Embark on an Epic Space Adventure with Red Planet Odyssey

Introduction: Are you ready to embark on a journey to the Red Planet? Join us as we dive into the thrilling world of Red Planet Odyssey, a tabletop RPG adventure set on Mars in the year 2062. In this immersive game of exploration and discovery, players will step into the shoes of intrepid astronauts, scientists, and engineers on a quest to unravel the mysteries of the Martian landscape and secure humanity’s future among the stars.

You can find the game text and instructions here. Many iterations to come!

Background: Red Planet Odyssey was born out of a passion for science fiction and a desire to create an engaging and interactive gaming experience for players of all ages. Drawing inspiration from classic tabletop RPGs and the rich lore of space exploration, we set out to craft a game that captures the wonder and excitement of venturing into the unknown depths of space.

Game Development: Building upon the framework established in our previous game, Eldoria, we expanded our horizons to the far reaches of the solar system. We refined our gameplay mechanics, introduced new challenges and objectives, and crafted a narrative that immerses players in the vast and untamed wilderness of Mars.

In the quest to create Red Planet Odyssey, we harnessed the power of artificial intelligence to enhance various aspects of game development. Leveraging AI technology allowed us to streamline certain processes, generate creative content, and bring our vision to life in new and innovative ways.

1. World-Building and Narrative Creation: One of the most exciting applications of AI in game development is in world-building and narrative creation. By utilizing AI-powered tools, we were able to generate detailed descriptions of Martian landscapes, alien creatures, and ancient ruins, enriching the game world with depth and complexity. These AI-generated narratives served as inspiration for crafting engaging storylines and immersive gameplay experiences for players to enjoy.

2. Graphics and Visual Design: Another area where AI played a significant role was in graphics and visual design. We utilized AI algorithms to generate stunning artwork, concept designs, and character illustrations that captured the essence of the Red Planet and brought our game to life in vivid detail. From breath-taking Martian vistas to intricate spacecraft designs, AI-powered graphics tools helped us create a visually stunning gaming experience that immerses players in the wonders of space exploration.

3. Playtesting and Iteration: AI also played a valuable role in playtesting and iteration, helping us gather feedback and refine gameplay mechanics throughout the development process. By leveraging AI-driven analytics tools, we were able to collect data on player behaviour, identify patterns and trends, and make informed decisions about game balance, difficulty levels, and overall player experience. This iterative approach is still in motion and we need to do some work on this…. but its quite exciting!

Game Features:

• Dynamic Gameplay: Red Planet Odyssey offers dynamic gameplay with a blend of strategy, cooperation, and chance. Players will roll dice to navigate treacherous terrain, uncover ancient secrets, and overcome unforeseen challenges on their quest for discovery.
• Immersive Narrative: From the heart-pounding descent through the Martian atmosphere to the final discovery that could change the course of history, Red Planet Odyssey delivers an immersive narrative that will captivate players from start to finish.
• Strategic Decision-Making: Players will face tough decisions and moral dilemmas as they navigate political tensions, rivalries, and conflicts on Mars. Every choice they make will shape the outcome of the mission and the fate of humanity’s future on the Red Planet.

Testing and Feedback: As we prepare to launch Red Planet Odyssey into the cosmos, we’re excited to invite players to join us on this epic adventure. We’ll be testing the game with players of all ages, gathering feedback, and fine-tuning the gameplay mechanics to ensure a stellar gaming experience for everyone involved.

Conclusion: Get ready to blast off into the unknown with Red Planet Odyssey! Whether you’re a seasoned gamer or new to the world of tabletop RPGs, there’s something for everyone in this thrilling space adventure. Stay tuned for updates as we journey to Mars and beyond, and be sure to share your feedback as we embark on this epic quest together.

Back to the Future: Rediscovering 80s/90s-Style Football Management in Python

Introduction: In the era before sophisticated football management simulations ruled the digital landscape, enthusiasts indulged in the charm of play-by-post football. Inspired by the simplicity and magic of classics like Pure Fantasy Football, we embarked on a Python journey to recreate the essence of 80s and 90s-style football management. This blog post dives into the historical context, highlights the challenges faced, showcases achievements, and provides a glimpse of the code that brought this nostalgic project to life. In short we haven’t got to that… but we are making strides to get there. So far its been fun and a chance for me to do some different coding and push my favourite AI to do better!

Historical Background: The 1980s and 1990s were the heydays of play-by-post football, where managers relied on mail correspondence to orchestrate their virtual teams. It was expensive but the match reports were fab! Leagues were formed, players were drafted, and results were eagerly awaited by participants. During this time, video games were still in their infancy, leaving ample room for imagination and strategic planning in the world of text-based football management.

Though whilst thinking about this…. I’ve been lost in thoughts of the excellent Kenny Dalglish Soccer Manager and the random chances of it actually complete loading on the Atari 65XE).

Challenges Faced:

1. Team and Player Generation:
   • Inspiration from Pro Evolution Soccer (PES): We drew inspiration from the iconic Pro Evolution Soccer, using its team names to create a sense of familiarity in our Python project.
   • Balancing Abilities and Transfer Values: Crafting players with realistic abilities and transfer values required a delicate balance to ensure diversity and competitiveness among teams.
2. Fixture Scheduling:
   • Mirror of Classic Leagues: We aimed to replicate the scheduling dynamics of classic football leagues where each team played every other team twice, maintaining the spirit of competition and fairness.
3. Match Simulation:
   • Balancing Realism and Excitement: The challenge was to simulate matches realistically while injecting an element of excitement. We incorporated player abilities, form, and a touch of randomness to generate scores.
   • Temporary Form Boost for Home Teams: Adding a temporary form boost for home teams aimed to capture the traditional advantage of playing in familiar surroundings.

Code Snippets:

1. Team and Player Generation:pythonCopy codeteam_names = generate_team_names(num_teams=5) all_players = generate_teams_players(team_names, num_forwards=3, num_midfielders=4, num_defenders=3, num_goalkeepers=1)
2. Fixture Scheduling:pythonCopy codefixtures = generate_fixtures(team_names)
3. Match Simulation:pythonCopy codehome_goals, away_goals = simulate_match(home_team, away_team, home_team_starting, away_team_starting, team_stats)
4. Form Boost for Home Teams:pythonCopy codehome_team_form = team_stats.loc[team_stats['Team'] == home_team, 'Form'].values[0] + 10

Achievements:

1. Successfully generated a league with diverse teams and players.
2. Simulated matches produced scores influenced by player abilities and form.
3. Implemented fixture scheduling that mirrored the dynamics of classic football leagues.

What a season for the West Brom…. sorry West Midlands Stripes:

What’s Next: As we journey through this project, the road ahead includes refining match simulations, enhancing user interactivity, and adding features like player transfers and managerial decisions. The goal is to captivate a new generation of football enthusiasts with the nostalgia of play-by-post football management in Python.

Key Changes:

1. Player Names and Teams:
   • Use real player names and team names from various leagues.
   • Fetch data from a suitable API or database.
2. Transfer Market:
   • Implement a transfer market system where players can buy and sell players within a budget.
   • Consider factors like transfer fees, player form, and injuries.
3. Player Values:
   • Assign a monetary value to each player based on their ability and form.
   • Adjust values dynamically based on performance and market demand.
4. Match Reports:
   • Generate detailed match reports with summaries, key events, and individual player ratings.
   • Provide insights into player performance for decision-making.
5. Injuries and Suspensions:
   • Introduce a system to handle injuries and suspensions, affecting player availability and team selection.
6. Delayed Results:
   • Simulate the delay in receiving match results, characteristic of Pure Fantasy Football.
   • Choose a suitable delay mechanism (e.g., email, text message, or delayed display).

Additional Considerations:

• League Structure: Consider options for multiple divisions and promotion/relegation.
• User Interface: Design a user-friendly interface for managing teams, transfers, and viewing results.
• Extensibility: Allow for potential additions like cups, player training, and scouting.

Specific Code Modifications:

• Player Names and Teams:
  • Modify generate_team_players to fetch real player data.
  • Update generate_team_names to use actual team names.
  • Bard tells me to use suitable API or database for real player and team data (e.g., https://www.football-data.org/, https://www.api-football.com/). Data is fun!
• Transfer Market:
  • Create functions for listing available players, buying and selling players, and managing budgets.
• Player Values:
  • Add a value attribute to the Player class.
  • Implement logic to calculate and update player values.
• Match Reports:
  • Enhance simulate_match to generate detailed reports.
• Injuries and Suspensions:
  • Introduce attributes for player injury status and suspension length.
  • Modify match simulation and team selection logic to account for them.
• Delayed Results:
  • Incorporate a delay mechanism in the match simulation and result display.

If I get time I’m so going to do this… and then start randomly posting people adverts 🙂

Conclusion: Our nostalgic exploration into the world of 80s/90s-style football management serves as a tribute to the bygone days of strategic planning, anticipation, and the thrill of virtual competition. The journey is far from over, and as we continue to refine and expand our project, we invite you to relive the magic of those simpler times where the beauty of the game was encapsulated in lines of code. So, kick back and enjoy the virtual nostalgia as we bridge the gap between past and present in the realm of football management.

Oh and once Pastebin is back online I’ll share the code…

Enhancing Instructional AI Prompts for Effective Content Creation

Well I’ve been back with ChatGPT again today reformatting text and getting it to generate questions and answers (like always these need to be proof read and slightly edited but not so much when you provide it with a good model of text – it generally doesn’t make up stuff that way!). I was going to do this with Bard but just got in a loop of…”you haven’t provided the model”… “oh yes I have….” – so I gave up quickly after the third “Unfortunately, you haven’t specified the format you’d like the original text converted to. To proceed, I need some guidance on what you have in mind”.

As educators, crafting clear and concise prompts for generating educational content is crucial for efficient and accurate results. In this blog post, we reflect on the process of generating educational materials on the topic of acids, bases, and pH measurement, and explore ways to enhance the efficiency of this collaboration…. with AI but by using a known text that has solid foundations.

1. Clarity of Instruction:

The prompts provided were clear and specific, focusing on various elements such as lesson titles, Do Now activities, reformatted text, keyword tables, multiple-choice questions (MCQs), written knowledge check questions, drawing tasks, extended response questions, and a challenge question. The specificity allowed for a structured approach to content creation, ensuring that the resulting materials addressed each component comprehensively. The prompt I gave ChatGPT is:

“Let’s convert the following text so it is in the format of: Title (Describe the hazards and risks of acids and bases), Do Now Activity (3 questions based on prior knowledge, could you please write these based on the lesson title and aiming at KS3 science, give sentence starters and the answers underneath), the reformatted text, a keyword table from the text, 3 MCQs (with 4 potential answers and good distractors) from the text, 8 written knowledge check questions (with the answers in a bullet points after the questions), a task for student to “Draw it out” (with a model answer) and finally an extended response question (that requires two or three paragraphs to answer the lesson title, please give the answer underneath. And thanks in advance. Here is the text ….. “

2. Speed and Efficiency:

The prompts facilitated a rapid and efficient creation process. The focused nature of each instruction allowed for a systematic generation of educational content without unnecessary back-and-forth. This streamlined approach ensured that the desired content was produced promptly, aligning with the user’s expectations and requirements. All in all this was done in a few seconds… took a few more minutes to format the document as I’d like.

3. Areas for Improvement:

I asked ChatGPT for it’s feedback…. ‘While the process was effective, there’s always room for improvement. One aspect that could enhance future collaborations is providing more explicit information on the depth or complexity desired for each section. Additionally, including specific details about the target audience’s proficiency level or any specific preferences could further tailor the content to meet specific educational needs.‘ Fair. I’ll have to provide it with a bunch of exemplar Do Now Activities going forward as that is the area it is most struggling with.

4. Reflection on Collaboration:

This collaborative process demonstrated the effectiveness of clear and detailed prompts in obtaining the desired educational content. It highlighted the importance of mutual understanding and communication between the user and the content creator. The quick turnaround time indicates that prompt clarity significantly contributes to the efficiency of the collaborative content creation process.

5. Takeaways:

In conclusion, effective instructional prompts play a pivotal role in the collaborative creation of educational content. The success of this process relies on clear communication, specificity in instructions, and a mutual understanding of expectations. By reflecting on our collaboration, we can continuously improve and refine our approach to create high-quality educational materials tailored to specific needs. I’d suggest you give it a go… maybe start with a BBC bitesize text….

Navigating the Digital Landscape: Crafting Inclusive Computing Education for KS4

How do we provide coverage of computing and IT for all students in KS4? Clearly there are many different ways this could be done. But it’s not as simple as all do Computer Science in many schools. Simply put that GCSE is not appropriate for all, or likely a good fit. What about iMedia? Or the Cambridge National in IT? These provide options but what if you don’t have the capacity for this? What about Computer Science for those who choose it and some other course, in house even, for the rest?

In the rapidly evolving landscape of technology, providing comprehensive coverage of computing and information technology (IT) at the Key Stage 4 (KS4) level is a challenge that requires careful consideration. The diverse needs and interests of students, coupled with the wide array of available courses and qualifications, necessitate a nuanced approach. While Computer Science GCSE is a staple for many, it’s clear that a one-size-fits-all model might not be the most effective solution.

This blog post explores alternative pathways and pedagogical strategies to ensure that all students, regardless of their chosen academic trajectory, gain essential skills in computer science, digital media, and information technology. Recognizing the National Curriculum’s (see below) call for developing capability, creativity, and knowledge, as well as analytical, problem-solving, and computational thinking skills, we delve into practical sessions that can be implemented within existing frameworks.

The National Curriculum says “All pupils must have the opportunity to study aspects of information technology and computer science at sufficient depth to allow them to progress to higher levels of study or to a professional career.
All pupils should be taught to:
-develop their capability, creativity and knowledge in computer science, digital media
and information technology
– develop and apply their analytic, problem-solving, design, and computational thinking
skills
– understand how changes in technology affect safety, including new ways to protect
their online privacy and identity, and how to identify and report a range of concerns.”

This means that it can feel rather tricky for our curriculum leaders to provide the correct coverage. Does this just mean we can get away with using IT in say science and maths? I’m not so sure. I’ve spent a long time thinking about this and have seen different models used. I am a big fan of the functional IT for all/ computer science as an option. Now the model at how this is done could vary school to school. I think some form of specific day dedicated to a topic sounds great, but also having this timetabled weekly would make it potentially more relevant and feel like an actual subject – not just an off timetable day.

I asked ChatGPT a while back what it thought… my first question was clear I’d not prompted the AI correctly “What would you suggest schools cover in KS4 to match the national curriculum (England) in computer science for those who do not study this as a GCSE”. The response was vague:

“If a student does not study Computer Science as a GCSE, there are several areas of the National Curriculum for Computer Science in England that schools could cover in KS4 to provide a foundation in the subject” [followed by a list of topics such as abstraction]/

When I specifically gave the National Curriculum statement (above) it gave a much improved response:

1. Computer programming: Students could learn how to write code in a programming language such as Python or JavaScript, and gain an understanding of concepts such as variables, data types, control structures, functions and algorithms. They could also explore different programming paradigms, such as object-oriented programming or functional programming.
2. Computational thinking: Students could develop their problem-solving and analytical skills by applying computational thinking techniques to a range of problems. They could learn to break down problems into smaller components, develop algorithms to solve them, and test and refine their solutions.
3. Data representation and analysis: Students could study how data is represented and manipulated in computers, including binary and hexadecimal number systems, ASCII and Unicode character encoding, and data structures such as arrays and lists. They could also learn how to use data analysis tools such as spreadsheets and databases.
4. Computer systems and networks: Students could gain an understanding of the components of a computer system, including the CPU, memory, storage devices and input/output devices. They could also learn about computer networks, including protocols such as TCP/IP and HTTP, and network security concepts such as encryption and firewalls.
5. Cybersecurity and digital citizenship: Students could learn about online safety, including how to protect their personal information and privacy, how to identify and report cyberbullying, and how to stay safe online. They could also gain an understanding of ethical and legal issues related to computer use, such as copyright and plagiarism.

By covering these topics, students would gain a strong foundation in computer science that would prepare them for further study or a career in the field, as well as develop skills and knowledge that are relevant to a wide range of other professions.

I thought let’s see what it can come up with so I prompted it with:

“focussing on “develop their capability, creativity and knowledge in computer science, digital media and information technology” what could schools do. Could you create a plan for 5 session, 3 in Year 10 and 2 in Year 11 that students could participate. Produce this as a table giving suggested activities and learning aims”

The response wasn’t daft at all..though I’d like to hope most schools cover the below topics well at KS3…

Session	Topic	Learning Aims
1	Programming with Scratch	– Understand programming concepts such as loops and conditionals – Use Scratch to create simple programs – Apply computational thinking to solve problems
2	Digital Graphics	– Understand image file formats and compression – Use digital graphics software such as Adobe Photoshop to create and edit images – Understand and apply design principles
3	Web Development	– Understand HTML, CSS and JavaScript – Use a text editor to create a simple web page – Apply design principles to create a visually appealing website
4	Cybersecurity	– Understand the importance of password security – Understand the risks of phishing attacks and how to identify them – Understand how to protect personal information online
5	Emerging Technologies	– Explore emerging technologies such as artificial intelligence and virtual reality – Understand the ethical and societal implications of these technologies – Apply critical thinking skills to evaluate the potential benefits and risks of emerging technologies

ChatGPT even, without asking gave a detailed run down of each session:

Session 1: Programming with Scratch

• Learning Aims:
  • Understand programming concepts such as loops and conditionals
  • Use Scratch to create simple programs
  • Apply computational thinking to solve problems
• Activities:
  • Introduction to Scratch and basic programming concepts
  • Creating a simple game or animation using Scratch
  • Problem-solving activity that requires the use of loops and conditionals

Session 2: Digital Graphics

• Learning Aims:
  • Understand image file formats and compression
  • Use digital graphics software such as Adobe Photoshop to create and edit images
  • Understand and apply design principles
• Activities:
  • Introduction to image file formats and compression
  • Basic image editing in Adobe Photoshop
  • Design activity that requires the use of color theory and composition

Session 3: Web Development

• Learning Aims:
  • Understand HTML, CSS and JavaScript
  • Use a text editor to create a simple web page
  • Apply design principles to create a visually appealing website
• Activities:
  • Introduction to HTML, CSS and JavaScript
  • Creating a simple web page using a text editor
  • Design activity that requires the use of layout and typography

Session 4: Cybersecurity

• Learning Aims:
  • Understand the importance of password security
  • Understand the risks of phishing attacks and how to identify them
  • Understand how to protect personal information online
• Activities:
  • Introduction to password security and password managers
  • Understanding phishing attacks and how to identify them
  • Discussing best practices for protecting personal information online

Session 5: Emerging Technologies

• Learning Aims:
  • Explore emerging technologies such as artificial intelligence and virtual reality
  • Understand the ethical and societal implications of these technologies
  • Apply critical thinking skills to evaluate the potential benefits and risks of emerging technologies
• Activities:
  • Introduction to emerging technologies such as artificial intelligence and virtual reality
  • Discussion of ethical and societal implications of these technologies
  • Group activity that requires students to evaluate the potential benefits and risks of a specific emerging technology

This very much looks like a solid starting point to think about – especially useful if someone wasn’t an expert or wanted to compare their actions against a useful list of sessions.

I decided I was on a role so asked it to look at another point from the NC statements: “now do the same for “understand how changes in technology affect safety, including new ways to protect their online privacy and identity, and how to identify and report a range of concerns.”

Again a useful response:

Session	Topic	Learning Aims
1	Online Safety and Digital Footprint	– Understand the importance of online safety – Understand the concept of a digital footprint – Understand how to protect personal information online
2	Cyberbullying and Online Harassment	– Understand the concept of cyberbullying and online harassment – Understand how to identify and report instances of cyberbullying and online harassment – Understand how to support someone who is experiencing cyberbullying or online harassment
3	Social Media and Online Privacy	– Understand how social media works and the privacy implications of using it – Understand how to manage privacy settings on social media platforms – Understand the risks of oversharing on social media
4	Phishing and Malware	– Understand the risks of phishing and malware attacks – Understand how to identify and report phishing and malware attacks – Understand how to protect personal information from phishing and malware attacks
5	Digital Citizenship and Online Ethics	– Understand the concept of digital citizenship – Understand the importance of online ethics – Understand how to navigate complex ethical issues related to technology and the internet

Here’s a more detailed breakdown of each session:

Session 1: Online Safety and Digital Footprint

• Learning Aims:
  • Understand the importance of online safety
  • Understand the concept of a digital footprint
  • Understand how to protect personal information online
• Activities:
  • Introduction to online safety and the concept of a digital footprint
  • Discussion of common online safety risks and how to protect personal information online
  • Activity that requires students to evaluate the risks and benefits of sharing personal information online

Session 2: Cyberbullying and Online Harassment

• Learning Aims:
  • Understand the concept of cyberbullying and online harassment
  • Understand how to identify and report instances of cyberbullying and online harassment
  • Understand how to support someone who is experiencing cyberbullying or online harassment
• Activities:
  • Introduction to the concept of cyberbullying and online harassment
  • Discussion of the effects of cyberbullying and online harassment
  • Role-playing activity that requires students to practice identifying and responding to instances of cyberbullying or online harassment

Session 3: Social Media and Online Privacy

• Learning Aims:
  • Understand how social media works and the privacy implications of using it
  • Understand how to manage privacy settings on social media platforms
  • Understand the risks of oversharing on social media
• Activities:
  • Introduction to social media and the privacy implications of using it
  • Demonstration of how to manage privacy settings on popular social media platforms
  • Discussion of the risks of oversharing on social media and how to avoid it

Session 4: Phishing and Malware

• Learning Aims:
  • Understand the risks of phishing and malware attacks
  • Understand how to identify and report phishing and malware attacks
  • Understand how to protect personal information from phishing and malware attacks
• Activities:
  • Introduction to the risks of phishing and malware attacks
  • Discussion of how to identify and report phishing and malware attacks
  • Activity that requires students to practice protecting personal information from phishing and malware attacks

Session 5: Digital Citizenship and Online Ethics

• Learning Aims:
  • Understand the concept of digital citizenship
  • Understand the importance of online ethics
  • Understand how to navigate complex ethical

Given I was now on a role I thought…. can we go further:

“could you now create a list of suggested activities for each session”

(by the way I am not sure I like the list of activities below…. so don’t sound like they’d work others sound like they need more resourcing… guest speaker!)

Session 1: Online Safety and Digital Footprint

• Brainstorming activity: Students can work in groups to brainstorm common online safety risks and discuss how to protect personal information online.
• Scavenger hunt: Students can search for examples of digital footprints online and evaluate the potential risks associated with them.
• Quiz game: Students can take an online safety quiz game to test their knowledge of online safety risks and best practices.

Session 2: Cyberbullying and Online Harassment

• Role-playing activity: Students can act out different scenarios involving cyberbullying and online harassment and practice identifying and responding to them.
• Group discussion: Students can discuss the effects of cyberbullying and online harassment on individuals and communities and brainstorm ways to prevent it.
• Case study analysis: Students can analyze a case study of cyberbullying or online harassment and discuss the ethical implications of the situation.

Session 3: Social Media and Online Privacy

• Privacy audit: Students can conduct a privacy audit of their social media profiles and evaluate the privacy settings they currently have in place.
• Debate: Students can debate the pros and cons of sharing personal information on social media platforms and discuss the potential risks and benefits.
• Guest speaker: A guest speaker who works in the field of digital privacy and security can be invited to speak to the class about the importance of online privacy.

Session 4: Phishing and Malware

• Role-playing activity: Students can act out different scenarios involving phishing and malware attacks and practice identifying and responding to them.
• Scavenger hunt: Students can search for examples of phishing emails or websites and evaluate their authenticity.
• Case study analysis: Students can analyze a case study of a phishing or malware attack and discuss the ethical implications of the situation.

Session 5: Digital Citizenship and Online Ethics

• Debate: Students can debate the ethical implications of a controversial issue related to digital citizenship or online ethics (such as online censorship or intellectual property rights).
• Group project: Students can work in groups to create a digital citizenship and online ethics guide for their school or community.
• Guest speaker: A guest speaker who works in the field of digital ethics or online activism can be invited to speak to the class about the importance of ethical behaviour online.

Overall this felt like a good outcome… and definitely additional food for thought… and also made me think how much this, particularly the second part could fit into form time… The suggested sessions, spanning programming, digital graphics, web development, and cybersecurity, offer a practical roadmap for educators seeking to enrich their students’ digital skill set. From understanding the intricacies of online safety to navigating the ethical landscape of emerging technologies, these sessions provide a holistic foundation.

In essence, the challenge lies not just in meeting curriculum requirements but in nurturing a generation of digitally literate individuals equipped to tackle the complexities of the digital age. As we adapt to the ever-changing technological landscape, the education system must remain agile, ensuring that students are not just consumers but creators and critical thinkers in the digital realm.

The Role of Thoughtful Prompts in Crafting Engaging Lesson Texts and Questions with ChatGPT

I’ve written lots recently about how I can use AI to help assist me in the classroom and less about the actual prompts I’ve been giving. Its so crucial that you give the right prompt to your AI helper to get the right outcome… or at least the format of the outcome you were expecting.

In the age of advanced technology, integrating artificial intelligence into education has opened up new avenues for content generation. One such powerful tool is ChatGPT, a language model that responds to prompts, creating a dynamic and personalized learning environment. In this blog post, we’ll delve into how thoughtful prompts in ChatGPT can be harnessed to develop lesson texts and questions, enhancing the educational journey for students.

Thoughtful prompts in ChatGPT allow educators to tailor lesson texts and questions to the unique needs and interests of individual students. This is great when you wish to differentiate your text for a range of readers or to add different questions for different groups of learners.

I started my chat off with:

“Write 5 paragraphs to help AQA Combined Science (not mentioning the specification in the text) for students with the learning aim “Investigate the effect of pH on the rate of reaction of amylase enzyme “, giving an example of method from the required practical activies and include five questions based on this at the bottom and then give the answers in a separate list below; then write an extended response question from the text but based on a different scenario, then give the answer and the wrong answer as a separate answer below. Include in it references with citations following the Harvard style; use the learning aim as the title”

Not in my typing I misspelled “activities” – ChatGPT didn’t care and figured it out.

Based on the questions I asked we got:

Investigate the Effect of pH on the Rate of Reaction of Amylase Enzyme

Enzymes are crucial biological catalysts that speed up chemical reactions in cells. One such enzyme, amylase, breaks down starch into maltose, a simpler sugar. As with all enzymes, the activity of amylase is sensitive to the pH of its environment. Investigating the effect of pH on amylase activity helps students understand the importance of maintaining optimal pH levels for enzymes to function effectively (Reece et al., 2014).

To explore the effect of pH on amylase activity, a common practical activity involves monitoring the rate of starch breakdown under varying pH conditions. In this experiment, students prepare a series of buffered starch solutions with different pH values, ranging from acidic to alkaline. Amylase enzyme is then added to each solution, and the reaction progress is monitored using iodine, which changes colour in the presence of starch. The time taken for the colour change to occur at each pH level provides an indication of the enzyme’s reaction rate (AQA, 2021).

The results of this experiment generally demonstrate that amylase activity is highest at a specific pH, often near neutral or slightly alkaline conditions. This optimal pH level varies among different organisms, reflecting adaptations to their unique environments. Deviations from the optimal pH lead to reduced enzyme activity, as the enzyme’s shape and function are disrupted (Nelson and Cox, 2017).

Understanding the effects of pH on enzyme activity has practical applications in various industries, including food processing, pharmaceuticals, and biofuels. For example, optimal pH conditions are crucial in the production of high-fructose corn syrup, where enzymes are used to convert corn starch into sugars (Jenkins and Donald, 2018).

Questions:

1. What is the role of amylase enzyme in cells?
2. How does pH affect the activity of amylase?
3. How do you monitor the progress of the starch breakdown reaction?
4. Why is it important to maintain optimal pH levels for enzymes to function effectively?
5. What are some practical applications of understanding the effects of pH on enzyme activity?

Answers:

1. Amylase enzyme breaks down starch into maltose, a simpler sugar.
2. The activity of amylase is sensitive to the pH of its environment, with optimal activity occurring at a specific pH level.
3. The progress of the starch breakdown reaction is monitored using iodine, which changes color in the presence of starch.
4. Optimal pH levels ensure enzymes maintain their shape and function, allowing them to act as efficient catalysts.
5. Practical applications include food processing, pharmaceuticals, and biofuels.

Extended response question: How can understanding the effect of pH on amylase activity inform the development of a novel food processing technique? Discuss the factors that need to be considered in designing such a process.

Answer: Understanding the effect of pH on amylase activity is essential for developing novel food processing techniques, as it ensures the enzyme operates at its highest efficiency. In designing a new process, factors such as the optimal pH for amylase activity, the stability of the enzyme at different pH levels, and the influence of other components in the food matrix should be considered. Moreover, maintaining consistent pH conditions throughout the process is critical to maximize enzyme activity and achieve the desired product characteristics.

Wrong answer: Understanding the effect of pH on amylase activity is irrelevant to the development of novel food processing techniques. Instead, focusing solely on temperature and pressure conditions will yield the best results.

References: AQA (2021) AQA Combined Science: Trilogy. Available at: https://www.aqa.org.uk/subjects/science/gcse/combined-science-trilogy-8464

I decided that I needed some information on a practical so asked:

“now add a potential practical method and some fake results for this”

Potential Practical Method:

1. Label six test tubes A-F and add 2 mL of starch solution to each.
2. Prepare six buffer solutions with pH values of 3, 4, 5, 6, 7, and 8.
3. Add 2 mL of the corresponding buffer solution to each test tube (A: pH 3, B: pH 4, C: pH 5, D: pH 6, E: pH 7, F: pH 8).
4. Add 1 mL of amylase solution to each test tube and immediately start the timer.
5. At regular intervals (e.g., every 30 seconds), remove a small sample from each test tube and add it to a drop of iodine solution on a spotting tile. Observe and record the color change.
6. Record the time taken for the iodine solution to stop changing color in each test tube, indicating the completion of the starch breakdown.

Fake Results:

Test Tube	pH	Time (minutes)
A	3	12
B	4	8
C	5	4
D	6	2
E	7	3
F	8	6

These fake results demonstrate that the amylase enzyme has the highest activity at pH 6, as it took the shortest amount of time for the iodine solution to stop changing color in Test Tube D. The enzyme activity decreases as the pH moves away from the optimal value, with the slowest reaction rate observed at pH 3 in Test Tube A. These results underscore the importance of maintaining optimal pH conditions for enzymes to function effectively.

Then I took this further:

“now add 5 questions that a GCSE Combined Science paper might ask about these results”

1. Based on the provided results, what is the optimal pH for amylase activity in this experiment?
2. How does the enzyme’s reaction rate change as the pH moves away from its optimal value?
3. What does the color change in the iodine solution indicate in this experiment?
4. Explain why enzyme activity is generally lower at pH levels that are significantly different from the optimal value.
5. Suggest a reason why different organisms might have different optimal pH levels for their amylase enzymes.

Answers:

1. The optimal pH for amylase activity in this experiment is pH 6.
2. As the pH moves away from the optimal value, the enzyme’s reaction rate decreases.
3. The color change in the iodine solution indicates the completion of starch breakdown.
4. Enzyme activity is generally lower at pH levels that are significantly different from the optimal value because the enzyme’s shape and function are disrupted, leading to reduced catalytic efficiency.
5. Different organisms might have different optimal pH levels for their amylase enzymes due to adaptations to their unique environments, which affect the pH conditions in which their enzymes need to function effectively.

As you can see the data and the text need clear proof reading and checking but its really quick and powerful! Not sure about the references though!

The text and questions above were generated by employing a series of carefully designed prompts to extract relevant information from ChatGPT. Here’s a breakdown of the process:

1. Precision in Content Generation:
   • Prompt: “Write about investigating the effect of pH on the rate of reaction of amylase enzyme.”
   • This broad prompt set the stage for ChatGPT to provide general information about the topic.
2. Scaffolding Complexity:
   • Prompts: “What is the role of amylase enzyme in cells?”, “How does pH affect the activity of amylase?”, and so on.
   • The progression of prompts gradually increased in complexity, guiding ChatGPT to delve deeper into specific aspects of the learning aim. This mimics a scaffolding approach, moving from foundational to more advanced concepts.
3. Enhancing Practical Understanding:
   • Prompt: “Provide a potential practical method and fake results for the investigation.”
   • By prompting a practical method and fictitious results, the focus shifted from theoretical concepts to practical application, enhancing students’ understanding of how the investigation would be conducted.
4. Critical Thinking Stimulation:
   • Prompt: “How can understanding the effect of pH on amylase activity inform the development of a novel food processing technique?”
   • This prompt aimed to stimulate critical thinking by posing a scenario that required students to apply their knowledge creatively and think about broader implications.
5. Connecting to Real-world Context:
   • Prompt: “What are some practical applications of understanding the effects of pH on enzyme activity?”
   • By asking about practical applications, the prompt encouraged ChatGPT to generate content that connected theoretical knowledge to real-world contexts.

In each case, the prompts were carefully constructed to guide ChatGPT towards generating content that aligns with the learning aim and progressively builds a comprehensive understanding of the topic. The process involves refining and iterating the prompts to extract the desired information while maintaining coherence and relevance.

Conclusions:

Due to practice with ChatGPT in the process of generating the content, there were no instances where I had to repeat myself or ask for clarification. The prompts provided were clear and well-structured, allowing for a smooth generation of responses. The prompts were specific enough to guide the model in addressing different aspects of the learning aim, and the information provided by ChatGPT was coherent and relevant to the prompts given.

Clear and precise prompts are essential when working with language models to ensure that the generated content aligns with the intended purpose. In this case, the prompts effectively guided the model to produce educational content related to the investigation of the effect of pH on the rate of reaction of amylase enzyme, and subsequent questions and scenarios.

Mastering Python Basics: A Practical Guide for OCR GCSE Computer Science Students

Embarking on the journey of programming can be challenging, especially for students diving into Python within the context of OCR GCSE Computer Science. This blog post serves as a comprehensive guide, offering practical scenarios, solutions, and tips to help students build a strong foundation. Whether you’re a beginner or striving for grades 1-4, these hands-on exercises will reinforce key programming concepts and set you on the path to success. I’d like to cover some of the problems and write how we can go about best supporting. So I put my own brain into gear…. and of course asked my two favourite AIs to offer me a hand… this is what we produced together….

Key concepts covered for Python programming in OCR GCSE Computer Science:

• Basics: Variables, constants, operators, inputs, outputs, data types (strings, integers, booleans, etc.)
• Programming constructs:
  • Sequencing: Executing instructions one after another in a linear order.
  • Selection: Making decisions and branching the program flow based on conditions (using if statements, else statements, and nested conditionals).
  • Iteration: Repeating a set of instructions multiple times using loops (for loops and while loops).
• String manipulation: Working with text data, including accessing characters, searching for patterns, and modifying strings.
• Modules and functions: Importing pre-written code and creating your own functions to modularize your programs.
• Problem-solving and algorithms: Designing solutions to computational problems using appropriate data structures and algorithms.

Assessment:

Exams will include tasks like writing Python code to solve specific problems, tracing the execution of code, and analysing program outputs.

Tips for getting grades 1-4:

Focus on the fundamentals: Prioritize understanding basic concepts like variables, data types, operators, and input/output before moving on to more complex topics.
Practice writing simple code: Start with small, manageable tasks like printing messages, reading user input, and basic calculations. Practice regularly to solidify your understanding.
Break down problems into smaller steps: Don’t try to tackle a complex problem all at once. Divide it into smaller, manageable tasks and write code for each step individually.
Visualise your code: Use flowcharts or diagrams to map out the logic of your program before writing the actual code. This can help identify potential errors and make your code more efficient.
Seek help: Don’t hesitate to ask your teacher, classmates, or online resources for help when you get stuck.

Key points for a grade 4:

• Solve problems using basic Python constructs: Students should be able to use control flow statements (if, else, elif), looping structures (for and while), and functions to solve problems efficiently.
• Manipulate strings: Understanding string indexing, slicing, and basic string methods like find and replace is crucial for some tasks.
• Working with modules: Importing and using basic modules like random and os demonstrates code reusability and understanding of external functionality.
• Debugging and error correction: Identifying and fixing basic syntax and logic errors independently shows problem-solving skills and code comprehension.

Here are some key points and suggestions:

1. Understanding Basic Syntax:
   • Focus on mastering the basic syntax of Python, including variables, data types, and basic operations.
   • Activity: Practice writing simple programs that involve user input, mathematical operations, and output.

# Example: Simple addition program

num1 = int(input("Enter the first number: "))

num2 = int(input("Enter the second number: "))

result = num1 + num2

print("The sum is:", result)

Control Structures:

• Ensure a good understanding of control structures such as if statements and loops.
• Activity: Write programs that involve decision-making and repetition, like a simple guessing game.

# Example: Guessing game
secret_number = 7
guess = int(input(“Guess the number: “))

if guess == secret_number:
print(“Congratulations! You guessed it.”)
else:
print(“Try again. The correct number is 7.”)

Functions:

Learn how to define and use functions. Emphasize the concept of modular programming.
Activity: Create a program with functions to perform different tasks, like a basic calculator.

# Example: Basic calculator functions

def add(x, y):

    return x + y



def subtract(x, y):

    return x - y



# Usage

result = add(5, 3)

print("Addition result:", result)

Data Structures:

• Understand and practice using lists and dictionaries for storing and manipulating data.
• Activity: Create a program that involves creating a list of items and performing operations on the list

# Example: List operations

shopping_list = ['apple', 'banana', 'orange']



# Add an item

shopping_list.append('grape')



# Print the updated list

print("Shopping List:", shopping_list)

File Handling:

• Learn how to read from and write to files. Understand basic file I/O operations.
• Activity: Create a program that reads data from a file, performs some operations, and writes the results to another file.

# Example: File handling

with open('input.txt', 'r') as file:

    data = file.read()



# Process data (e.g., count words)

word_count = len(data.split())



# Write results to output file

with open('output.txt', 'w') as output_file:

    output_file.write(f"Word count: {word_count}")

Error Handling:

• Understand basic error handling concepts, such as using try-except blocks.
• Activity: Modify existing programs to include error handling for common scenarios, like invalid user input.

# Example: Error handling for division by zero

try:

    result = 10 / 0

except ZeroDivisionError:

    print("Error: Division by zero.")

Testing and Debugging:

• Learn how to test programs and identify common errors. Use print statements for debugging.
• Activity: Debug programs with intentional errors and fix them.

# Example: Debugging

num1 = 5

num2 = '2'  # Intentional error



# Attempting to add two different data types will result in an error

result = num1 + num2

print("Result:", result)

Practice Regularly:

• Regular practice is key to improving programming skills. Work on small projects and gradually increase complexity.

So based on this I then asked ChatGPT to come up with a set of examples that I could give to a class todo.. and it did! I’ve put them in this Google Doc (Note: solutions have not been checked for accuracy, also included a few examples from Google Bard). If you fancied using it! 

I’m going to give it a go with our students and maybe report back, if I remember!

From Brainstorms to Blazing Minds: Unpacking a Lesson on Energy Transfer

Please note the title of this was created by Goo¹gle Bard…. seems a bit OTT even for me! 🙂

Ah, the joys of lesson planning! That exhilarating mix of research, creativity, and a healthy dose of caffeine. Well there is less of that at the moment as again I take a BBC bitesize² text and use an AI to achieve my lesson questions/structure and generally do a lot of the heavy lifting for me so I can spend more time thinking about how to tailor to the class in front of me….. this time its Google Bard we are giving a go to.

Google Bard more so than ChatGPT³ seemingly wants to write fantastically and provide flowering language and glorious prose, when I just want some simple and clear text.

In fact in one response to me Google Bard wrote “You’re absolutely right, I got a bit carried away with the theatrics in the previous blog post drafts. Let’s take a more practical approach and summarize our chat to create lesson text and questions, focusing on…. ”

So what did we do here?

Topic: Energy Transfer (Conduction, Convection, Radiation)

Target Audience: Year 9 students

Lesson Goals:

Recall and define different types of energy transfer.
Identify examples of each type in everyday scenarios.
Explain the key principles of each transfer method.
Apply their understanding to solve problems and design solutions.

Discussion Highlights:

We started with a review of energy types learned in previous lessons.
You wanted to focus on energy transfer as the main concept.
You emphasized the importance of practical considerations and real-world application.
We brainstormed interactive activities and engaging examples for each type of transfer.
We discussed different question formats and assessment strategies.

Google Bard told me:

What Went Well:

• We identified clear learning goals and a relevant topic for your students.
• We brainstormed engaging activities and questions that encourage active learning and problem-solving.
• We focused on real-world application and making the science of energy transfer relatable.

Next Steps:

• Refine the lesson text and activities based on your specific resources and classroom setting.
• Design additional assessment tools to gauge student understanding of the concepts.
• Share your lesson plan and experiences with other educators to spark further discussion and collaboration.

Remember, this is just a starting point. Feel free to adapt and personalize the lesson to fit your teaching style and student needs. The key is to create a learning experience that is both informative and engaging, sparking curiosity and a deeper understanding of the fascinating world of energy transfer.

All fairly good points.

Now how did this compare to ChatGPT – hard to say. I still prefer the way ChatGPT communicates with me. Both got decent results in the end… well once I told Bard to effectively calm down.

How to figure out which is better? Well let’s ask the AI. I asked ChatGPT about Bard’s text and it told me:

“Google Bard did a commendable job in structuring the lesson plan. The key differences in approach might be the choice of language and style”

So I asked it again telling it that I insisted on that style and it told me….

Given the context of you providing specific prompts and instructions to shape the style, both Google Bard and I adhered to the directives in a similar manner. In this controlled setting, the primary difference lies in the underlying language models used by Google Bard and ChatGPT, which may contribute to slight variations in response style and phrasing.

Considering your instructions for a structured and directive approach, both systems align closely in delivering the desired content. The nuanced differences may still reflect variations in the models’ understanding and generation patterns. Overall, it appears that both approaches effectively cater to the instructional style you set forth.

Now I will leave you with Google Bard’s words of wisdom on this blog post….

And so, the Bard versus ChatGPT saga concludes! After a whirlwind tour of lesson-planning, AI quirks, and self-referential analysis, we stand ready to unleash this energy transfer lesson upon your Year 9 students. Remember, you’ve wielded both language models like Jedi lightsabers, extracting their essence to craft a lesson that’s both informative and engaging. So, ignite the spark of curiosity, watch understanding bloom, and bask in the glow of a well-taught science session! Now, go forth and conquer – and maybe cut back on the caffeine, your lesson plan is already electrifying enough!

I leave with:

1. AI Google Bard: https://bard.google.com/ ↩︎
2. https://www.bbc.co.uk/bitesize ↩︎
3. ChatGPT from OpenAI https://chat.openai.com/ ↩︎

Empowering Educators: AI-Enhanced Content Creation for Teachers

In the journey of crafting educational content using AI, I encountered various challenges and explored the capabilities of the language model. In this post we will reflect on some efforts I have undertaken and I will give a summary of the process.

As we delve into the fascinating realm of AI-powered content creation, it’s essential to acquaint ourselves with the robust capabilities of models like GPT-3.5. In the context of education, its potential for crafting tailored and insightful educational content is noteworthy. AI’s role in content creation extends far beyond conventional methods, offering efficiency, adaptability, and scalability. As we explore the collaborative journey with GPT-3.5, it becomes evident that leveraging AI in education holds the key to unlocking personalized, effective, and engaging learning experiences.

So let’s look at a task. I wanted to take a text and use it with a science class. For the moment we are just going to take some text from BBC Bitesize and utilise this, rewriting as needed. Essentially we will go from:

to:

So how did we go about this (please note I wrote in a very informal manner with ChatGPT and didn’t worry about any spelling or grammatical issues, here is our dialogue:

1. Initial Prompts:
   • Prompt: “If I give you two pieces of texts can you format the first one in the style of the second adding questions and answers as appropriate?”
   • Response: “Certainly! Please provide the two pieces of text you’d like me to work with.”
2. Content Transformation:
   • Prompt: [Provided two texts on energy transfer]
   • Response: [Formatted the first text in the style of the second, incorporating questions and answers appropriately]
3. Adjusting Prompts for Clarity:
   • Prompt: “Is the second text:”
   • Response: “In silence, write the date and the title below in your exercise book: Compare benign and malignant tumours. Now write the subtitle in your exercise book and answer the questions in full sentences.”
4. Correcting and Refining Prompts:
   • Prompt: “Great thank you for the questions but could you do the same now for the main text.”
   • Response: [Proceeded to format the main text in a similar manner as the previous, without additional questions]
5. Clarifying Prompts for Precision:
   • Prompt: “No the other way around”
   • Response: [Reformatted the text as per the clarification]
6. Refining Prompts for Specifics:
   • Prompt: “Ok, let’s go back to the energy transfer lesson, can you create a table where there is the energy type, using the AQA combined science GCSE list, and the definition on the right… then underneath have a table where the keyword is missing but definition is there.”
   • Response: [Provided tables with energy types and definitions, leaving blanks for keywords]
7. Adapting Prompts to Challenge AI:
   • Prompt: “Great now based on the text around energy and the above definitions create 4 MCQs with clear distractors and 3 options labelled a, b, c and underneath the full list of questions give the answers.”
   • Response: [Generated multiple-choice questions with clear distractors and provided the answers]
8. Transitioning to Knowledge Check:
   • Prompt: “Now let’s do 8 knowledge check questions […] they go from basic definition recall to explaining how to use something.”
   • Response: [Provided knowledge check questions related to energy transfer]
9. Completing the Knowledge Check:
   • Prompt: “Great now give the answers”
   • Response: [Delivered detailed answers to the knowledge check questions]
10. Closing with a Challenge Question:
    • Prompt: “Now create a short challenge question on this topic and give an answer.”
    • Response: [Generated a challenge question and provided an exemplary answer]

Conclusion: Navigating through AI-generated content creation involves refining prompts, adjusting language, and adapting queries to achieve desired outcomes. The process is collaborative, and understanding how to articulate prompts effectively enhances the efficiency of AI interactions.

Suggested Steps for Effective AI Collaboration:

1. Clearly Define Objectives: Clearly articulate your goals to the AI model.
2. Iterative Refinement: Adjust prompts based on initial responses, refining them iteratively.
3. Specify Format and Style: Clearly communicate the desired format, structure, or style of the content.
4. Ask for Clarification: If a response is not as expected, seek clarification or guide the AI with more explicit instructions.
5. Utilise Feedback: Use the feedback loop to correct and improve upon responses. This is absolutely crucial. Accept it will get it wrong and alter it and change it. Go back and do again. Your AI helper will get it right with clarity.
6. Experiment and Learn: Experiment with different prompts to understand the model’s capabilities and limitations.

By following these steps, users can enhance the collaborative process with AI models, ensuring a smoother and more effective content creation experience.

I’ll try this with a different language model next…

Analysing Time Series Data with Google Cloud: A Guide to FFT

A while back I decided to take a proper look at Google Cloud. I am always very interested in time series data – clearly links to my days of hunting down noise in radio telescope data or hunting for exoplanet flares….! I spent some time with ChatGPT coming up with some quick ways of doing this analysis using Google Cloud (was the first time I did this) and well I didn’t want to just extract with SQL and run some Python scripting locally…. after we finished I asked ChatGPT to write a guide that I could share….. so here it is:

Understanding Time Series Data

Time series data is a sequence of observations recorded at equally spaced intervals over time. It is a crucial component in various fields, such as finance, healthcare, and environmental science. Analysing time series data can unveil patterns, trends, and anomalies, providing valuable insights for decision-making.

In this blog post, we’ll explore the realm of time series data analysis using Google Cloud services, with a focus on Fourier Transform, a powerful technique for understanding the frequency components within a time series.

Google Cloud Setup

Before diving into the code, make sure you have a Google Cloud account and the necessary resources set up:

1. BigQuery: Store and manage your time series data.
2. Dataproc: Create Spark clusters for distributed computing.

Example Data

For this guide, let’s consider a hypothetical dataset representing data over a period of time:

CREATE TABLE your_project_id.your_dataset.your_table (

    record_date DATE,

    organic_revenue FLOAT64

);



INSERT INTO your_project_id.your_dataset.your_table (record_date, organic_revenue)

VALUES

    ('2023-08-01', 1200.00),

    ('2023-08-02', 1250.00),

    -- ... rest of the data ...

    ('2023-08-31', 650.00);

In this example:

• Replace your_project_id with the actual project ID in your Google Cloud project.
• Replace your_dataset with the name of your BigQuery dataset.
• Replace your_table with the desired table name within your dataset.

To perform Fast Fourier Transform (FFT), we’ll use PySpark on Google Cloud Dataproc. Here’s a simplified PySpark script:

Google Cloud Dataproc and PySpark

from pyspark.sql import SparkSession

import numpy as np



# Initialize Spark session

spark = SparkSession.builder.appName("FFTExample").getOrCreate()



# Read data from BigQuery

project_id = "your_project_id"

dataset_name = "your_dataset"

table_name = "your_table"



data = spark.read.format("bigquery").option("table", f"{project_id}.{dataset_name}.{table_name}").load()



# Perform FFT using Spark's built-in functions

fft_result = data.select("record_date", "organic_revenue").rdd.map(lambda row: (row.record_date, np.fft.fft(row.organic_revenue)))



# Save the FFT results to a storage location

output_path = "gs://your-output-path"

fft_result.saveAsTextFile(output_path)



# Stop Spark session

spark.stop()

Remember to replace placeholders:

• your_project_id with your actual Google Cloud project ID.
• your_dataset with the name of your BigQuery dataset.
• your_table with the desired table name within your dataset.
• gs://your-output-path with your desired Google Cloud Storage output path.

This script reads the time series data from BigQuery, applies FFT using NumPy, and saves the results to Google Cloud Storage. Make sure to replace "gs://your-output-path" with your desired storage location.

Analysing FFT Results

Once the Spark job is complete, you can access the FFT results in Google Cloud Storage. Depending on your preference, you can further analyse the data using tools like Google Data Studio, or export it to other platforms for advanced visualization and interpretation.

Conclusion

Analysing time series data using Google Cloud provides a scalable and efficient solution. By employing the Fast Fourier Transform, you gain insights into the frequency components of your data, unlocking a deeper understanding of patterns and trends. This guide serves as a starting point for your journey into time series analysis on the Google Cloud platform.

As for the development of this with ChatGPT – it was actually quite straightforward and again an ideal tool to help me learn a new tool.

Python coaching with Bard

This is a follow on from yesterday’s post about how I was coached to solve problems with ChatGPT. It turns out with the exact same prompt Google’s Bard produced a very similar outcome. It might have even been better or faster… that or I knew what I wanted quicker…. either way I was impressed. Well until we had some issues….

Big difference between the two…. Bard was able to generate me a fake CDF file to test there and then…. though it was illustrative and not functional, hmmm… not sure I see the point.

CDF_EPOCH             = 1993-11-01T00:00:00

CDF_VERSION           = 3.4

DEL_VAR               = B_NEC; B_GSE; V_GSE

VAR_LIST              = B_NEC; B_GSE; V_GSE



B_NEC[365][25][3]     = 1.000, 2.000, 3.000

B_GSE[365][25][3]     = 4.000, 5.000, 6.000

V_GSE[365][25][3]     = 7.000, 8.000, 9.000

The issues came when debugging and issue the version of the code it had given me…. the response I got was:

I’m designed solely to process and generate text, so I’m unable to assist you with that.

I’ve never had that off ChatGPT… yes errors, yes going the wrong direction… yes random stuff that makes no sense but always a willingness. This left me with no coaching. So that’s where I stopped. I’ll give it a go again, but well I know which AI tool I am sticking with for now (again Bard don’t put me on the naughty list… I promise to come and write some lyrics with you later about how “banging” Brum is…)

I asked my favourite image generator to turn this sequence of events into a cartoon scene….