artificial intelligence tools and the need for human oversight.

· Observes principles related to digital safety, copyright, personal data, privacy and artificial intelligence ethics.

· Shares responsibilities within a team, respects different perspectives and develops a collaborative product.

· Tests, debugs and improves the developed product and presents it with justification.

· Contributes to an inclusive working culture by taking differences in access to technology and prior experience into consideration.

5. Alignment with the Türkiye Yüzyılı Maarif Modeli

The plan has been structured on the basis of integrating knowledge and skills, monitoring learning outcomes through tangible evidence of learning, embedding cross-program components into learning experiences, differentiated instruction, and a school-based implementation approach.

Dimension

Correspondence within the Activity

Student Profile

A productive, inquisitive and wise learner who demonstrates aesthetic sensitivity, assumes responsibility and generates solutions.

Conceptual Skills

Analysis, comparison, classification, association, inference, problem solving, decision-making and design.

Social-Emotional Learning

Collaboration, communication, self-regulation, division of responsibilities, giving and receiving feedback, and self-assessment.

Literacies

Digital, information, data, visual, scientific and technological literacy; artificial intelligence awareness.

Erdem–Değer–Eylem

The overarching values of respect, responsibility and justice; diligence, helpfulness, sensitivity, aesthetics and privacy.

Holistic Education

Integration of cognitive, social, emotional, ethical, and production/design dimensions within the same learning process.

Differentiation

Task difficulty based on age and readiness, block-/text-based options, peer support and diversification of roles.

Evidence of Learning

Code, algorithm, circuit/simulation, prototype, game/web product, AI model, process journal, presentation and self-assessment.

6. Content Framework

· Web applications and basic software development

· Robotic systems and smart devices

· Visual/block-based programming

· Game design and interactive storytelling

· Fun unplugged coding activities

· Arduino and electronic circuit simulations

· Physical computing with PicoBlocks / mBlock

· Machine learning: data–label–training–testing–prediction

· Awareness of artificial intelligence and generative/agentic artificial intelligence

· Digital art, design and 3D production

· Coding awareness, inspiration, inclusion and team culture

· Coding@Home tasks and coding awareness in the family/home environment

· Project presentation, exhibition, peer assessment and reflection

7. Programs and Digital Tools That May Be Used

Tool / Program

Recommended Grade Level

Purpose of Use

Scratch

6–8

Block-based coding, games, animation, event–condition–loop structures.

mBlock

6–10

Block-based coding, Arduino/robotics, sensors and physical computing.

Tinkercad Circuits

8–12

Arduino circuit simulation; sensor, LED, buzzer and motor applications.

PicoBlocks

8–12

Physical computing, board/sensor-based experiments and data-oriented activities.

Arduino IDE

10–12

Text-based programming and hardware control for Arduino.

micro:bit / MakeCode (if available)

6–10

Introductory physical computing and sensor-based mini-projects.

HTML / CSS / JavaScript

10–12

Simple web pages, user interfaces and interaction.

Machine learning educational tools

10–12

Classification, data collection, model training and testing.

Generative artificial intelligence tools

10–12

Idea development, prompt design and prototyping; ethics and verification activities.

3D design/printer tools

8–12

Robot bodies, sensor housings, product components and creative digital production.

8. Materials, Equipment and Hardware List

· Computers or tablets; internet connection; interactive whiteboard/projector.

· Arduino UNO or compatible boards; Raspberry Pi Pico and/or micro:bit, if available.

· Breadboard, jumper wires, USB cables, LEDs, resistors, buttons and buzzers.

· LDR, ultrasonic sensor, temperature/humidity sensor and other suitable sensors available at school.

· Servo motors and/or low-voltage DC motors; appropriate driver modules.

· Cardboard, mounting board, coloured paper, sticky notes, felt-tip pens, tape, scissors and adhesive.

· Algorithm/direction cards and unplugged coding floor grids.

· 3D printer and filament, if available; prototyping materials.

· Team task card, project worksheet, testing/error log and assessment rubric.

9. Two-Week Implementation Schedule

Date

Grade

Workshop

Implementation

Tools/Materials

Evidence of Learning

12 Oct, Mon.

All

Opening + Coding Awareness

Introduction to Code Week; the place of STEM and coding in daily life; team formation; introductory algorithm game.

Interactive whiteboard, Code Week resources, cards

Team task card + pre-assessment

13 Oct, Tue.

Unplugged Coding

Human robot, direction commands, sequencing, identifying and correcting errors.

Direction cards, tape, floor grid

Correctly functioning algorithm

14 Oct, Wed.

Visual Coding and Game

Mini-game using character, event, condition and loop structures in Scratch/mBlock.

Computer, Scratch/mBlock

Mini educational game

15 Oct, Thu.

STEM + Smart System

Sensor-based solution to a school problem; circuit and code simulation in Tinkercad.

Tinkercad, Arduino components

Simulation/prototype

16 Oct, Fri.

Game Design + Digital Art

Game/animation on an environment, energy or safety theme; visual design principles.

Scratch/mBlock, drawing tools

Game + design poster

17–18 Oct

All

Coding@Home

Algorithm, coding puzzle or unplugged family task at home; brief process record.

Coding@Home, task form

Task evidence / brief reflection

19 Oct, Mon.

Web & Software Lab

Simple web interface/application prototype addressing a school need.

HTML/CSS/JS or an appropriate visual tool

Functional web prototype

20 Oct, Tue.

Physical Computing

Sensor–process–output architecture; smart device using Arduino/mBlock/PicoBlocks.

Arduino, sensors, mBlock/PicoBlocks

Smart device prototype

21 Oct, Wed.

Machine Learning Lab

Data collection, labelling, model training, testing and error analysis.

ML educational tool, computer

Simple classification model

22 Oct, Thu.

AI & Responsible Design

Generative/agentic AI; prompt design, verification, privacy, copyright and human oversight.

Appropriate AI tools, case cards

AI use principles + solution draft

23 Oct, Fri.

All

STEM CodeLab Final

Project exhibition, team presentation, peer feedback, assessment and closing.

All products, posters, rubric

Exhibition + presentation + self-assessment

23 Oct, 23:59

—

End of Activity

Official closure of the camp process and archiving of products/evidence.

Digital folder

Activity file

10. Detailed Workshops by Grade Level

Grade 6 – Algorithm and Visual Coding Workshop

Aim: To develop computational thinking through gamification and prepare students for block-based programming.

Topics: Algorithm and instruction, Sequencing, Event, Condition, Loop, Debugging

Tools/Materials: Scratch or mBlock, Computer/tablet, Direction cards, Coloured paper and pencil

Implementation Steps:

1. Warm-up: a 'human robot' follows the teacher's commands.

2. Teams use cards to construct an algorithm that reaches the target.

3. The algorithm is transferred to the Scratch/mBlock environment.

4. The student creates a short game/animation.

5. Teams test one another's products and provide one error/improvement suggestion.

Expected Product / Evidence of Learning: Mini-game/animation + algorithm card

Grade 8 – STEM Smart System Design Workshop

Aim: To enable students to experience the engineering design process by connecting a real-life problem with a sensor, code and physical output.

Topics: Problem and need, Sensor, Input–process–output, Conditional structures, Circuit, Testing and improvement

Tools/Materials: Tinkercad Circuits, Arduino UNO, LDR/ultrasonic sensor, etc., LED, buzzer, resistor, breadboard, jumper wires

Implementation Steps:

6. The team selects a problem that can be addressed at school.

7. The team completes the problem → need → sensor → process → output diagram.

8. The circuit is constructed and the code is written in Tinkercad.

9. Threshold values/test scenarios are trialled.

10. Non-functioning points are recorded in the error log and the system is improved.

Expected Product / Evidence of Learning: Functional simulation or physical prototype + STEM diagram

Grade 10 – Web, Software and Physical Computing Workshop

Aim: To develop a user-centred digital solution by considering software interface design together with physical system logic.

Topics: User need, Interface, Algorithm, Sensor/data, Physical output, Prototype

Tools/Materials: HTML/CSS/JS or an appropriate web tool, mBlock/PicoBlocks, Arduino/Pico/micro:bit (if available), Sensors and actuators

Implementation Steps:

11. A user and problem scenario is identified.

12. A simple screen/interface is designed on paper.

13. A digital prototype is developed.

14. Where a sensor-based system is used, a physical computing connection is established.

15. User testing is conducted and revisions are made based on feedback.

Expected Product / Evidence of Learning: Web/application prototype or sensor-based smart device

Grade 12 – Artificial Intelligence and Machine Learning Workshop

Aim: To understand how a data-driven model is developed and to use artificial intelligence for problem solving in an ethical, safe and critical manner.

Topics: Data, Label, Model, Training, Testing, Prediction, Bias, Verification, Privacy and copyright

Tools/Materials: Machine learning educational tool, Computer, Sample datasets, Appropriate generative AI tool, Ethics case cards

Implementation Steps:

16. Students identify a classification problem.

17. A small and balanced dataset is prepared/labelled.

18. The model is trained and tested using new examples.

19. Errors and possible data bias are discussed.

20. A solution idea is developed using generative/agentic AI; output verification and human oversight are discussed.

21. The team develops 'Responsible AI Use Principles'.

Expected Product / Evidence of Learning: Simple ML model + model card + responsible AI principles

11. Group Work and Roles

Teams may consist of 3–5 students. Roles may be rotated:

· Problem Analyst: Explains the problem, the user and the need.

· Designer: Develops the visual/physical design of the solution.

· Coder: Conducts the algorithm and coding process.

· Testing Officer: Implements test scenarios and records errors.

· Presentation and Documentation Officer: Organises the process, evidence and final presentation.

12. Seminar Topics

· What Is Code Week? Why Is Coding for Everyone?

· Solving Real-Life Problems through STEM

· How Do Robots and Smart Devices Make Decisions?

· The Difference between Artificial Intelligence, Machine Learning and Generative Artificial Intelligence

· Digital Ethics: Privacy, Copyright, Data Security and Verification

· Inclusion in Coding: Creating Together with Different Abilities

13. Inclusion and Differentiated Instruction

· Ready-made code blocks, template circuits and peer support are provided for students with no prior experience.

· Advanced students are given extension tasks involving an additional sensor, a second condition, data logging, an interface or text-based programming.

· Team roles are diversified according to students' strengths; writing code is not treated as the sole measure of success.

· Visual instructions, step cards and sample prototypes are used.

· For students with accessibility needs, task duration, tools, output format and presentation method are adapted.

· The active participation of female and male students, students with different levels of experience and students with different learning needs is taken into consideration.

14. Safety, Digital Ethics and Responsible Artificial Intelligence

· Low-voltage educational circuits should be used; connections should be checked before power is supplied.

· A safe physical distance should be maintained around motors and moving components.

· Students' personal data, facial images or identifying information should not be uploaded to AI/ML systems without permission.

· Artificial intelligence outputs should be verified against reliable sources before being accepted as accurate.

· Awareness of attribution and licensing should be developed when copyrighted images, code and content are used.

· AI should be positioned not as a tool that completes the product on behalf of the student, but as support for idea generation, experimentation and feedback.

15. Measurement and Assessment

Assessment is conducted on the basis of the process and evidence of learning, rather than solely on the final product.

Criterion

Weight

Evidence to Be Observed

Understanding the problem and STEM connection

20%

Problem definition, need and interdisciplinary relationship

Algorithm/coding

20%

Logic of the code, appropriate programming structures, debugging

Design and functionality

20%

Fitness for purpose and usability of the prototype/product

Testing and improvement

15%

Test scenario, error log, revision

Teamwork and values

15%

Division of responsibilities, respect, responsibility, inclusion

Presentation and reflection

10%

Explaining the product, presenting evidence, self-assessment

Recommended tools: observation form, team rubric, peer assessment, self-assessment, product checklist and brief exit ticket.

16. STEM CodeLab Final Exhibition

On 23 October, each team establishes a product table. The table includes a problem card, solution diagram, the program/hardware used, a code or algorithm sample, the prototype, and evidence of testing/improvement. Each team delivers a 3–5 minute presentation.

· What was our problem?

· For whom / for what need did we develop a solution?

· Which STEM fields and coding tool did we use?

· How does our system work?

· What error did we identify during testing and what did we change?

· What would we improve in the next version?

17. Code Week Registration Text – Short Version

STEM CodeLab: Code, Design, Solve! is a two-week school coding camp to be conducted with students in Grades 6, 8, 10 and 12 between 12–23 October 2026. Students will participate in workshops on unplugged coding, visual programming, game design, web/software development, Tinkercad and Arduino applications, physical computing, machine learning, artificial intelligence and digital production. Teams will address real-life problems through the STEM design process; develop algorithms, code, simulations and prototypes; and share their products at the final exhibition. The activity supports collaboration, inclusion, digital ethics, responsibility and creative problem solving.

18. Teacher Reflection Questions

· Which activities made students' problem-solving and computational-thinking skills most visible?

· At which grade level should task difficulty be readjusted?

· To what extent did team roles support inclusive participation?

· How did students address coding errors; did a culture of debugging develop?

· Did awareness of ethics, verification and privacy emerge in AI/ML activities?

· Which tool, duration or assessment method should be changed in the next Code Week implementation?

19. Reference and Rationale Note

The plan has been prepared by taking into consideration the Türkiye Yüzyılı Maarif Modeli approach to holistic education, learning outcomes, skills, cross-program components, Erdem–Değer–Eylem, differentiated instruction, evidence of learning and teacher reflection. The Code Week component has been associated with EU Code Week's school-/home-based activity approach to expanding coding and digital skills.

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