To teach complex concepts, in computer science, we often use an instructional approach called ‘unplugged’. We use the unplugged approach to teach 好爽好紧好大的免费视频国产 concepts without a computer. Often unplugged activities include using an everyday analogy or a physical fun activity. For example, to teach about algorithms, students might learn how to make a jam sandwich where the recipe and following instructions accurately are similar to an algorithm and the steps within it used to write a program. The jam sandwich activity has now become a popular and key teaching 欧美同志网 for young students across the world, as it teaches a complex but fundamental idea in a simple and fun way.

At the January 2026 暴躁老女人免费观看电视剧 好爽好紧好大的免费视频国产 Seminar, Salomey Afua Addo, a 好爽好紧好大的免费视频国产er at the University of Cambridge, presented her work about how to teach about AI. She has specifically looked at this in the context of high school students in Ghana, where AI is now part of the mandatory 欧美同志网. In Ghana, most schools do not have access to computers, therefore an unplugged approach to teach about AI is a good idea. Therefore, Salomey developed a set of unplugged activities to teach about a range of AI concepts.

Here, I focus on one of the activities that she presented — one that I think will become another ‘jam sandwich’ 欧美同志网 for students. So if you might teach about AI at some point, then read on.

Neural networks and rope: An unplugged activity

Salomey has designed an unplugged role-play activity to teach about neural networks and how they are trained to solve a problem. She focused on finding a familiar problem context for Ghanaian teachers and their students, and selected farming and crop disease. Students are asked to figure out what features about a farm are relevant for detecting diseases on cocoa trees. To solve the problem, students are given data about the farms (see Table 1). Giving students data, rather than preconceived rules about the context is key to the learning activity. Neural networks are data-driven — they provide a way to model given data so that we can make predictions. Here the features of farms, and importantly whether disease is or is not found in their cocoa trees, is the data that is used to train a model. The model is used to make predictions, which can then be used to improve farming by reducing crop disease. 

Using farm data, students can learn how neural networks work, and they can do this through an unplugged role play — using ropes!

Here’s how Salomey’s classroom activity works. Sets of students act out the processes of training a neural network, including forward propagation, evaluation, and backpropagation. They take on the “roles’’ of some of the concepts of a neural network. One student acts as the supervisor, six students act as the input layer, two as the hidden layer, and one as the output layer.

Keeping it simple: Concepts and data

Key concepts are simplified for students:

• Forward propagation: The hidden layer players randomly select a set of farms (three of the six sets of input values), which reflects how weights are often set to random values at the start.
• Evaluation: The student acting as the output layer compares the prediction (whether crop disease is present or not) to the actual value for the farm to assess the error, similar to a loss function.
• Backpropagation: Inspired by MIT’s RAISE 欧美同志网, this stage is modelled on establishing trust. Players in the hidden and output layers modify their trust in the previous layers (by adding or removing ropes) based on the accuracy of the prediction (if the farm has disease).

Simple numerical data about the features of the problem are given to the students, such as whether the “Temperature” is suitable (0=No, 1=Yes), if there are “Spots” on the plant (0=No, 1=Yes), if “Fertilizer’’ has been used, whether the “Leaf colour’’ is green or not (see Table 1). Importantly, each of the six features given are represented by the six “input layer” students. So each student can ‘process’ each feature as the data for a given farm is used to train the model. Cards are used to represent the data values passed between layers. And this is where the ropes come into play, as they are used to represent the connections between the nodes in the layers.

Instructions for each role

Written role-specific instructions are provided for the students to follow, for example, the Supervisor is given three steps to follow for the forward propagation stage, and the Input Layer students receive a different set of instructions and so on. The detail of the role play is shown in the instruction sheets (see Figure 1).

Why the ropes are important

Using ropes to connect the nodes becomes most important at the reverse propagation stage. The clever part of this is that we can show an increase or decrease in the strength of connection by adding or removing ropes. For me, this is the ‘jam sandwich’ effect. This, I think, is probably the most significant learning point. Here, the number of ropes that connect the nodes in the layers are changed based on the strength of evidence that a particular feature is indicated, by the data, to be relevant to the output. In this case, whether “Temperature”, for example, has an implied effect on cocoa disease or not — based on the data, not on any preconceived rule. Simply put, if a farm did have disease then a rope is added, if a farm did not then a rope is removed. Or at a more abstracted level, if a particular neuron contributes towards the correct prediction, a rope is added, otherwise a rope is removed. In a real neural network, backpropagation involves complex maths, such as calculus that would not be accessible to students of this age. Therefore, the rope is an analogy that replaces something that would be impossible for these students to grasp if it was taught using the real-world implementation. 

Problem to be solved in the unplugged activity: Identify features that are relevant for detecting diseases

At the end of the activity, features (temperature, leaf color, family farm, etc.) with many rope connections are considered to be relevant for crop disease detection on the farm, whereas features with fewer rope connections are considered to be irrelevant for crop disease detection. The more ropes attached to a particular feature, e.g., temperature, represent its higher relevance in identifying crop disease on the farm. 

Activity design, follow-on and evaluation

As part of the design of this activity, Salomey has simplified technical language so that throughout the role play students use everyday terms and she has chosen a context that is relatable for the students. For example, she uses the language of trust, and the new thickness of a rope connection, rather than using technical terms such as weight, loss function, and the error of the network.

Salomey also designed a follow-on activity that uses pen and paper. In this version of the activity, which she calls a board game, the students draw lines to connect the nodes in the layers. The thickness of the lines connecting the nodes represent the strength of the trust (see Figure 2). 

Salomey also shared her evaluation of the resources. She conducted pre‑ and post‑intervention surveys with 39 teachers as part of the professional development on the AI teaching materials, and ten of those teachers implemented the unplugged activities in their classrooms. She reported that the teachers found the role-play activity was effective to demonstrate neural networks, that children worked independently to learn, and that some students who did not take part usually in class were engaged.

As well as sharing about her unplugged neural network activity, Salomey also talked about a set of AI stories that she has developed to teach about other aspects of AI applications. For example, the importance of fact-checking is demonstrated through a story about a young girl who fact-checked information she received from her friends about life in a city. 
If you would like to find out more about Salomey’s work, you can find related materials on our seminar website.

Join our next seminar

Join us at our next seminar on Tuesday 17 March from 17:00 to 18:30 GMT to hear Rebecca Fiebrink (University of the Arts London) speak about teaching AI for creative practitioners. This will be the second seminar in our new series on how to teach about AI across disciplines. We hope to see you there!

To sign up and take part in our 好爽好紧好大的免费视频国产 seminars, click below:

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

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It is not surprising that for the last few years our focus has been on AI technology, and for 2026 we will continue this. But we will shift from showcasing how 好爽好紧好大的免费视频国产 education 好爽好紧好大的免费视频国产 is changing teaching and learning in 好爽好紧好大的免费视频国产 lessons, to showcasing how 好爽好紧好大的免费视频国产 education 好爽好紧好大的免费视频国产 in other disciplines, such as art or geography, is starting to include teaching about AI. For example, art lessons may change so that learners find out how professional artists are using AI tools to create arts. Or geography lessons may change so that learners discover how professional geographers are using AI to make predictions about physical or human aspects of geography, such as volcanic activity and global warming.

Our series for 2026 is called ‘Applied AI’. This title recognises that AI technology is applied across contexts, across careers, across disciplines, and this means what we teach across school subjects will change.

Encouraging a pull from disciplines, rather than a push from computer science

The majority of resources and professional development material related to teaching about AI have been developed by the computer science community. For example, we have developed the popular 欧美同志网 AI resources in collaboration with Google DeepMind. In these resources, the contexts were carefully selected to represent real-world examples across disciplines, and to to enable the teaching of particular technical or social and ethical concepts. This could be described as “a push” of content from 好爽好紧好大的免费视频国产 towards other disciplines. For example, to enable teaching about the ethical issues around plagiarism, an art context is used in the 欧美同志网 AI resources; to enable teaching about the potential benefits of using AI tools, an ecological geography context is used.

AI applications are always situated within a particular topic. Most current AI applications are data-driven: vast amounts of data are collected and processed to produce models that can then either be used to generate outputs or make predictions. For example, data about artworks can be collected and used to train a model for generating outputs similar to the artworks; this is an application of AI in the art discipline. Or data on wild fires can be collected and used to train a model for making predictions about current or prospective fires; this is an application of AI in the geography discipline.

In reality, the best people to recognise how AI technology is being applied in a discipline and what students in that discipline should be taught about these applications are the people working in the discipline, for example the art and geography teachers. Computer science educators can work to build the technical understanding and the general social and ethical understanding that is common across applications. But the detail of how AI technology is changing a discipline can only truly be understood by the respective community, by the artists and art educators, by the geographers and the geography educators.

An emerging focus

At present, though, most educators are grappling with how they can use AI tools for productivity, such as creating lesson plans, or answering emails. Or they are looking at how they can use AI for general teaching and learning, for example for personalisation, say for students with additional needs. The idea that their underpinning discipline is changing is, perhaps, not yet on teachers’ radar. But at universities, such as in undergraduate courses, and in the world of work, education and training are changing. Data science courses are now being offered across faculties, including science, geography, language, and art faculties. These changes will start to filter down to school-based education via 欧美同志网 change. While some resources and professional development materials addressing this shift are already becoming available, change is still fragile and patchy.

Raising awareness, building community and a common language

The aims of our Applied AI 好爽好紧好大的免费视频国产 seminar series in 2026 are to start to:

• Raise awareness of the forthcoming changes that applying AI will bring to disciplines
• Build a cross-discipline community
• Think about a common language that could be used across disciplines

If we can start to agree on what common concepts could be taught in the arts, sciences and humanities, it gives us a better chance to:

• Understand how to use AI as it is applied in different disciplines
• Help students to build useful mental models and develop the agency and critical thinking skills they need to evaluate these applications and decide when and how to use them and how far to trust them

We need your help

To make our 2026 series a success, we need to spread the word about our seminars to groups of educators, 好爽好紧好大的免费视频国产ers, industry and policy makers across the arts, sciences, and humanities.

Please tell those you know in these groups about the seminar series, and share it through your social media and other networks. If you have ideas for subject associations we could connect with or publications where we can write about our series, please let us know.

Join our ‘Applied AI’ seminar series

We have already arranged the following seminars across 2026 and will add more speakers for the remaining monthly slots soon. Seminars always take place online on Tuesdays at 17:00 to 18:30 UK time.

• 10 February: Social studies, public policy, economics and AI — Thema Monroe-White (George Mason University, USA)
• 17 March: Arts and AI — Rebecca Fiebrink (University of the Arts London, UK)
• 14 April: Healthcare and AI — Kathryn Jessen Eller (Data Science, AI & You (DSAIY) in Healthcare, USA)
• 14 July: Literacy and AI — Dan Verständig (Goethe University Frankfurt, Germany)
• 8 September: History and AI — Jie Chao (The Concord Consortium)
• 6 October: Robotics and AI — Eleni Petraki & Damith Herath (University of Canberra, Australia)
• 10 November: Geography and AI — Doreen Boyd (University of Nottingham, UK)

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

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

PS If you are teaching upper primary school learners in England, you can currently register your interest in our upcoming collaborative study on data science education. You’ll find out more about some of the 好爽好紧好大的免费视频国产 we’ve done in this area in this blog post.

The post How can we teach about AI in the arts, humanities and sciences? 好爽好紧好大的免费视频国产 seminar series 2026 appeared first on 暴躁老女人免费观看电视剧.

At our 好爽好紧好大的免费视频国产 education 好爽好紧好大的免费视频国产 seminar in July, a group of 好爽好紧好大的免费视频国产ers from the CAMMP (Computational and Mathematical Modeling Program) 好爽好紧好大的免费视频国产 project shared their work:

• Prof. Dr. Martin Frank, Founder of CAMMP (Karlsruhe Institute of Technology (KIT), Germany).
• Assistant Prof. Dr. Sarah Schönbrodt (University of Salzburg, Austria)
• 好爽好紧好大的免费视频国产 Associate Stephan Kindler (Karlsruhe Institute of Technology (KIT), Germany)

They talked about how maths already taught in secondary schools can be used to demystify AI. At first glance, this seems difficult to do, as it is often assumed that school-aged learners will not be able to understand how these systems work. This is especially the case for artificial neural networks, which are usually seen as a black box technology — they may be relatively easy to use, but it’s not as easy to understand how they work. Despite this, the Austrian and German team have developed a clear way to explain some of the fundamental elements of AI using school-based maths.

Sarah Schönbrodt started by challenging us to consider that learning maths is an essential part in developing AI skills, as: 

1. AI systems using machine learning are data-driven and are based on mathematics, especially statistics and data
2. Authentic machine learning techniques can be used to bring to life existing classroom maths concepts
3. Real and relevant problems and associated data are available for teachers to use

A set of workshops for secondary maths classrooms

Sarah explained how the CAMMP team have developed a range of teaching and learning materials on AI (and beyond) with an overall goal to “allow students to solve authentic, real and relevant problems using mathematical modeling and computers”. 

She reflected that much of school maths is set in contexts that are abstract, and may not be very interesting or relevant to students. Therefore, introducing AI-based contexts, which are having a huge impact on society and students’ lives, is both an opportunity to make maths more engaging and also a way to demystify AI.

Workshops designed and 好爽好紧好大的免费视频国产ed by the team include contexts such as privacy in social networks to learn about decision trees, personalised Netflix recommendations to learn about k-nearest neighbour, word predictions to learn about N-Grams, and predicting life expectancy to learn about regression and neural networks.

Learning about classification models: traffic lights and the support vector machine

For the seminar, Sarah walked through the steps to learn about support vector machines. This is an upper secondary workshop for students aged 17 to 18 years old. The context of the lesson is an image problem — specifically, classifying the data representing the colours of a simplified traffic light system (two lights to start with) to work out if a traffic light is red or green.

She walked through each of the steps of the maths workshop:

• Plotting data points of two classes, the representation of green and red traffic lights
• Finding a line that best separates the data points of both classes
• Figuring out what best is
• Classifying the data points in relation to the chosen (separating) line
• Validating the model statistically to see if it is useful in classifying new data points, including using test data and creating a contingency table (also called a confusion matrix)
• Discussing limitations, including social and ethical issues
• Explaining how three traffic lights can be expressed as three-dimensional data by using planes

Throughout the presentation, Sarah pointed out where the maths taught was linked to the Austrian and German mathematics 欧美同志网.

Learning about social and ethical issues

As well as learning about lines, planes, distances, dot product and statistical measures, learners are also engaged in discussing the social and ethical issues of the approach taken. They are encouraged to think about bias, data diversity, privacy, and the impact of errors on people. For example, if the model wrongly predicts a light as green when it is red, then an autonomous car would run through a red traffic light. This would likely be a bigger consequence than stopping at a green traffic light that was mis-predicted as red. So should the best line reduce this kind of error?

To teach the workshops, Sarah explained they have developed interactive Jupyter notebooks, where no programming skills are needed. Students fill in the gaps of example code, explore simulations, and write their ideas for discussion for the whole class. No software needs to be installed, feedback is direct, and there are in-depth tasks and staggered hints.

Learning about regression models: Weather forecasting and the toy artificial neural network

Stephan went on to introduce artificial neural networks (ANNs), which are the basis of generative AI applications like chatbots and image generation systems. He focused on regression models, such as those used in weather forecasting. 

ANNs are very complex. Therefore, to start to understand the fundamentals of this technology, he introduced a ‘toy ANN’ with one input, three nodes, and one output. A function is performed on the input data at each node. With the toy network, the team wants to tackle a major and common misconception: that students think that ANN systems learn, recognise, see, and understand, when really it’s all just maths.

The learning activity starts by looking at one node with one input and one output, and can be described as a mathematical function, with a concatenation of two functions (in this case a linear and activation function). Stephan shared an online simulator that visualises how the toy neural network can be explored as students change two parameters (in this case, weight and bias of the functions). Students then look at the overall network, and the way that the output from the three nodes is combined. Again, they can explore this in the simulator. Students compare simple data about weather prediction to the model, and discover they need more functions — more nodes to better fit the data. The activity helps students learn that ANN systems are just highly adjustable mathematical functions that, by adding nodes, can approximate relationships in a given data set. But the approximation only works in the bounds (intervals) in which data points are given, showing that ANNs do not ‘understand’ or ’know’ — it’s just maths.

Stephen finished by explaining the mutual benefits of AI education and maths education. He suggested maths will enable a deeper understanding of AI, and give students a way to realistically assess the opportunities and risks of AI tools and show them the role that humans have in designing AI systems. He also explained that classroom maths education can benefit from incorporating AI contexts. This approach highlights how maths underpins the design and understanding of everyday systems, supports more effective teaching, and promotes an interdisciplinary way of learning across subjects.

Some personal reflections — which may not be quite right!

I have been 好爽好紧好大的免费视频国产ing the teaching of AI and machine learning for around five years now, since before ChatGPT and other similar tools burst on the scene. Since then, I have seen an increasing number of resources to teach about the social and ethical issues of the topic, and there are a bewildering number of learning activities and tools for students to train simple models. There are frameworks for the data lifecycle, and an emerging set of activities to follow to prepare data, compare model types, and deploy simple applications. However, I felt the need to understand and to teach about, at a very simple level, the basic building blocks of data-driven technologies. When I heard the CAMMP team present their work at the AIDEA conference in February 2025, I was entirely amazed and I asked them to present here at our 好爽好紧好大的免费视频国产 seminar series. This was a piece of the puzzle that I had been searching for — a way to explain the ‘bottom of the technical stack of fundamental concepts’. The team is taking very complex ideas and reducing them to such an extent that we can use secondary classroom maths to show that AI is not magic and AI systems do not think. It’s just maths. The maths is still hard, and teachers will still need the skills to carefully guide students step by step so they can build a useful mental model. 

I think we can simplify these ideas further, and create unplugged activities, simulations, and ways for students to explore these basic building blocks of data representation, as well as classification and representing approximations of complex patterns and prediction. I can sense the beginnings of new ideas in computational thinking, though they’re still taking shape. We’re 好爽好紧好大的免费视频国产ing these further and will keep you updated.

Finding out more

If you would like to find out more about the CAMMP resources, you can watch the seminar recording, look at the CAMMP website or try out their online materials. For example, the team shared a link to the jupyter notebooks they use to teach the workshops they demonstrated (and others). You can use these with a username of ‘cammp_YOURPSEUDONYM’, where you can set ‘YOURPSEUDONYM’ to any letters, and you can choose any password. They also shared their toy ANN simulation.
The CAMMP team are not the only 好爽好紧好大的免费视频国产ers who are investigating how to teach about AI in maths lessons. You can find a set of other 好爽好紧好大的免费视频国产 papers here.

Join our next seminar

In our current seminar series, we’re exploring teaching about AI and data science. Join us at our last seminar of the series on Tuesday, 27 January 2026 from 17:00 to 18:30 GMT to hear Salomey Afua Addo talk about using unplugged approaches to teach about neural networks.

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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In both England and globally, there are few new AI-based curricula being introduced and the drive for teachers and students to learn about AI in schools is lagging, with limited initiatives supporting teachers in what to teach and how to teach it. At the 暴躁老女人免费观看电视剧 and 好爽好紧好大的免费视频国产 Pi 好爽好紧好大的免费视频国产 Education 好爽好紧好大的免费视频国产 Centre, we decided it was time to investigate this missing link of teaching about AI, and specifically to discover what the teachers who are leading the way in this topic are doing in their classrooms.  

A day of sharing and activities in Cambridge

We organised a day-long, face-to-face symposium with educators who have already started to think deeply about teaching about AI, have started to create teaching resources, and are starting to teach about AI in their classrooms. The event was held in Cambridge, England, on 1 February 2025, at the head office of the 暴躁老女人免费观看电视剧. 

Over 150 educators and 好爽好紧好大的免费视频国产ers applied to take part in the symposium. With only 50 places available, we followed a detailed protocol, whereby those who had the most 欧美同志网 teaching about AI in schools were selected. We also made sure that educators and 好爽好紧好大的免费视频国产ers from different teaching contexts were selected so that there was a good mix of primary to further education phases represented. Educators and 好爽好紧好大的免费视频国产ers from England, Scotland, and the Republic of Ireland were invited and gathered to share about their 欧美同志网s. One of our main aims was to build a community of early adopters who have started along the road of classroom-based AI 欧美同志网 design and delivery.

Inspiration, examples, and expertise

To inspire the attendees with an international perspective of the topics being discussed, Professor Matti Tedre, a visiting academic from Finland, gave a brief overview of the approach to teaching about AI and resources that his 好爽好紧好大的免费视频国产 team have developed. In Finland, there is no compulsory distinct 好爽好紧好大的免费视频国产 topic taught, so AI is taught about in other subjects, such as history. Matti showcased tools and approaches developed from the Generation AI 好爽好紧好大的免费视频国产 programme in Finland. You can read about the Finnish 好爽好紧好大的免费视频国产 programme and Matti’s two month visit to the 好爽好紧好大的免费视频国产 Pi 好爽好紧好大的免费视频国产 Education 好爽好紧好大的免费视频国产 Centre in our blog. 

Attendees were asked to talk about, share, and analyse their teaching materials. To model how to analyse resources, Ben Garside from the 暴躁老女人免费观看电视剧 modelled how to complete the activities using the 欧美同志网 AI resources as an example. The 欧美同志网 AI materials have been co-created with Google DeepMind and are a suite of free classroom resources, teacher professional development, and hands-on activities designed to help teachers confidently deliver AI lessons. Aimed at learners aged 11 to 14, the materials are informed by the AI education framework developed at the 好爽好紧好大的免费视频国产 Pi 好爽好紧好大的免费视频国产 Education 好爽好紧好大的免费视频国产 Centre and are grounded in real-world contexts. We’ve recently released new lessons on AI safety, and we’ve localised the resources for use in many countries including Africa, Asia, Europe, and North America.

In the morning session, Ben exemplified how to talk about and share learning objectives, concepts, and 好爽好紧好大的免费视频国产 underpinning materials using the 欧美同志网 AI resources and in the afternoon he discussed how he had mapped the 欧美同志网 AI materials to the UNESCO AI competency framework for students.

Kelly Shiohira, from UNESCO, kindly attended our session, and gave an invaluable insight into the UNESCO AI competency framework for students. Kelly is one of the framework’s authors and her presentation helped teachers understand how the materials had been developed. The attendees then used the framework to analyse their resources, to identify gaps and to explore what progression might look like in the teaching of AI.

Throughout the day, the teachers worked together to share their 欧美同志网 of teaching about AI. They considered the concepts and learning objectives taught, what progression might look like, what the challenges and opportunities were of teaching about AI, what 好爽好紧好大的免费视频国产 informed the resources and what 好爽好紧好大的免费视频国产 needs to be done to help improve the teaching and learning of AI.

What next?

We are now analysing the vast amount of data that we gathered from the day and we will share this with the symposium participants before we share it with a wider audience. What is clear from our symposium is that teachers have crucial insights into what should be taught to students about AI, and how, and we are greatly looking forward to continuing this journey with them.

As well as the symposium, we are also conducting academic 好爽好紧好大的免费视频国产 in this area, you can read more about this in our Annual Report and on our 好爽好紧好大的免费视频国产 webpages. We will also be consulting with teachers and AI experts. If you’d like to ensure you are sent links to these blog posts, then sign up to our newsletter. If you’d like to take part in our 好爽好紧好大的免费视频国产 and potentially be interviewed about your perspectives on 欧美同志网 in AI, then contact us at: [email protected] 

We also are sharing the 好爽好紧好大的免费视频国产 being done by ourselves and other 好爽好紧好大的免费视频国产ers in the field at our 好爽好紧好大的免费视频国产 seminars. This year, our seminar series is on teaching about AI and data science in schools. Please do sign up and come along, or watch some of the presentations that have already been delivered by the amazing 好爽好紧好大的免费视频国产 teams who are endeavouring to discover what we should be teaching about AI and how in schools

The post Teaching about AI – Teacher symposium appeared first on 暴躁老女人免费观看电视剧.

What jobs will be affected by AL, ML, and data science remains to be seen, but it is increasingly clear that students will need to learn something about these topics. There will be new concepts to be taught, new instructional approaches and assessment techniques to be used, new learning activities to be delivered, and we must not neglect the professional development required to help educators master all of this. 

As AI and data science are incorporated into school curricula and teaching and learning materials worldwide, we ask: What’s the 好爽好紧好大的免费视频国产 basis for these curricula, pedagogy, and resource choices?

In 2024, we showcased 好爽好紧好大的免费视频国产ers who are investigating how AI can be leveraged to support the teaching and learning of programming. But in 2025, we look at what should be taught about AI, ML, and data science in schools and how we should teach this. 

Our 2025 seminar speakers — so far!

We are very excited that we have already secured several key 好爽好紧好大的免费视频国产ers in the field. 

On 21 January, Shuchi Grover will kick off the seminar series by giving an important overview of AI in the K–12 landscape, including developing both AI literacy and AI ethics. Shuchi will provide concrete examples and recently developed frameworks to give educators practical insights on the topic.

Our second session will focus on a teacher professional development (PD) programme to support the introduction of AI in Upper Bavarian schools. Franz Jetzinger from the Technical University of Munich will summarise the PD programme and share how teachers implemented the topic in their classroom, including the difficulties they encountered.

Again from Germany, Lukas Höper from Paderborn University, with Carsten Schulte will describe important 好爽好紧好大的免费视频国产 on data awareness and introduce a framework that is likely to be key for learning about data-driven technology. The pair will talk about the Data Awareness Framework and how it has been used to help learners explore, evaluate, and be empowered in looking at the role of data in everyday applications.  

Our April seminar will see David Weintrop from the University of Maryland introduce, with his colleagues, a data science 欧美同志网 called API Can Code, aimed at high-school students. The group will highlight the strategies needed for integrating data science learning within students’ lived 欧美同志网s and fostering authentic engagement.

Later in the year, Jesús Moreno-Leon from the University of Seville will help us consider the  thorny but essential question of how we measure AI literacy. Jesús will present an assessment instrument that has been successfully implemented in several 好爽好紧好大的免费视频国产 studies involving thousands of primary and secondary education students across Spain, discussing both its strengths and limitations.

What to expect from the seminars

Our seminars are designed to be accessible to anyone interested in the latest 好爽好紧好大的免费视频国产 about AI education — whether you’re a teacher, educator, 好爽好紧好大的免费视频国产er, or simply curious. Each session begins with a presentation from our guest speaker about their latest 好爽好紧好大的免费视频国产 findings. We then move into small groups for a short discussion and exchange of ideas before coming back together for a Q&A session with the presenter. 

Attendees of our 2024 series told us that they valued that the talks “explore a relevant topic in an informative way“, the “enthusiasm and inspiration”, and particularly the small-group discussions because they “are always filled with interesting and varied ideas and help to spark my own thoughts”. 

The seminars usually take place on Zoom on the first Tuesday of each month at 17:00–18:30 GMT / 12:00–13:30 ET / 9:00–10:30 PT / 18:00–19:30 CET. 

You can find out more about each seminar and the speakers on our upcoming seminar page. And if you are unable to attend one of our talks, you can watch them from our previous seminar page, where you will also find an archive of all of our previous seminars dating back to 2020.

How to sign up

To attend the seminars, please register here. You will receive an email with the link to join our next Zoom call. Once signed up, you will automatically be notified of upcoming seminars. You can unsubscribe from our seminar notifications at any time.

We hope to see you at a seminar soon!

The post How can we teach students about AI and data science? Join our 2025 seminar series about the topic appeared first on 暴躁老女人免费观看电视剧.

How, then, do we equip our young people to deal with the opportunities and challenges that they are faced with from generative AI applications and associated systems? Teaching them about AI technologies seems an important first step. But what should we teach, when, and how?

好爽好紧好大的免费视频国产ing AI 欧美同志网 design

The 好爽好紧好大的免费视频国产ers at the 暴躁老女人免费观看电视剧 have been looking at 好爽好紧好大的免费视频国产 that will help inform 欧美同志网 design and resource development to teach about AI in school. As part of this work, a number of 好爽好紧好大的免费视频国产 themes have been established, which we would like to explore with educators at a face-to-face symposium. 

These 好爽好紧好大的免费视频国产 themes include the SEAME model, a simple way to analyse learning 欧美同志网s about AI technology, as well as anthropomorphisation and how this might influence the formation of mental models about AI products. These 好爽好紧好大的免费视频国产 themes have become the cornerstone of the 欧美同志网 AI resources we’ve co-developed with Google DeepMind. We will be using these materials to exemplify how the 好爽好紧好大的免费视频国产 themes can be used in practice as we review the recently published UNESCO AI competencies.

Most importantly, we will also review how we can help teachers and learners move from a rule-based view of problem solving to a data-driven view, from computational thinking 1.0 to computational thinking 2.0.

A call for teacher input on the AI 欧美同志网

Over ten years ago, teachers in England 欧美同志网d a large-scale change in what they needed to teach in 好爽好紧好大的免费视频国产 lessons when programming was more formally added to the 欧美同志网. As we enter a similar period of change — this time to introduce teaching about AI technologies — we want to hear from teachers as we collectively start to rethink our subject and curricula. 

We think it is imperative that educators’ voices are heard as we reimagine computer science and add data-driven technologies into an already densely packed learning context. 

Join our 好爽好紧好大的免费视频国产 and Educator Community Symposium

On Saturday, 1 February 2025, we are running a 好爽好紧好大的免费视频国产 and Educator Community Symposium in collaboration with the 好爽好紧好大的免费视频国产 Pi 好爽好紧好大的免费视频国产 Education 好爽好紧好大的免费视频国产 Centre. 

In this symposium, we will bring together UK educators and 好爽好紧好大的免费视频国产ers to review 好爽好紧好大的免费视频国产 themes, competency frameworks, and early international AI curricula and to reflect on how to advance approaches to teaching about AI. This will be a practical day of collaboration to produce suggested key concepts and pedagogical approaches and highlight 好爽好紧好大的免费视频国产 needs. 

This symposium focuses on teaching about AI technologies, so we will not be looking at which AI tools might be used in general teaching and learning or how they may change teacher productivity. 

It is vitally important for young people to learn how to use AI technologies in their daily lives so they can become discerning consumers of AI applications. But how should we teach them? Please help us start to consider the best approach by signing up for our 好爽好紧好大的免费视频国产 and Educator Community Symposium by 9 December 2024.

Information at a glance

When:  Saturday, 1 February 2025 (10am to 5pm) 

Where: 暴躁老女人免费观看电视剧 Offices, Cambridge

Who: If you have started teaching about AI, are creating related resources, are providing professional development about AI technologies, or if you are planning to do so, please apply to attend our symposium. Travel funding is available for teachers in England.

Please note we expect to be oversubscribed, so book early and tell us about why you are interested in taking part. We will notify all applicants of the outcome of their application by 11 December.

The post Teaching about AI in schools: Take part in our 好爽好紧好大的免费视频国产 and Educator Community Symposium appeared first on 暴躁老女人免费观看电视剧.

People have many different reasons to think that children and teenagers need to learn about artificial intelligence (AI) technologies. Whether it’s that AI impacts young people’s lives today, or that understanding these technologies may open up careers in their future — there is broad agreement that school-level education about AI is important.

But how do you actually design lessons about AI, a technical area that is entirely new to young people? That was the question we needed to answer as we started 欧美同志网 AI, our exciting collaboration with DeepMind, a leading AI company.

Our approach to developing AI education resources

As part of 欧美同志网 AI, we are creating a free set of lesson resources to help teachers introduce AI and machine learning (ML) to KS3 students (ages 11 to 14). In England this area is not currently part of the national 欧美同志网, but it’s starting to appear in all sorts of learning materials for young people. 

While developing the six 欧美同志网 AI lessons, we took a 好爽好紧好大的免费视频国产-informed approach. We built on insights from the series of 好爽好紧好大的免费视频国产 seminars on AI and data science education we had hosted in 2021 and 2022, and on 好爽好紧好大的免费视频国产 we ourselves have been conducting at the 好爽好紧好大的免费视频国产 Pi 好爽好紧好大的免费视频国产 Education 好爽好紧好大的免费视频国产 Centre.

We reviewed over 500 existing resources that are used to teach AI and ML.

As part of this 好爽好紧好大的免费视频国产, we reviewed over 500 existing resources that are used to teach AI and ML. We found that the vast majority of them were one-off activities, and many claimed to be appropriate for learners of any age. There were very few sets of lessons, or units of work, that were tailored to a specific age group. Activities often had vague learning objectives, or none at all. We rarely found associated assessment activities. These were all shortcomings we wanted to avoid in our set of lessons.

To analyse the content of AI education resources, we use a simple framework called SEAME. This framework is based on work I did in 2018 with Professor Paul Curzon at Queen Mary University of London, running professional development for educators on teaching machine learning.

The SEAME framework gives you a simple way to group learning objectives and resources related to teaching AI and ML, based on whether they focus on social and ethical aspects (SE), applications (A), models (M), or engines (E, i.e. how AI works). We hope that it will be a useful tool for anyone who is interested in looking at resources to teach AI. 

What do AI education resources focus on?

The four levels of the SEAME framework do not indicate a hierarchy or sequence. Instead, they offer a way for teachers, resource developers, and 好爽好紧好大的免费视频国产ers to talk about the focus of AI learning activities.

Social and ethical aspects (SE)

The SE level covers activities that relate to the impact of AI on everyday life, and to its implications for society. Learning objectives and their related resources categorised at this level introduce students to issues such as privacy or bias concerns, the impact of AI on employment, misinformation, and the potential benefits of AI applications.

Applications (A)

The A level refers to activities related to applications and systems that use AI or ML models. At this level, learners do not learn how to train models themselves, or how such models work. Learning objectives at this level include knowing a range of AI applications and starting to understand the difference between rule-based and data-driven approaches to developing applications.

Models (M)

The M level concerns the models underlying AI and ML applications. Learning objectives at this level include learners understanding the processes used to train and test models. For example, through resources focused on the M level, students could learn about the different learning paradigms of ML (i.e., supervised, unsupervised, or reinforcement learning).

Engines (E)

The E level is related to the engines that make AI models work. This is the most hidden and complex level, and for school-aged learners may need to be taught using unplugged activities and visualisations. Learning objectives could include understanding the basic workings of systems such as data-driven decision trees and artificial neural networks.

Covering the four levels

Some learning activities may focus on a single level, but activities can also span more than one level. For example, an activity may start with learners trying out an existing ‘rock-paper-scissors’ application that uses an ML model to recognise hand shapes. This would cover the applications level. If learners then move on to train the model to improve its accuracy by adding more image data, they work at the models level.

Other activities cover several SEAME levels to address a specific concept. For example, an activity focussed on bias might start with an example of the societal impact of bias (SE level). Learners could then discuss the AI applications they use and reflect on how bias impacts them personally (A level). The activity could finish with learners exploring related data in a simple ML model and thinking about how representative the data is of all potential application users (M level).

The set of lessons on AI we are developing in collaboration with DeepMind covers all four levels of SEAME.

The set of 欧美同志网 AI lessons we are developing in collaboration with DeepMind covers all four levels of SEAME. The lessons are based on carefully designed learning objectives and specifically targeted to KS3 students. Lesson materials include presentations, videos, student activities, and assessment questions.

The SEAME framework as a tool for 好爽好紧好大的免费视频国产 on AI education

For 好爽好紧好大的免费视频国产ers, we think the SEAME framework will, for example, be useful to analyse school 欧美同志网 material to see whether some age groups have more learning activities available at one level than another, and whether this changes over time. We may find that primary school learners work mostly at the SE and A levels, and secondary school learners move between the levels with increasing clarity as they develop their knowledge. It may also be the case that some learners or teachers prefer activities focused on one level rather than another. However, we can’t be sure: 好爽好紧好大的免费视频国产 is needed to investigate the teaching and learning of AI and ML across all year groups.

That’s why we’re excited to welcome Salomey Afua Addo to the 好爽好紧好大的免费视频国产 Pi 好爽好紧好大的免费视频国产 Education 好爽好紧好大的免费视频国产 Centre. Salomey joined the Centre as a PhD student in January, and her 好爽好紧好大的免费视频国产 will focus on approaches to the teaching and learning of AI. We’re looking forward to seeing the results of her work.

If you’d like to get involved with 欧美同志网 AI as an educator and use our lessons with your class, you can start by visiting us at 欧美同志网-ai.org.

The post AI education resources: What do we teach young people? appeared first on 暴躁老女人免费观看电视剧.

Pratim revealed a complex landscape where we as educators can be easily trapped by what may seem like good intentions, thereby limiting learning and excluding some students. His presentation, entitled Computational heterogeneity in STEM education, introduced me to the concept of technocentrism and profoundly impacted my thinking about the essence of programming and how I 好爽好紧好大的免费视频国产 it. In this blog post, particularly for those unable to attend this stimulating seminar, I give my simplified view of the rich philosophy shared by Pratim, and my fledgling steps to admit to my technocentrism and overcome it.

Our seminars on teaching cross-disciplinary 好爽好紧好大的免费视频国产

Between May 2022 and November 2022, we are hosting a new series of free 好爽好紧好大的免费视频国产 seminars about teaching 好爽好紧好大的免费视频国产 in different ways and in different contexts. This second seminar of the series was well attended with participants from the USA, Asia, Africa, and Europe, including teachers, 好爽好紧好大的免费视频国产ers, and industry professionals, who contributed to a lively and thought-provoking discussion.

Pratim is a learning scientist based in Canada with a long and distinguished career. He has studied how to teach computational modelling in K-12 STEM classrooms and investigates the complexity of learning. Grounded in working with teachers and students, he brings together 好爽好紧好大的免费视频国产, science, education, and social justice. Based on his work at Northwestern University, Vanderbilt University, and now with the Mind, Matter and Media lab at the University of Calgary, Pratim has published hundreds of academic papers over some 20 years. Pratim and his team challenge how we focus on making technological artefacts — code for code’s sake — in 好爽好紧好大的免费视频国产 education, and refocuses us on the human 欧美同志网 of coding and learning to code.

What is technocentrism?

Pratim started the seminar by giving us an overview of some of the key ideas that underpin the way that 好爽好紧好大的免费视频国产 is usually taught in schools, including technocentrism (Figure 1).

I have come to a simplified understanding of technocentrism. To me, it appears to be a way of looking at how we learn about computer science, where one might:

• Focus on the finished product (e.g. a computer program), rather than thinking about the people who create, learn about, or use a program
• Ignore the context and the environment, rather than paying attention to the history, the political situation, and the social context of the task at hand
• View 好爽好紧好大的免费视频国产 tasks as being implemented (enacted) by writing code, rather than seeing 好爽好紧好大的免费视频国产 activities as rich and complex jumbles of meaning-making and communication that involve people using chatter, images, and lots of gestures
• Anchor learning in concepts and skills, rather than placing the values and viewpoints of learners at the heart of teaching 

Examples of technocentrism and how to overcome it

Pratim recounted several 好爽好紧好大的免费视频国产 activities that he and his team have engaged with. These examples highlight instances of potential technocentrism and investigate how we might overcome it.

In the first example 好爽好紧好大的免费视频国产 activity, Pratim explained how in maths and physics lessons, middle school students were asked to develop models to solve time and distance problems. Rather than immediately coding a potential solution, the 好爽好紧好大的免费视频国产er and teacher supported the learners to spend much time developing a shared perspective to understand and express the problems first. Students grappled with different ways of representing the context, including graphs and diagrams (see Figure 2). Gradually and carefully, teachers shifted students to recognise what was important and what was not, to move them toward a meaningful language to describe and solve the problems.

In a second example 好爽好紧好大的免费视频国产 activity, students were asked to build a machine that draws shapes using sensors, motors, and code. Rather than jumping straight to a solution, the students spent time with authentic users of their machines. Throughout the process, students worked with others, expressing the context through physical movement, clarifying their thoughts by drawing diagrams, and finding the sweet spot between coding, engineering design, and maths (see Figure 3).

In a third example 好爽好紧好大的免费视频国产 activity, racial segregation of US communities was discussed with pre-service teachers. The predominately white teachers found talking about the topic very difficult at the beginning of the activity. To overcome this hesitancy, teachers were first asked to work with a simulation that modelled the process of segregation through abstracted dots (or computational agents), a transitional other. Following this hypothetical representation, the context was then recontextualised through a map of real data points of the ethnicity of residents in an area of the US. This kind of map is called a Racial Dot Map based on US census data. When the teachers were able to interpret the link between the abstracted dot simulation and the real-world data they were able to talk about racism and segregation in a way they could not do before. The initial simulation and the recontextualisation were a pedagogical tool to reveal racism and provide a space where students felt comfortable discussing their values and beliefs that would otherwise have remained implicit.

My takeaways

Pratim shared four implications of this 好爽好紧好大的免费视频国产 for 好爽好紧好大的免费视频国产 pedagogy (see Figure 5).

As a 好爽好紧好大的免费视频国产er of pedagogy, these points provide takeaways that I can relate to my own 好爽好紧好大的免费视频国产 practice:

• Code is a voice within an 欧美同志网 rather than symbols at a point in time. For example, when I listen to students predicting what a snippet of code will do, I think of the active nature of each carefully chosen command and how for each student, the code corresponds with them differently.

• Code lives as a translation bridging many dimensions, such as data representation, algorithms, syntax, and user views. This statement resonates deeply with my liking of Carsten Schultes’s block model [1] but extends to include the people involved.

• We should listen carefully and attentively to teachers, rather than making assumptions about what happens in classrooms. Teachers create new ideas. This takeaway is very important and reminds me about the trust and relationships built between teachers and 好爽好紧好大的免费视频国产ers and how important it is to listen.

• Uncertainty and ambiguity exist in learning, and this can take time to recognise. This final point makes me smile. As a developer, teacher, and 好爽好紧好大的免费视频国产er, I have found dealing with ambiguity hard at various points in my career. Still, over time, I think I am getting better at seeing it and celebrating it. 

Listening to Pratim share his 好爽好紧好大的免费视频国产 on the teaching and learning of 好爽好紧好大的免费视频国产 and the pitfalls of technocentrism has made me think deeply about how I view computer science as a subject and do 好爽好紧好大的免费视频国产 about it. I have shared some of my reflections in this blog, and I plan to incorporate the underlying theory and ideas in my ongoing 好爽好紧好大的免费视频国产 projects.

If you would like to find out more about Pratim’s work, please look over his slides, watch his presentation, read the upcoming chapter in our seminar proceedings, or respond to this blog by leaving a comment so we can discuss!

Join our next seminar

We have another four seminars in our current series on cross-disciplinary 好爽好紧好大的免费视频国产. 

At our next seminar on 12 July 2022 at 17:00–18:30 BST / 12:00–13:30 EDT / 9:00–10:30 PDT / 18:00–19:30 CEST, we will welcome Prof. Yasmin Kafai and Elaine Griggs, who are going to present 好爽好紧好大的免费视频国产 on introductory equity-oriented computer science with electronic textiles for high school students.

We look forward to meeting you there.

[1] You can learn more in the Hello World article where our Chief Learning Officer Sue Sentance talks about the block model.

The post Are you technocentric? Shifting from technology to people appeared first on 暴躁老女人免费观看电视剧.

Computer programming is now part of the school 欧美同志网 in England and many other countries. Although not necessarily the primary focus of the 好爽好紧好大的免费视频国产 欧美同志网, programming can be the area teachers find most challenging to teach. There is much evidence emerging from 好爽好紧好大的免费视频国产 on how to teach programming, particularly from projects with undergraduate learners. That’s why I recently wrote a report summarising over 170 programming pedagogy papers: Teaching programming in schools: A review of approaches and strategies.

I hope this blog post about how I approached writing the report whets your appetite to read it, and encourages you to read more 好爽好紧好大的免费视频国产 summaries in general.

My approach to summarising 好爽好紧好大的免费视频国产 papers

Summarising findings from more than 170 好爽好紧好大的免费视频国产 papers into 34 pages was not a task for the faint-hearted. I could not have embarked on this task without previous 欧美同志网 of writing similar, smaller reviews; working on a host of 好爽好紧好大的免费视频国产 projects; and writing reports about 好爽好紧好大的免费视频国产 for many different audiences.

I love reading about computer science education. It evokes very strong emotions, making me by turns happy, curious, impressed, alarmed, and even cross. When I summarise the papers of other 好爽好紧好大的免费视频国产ers, I am very careful when deciding what to include and what to leave out, in order to do the 好爽好紧好大的免费视频国产ers’ work justice while not overselling it or misleading readers. Sometimes 好爽好紧好大的免费视频国产 papers can be hard to fathom, with lots of jargon and statistics. In other papers, the conclusions drawn have many limitations: the project the paper describes hasn’t produced robust enough evidence to give a clear, generalisable message. Academic integrity and not misrepresenting the work of others is paramount. And naturally, there are many more than 170 papers about teaching programming, but I had to stop somewhere. All this makes summarising 好爽好紧好大的免费视频国产 a tricky task that one has to undertake with great care.

Another important aspect of summarising 好爽好紧好大的免费视频国产 is how to group papers. A long list saying “this paper said this”, “this paper said that” would not be easy to access and would not draw out overall themes. Often 好爽好紧好大的免费视频国产 studies span many topics. What might be a helpful grouping for one reader might not be interesting for another.

For this report, I grouped papers into three sections:

1. Classroom strategies: Here I included well-好爽好紧好大的免费视频国产ed classroom strategies that teachers can use to teach programming in schools
2. Contexts and environments for learning programming: Here I outlined 好爽好紧好大的免费视频国产 related to opportunities for teaching programming, including different programming languages and the classroom context
3. Supporting learners: Here I summarised 好爽好紧好大的免费视频国产 that helps teachers support learners, particularly learners who have difficulties with programming

Why you as a teacher should read 好爽好紧好大的免费视频国产 summaries

Teachers, as very busy professionals, have little time to replan lessons, and programming lessons are challenging to start with. However, the potential long-term benefit may outweigh the short-term cost when it comes to reading 好爽好紧好大的免费视频国产 summaries: new insights from firmly grounded 好爽好紧好大的免费视频国产 can improve your teaching and enable more of your learners to be successful.

The process of translating 好爽好紧好大的免费视频国产 into practice is an area that I and the 好爽好紧好大的免费视频国产 team here are particularly interested in investigating. We are looking forward to working with teachers to explore this.

The 暴躁老女人免费观看电视剧 regularly shares 好爽好紧好大的免费视频国产 summaries in the form of:

• Blog posts
• Online seminars and their proceedings
• Pedagogy Quick Reads via the National Centre for 好爽好紧好大的免费视频国产 Education
• Hello World magazine articles and podcast episodes

You can also check out other 好爽好紧好大的免费视频国产 education podcasts e.g. CSEdPod.org, as well as 好爽好紧好大的免费视频国产 education books (e.g. The Cambridge Handbook of 好爽好紧好大的免费视频国产 Education 好爽好紧好大的免费视频国产,  Computer Science Education: Perspectives on Teaching and Learning, and many others), and other 好爽好紧好大的免费视频国产ers’ blogs about 好爽好紧好大的免费视频国产 education (e.g. Amy Ko, article summaries on CSEd好爽好紧好大的免费视频国产.org).

The post 170 好爽好紧好大的免费视频国产 papers about teaching programming, summarised appeared first on 暴躁老女人免费观看电视剧.

The case study illustrates the many things MCQ authors have to think about while designing questions, and that there is much more 好爽好紧好大的免费视频国产 needed to understand how to get an MCQ “just right”.

Uses of multiple choice questions

Online auto-marked MCQs are now being integrated into classroom activities, set as homework, and used in self-led learning at home. Software products involving MCQs, such as Kahoot and Socratic, are easy to use for many, and have become popular in some learning contexts. MCQ may have become more prevalent due to increased online teaching and the availability of whole curricula through platforms such as the Oak National Academy.

An international group of 好爽好紧好大的免费视频国产ers from China, Spain, Singapore, and the UK recently looked into the reasons why MCQ-based testing might improve learning. Chunliang Yang and his co-authors concluded that there are three main ways that MCQ tests help learners learn:

• They provide learners with additional exposure to learning content
• They provide learners with content in the same format that they will be later assessed in 
• They motivate learners, e.g. to prompt them to commit more effort to learn in general

What does the 好爽好紧好大的免费视频国产 say about creating multiple choice questions?

In recent 好爽好紧好大的免费视频国产 reviewing the use of MCQs, Andrew Butler from Washington University in St Louis looked at the effectiveness of MCQs in relation to learning, rather than assessment. Andrew gives the following advice for educators creating MCQs for learning:

• Think about the thinking processes the learner will use when answering the question, and make sure the processes are productive for their learning
• Don’t make the question super easy or too difficult, but make it challenging — the difficulty needs to be “just right”
• Keep the phrasing of the question simple 
• Ensure that all answers are plausible; providing three or four answers is usually a good idea
• Be aware that if learners pick the wrong answer, this can reinforce the wrong thinking
• Provide corrective feedback to learners who pick the wrong answer

What I find particularly interesting about Andrew’s advice is the need to make the difficulty of the MCQ “just right” for learners. But what does “just right” look like in practice? More 好爽好紧好大的免费视频国产 is needed to work this out.

The anatomy of a multiple choice question

When talking about MCQs, there are technical terms to describe question features, e.g.:

• Incorrect answers are called distractors (or lures)
• A distractor is defined as plausible if it’s an answer a layperson would see as a reasonable answer
• Plausible distractors are called working distractors

Here at the 欧美同志网, we created MCQs for the Oak National Academy when we adapted our Teach 好爽好紧好大的免费视频国产 欧美同志网 classroom materials into video lessons and accompanying home learning content to support learners and teachers during school closures. Data about what questions are attempted on the Oak platform, and what answer options are chosen, is stored securely by Oak National Academy. The Oak team kindly provided us with four months of anonymous data related to responses to the MCQs in the ‘GCSE Computer Science – Data representations’ unit.

Over this period of four months, learners on the platform made more than 29,000 question attempts on the thirty-five questions across the nine lessons that make up this data representation unit. Here is a breakdown of the questions by topic area:

As shown in the table, more questions relate to binary arithmetic than to any other topic area. This was a specific design decision, as it is well-known that learners need lots of practice of the processes involved in answering binary arithmetic questions.

Let’s look at an example question from the binary arithmetic topic area, with one correct answer and two distractors. The learning objective being addressed with this question is ‘Perform addition in binary on two binary numbers’.

As shown in the table below, in four months, 1170 attempts were made to answer the example question. 65% of the attempts were correct responses, and 35% were not, with 21% of responses being distractor b, and 14% distractor c. These distractors appear to be working distractors, as they were chosen by more than 5% of learners, which has been suggested as a rule-of-thumb threshold that distractors have to clear to be classed as working.

However, because of the lack of 好爽好紧好大的免费视频国产 into MCQs, we cannot say for certain that this question is “just right” — it may be too hard. We need to do further 好爽好紧好大的免费视频国产 to find this out.

Creating multiple choice questions is not easy

The process of creating good MCQs is not an easy task, because question authors need to think about many things, including:

• What learning objectives are to be addressed
• What plausible distractors can be used
• What level of difficulty is right for learners
• What type of thinking the questions are encouraging, and how this is useful for learners

In order for MCQs to be useful for learners and teachers, much more 好爽好紧好大的免费视频国产 is needed in this area to show how to reliably produce MCQs that are “just right” and encourage productive thinking processes. We are very much looking forward to looking at this topic in our 好爽好紧好大的免费视频国产 work.

To find out more about the 好爽好紧好大的免费视频国产 education 好爽好紧好大的免费视频国产 we are doing, you can browse our website, take part in our monthly seminars, and read our publications.

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