AI Readiness: Step 4
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It is not always the best idea to select data sources that are easiest to access. Many times, it is worth going to a little more trouble to access data sources that are of the greatest relevance to our challenge
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Once we have identified works of relevance and we have identified relevant data sources, the next step is to synthesise all this data and information together. And don’t forget, it is not just the data that you yourself might have collected or that from others in your organisation, there are sources out there that are publicly available and that can contribute to your understanding
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A few initial questions that need to be addressed for new data collection could be:
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Who is going to have responsibility for collecting the data? Perhaps it will be a course or module leader, or a class teacher, a head of the department, or a teaching and learning policy lead
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Timeframes: when will the data be collected? Today, next week, next month, next year?
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Over what period of time will the data collection happen?
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And it is important to consider that this data collection could be occurring whilst teachers might be trying to keep up with their schedules
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Data can be collected through some common means, such as:
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Surveys
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Interviews
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But there is also multimodal data, such as video-enabled platforms, which can allow analysis and reflection of language used in classroom organisation or the addressing of misconceptions, or the style of scaffolding provided to individual students, or student-to-teacher conversation
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Key Takeaway: Data collection needs to be designed carefully and must complement that data which is already available. Thoroughly examining your challenge in the earlier steps should allow you to frame exactly what data you do and don’t want, so that you’re not wasting your precious time or opening yourself up to risk, collecting something that will later turn out to be irrelevant
- Professor Rose Luckin, Founder, EDUCATE Ventures Research, July 2022
Step 4: Modules
How to Get Started with Data Collection
Don’t just consider the data you’ve collected yourself, there are sources of data that you can access that are publicly available
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What New Data Should You Have?
Spend time planning what you want to collect, as whatever you find out about your challenge or concerns is only as good as the data you have
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Methods for Data Capture
Although surveys yield self-reported data from individuals, they are exceptionally useful for gathering structured and unstructured data
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Types of Questions Used in Data Capture
No single question will yield the complete data that you need to address your challenge, so think broadly about what to ask and how much to ask
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Avoiding Survey Mistakes
Poor quality questions can provide you with conflicting data, no data, or unsubscribed respondents. Think about how your questions will be received!
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Sampling
In developing your surveys and data gathering exercises, consider who you want to complete them, and what the results might look like if your entire audience does complete them
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Interviews as Data Capture
The interview is an extremely useful way to get a lot of detailed information from a small number of subjects
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Step 4: Further Reading
Below you can find a selection of resources, books, podcasts, webinars, and research papers appropriate to your stage of AI Readiness. Good luck!
Download this step as a PDF here.
AI for School Teachers, Byte-Sized Edition
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An easy-to-read 10-page byte-sized summary of the book of the same name, written by Professors Rose Luckin, Mutlu Cukurova, and Headteacher Karine George, members of the senior team actively developing and using the AI Readiness Framework from which these recommendations derive
Hey Mum, the Fridge Just Let in the Burgulars
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A short blog from the Institute of Education featuring the dangers of unethical data capture in devices with poor online security
AI Readiness: Step 4 Webinar for Educators/Businesses
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Two separate webinars introducing Step 4 of the AI Readiness Framework, one targeted toward educationalists, and the other targeted to educational businesses
Understanding and Measuring Emotions in Technology-Rich Learning Environments
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Following a review, empirical research is presented on measuring emotions in the context of learning with TREs in multiple domains, including medicine, history, and mathematics. The researchers use concurrent measures to capture students’ cognitive, metacognitive and affective processes before, during, and after solving problems, documenting the complex role of such processes as individuals and groups learn with technology. The paper concludes with two commentaries that point the way to next steps in this field of research
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The complete book on the AI Readiness Framework, specifically for teachers and headteachers in schools. It will help teachers and heads understand enough about AI to build a strategy for how it can be used in their school. Though it is pitched to teachers and contains familiar examples, the approach should still be used by education and training businesses working with technology