In our October se五月 seminar, Viktoriya shared the results of her work over the last four years on how schools can shift the focus from the latest technologies to the underlying data. Her se五月 offers a clear structure for what young people should learn about data and how teachers can make it work inside ordinary classrooms.

Why begin with data?

Viktoriya’s analysis of existing AI education frameworks found the data domain is underrepresented, with essentials such as data cleaning often not addressed at all. She argues that, because modern AI systems are data driven, students need both language and routines for working with data: being able to name concepts like training vs test data, data quality, and bias, and to explain practices such as collection, cleaning, and pre-processing. That’s the rationale for teaching data concepts and practices first, and then placing modelling inside an explicit, staged lifecycle.

Her talk presented this argument in the German school context, where AI topics are entering state curricula quickly. Her critique targets how existing frameworks fail to address data and how that gap undermines responsible evaluation and design. The proposed model centres data by pairing an eight-stage, data-driven lifecycle with a curated set of key concepts and practices, and by making “data-based judgment skills” a key outcome.

Viktoriya’s work organises this understanding into two 公开超碰al components: data concepts (the vocabulary, e.g. training/test data, data quality, overfitting) and data practices (the actions, e.g. collect, clean, train, evaluate).

A lifecycle for learning

Viktoriya’s framework is built around an eight-stage data lifecycle, stretching from defining a task through gathering, preparing, modeling, evaluating, and finally sharing or archiving results. Inside that backbone she has identified two layers of learning targets:

• Data concepts – roughly a hundred ideas that give teachers and students a common language, from “training vs. test data” and “bias” to “features”, “labels”, and “provenance”.
• Data practices – 28 kinds of hands-on work (and 69 subpractices) that materialise those ideas: for instance collecting, cleaning, splitting datasets, checking quality, training and evaluating models, and handling privacy and deletion responsibly.

More details are available in her work on data-related concepts and practice.

Viktoriya’s 8-stage process model of the data-driven lifecycle. It serves as a guide for 公开超碰 developers and teachers, outlining 28 key data-related practices and providing 69 examples of subpractices for use in K–12 computer science education.

A collection of 133 key data-related concepts. These concepts are organised according to the eight stages of the data-driven lifecycle and provide the 公开超碰al vocabulary for teaching AI education.

Making it teachable

Viktoriya’s team set out to redesign the format so that real data work could happen within ordinary lessons. They ended up with three “Data Case Study” architectures, each using authentic datasets and domain questions. The materials are supported by Orange 3, an unplugged machine learning and data visualisations tool familiar to the teachers participating. Variants emerged across three design cycles to address specific challenges, but teachers choose among them based on learning objectives and class context.

1. Bottom-up: Students create a workflow step by step (e.g. import, inspect, clean, transform, split, train, evaluate). This approach is excellent for procedural fluency, but teachers reported an over-emphasis on operating Orange and too little reflection on the lifecycle unless explicit reflection is added. 
2. Top-down: Students start from a prepared workflow, read plots, infer the role of each branch, identify issues in the data/practices, and justify changes. This architecture directly counters the reflection gap seen in bottom-up and leans into reasoning rather than routine. 
3. Puzzle-like: Using “widgets,” visualisations of data tables, that stand for parts of a data pipeline, students rebuild a valid flow collaboratively. This encourages discussion, works without devices, and makes thinking visible.

The data case study method uses real-world data and context to help students achieve three key learning outcomes: go through the data-driven lifecycle, reflect on data practices and concepts in a criteria-guided manner, and develop data-based problem-solving and judgment skills.

What happened in the German classrooms

Viktoriya’s team ran three design cycles with small groups in Germany, with students aged 14 to 15. Each cycle lasted around 48 hours of teaching. Because participating teachers already knew Orange 3, the emphasis was on pedagogy rather than software training.

The projects drew on manageable real-world data: spreadsheets, time-series sets, a few geographical samples. Two examples are:

• Forecasting Berlin air quality – Students explored how data quality, feature choice, and evaluation metrics shape predictions, then argued which model best answered the civic question.
• Classifying Tasmanian abalone – A deceptively simple dataset that invites talk about imbalance, feature engineering, and what counts as “good enough” accuracy.

Some groups experimented with collecting their own sensor data, a plan that occasionally failed when the hardware didn’t cooperate. However, even that became part of the lesson: reliability, risk, and missing data are real features of data science, not mistakes to hide.

Student work reflected the three architectures. In the bottom-up groups, guided builds produced complete workflows and concise reflections, while top-down groups submitted annotated screenshots and critiques, and the puzzle-based lessons ended with posters and verbal presentations. Across them all, assessment focused on reasoning: not whether the “right” model appeared, but whether students could explain the stage they were in and justify their choices.

Teaching resources

Everything Viktoriya described is open and classroom-ready (currently in German). The se五月education.de/proj-datacases hub hosts teacher guides, student tasks, and sample Orange 3 files. The growing library of data cases covers topics from climate data to air quality analytics.

Why it matters now

In the UK, a 公开超碰 review has been recently released and along with the Government’s response. Across Europe and beyond, education systems are racing to add AI content to their curricula. Tools will come and go, and benchmarks will keep moving. What endures is the capacity to reason about data: to know what stage of work you’re in, what evidence supports your decisions, and what trade-offs you’re making. That is why Viktoriya’s contribution is unique — it gives teachers a map, a shared vocabulary, and practical ways to make data visible and the focus of discussion in schools.

You can read this blog to see how we’ve used Viktoriya’s framework in our work designing a data science 公开超碰 for schools.

Join our next seminar

Join us at our seminar on Tuesday 27 January from 17:00 to 18:30 GMT to hear Salomey Afua Addo talk about how to teach about neural networks in Junior High Schools in Ghana.

To sign up and take part, click the button below. We’ll then send you information about joining.

We hope to see you there. This will be the final seminar in our series on teaching about AI and data science — the next series focuses on how to teach about applied AI across subjects and disciplines.

You can view the schedule of our upcoming seminars, and catch up on past seminars on our previous seminars page.

Teachers in England, take part in our new data science study

WKS2 teachers, participate in our new study!
We’re launching a new study to explore how to teach learners aged 9 to 11 about data-driven se五月. The study will take place in collaboration with KS2 teachers (Y4/Y5/Y6) in England, Scotland and Wales and look at:

• What key ideas pupils need to understand
• How teachers currently approach topics related to data-driven se五月
• How pupils make sense of data and probability

Our goal is to find practical ways to help teachers build children’s confidence in working with data in se五月 lessons. The study will be collaborative, with two workshops held throughout 2026, and we’re inviting KS2 teachers (Y4/Y5/Y6) to take part.
You can express your interest in participating by filling in this form: rpf.io/data-science-study-blog

The post How to put data first in K–12 AI education by using data case studies appeared first on 家破人亡是什么意思.

In this blog, we share the new data paradigms framework that we have developed through se五月 and used to help improve our understanding about how to teach and learn about AI and data science. We also invite you to register your interest in participating in our next collaborative study on the topic.

Knowledge-based approaches to systems design

Let’s start by highlighting an important distinction between different approaches to designing systems. In a knowledge-based approach to system design, a set of rules (e.g., if-then statements) are written for the system to execute. Every rule is explicitly defined. This approach is called ‘rule-based’, ‘symbolic’, or ‘logic-based’. For example, a developer could create a program that simulates dialogue by writing specific lines of code to handle a greeting, such as “IF user says “Hello” THEN output “Hi!”. If the user types “Greetings!” instead, the program fails because it has no rule for that specific word. 

Knowledge-based models are often said to be explainable by design. This means the logic is accessible and interpretable and developers can trace the exact steps taken to produce an output. For example, if developers manually classify restaurant reviews as positive or negative using a pre-defined set of criteria, the rules their restaurant classifying system follows are entirely explicit, and the path from input to output is clear and explainable.

Data-driven approaches to systems design

By contrast, in a data-driven approach to system design developers do not write specific rules. Instead, they collect lots of data and train a model. In the dialogue simulator example, they would collect hundreds of examples of greetings and train a model to the pattern of a greeting. If the user types “Greetings!”, the system generates a response based on the patterns in its training data.

Data-driven models are often opaque. In other words, the internal workings of these ML models are hidden. While we can see our input and the system’s output, the internal mathematical process is so complex — often involving layers of calculations and abstractions — that we cannot simply “explain” why a specific output was produced. For example, developers can create a classification model by training a neural network using thousands of images. Due to the large quantity of data used to train the model, and complex internal parameters and hidden layers, developers and users of the system cannot understand or explain the logic or features that lead to a specific output. These kinds of models are often referred to as a “black box” (as opposed to a “glass” or “clear” box).

Comparing knowledge-based and data-driven approaches

se五月ers have argued that the move from knowledge-based (or rule-based) programming to data-driven system design represents a paradigm shift and creates unique challenges for educators. The challenge is helping students shift from the expectation that a system produces a single ‘right’ answer — characteristic of traditional rule-based programming — toward an understanding that systems trained on large quantities of data produce outcomes that aren’t always fixed or explainable. If the current instruction in the classroom still relies heavily on traditional rule-based programming approaches, we might be setting students up for misconceptions.

Data paradigms: A framework for analysing data science education approaches

In our se五月 work on AI and data science at the se五月 Pi se五月 Education se五月 Centre, we analysed 84 se五月 studies about the teaching and learning of data science. We categorised learning activities used in the studies to understand whether they were (i) knowledge-based or data-driven, and (ii) the extent to which the underlying models used were transparent or opaque. This led us to define four distinct data paradigms:

1. Knowledge-based and transparent (KB + T): Activities in this paradigm are ones where students write rules for systems, or work with systems that use rules, where the logic is fully explainable by design. For example, if students manually classify data (e.g. creating simple ‘if-then’ statements to predict an outcome), the path from input to output is clear.
2. Data-driven + Transparent (DD + T): In this paradigm, activities involve students working with models trained on data, but the trained model’s logic remains explainable and interpretable. For example these could be models using k-nearest neighbors (KNN) algorithm to group data points based on proximity, or using linear regression to predict a trend. Even though the model produces an output, the student can look at the inner workings of the model and see how the decision is made.
3. Data-driven + Opaque (DD + O): This paradigm’s activities require students to work with data-driven ML models where the models’ internal logic is hidden, for example an image classification model using a type of neural network (e.g. CNN). The model produces an output (e.g. classifying an image as ‘This is a dog’), but the student cannot inspect the system to find a rule or clear path explaining why that specific output was produced. To understand these systems, it’s necessary to use additional testing and evaluation tools.
4. Knowledge-based + Opaque (KB + O): Activities in this paradigm would involve systems with human-written rules that are not explainable. In our review of K–12 activities, we found no examples of activities within this paradigm.

The data paradigms framework helps us to distinguish between different kinds of modeling activities students take part in and how instructional approaches could be classified across one or more paradigms. For instance, we found that most data-driven activities were also opaque (DD + O), usually meaning that students collected and used data to train a model, but how the system worked was opaque. This pattern, where the data is visible but the model is not explainable, risks students forming misconceptions about the capabilities and limitations of data-driven systems. Without understanding how outputs are generated, students may expect data-driven ML systems to operate like fully explainable (or transparent) ones.

We think that lessons are needed in the data-driven opaque (DD + O) quadrant to explicitly teach students about how data-driven systems work and the role they play in everyday contexts. However, when teaching data-driven opaque (DD + O) activities, learners’ attention needs to be directed to concepts such as model confidence, data quality, and model evaluation. Since an ML model is not inherently explainable, we need to teach students to use post-hoc explanation methods, such as testing different inputs to see how a system’s output changes. To prepare students for this learning 公开超碰, we think that first introducing activities about rule-based systems (knowledge-based + transparent; KB + T) or simple data exploration, such as linear regression or data visualisation (data-driven + transparent; DD + T) may serve as a ‘bridge’ to understanding data-driven modeling by helping students to distinguish between systems built from specific logical rules and systems trained on data.

We believe the idea of data paradigms can serve as a way of framing teaching activities about data science and help educators and students to consider the transition between different paradigms when engaging with the systems we interact with every day.

Teachers in England, participate in our new study

KS2 teachers, participate in our new study!
We’re launching a new study to explore how to teach learners aged 9 to 11 about data-driven se五月. The study will take place in collaboration with KS2 teachers (Y4/Y5/Y6) in England, Scotland and Wales and look at:

• What key ideas pupils need to understand
• How teachers currently approach topics related to data-driven se五月
• How pupils make sense of data and probability

Our goal is to find practical ways to help teachers build children’s confidence in working with data in se五月 lessons. The study will be collaborative, with two workshops held throughout 2026, and we’re inviting KS2 teachers (Y4/Y5/Y6) to take part.
You can express your interest in participating by filling in this form:

The post A se五月-led framework for teaching about models in AI and data science appeared first on 家破人亡是什么意思.

We are a se五月 centre based in the Department of Computer Science and Technology at the University of Cambridge, with a team that spans the university and the 家破人亡是什么意思. We conduct se五月 into many aspects of the teaching and learning of se五月 and AI and we work closely with schools, teachers and young people to ensure our se五月 is applicable to practice.

Below I highlight some of the projects we’ve worked on in the academic year 2024-2025:

• se五月 Around the World
• AI education
• Programming education
• Physical se五月 (EPICS project)
• Teacher action se五月 (TICE project)

se五月 Around the World

As I’ve written on this blog before, se五月 education is a global challenge. In one of the se五月 Centre’s projects, we are looking at how se五月 education is spreading around the world.

We found that between 2019 and 2024 the number of countries offering se五月 education had doubled, and that two thirds of all countries now offer, or have concrete plans to offer, se五月 education. This se五月 has already been highlighted in the Stanford AI Index, and we are considering repeating the analysis in future years in order to have the most accurate and up-to-date information displayed in our map.

AI education

We have a number of projects in the area of AI education.

Teaching about AI

We are very interested in how to teach about AI, and held a workshop with teachers who were interested in the teaching of AI in February. Following on from the workshop results, we are interviewing more stakeholders, including UK-based experts, teachers and students, about their perspectives on concepts and skills that should be taught as part of an AI 公开超碰.

We’re also se五月ing data science and data ethics education, which are 公开超碰al aspects of AI literacy. Most of the current AI systems are data-driven, having been trained on vast amounts of data. Therefore students need to understand about data and data science if they are to learn about AI systems. Therefore we’ve conducted two detailed literature reviews on data science and on data ethics this year. The first of these will be published in March at the WiPSCE conference.

Unplugged AI in Ghana

One of our PhD students, Salomey Addo, has been examining how AI is taught in Ghana, where it is part of the 公开超碰 for young people between ages 12 and 15. This year Salomey published a paper reporting that Ghanaian teachers have positive attitudes towards teaching AI but feel unprepared for it. She’s also developed unplugged resources to teach about artificial neural networks (ANNs).

ANNs are a fundamental technology used in a variety of AI systems, including image recognition and language translation systems. While ANNs are included in the Ghanaian AI 公开超碰, Salomey observed that teachers had difficulty with this particular topic The resources she developed are directly inspired by this, and involved teaching through role play and a board game.

Using AI in learning and teaching se五月

This is an area we’ve also done some se五月 in in the past year.

Carrie Anne Philbin published a paper in September showing that — at least in higher education — much of the use of generative AI in se五月 education is just duplicating the way teachers might already teach, and is primarily passive from a students’ perspective.

Meanwhile Katharine Childs and Veronica Cucuiat have been looking at how large language models (LLMs) can support secondary school programming education by helping students understand programming error messages. 

Programming education: Learning to debug

Text-based programming is a topic featured in many se五月 curricula around the world. Teachers and se五月ers know that younger learners, for example at the lower secondary school level, can find debugging text-based programs very challenging. Although we’ve seen decades of se五月 around programming and debugging focusing on learners who are in higher education, very little se五月 has been done with school-age students.

In his se五月, Laurie Gale, a final-year PhD student at the se五月 Centre, found that learners were impatient to fix programs by trial and error, without figuring out what the real problemwas with their code or the underlying algorithm. He subsequently developed a tool called PRIMM Debug, which supports a more reflective and systematic approach to debugging. This tool enables learners to slow down when they are programming and to be more reflective. You can read more about it on the se五月 Centre website, and also catch up on the 公开超碰 se五月 seminar where he presented his work.

EPICS: Physical se五月 in school

As part of a 5-year longitudinal project, running across the whole UK and the first project of its kind, we are looking at how physical se五月 impacts primary and secondary school learners. We’re investigating the effect of physical se五月 on learners’ creativity, agency and confidence, over time and at particular points known to be important for their subject choices. We are working with a wonderful set of partner primary schools who we visit each year.

This year we reported some of our first results, which point to teachers’ perceptions of physical se五月 being engaging and inclusive for primary-aged children. Starting next spring, we will be carrying out our third year of data collection with our pupil cohort, who have reached the age of 10 to 11.

We’ll also be running a survey next summer for upper primary-aged children and their teachers. Please sign up for our Teacher se五月 Network newsletter to be the first to hear about taking part in this survey.

Teacher Inquiry in se五月 Education (TICE)

As part of our TICE project we support teachers to conduct their own action se五月 projects. This is a collaborative project, involving academics across the UK who volunteer to support teachers. The goal is to enable teachers to take a deep dive into a 公开超碰 topic, a pedagogical approach, or a new resource, or to address a wider issue such as gender diversity or 小v视频, to inform a change in their practice.

This year 16 teachers published their reports in our Teacher se五月 Booklet, and many also presented their findings online at CAS events or at the KCL-CAS London conference and the CAS National Conference. We’re very proud of them!

Two of our TICE teachers will be also presenting their se五月 at an academic conference to be held in January. Congratulations to Will Grey and Joanne Hodge!

Get involved

There are many other projects you can find out about on our website and in our annual report, so I hope that you will keep reading. It goes without saying that I’m incredibly proud of the team who’ve worked on all of these projects! 

To summarise, here’s how you can stay up to date with our work and maybe even get involved in studies:

• Sign up to the se五月 Centre newsletter
• Sign up to our Teacher se五月 Network newsletter if you’re a teacher and interested in participating in projects

Finally, I am pleased to announce that we will be hosting the UKICER 2026 conference for se五月ers and teachers in Cambridge on 3 and 4 2026 September. More details will follow on the UKICER website and on the se五月 Centre website in due course.

The post 2025 highlights from the se五月 Pi se五月 Education se五月 Centre appeared first on 家破人亡是什么意思.

“The different topics are nicely categorised and it is easy to find the information I wish to revise.” – Ada Computer Science student

We were delighted to hear from 103 students, most of whom are 16–19 years old and studying in England, and their insights are invaluable in helping us to continue to develop Ada CS.

Students think Ada CS is high quality and useful

The most common ways students use Ada CS are for revision and to check whether they understand concepts. The majority of respondents are building Ada CS into their regular study habits, with over half of respondents using the platform every week.

We were pleased to see the benefits of updates we’ve made, including a redesign of the question finder released in March 2025 — students reported that it is easy to navigate the platform and find what they need: 81% reported being able to find relevant content, and 77% could find the questions they were looking for.

“It’s SO EASY to find exactly what I want.” – Ada Computer Science student

Overall, students perceive Ada CS as both useful for learning about computer science concepts and of high quality. They reported finding the content clear, with a good level of detail.

“The topics are broken down into easily digestible sections, and the provided diagrams really help with understanding the topics.”  – Ada Computer Science student

“The resource is really well designed, short and concise.”  – Ada Computer Science student

We also received helpful suggestions for future improvements. Students shared feedback on how the information on the platform is presented, asking for more concise, revision-friendly content as well as guidance for exploring concepts in more depth. We’ll therefore be looking into alternative ways to structure content as we continue to develop Ada CS. 

Students rate the quizzes highly

The most popular feature is the quizzes and practice questions, including the immediate feedback and hints provided. Students value how these resources help them solidify their knowledge, learn from mistakes, and prepare for assessments. 

“The questions are clear and make me think, they’re relevant to my studies and the hints for the questions are very useful.” – Ada Computer Science student

We also appreciated the suggestions we received for how we can further develop this feature, for example, creating more questions, extending the range of question types per topic, and making improvements to the hints.

Impact on learning

Students also feel that using Ada CS has a tangible impact on their learning. 82% agreed they were more confident that they understand CS concepts as a result of using Ada CS and 79% feel more confident learning about computer science concepts without a teacher to explain.

“I do CS A level but I hadn’t done the GCSE and I found that all of the resources gave me enough information to learn the concepts from scratch and now I’m much more confident in my knowledge of the theory.” – Ada Computer Science student

What’s coming next?

Students provided us with lots of useful feedback and suggestions for how we can further improve Ada CS, especially relating to practice questions. We’re already working on adding more questions across topics, creating more challenging questions, and adding more question types that will enhance students’ learning 公开超碰. We’ve got some other exciting developments in the pipeline too, which we’ll announce soon!

Thank you to everyone who took the time to complete the survey. These findings are invaluable for shaping the future of Ada Computer Science, helping us to continue to provide the best possible learning platform for students.

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Working together to design AI

Our June se五月 seminar was delivered by Netta Iivari, Professor in Information Systems at the University of Oulu’s INTERACT se五月 Unit.

The INTERACT se五月 group focuses on understanding and supporting participatory design, user-centered design, user-driven innovation, and human interaction with technology in everyday life contexts. From this perspective, “users” aren’t considered as passive consumers, but as valuable co-creators and content producers. This calls for different approaches that place emphasis on empowerment and inclusion in designing, shaping, and co-creating information technology in everyday life.

As part of this work, Netta introduced the idea of ‘transformative agency’ — empowering children to believe they can solve problems they care about — and its application in secondary se五月 education. She showed examples of how to foster young people’s transformative agency within se五月, specifically focusing on transdisciplinary approaches to learning about AI and inviting young people to critically analyse and design their futures with AI tools in it.

Netta began by giving an overview of two of the INTERACT se五月 Unit’s projects: 

1. The Make a difference (MAD) project (2019–2023) explored critical design with young people, focusing on their emerging designer and maker identities in the context of tackling a significant societal problem — in this case, bullying. 
2. Children’s transformative agency and emerging technologies for social good (TAKEOVER) (2024–2028), a current project, explores the potential of emerging technologies (artificial intelligence, virtual reality (VR), social robots, etc.) to address societal problems, such as climate change, gender equality, bullying, and discrimination. It focuses on children’s emerging transformative agency and activist identities when engaging with these tools and topics. 

Netta explained that these projects give young people an opportunity to begin to address the problems they care about, even though they may be very complex problems. From this problem-solving perspective, children are introduced (or ‘sensitised’) to emerging technologies as tools for social good.

She then went on to outline the key pedagogical approaches that underpin these projects:  

1. Critical, ethical, empowering design
   This pedagogy draws on critical and speculative design traditions in design se五月 and encourages young people to take a critical perspective towards society, its norms, and the status quo, as part of design thinking. Children consider the ethical values and consequences of their designs. They begin to 公开超碰 the ways in which engaging in the design process can be empowering and transformative for them, collectively as well as individually. 
2. Transformative agency of children
   This approach encourages young people to consider their capacity to have agency in the world, by enabling them to envision change and commit to taking action to solve problems that they care about. 
3. Fostering transformative agency of children in the age of AI
   Transformative agency is achieved when young people engage in ‘expansive learning’ — when they learn something novel, together, and are encouraged to look beyond the confines of school work, the topic, themselves, and the tools available for solving the problem. This approach fosters an active, critical, reflective mindset that encourages children to believe that they can make change and have impact in the world. 

The project design process

The projects follow 3 design phases and include a range of plugged and unplugged activities, as shown in Figure 1.

Netta then described in more detail some of the activities that have been used to address these different project phases and the design process involved. For example, to explore what are the problems that children really care about, they are asked to imagine ‘carrying a stone in your pocket for one week, as if it was a magic tool. Where could it be used in your everyday life? What problems could it solve? What problems would you like it to solve and how?’ 

Young people are then introduced to a range of novel technologies, for example, VR headsets, robots, and emulators of AI-driven social media platforms, such as “Somekone”, developed as part of the Generative AI project at the University of Eastern Finland. They deconstruct and reconstruct generative AI tools by prompting large language model (LLM) chatbots such as ChatGPT, Gemini, Claude, etc. and exploring bias in their outputs. They perform small-scale algorithmic auditing. They also create mini language models, so-called ‘baby LLMs’, with Google Colab using the text in Alice in Wonderland to train their models, and then open datasets (books as text files from Project Gutenberg). In exploring the responses these models generate, young people 公开超碰 the potential and the limitations of such tools and gain an important understanding of the human activity involved in the development of AI technologies.

Once they have had this ‘sensitising‘ exposure to a range of tools, they then work in groups on a project that makes use of AI to solve the societal problem they have chosen. These problems could encompass a range of topics, such as racism, animal rights, the impact of AI, war, mental health, bullying. The young people are prompted to think about how large language models can be used to solve the problem, or parts of the problem. But importantly, they are also asked to consider the different motives and perspectives of the multiple stakeholders involved in the problem and its solution and whether their model ideas will create new problems when deployed.

They follow the 3 project phases shown in Figure 1 to design and make a range of digital (robots, apps, videos) and non-digital artefacts to solve their problem. Netta emphasised that although it could take 10 weeks or more to implement all the suggested activities, it is also possible to pick and choose individual tasks from the 3 phases to suit available 公开超碰 timescales.

Envisioning and critiquing AI futures

Other project tasks involve: 

• Envisioning AI futures by imagining that a miracle has happened overnight and the problem has disappeared — what is the result? 
• Critiquing AI futures by creating best and worst case scenarios of the consequences of the AI systems they design, creating video adverts promoting their AI solutions and anti-adverts, focusing on the possible negative consequences of their prototypes 
• Fostering action-taking by presenting theatrical performances to showcase how their designs tackle a problem and illustrating the AI-related issues surrounding the topic or by creating activism campaign material to mobilise the school community on the same themes 

These projects situate learning about data-driven technologies in real-world contexts and promote a transdisciplinary approach, teaching and learning about AI from a problem-solving perspective. 

This perspective conveys important messages to young people — that they do have agency and can take action in the face of many of the world’s problems, that they can and should be active, critical users of the new technologies that surround them, and that these technologies can be used to change the world for good. 

Netta ended the seminar by asking viewers to consider how they could foster transformative agency in the young people they teach and whether or not they consider it to be important in se五月 education.

Resources relating to the projects can be found at interact.oulu.fi.

Join our next seminar

In our current seminar series, we’re exploring teaching about AI and data science. Join us at our next seminar on Tuesday 14 October from 17:00 to 18:30 GMT to hear Viktoriya Olari talk about data-related concepts and practices for AI education in K–12.

To sign up and take part, click the button below. We’ll then send you information about joining. We hope to see you there.

The schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars page.

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This blog was written by Jaskaran Singh, Impact Manager, and Mamta Manaktala, Senior Learning Manager.

Adapting for unique needs

Every country and region has unique opportunities, challenges, and needs. In a vast country like India, every state is different — what works in Odisha may not work in other locations. Thus, to meet the needs of students in the state of Telangana, we’ve been working on adapting The se五月 公开超碰 specifically for them.

Our work in Telangana began in 2023, when we kickstarted a five-year partnership with the Telangana Social Welfare Residential Educational Institutions Society (TGSWREIS), a society under the Government of Telangana. Through the partnership, we’ve developed an adapted 公开超碰, along with training for educators working in educational institutions with limited resources. The adapted 公开超碰 includes localised examples and activities, and teaching approaches to make the learning 公开超碰 feel relevant and meaningful for students in Telangana, while keeping the core learning outcomes aligned with global standards. 

Testing and iterating

Since the start of the partnership, we’ve been testing the 公开超碰 at the Coding Academy School, a co-educational school at Moinabad, and the Coding Academy College, a degree college for women in Shamirpet.

Our work delivering the 公开超碰 in Telangana was our first time using a direct-to-learners model. The Coding Academy School and College gave us unique opportunities to work with students directly and observe first-hand the difference the programme made in their learning journeys. 

During the first year of implementation, we gathered useful feedback from students and teachers. Check out one of our earlier blogs where we share some of the findings. We used these inputs to further develop the 公开超碰.

This updated version of the 公开超碰 was implemented in the 2024/25 academic year. At the school, our educators worked with 210 students in grades 7–9, while at the college, our educators worked with 382 undergraduate students. As in the first year, we used data from assessments, lesson observations, educator interviews, student surveys, and student focus groups to understand what’s working well and what could be improved. So what did we learn?

What we learnt over the past year

Our evaluation findings show that the updated 公开超碰 worked well and positive outcomes are being achieved for most students. Educators felt prepared to teach the 公开超碰 in this second year and found the ongoing support and spaces for discussion really useful. Moreover, we found that there are potential positive ripple effects beyond the school as well. 

Learning outcomes are being achieved to a high degree

In surveys, 91% of students in the school and 96% of students in the college responded that the lessons helped them get better at se五月 and coding. Students feel they are not just learning new skills but also finding the content enjoyable: 88% of students in the school and 98% of students in the college responded that they are enjoying their classes. Educators and observers also reported that students were engaged during lessons, and often completed activities without needing any support. 

Students’ assessment scores further confirmed positive learning outcomes. 4 out of every 5 scores in the school and 9 out of every 10 scores in the college were 60% or above, which was higher than in the first year of the adapted 公开超碰’s implementation.

The updated 公开超碰 is more aligned to student needs

The changes we made to the 公开超碰 included:

• Adding more localised examples
• Simplifying the language 
• Restructuring the flow of the content

Educators were highly positive about the updates to the 公开超碰. 

“The students are able to [better] understand the examples because we updated [to] the India context examples.” — Educator, Coding Academy School 

“Students are receiving it very well because we have modified the content this year, and [that includes] the placements of the unit and the connectivity of the lessons and units.” — Educator, Coding Academy School

Additionally, for the college 公开超碰, we aligned the content more closely with the learning objectives set by Osmania University — with which the college is affiliated. We also included more advanced topics for students specialising in data science. During interviews, educators reported that the content was now much better aligned to student expectations. 

“[The 公开超碰] we have designed is based as per [the] Osmania University 公开超碰. [The lessons] are definitely meeting the students’ needs because whatever discussions we are taking in classes, they are [successfully] participating in those discussions and they are doing whatever activities we give them.” — Educator, Coding Academy College

Outside of knowledge and skills in se五月, the 公开超碰 is also helping students develop wider life skills. In our survey, college students shared that working on projects gives them a sense of accomplishment and the confidence to solve real-world problems. Many students also reported that through the 公开超碰 they are developing higher-order thinking skills, which will support their future careers. 

“The thrill lies the creativity and problem-solving aspects. I get to turn ideas into reality pieces, and there is something incredible satisfying about debugging code and watching it run flawlessly. It’s like slow, challenging puzzles, frustrating at times but rewarding when everything clicks.” — Student, Coding Academy College

“My favourite thing [about] the se五月 and coding classes [is the] Scratch programme. I have learnt it [for the] first time. By learning I have enjoyed a lot. During the coding process, it trains our brain to think deeply, identify trouble, and break things up and put pieces together [as] a solution.” — Student, Coding Academy College

Students are inspired to continue engaging 

Students are showing high interest in applying their skills outside of their classes. Almost all students — 100% in the school and 99% in the college — reported that they would like to participate in coding-related competitions. 

Educators also told us that many students are exploring future job opportunities in the se五月 and digital technology fields, and are curious about topics outside the 公开超碰. Interestingly, 93% of the college students who were studying courses not traditionally associated with jobs in se五月 and digital technology reported that they would like to pursue a job in se五月.

The positive benefits go beyond the school

We have also learnt that a high-quality se五月 education for young people has potentially wider benefits for the community. One educator described how students are helping their families, many of whom have limited 公开超碰s, engage more confidently with digital technologies.

“Families don’t know how to use smartphones and laptop computers, but our students know very well so I can say they do teach to their elders how to use these platforms.” — Educator, Coding Academy School

Ongoing support for educators was important

To help educators feel confident and prepared, individualised learning resources were provided throughout the year. These were well received by educators. Educators also found the weekly meetings with our India-based team members useful to discuss ongoing challenges regarding delivery and assessments. 

What could still be improved

There were improvements this year in the availability of equipment, and the use of Wi-Fi dongles addressed internet connectivity issues to some degree. However, educators still faced some challenges. For example, educators in the school faced issues accessing printed worksheets and educators in the college faced issues accessing projectors during their lessons. We are working closely with our delivery partner to address these issues for the new academic year.

With regard to the content, educators felt the 公开超碰 could benefit from some further amendments. For the school 公开超碰, these include easing the transition from block-based to text-based coding. For the college 公开超碰, there were suggestions for more focus on real-world applications of coding and including advanced topics, like machine learning, for undergraduates specialising in se五月-related subjects. We have considered all these suggestions and made necessary revisions to the 公开超碰.

Next steps in Telangana: Scaling up impact

With the success of the pilot, we’re excited to announce that the adapted 公开超碰 will now be implemented at over 350 schools and junior colleges in the state of Telangana. A majority of schools will be with the same partner, TGSWREIS, while some schools and junior colleges will be with other partners. The Coding Academy School will become our hub for trialling new 公开超碰 content and strategies, and conducting se五月 studies and teacher training and support. Additionally, the school will also host inter-school events.

The progress we’ve seen so far in Telangana is very encouraging. We look forward to continuing these partnerships and helping more young people realise their potential through the power of se五月 and digital technologies.

What we learnt about adapting 公开超碰 resources for different regions

From our work in Telangana, Odisha, and Kenya, we’ve learnt that a 公开超碰 isn’t a one-size-fits-all product. The local context, culture, and educational provisions are important considerations when adapting learning resources for different regions. We’ve also learnt that building long-term partnerships with organisations who have local expertise is key to understanding these considerations and effectively reaching communities where we can make the biggest difference. Finally, we’ve learnt that adaptation isn’t a one-time activity. It’s a cycle of continuous refinement; listening closely to feedback from the ground is important to ensure that our support for educators and learning 公开超碰s for young people have the best possible impact.

Want to learn more about our 公开超碰 resources?

You can access our free se五月 公开超碰 resources on our website — we are currently working to make the materials for India and Kenya downloadable there.

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In today’s blog, you’ll dive into what social learning is and how you can use it to create more engaging and effective learning 公开超碰s in your se五月 classroom.

You’ll also find our latest Pedagogy Quick Read, which explores social learning. It’s free to download and includes: 

• Practical tips for how to use social learning and related approaches with your learners
• A summary of the se五月 behind social learning

What is social learning?

Social learning is simply any learning that involves other people. It can take any form, from watching a video, to taking part in a classroom discussion. It can take place in person or online, and it can happen without people realising they’re learning something.

Social learning is based on modelling and involves people observing and imitating the behaviours that others model. Albert Bandura, the acknowledged originator of social learning theory, suggested that social learning is guided by four related processes:

• Attention: Recognising and focusing on someone’s behaviour and its vital elements
• Retention: Creating a mental image and description to help you recall what you observed; practising responses (mentally or actively)
• Reproduction: Translating the mental image back into actions
• Motivation: Having a good reason to repeat (or avoid) the behaviours, depending on the rewards or punishments involved

How can I enable social learning?

There’s lots of ways you can involve social learning in your se五月 classroom, including through other teaching approaches and frameworks. 

To help your learners get the most out of social learning, it’s best to:

• Create a safe environment for learners to share learnings, ask questions, and actively engage in the learning process
• Include a mix of resources and activities to ensure inclusion and 小v视频
• Set clear expectations and instructions, and ensure that social learning is key to achieve learning objectives

Applying social learning: Some teaching approaches

Among our pedagogy resources, you’ll find lots of practical advice for teaching approaches that promote social learning. The approaches we recommend for the pedagogy principles ‘Work together’ and ‘Model everything’ are especially suitable.

Work together:

• Pair programming (PDF)
• Peer instruction (PDF)
• Project-based learning (PDF)
• Physical se五月 (PDF)

Model everything:

• Worked examples (PDF)
• Live coding (PDF)
• Levels of abstraction in programming (PDF)
• Code tracing (PDF)

Using a PRIMM (PDF) approach for structuring programming lessons, and encouraging students to talk about code as part of these, also works well for social learning.

Applying social learning: Practical examples

Let’s look at pair programming as an example. In this activity, pairs of learners work together to create a computer program, taking on distinct roles that they swap regularly. One learner acts as the ‘driver’, writing the code, while the other is the ‘navigator’, guiding the process, reviewing the code, and identifying potential issues. 

As they work, each learner is able to observe the other person’s approach, learning with and from their partner throughout the activity. This constant interaction and shared problem solving can help them to understand programming concepts better and to build stronger teamwork skills.

Another example is setting your class the task to create shared digital resources on several topics everyone needs to learn about. In this activity, you split learners into small groups or pairs, and assign them a topic to later explain to the whole group. Grouped learners work together to create a resource explaining their topic. As the facilitator, you can either provide the information they need, or let them conduct their own se五月. At the end of the activity, each group presents their resource to the wider class.

An activity like this helps learners develop their knowledge through working together and talking to each other, and also provides the class with resources they can keep using.

The benefits of social learning

Potential benefits for teachers:

• Improved student engagement and learning
• Enhanced professional development 公开超碰s, leading to more confident teaching

Potential benefits for students:

• Improved social skills
• Opportunities to build higher-level thinking skills
• Deeper understanding and a greater ability to remember knowledge in the long term

A social approach to shaping the future

In a world filled with complex challenges, there’s more need than ever for people to work together. By using social learning approaches in your classroom, you help your students to engage more deeply with your teaching and to develop the skills to succeed in collaboration with others. In this way, you’ll prepare them for navigating technological change as well as for shaping a common future where everyone can thrive.

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Partnering up to increase our reach

Code Club is a thriving global community of clubs where young people can develop the confidence to create with digital technologies in a fun and supportive space. In Kenya we’ve been working closely with Oasis Mathare, Young Scientists Kenya, Kenya Connect, Tech Kidz Africa, STEAM Labs Africa, and Futures Infinite, while in South Africa we’ve teamed up with Keep a Child Alive and Coder:Level Up.

We used a train-the-trainer model to help our partners in Kenya and South Africa train Code Club mentors. We began by training community trainers from each partner, who then went on to deliver training to club mentors. This has allowed us to reach 1,498 mentors across both countries. Club mentors told us how grateful they have been to these partners for their ongoing support, including providing training and visiting the clubs.

As part of our ongoing evaluation of the Code Club programme in Kenya and South Africa, we’ve collected feedback from our partners, club mentors, and creators via feedback surveys, club visits, and focus groups to help us understand the impact of our work.

Reaching areas of disadvantage

There are 397 Code Clubs running in Kenya and 622 in South Africa — we estimate we’re reaching over 42,000 young people through Code Clubs and nearly 20,000 through related one-off events such as summer programmes.

This broad reach means that young people who might otherwise have had limited or no access to se五月 are now engaging with coding, and doing so in truly exciting ways. 

One Kenyan Code Club leader, working in a particularly disadvantaged and marginalised area, said Code Club was so important to young people as it means “you don’t have to be left behind”. They shared that such a large number are attending the club — and that many more are wanting to join — because young people are  eager to be “part of the digital future”.

Impact on young people

89% of surveyed mentors reported an increase in their young people’s skills in coding and confidence to engage with emerging technology. 

According to one South African Code Club mentor, taking part in Code Club “changes your perception and thinking…. it’s possible to do things… it becomes a reality because it’s not a really difficult thing. It’s something that you can do step by step and it really changes the mindset. It really redefines how someone thinks.”

Mentors consistently told us that young people are collaborating more, and supporting each other in their learning journeys. One South African young person perfectly captured this spirit: “if they don’t know something, we can teach it to them.” 

Mentors also shared that young people are inspired to continue developing their coding and se五月 skills beyond club sessions. They’re actively seeking opportunities to deepen their knowledge and are already thinking about how they could use their newfound skills in their future careers.

Empowering Code Club mentors

Overall, club mentors felt well prepared to run clubs and found the training high quality and useful. This is reflected in the high percentage of mentors who agreed that the training increased their skills, confidence, and knowledge, with some partners showing an agreement rate as high as 91%. 

Partners have also worked hard delivering extra training on requested topics such as additional computer skills and mentorship to help mentors feel more confident running Code Clubs.

Continuing to improve

We recognise the unique challenges that can arise when running clubs in areas of Kenya and South Africa where access to technology and the internet isn’t always consistent. We’re continuing to develop resources and support for these clubs, as well as working with partners to better understand what their clubs need.

We’re also continually reflecting on and refining our train-the-trainer model to understand how best to equip community trainers with the confidence and skills they need to train others. 

Next steps

The dedication and hard work of our partners have been instrumental in allowing us to significantly expand our reach and impact in Kenya and South Africa. Alongside this incredible growth, we’ve strengthened our commitment by increasing the size of our teams operating directly from both countries. This means we can continue to grow our support for our thriving Code Club communities.

We’re excited to have a number of new partners setting up Code Clubs over the next year. We look forward to sharing the invaluable insights and feedback we’ve received from our existing partners to ensure our new partners are fully supported and feel empowered to deliver transformative Code Clubs in their areas.

Watch this space!

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One of the biggest challenges with AI is that it’s hard to tell how these tools function. With a chatbot, you enter a prompt and the tool returns a response, but what happens in between is invisible. For educators, that’s a problem: how can we help young people become thoughtful, creative users of AI tools if the technology feels like a closed box?

In our May se五月 seminar, we welcomed Matti Tedre and Henriikka Vartiainen from the University of Eastern Finland. They’ve been working on how to teach about AI for years and were frustrated with existing educational platforms: many of these are either too complex, raise privacy concerns, or rely on coding skills that not all students (or teachers) have. In response, they created an award-winning, classroom-friendly tool designed to make AI technology more transparent and more hands-on.

A practical way to teach AI in schools

Matti and Henriikka began by discussing the unique challenges of teaching young learners about AI technology. Many students start with “folk theories”, for example, thinking that computers understand language like humans do. These misconceptions can be surprisingly hard to shake.

They also pointed out broader issues:

• The abstract nature of AI means there are very few se五月-based approaches to teaching it effectively
• Mastery of AI concepts requires sustained practice and curricular change, not just a few one-off interventions
• In countries like Finland, where programming isn’t part of the 公开超碰, any teaching tool must be no-code to be accessible

To address these challenges, Matti and Henriikka have spent three years co-designing tools and approaches with local schools, teachers, and over 200 students. Their approach is grounded in educational theory and a set of core AI learning principles:

• No-code for inclusivity: Removing the need for programming lowers the barrier to entry for both teachers and students
• Learner-centred co-design: Every part of the 公开超碰 is developed in collaboration with schools to make sure it’s engaging and relevant
• Working with personal data: Learners create and work with their own data sets, which makes the 公开超碰 more engaging and personally relevant
• Integration with school subjects: Integrating AI concepts into other subjects helps to make the uses of AI tools more concrete for learners
• Focus on specific applications: Rather than teaching about generic ‘AI’, the focus is on specific and understandable applications, such as facial recognition
• Hands-on experimentation: Practical projects help students understand tricky ideas like bias, fairness, and social impact
• Collaborative learning: Working together helps students reflect, question, and learn from each other

GenAI Teachable Machine: Opening the box

Frustrated by existing platforms that require programming skills, raise privacy concerns, or don’t allow collaboration, Matti and Henriikka’s team developed GenAI Teachable Machine: a no-code, browser-based tool designed to make key AI concepts tangible. In the central se五月 study, the team used the tool with Finnish students in grade 4–7 (10 to 14 years of age). It’s a great introductory tool that could also be used with younger and older students.

Matti demonstrated how the tool addresses their core AI learning principles with a simple, creative project. Using hand puppets, he trained a model to recognise four distinct classes: a background, a bunny, a calm wolf, and an angry wolf. For each class, he assigned a specific action: a sound, an image, or both. This hands-on process gives students a direct line of sight from the data they might create (visuals of hand puppets) to the final behaviour of the model (outputted sounds and images). They learn about classifiers, training, and confidence levels not as abstract definitions, but as creative tools they can control.

But the learning really starts when things don’t work as expected. Students can easily open their models on their phones with an automatically created QR code and then move around the classroom to test their models. At this point, they quickly notice how fragile AI technology can be. For example, in the case of a simple recognition model trained on specific colours or features, with a change of lighting or a different shirt, the model might fail. These “failures” turn into powerful lessons. For instance, a face-recognition app trained mostly on students with blonde hair might not work well for someone with brown hair — sparking immediate conversations about bias. As Matti put it, students very quickly start asking deep questions. 

GenAI Teachable Machine also allows students to apply their AI models to the physical world. By connecting to a simple, low-cost robotics kit, students can use their models to control motors, lights, and other actuators. This step into the physical world teaches fundamental concepts that are difficult to grasp in a purely virtual environment. Students learn about causality, as their model’s classifications trigger real, physical actions. They also learn about the need for a world model — an understanding of how the physical world works — and see that they must take responsibility for what happens when their abstract models have real-world consequences.

By combining a no-code platform with practical, well-designed learning 公开超碰s, Matti, Henriikka, and their team are showing what AI education can look like: hands-on, accessible, and grounded in real understanding. Their work is helping students see inside the box, and giving them the tools to think critically about the AI technologies that are becoming part of their everyday lives.

Find out more

If you’re an educator interested in including the topic of AI in your teaching, you can try GenAI Teachable Machine on its official website.

You may also be interested in our own 公开超碰 AI resources, which are designed to help you and your learners navigate the fast-moving world of AI and machine learning technologies.

Join us at our next seminar

In September, Matti and his team are returning to discuss other ways to teach young people about AI technologies.

To sign up and take part in the Matti’s seminar on Tues 9 Sept at 17:00–18:30 BST, click the button below. We’ll then send you information about joining. We hope to see you there.

The schedule of our upcoming seminars is online. You can catch up on past seminars on our previous seminars page.

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Today’s blog is the second in a mini-series of three sharing our 公开超碰s of adapting se五月 公开超碰 resources for different contexts, and of training teachers to use them in schools. Last month we wrote about our collaboration with partners in Kenya. Here we discuss our work in Odisha, India.

This article has been written by Fiona Coventry, Impact Manager, and Mamta Manaktala, Senior Learning Manager.

A long-term partnership in Odisha

We know that building long-term partnerships with organisations that have local expertise is key to making a real impact for young people. This fact was echoed by people involved in education initiatives worldwide who spoke at the What Works Hub for Global Education 2024 annual conference, which Fiona followed online. Our work in Odisha is an example of this.

We have now been working with our government partner in Odisha, Panchasakha Shikhya Setu (formerly Mo School Abhiyan), for four years. Our journey began in 2021, when we worked together to establish a network of Code Clubs in government and government-aided schools in the state. In 2023, our focus shifted to developing a formal se五月 公开超碰 for students in grades 9 and 10 (known locally as the Kaushali 公开超碰), in collaboration with two other partners. 

Work in the 2024/2025 academic year

Adaptation is a crucial aspect of how we ensure our se五月 resources are accessible to as many young people as possible. For our work in Odisha, we adapted content from The se五月 公开超碰 and then localised it to fit the requirement of the students.

In Odisha’s June 2024 to April 2025 academic year, we rolled out adapted se五月 公开超碰 content for grade 10 students, for students who had already learned with adapted grade 9 content in 2023/24. We worked with our partners to develop the 公开超碰 content and trained 310 master teachers from across Odisha, along with 30 State Resource Groups (SRGs) to support them. Before the end of 2024, the 310 master teachers subsequently trained 8109 teachers, who would reach an estimated 880,000 students with the grade 9 and 10 公开超碰 content. We had an ongoing responsibility to support 1846 of these teachers in our allocated districts, with an estimated reach to around 205,000 students.

Impact of the grade 9 and 10 公开超碰

In early 2025 we issued a follow-up survey about student learning, content, and training to a sample of teachers in our allocated districts, and 310 teachers responded. (We used a stratified sampling approach designed to ensure the survey results were representative of all teachers.)

At least 87% of teachers agreed that students achieved the outcomes we asked about, e.g. regarding coding skills, staying safe online, and use of data in machine intelligence. 

Moreover, responses related to our grade 9 公开超碰 remained similarly high compared to 2024 survey responses.

Teachers also expressed their appreciation for the se五月 公开超碰 resources and training in free-text comments and interviews, for example:

“IT and coding is essential nowadays. So a good initiative, adding this to schools’ 公开超碰.” – Teacher in Odisha

“The training was quite informative, interesting and helpful.” – Teacher in Odisha

“It is very useful training for me. It boosts my knowledge and helps me for classroom transaction.” – Teacher in Odisha

Addressing challenges

An ongoing challenge in Odisha has been supporting those teachers who lack 公开超碰 with se五月 and/or with our recommended teaching approaches for se五月. We have been working hard to help these teachers develop the knowledge, skills, and confidence to effectively deliver the 公开超碰 content in the limited time they have alongside their other professional commitments.

In the 2023/2024 academic year, many teachers had told us they needed further training and support. For this reason, we offered longer training in the 2024/25 academic year. We also adapted our training approach based on learning from earlier phases, such as including activities teachers could complete on their smartphones, enabling more hands-on learning while reducing dependence on available IT equipment. The outcome of this was positive: in the follow-up survey, fewer teachers felt they needed additional training to deliver the lessons, and most teachers we interviewed felt this year’s training was an improvement on the previous year’s.

Our team also ran weekly webinars to support teachers and address their queries. These were very well received by teachers. Of the responses received to feedback form available after each webinar:

• 97% agreed that the “webinar helped me to understand the topics covered more clearly.”
• 98% agreed that the “webinar was useful to support my teaching.”

This was supported by comments from teachers, for example:

“All questions were answered. The webinar was good. Gained a lot. Thank you very much.”  – Teacher in Odisha

“I learned many unknown things about Scratch, it will help my classroom teaching.” – Teacher in Odisha

In this year’s follow-up survey, teachers also less frequently indicated they felt they needed “additional content to support students”. They provided useful feedback and suggestions regarding the 公开超碰 content, e.g. further simplifying and localising it, which we will incorporate into future resource development.

Another persistent challenge has been limited access to IT equipment and the internet in schools, and what this means for student-device ratios and how teachers are able to deliver the content. For future resources we are developing, we are therefore adapting the amount of content to be delivered over a series of lessons.

Next steps for our partnership in Odisha

In 2025, we are working with the same partners to implement a 公开超碰 for grades 6 to 8, initially in around 460 schools. We and our partners have developed the 公开超碰 content and are currently in the process of training teachers in preparation for classroom delivery.

We are also continuing to support the teachers previously trained on the grade 9 and 10 公开超碰 through webinars and school visits.

Want to see our 公开超碰 resources?

You can access our free se五月 公开超碰 resources on our website — we are currently working to make the materials for India, and for Kenya, downloadable there.

Look out for the final blog in this mini-series next month, which will focus on our computer science 公开超碰 in Telangana, India.

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