Global EdTech Landscape 1.0
Mapping the future of education: an open-source MVP.
10 Oct 2018. Check out the 2019 Global Learning Landscape. http://globallearninglandscape.org. An open source taxonomy for the future of education. Mapping the learning and talent innovation landscape.
In summary... we found 16 innovation clusters and grouped them into 6 themes in our first scan of the market we call Landscape 1.0. That’s over 2,000 companies around the world representing $16b of investment. We are launching Project Landscape as (eventually) a global, open source, community driven initiative to help us understand what the next 10 years in education might look like. If you are keen to get involved directly, please ping me on LinkedIn.
What will education look like in 10 years?
Ernest Hemingway suggested change happens two ways: gradually and then suddenly. I can’t think of a better way to describe how I expect education to evolve over the next 10 years.
At Navitas Ventures we have started talking about unleashing human potential. We use words like those deliberately to escape preconceived notions of the ‘supply’ or ‘provision’ of education and encourage a fresh conversation focused on the learner. We build, back and partner to establish and grow education focused ventures and the first logical step was to map out the education landscape. To reimagine the lines on the map that have defined our profession for the last 1,000 or so years and the change we anticipate over the next 10.
Like any explorer or pioneer; entrepreneurs and venture partners need a map of this emerging landscape. So too will traditional institutions. Whether you are looking for unchartered territory or hidden dangers, maps help you decide where and how to start your journey and ultimately your intended destination. So we set off on our own journey to map the new and emerging landscape of education and called it Project Landscape.
Project Landscape
Project Landscape is our vision for a global, open-source, community-driven map of education innovation and technology. Drawing on the diverse perspectives of Navitas teams in 50 countries, we hope to seed Project Landscape within the most vibrant education innovation communities around the world. We want to put those already exploring and pioneering on the map and inspire those who might not have otherwise to consider their own expedition.
Like any lean startup, we began by building a Minimum Viable Product (MVP), “that version of a new product which allows a team to collect the maximum amount of validated learning with the least effort”. Navitas is built on partnerships and we found the perfect partner for Project Landscape’s MVP in San Francisco based startup Quid .
Quid has access to millions of news articles, blog posts, company profiles, and patents and runs on natural language processing algorithms that can read and analyze massive amounts of data at one time, bringing a whole new meaning to “least effort” for an MVP. We ended up with a sample of over 2,000 companies from over 40 countries — most coming from the US, UK, China, India, Canada, France, Germany, Australia, Japan and Spain.
Landscape 1.0
Our MVP is based on the network below. Visualising networks in this way helps us make sense of a complex landscape or trends, so we can interpret them and make better decisions. We also like that we are not using a 1,000 year old rule book to organise innovation but rather a blank canvas that is more contemporary, organic and descriptive.
We found 16 clusters of similar companies but found it hard to digest in one bite so we created 6 broad groups or thematics represented below by different colours. I can’t stress MVP status enough; it’s incomplete, and overlaps but it’s more than enough to seed the project and collect feedback.
Understanding network diagrams. If nodes (companies) within the network are connected, they have similar products, technologies or services. If they are close together, this means their other connections are similar too. Larger nodes are more representative of the other nodes in their cluster. A cluster is a group of nodes that clump together because many of them are connected due to sharing a high degree of similarity.
We will deep dive into the thematics and clusters in a follow up note. The list below shows how the thematics above link to the clusters listed and shown in the network below.
Content. Publishers, Content Distribution, Digital Learning
Platforms. LMS & Analytics and Social Learning
Access. Recruitment & Employment, Admissions and Financing
Immersion. VR/AR and China Ed Tech
Learning. Formal/Accredited and Informal/Unaccredited
Progression. Peer to Peer and Tutoring, Language and Literacy and Testing & Credentialing.
The clusters feel more familiar and start to tell a story. Something tells me we will end up with micro-clusters but again this is plenty for an MVP. At a cluster level you can also start to see different business models and market leaders come to mind. China Ed Tech clustered together despite having members that will eventually sprinkle through the landscape. VR/AR is a clear cluster but likewise might eventually creep through the network as is AI.
Cluster leaderboards
We ranked the clusters below, in part explaining how the sample is made up and part the trends coming from the sample itself. Total investment and median investment are both in millions USD.
What’s next?
Building Landscape 1.0 has taken some effort, but the real work comes now. We need passionate and constructive feedback so we can understand where to focus. You can send feedback here. We greatly appreciate any and all thoughts.
Meanwhile, we’ll start putting together the technology to create a truly open source and global landscape, where our collective intelligence can create a strong and scalable community.
Over the coming months we will be sharing more of our research and learnings. Landscape 1.0, and what will follow, is equal parts human intuition and machine intelligence — because we believe that the future of technology is about investing in people and unleashing their potential.
