Angst and confusion about education is hardly new, but there is a lot of angst and confusion about almost everything at the moment. Critique can be a bit cheap, although it certainly has its place in identifying absences in current structures and processes. But you can't build a better system from absences - except, perhaps, by bureaucracy, but that then introduces its own absences.
In partnership with the Far Eastern Federal University, I'm wanting to do something positive and create a laboratory for education which can explore how things might be better in a practical way. The following is a working document towards a concrete proposal.
Intro - What is a Learning Futures Lab for?
A Learning Futures Laboratory exists to empirically explore ways in which education can be improved. The laboratory consists of three key elements between which it seeks to establish synergy:
- An institutional focus which explores how teachers and learners can be organised in flexible learning activities which lead to recognised certificates.
- A learner focus which explores how individual students (from all levels of education) can navigate a fast-changing and uncertain environment and make choices about their educational direction
- An employer focus which explores how employers can take the best advantage of institutional educational provision and the graduates which it produces, while also enabling employees to develop themselves in the context of their work.
Building on existing work - particularly around a transdisciplinary module I created in Russia called "Global Scientific Dialogue" (which is about to run in its 4th year) - the laboratory’s research focuses on the combination of data analysis, artificial intelligence and learning design alongside creative activities and conversational learning.
The laboratory will work with students of all ages. It serves the education of students in the University, as well as providing outreach to schools and younger students. In providing outreach, it creates opportunities for university students to engage with and help younger people.
Structurally, the lab can be imagined as a Venn-diagram:
For each of these dimensions, we propose a simultaneous top-down and bottom-up analytical approach: effectively transformation is achieved through a “pincer-movement” which unites approaches to institutional learning design with approaches to learner self-direction and inquiry, alongside active engagement with employers. In addition to this, there is a middle-out process where local educational interventions – either those in schools, or those in the university – can be expanded into other areas and engage a broader range of stakeholders.
For each of these top-down, bottom-up and middle-out processes, there are associated analytical activities which are intended to identify powerful questions to steer the process, whilst also providing metrics on progress, performance and (most importantly) "are we asking the right questions?".
Data analysis
After COVID, large amounts of educational activity are happening online. This means that there is now the possibility to analyse digital learning transactions in powerful ways. This also presents the possibility that strategic directions in education can be supported through improved data analysis and forecasting. The Learning Futures Laboratory will exploit the power of the Microsoft Graph API, Blackboard, and other data sources for educational activity, so as to provide an overview of educational activity and identify powerful questions to ask about educational processes.
The most important dimension in this process is the data concerning the learning activities that students engage in within their studies, and the “fit” between these activities and real-world needs among employers for specific skills. The Learning Futures Laboratory will focus on the establishment of new metrics which will identify the synergy between learning activity, curriculum content and employer needs, drawing on data from industry, government and curricula. This analysis will draw on existing work on the synergy and innovation within national economic systems, the Triple Helix (see Triple helix model of innovation - Wikipedia)
Progress and success of the Learning Futures Laboratory will be measured according to progress against new metrics derived from this data analysis.
Learner Focus and Self-Steering
The techniques designed for institutions to measure their performance can also be used with learners themselves. The Learning Futures Laboratory will seek to develop tools for individuals to use with which they can perform data analysis on their own educational progress and align it to indicators drawn from industry – for example, job advertisement requirement, key competency criteria, etc.
Drawing on existing work around medical diagnostics, artificial intelligence can be exploited to provide learners with the means of prioritising those areas of their work which they are most interested in, and automatically identifying potential new learning opportunities, new contacts, or new job opportunities.
Learning and Assessment Design for Educational Experiments
Core to developing new tools and greater flexibility in education is the experimentation with new kinds of educational process. Of particular importance is the design of educational experiences which are interdisciplinary, involve technology, and create spaces for learners to be creative in ways which individuals are comfortable with.
At the heart of the Learning and Assessment Design work is the current Global Scientific Dialogue programme. This is a flexible educational programme which has sufficient flexibility within it to accommodate a range of new tools and techniques.
Laboratory Philosophy and Methods
The laboratory focuses on strong relationships as being at the core of learning: it focuses on the relationships between teachers and learners, those between learners, and between graduates and employers.
In order to develop stronger relationships, the laboratory will use its data analysis activities to identify powerful new questions with which to engage different groups of people. Data analysis can take many forms. It can be analysis of creative work, or analysis of the current scientific discourse, or analysis of educational results. Whatever analysis is done, however, the focus is on using data to ask questions, not using data to form judgements or deliver “answers”.
The individual focus of the Learning Futures Laboratory will seek to encourage learners to generate their own data, and then to explore their own data. Through focus on user-generated data, the intention is to create a more direct connection between computational techniques and personal activities.
The learning design activities of the Learning Futures Laboratory will develop ways of teaching which will encourage engagement with questions raised by data analysis, as well as engage students in asking new questions of the data. In this way, the Laboratory both drives its activities with data, whilst engaging students in the technical challenges of improving the data driven research.
Projects
There are a range of possible projects that concern the laboratory. These will engage different kinds of stakeholders, and serve different purposes:
- The biology of learning with technology
- The data analysis of creativity
- The system dynamics of institutional organisation and viability in a fast-changing world
- Futures literacy
- Conversation and the construction of confident selves
- Simulation and prediction of learning process
In addition to these projects, the Learning Futures Laboratory can conduct training on its own techniques and technologies for teachers in other institutions, schools and employers.
Outreach: School involvement
One of the key goals of educational development in schools is the importance of “computational thinking”. The Learning Futures Laboratory can drive an innovative programme of educational events with schools, both using face-to-face settings and online.
In the spirit of the overall philosophy of the laboratory, these interventions will be based around interdisciplinary conversations and creative activities, where data is both generated and analysed by students, who gain an understanding of the relationship between conversation, creativity and technology.
Overall, in these activities, we aim to establish a “computational thinking” curriculum which is no constrained by the subject of computer science, but reaches across disciplines and inspires increased engagement and curiosity with technology and data analysis.
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