A teacher is wearing an Apple VisionPro headset. In front of her is a classroom full of students, all wearing the headsets too. The devices are being used to teach complex concepts in an immersive way.
The teacher notices concentration waning halfway through the lesson. So she taps a button that pulls up a chart visualising aggregate attention span over the duration of the class. As she suspects, concentration is dropping. Quick to respond, she begins asking the students questions to re-engage them. It works: attention span increases, and the class is re-energised.
I know this may sound like a dystopian vision of the future. But could it be one of the ways in which data and new technologies inform the next generation of learning? Similar applications of data are already being used to measure and improve learning through online classes and e-learning courses in real time. Wearable tech is already supporting everyday physical health, so it follows that data-driven devices will soon be charged with looking after our mental stimulation as learners, too.
Since ChatGPT’s introduction and mass consumer adoption, we’ve been seeing exciting use of the tools and opportunity in educational settings, contrary to early worries.
Harvard University has enrolled its own generative AI chatbot. Starting this fall semester, students enrolled in Computer Science 50: Introduction to Computer Science (CS50) will be encouraged to use AI to help them debug code, give feedback on their designs, and answer individual questions about error messages and unfamiliar lines of code. Closer to home, we’re seeing students switch to AI to learn languages.
This isn’t all about AI, though. Data has been used to improve learning and development outcomes for generations. Quizzes, tests and exams all pull in data to inform educators, students, employers, policy-makers and NGOs. Data answers key questions like:
The difference now is that we’re starting to see cutting-edge new ways in which data connects the dots to improve educational and development outcomes. Here are three ways you might not know about:
Big data is being harnessed at a macro level to review the quality of workforce development and training – by taking the dots that previously couldn’t be connected and finding insightful new ways to connect them.
The team at the Harvard Project on Workforce has two initiatives running right now. The first, the Workforce Almanac, attempts to bridge the gap in the US between a workforce development system that has historically been split into separate categories/silos for the purposes of funding and study. As a result, researchers and policymakers have struggled to understand workforce development from a holistic perspective – one that includes community college programs, the public workforce system, registered apprenticeships, and non-profit providers.
The Workforce Almanac is a first-of-its-kind initiative that aims to help us move away from this narrow and siloed conception of workforce development by analysing over 17,000 providers of workforce training. This exciting research will be developed and built out in the coming years, and the team hopes to inform policy-makers on improving how and where workforce training takes place.
As an example, this data can highlight anomalies within a given geography: Why does this sparsely populated county have a higher concentration of workforce development centres? Or does this area have low unemployment rates because public transport links are few and far between, and its workforce developments centres are only accessible by car?
The College-to-Jobs Map is another research project led by the Harvard Project on Workforce as part of a broader research effort to improve connections between colleges and the labour market. It uses data to answer important questions like:
Armed with such findings, educational institutions can look to better prepare their students for the realities of a fast-shifting job market.
Data is used (although not always very well) by academic institutions to adjust course curricula in a number of ways. First, decision-makers simply look at demand for courses, considering both internal factors (Is this course popular? Should we keep it or drop it?) and wider socio-economic factors (Are there policies in place to improve development in certain sectors and skills? Can this course support that?).
More practically, classes can be effectively re-allocated to maximise resources, especially among schools that may be less funded than others. For example, learning management systems can help students who may be struggling by raising alerts based on data collected from previous course-takers. The same logic can be applied to teachers: if student exam grades begin to decline in one class, the data can clarify whether it’s the students who aren’t performing, or the teacher.
The US has a huge student debt problem. The total federal student debt has more than tripled over the past 15 years, rising from about $500bn in 2007 to $1.6tn today. Data can help students financially in many ways. Here are just a few:
Data’s role in informing the latest generation of learning and development is only just getting started. Could we see courses that evolve in real-time based on user behaviour, where the sections of a program become harder or easier to boost chances of student success?
It’s an exciting time to be involved in EdTech. Here are some predictions for areas of L&D that are ready to reap the benefits of visual data:
Our EdTech pod places data-driven solutions in the hands of next-generation educators and workforce developers. Get in touch about running one of our Lunch & Learn sessions for your team.
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