How can schools change? How can–and will–the concept of education change in the next thirty years? this is a question I ask my co digital workers at JBKlutse.com every day to find a solution.

Medium shot student studying with laptop Free Photo

These are critical questions that are difficult to even begin to answer (imagine answering the same kind of question in 1992 to clarify the schools of 2022). But let’s speculate–identify some specific ways or areas we might look to for this kind of evolution. There’s too much here in terms of quantity to qualify exactly what this might look like, how feasible it might be, etc. The point here isn’t a how-to guide to change school systems–especially those not looking or built for change.

 

Further, I’ve written about this many times with many scholars like Chukwuma Chinaza Adaobi, and Prof. Daniel Obeng-Ofori all from Catholic University College of Ghana–a few examples:

 

Three of the biggest questions that fascinate me as an educator and shape much of TeachThought’s work are:

What are students are learning?

How are they learning it?

What are they doing with what they learned?

Add to this the concept of knowledge demands: what’s worth knowing in a modern world? Who gets to decide? How will we know if we’re correct? What’s at stake if we’re not?

New Content Areas

From content to ‘thinking.’ Unlike the above, this wouldn’t be ‘new content areas, but rather ‘no content areas’–a shift from academics to the ability to use research and data-grounded reasoning–from teaching content to teaching thinking.

Well-being-focused systems of teaching and learning (e.g., cognitive behaviour, the ability to reason, inside-out schooling, empathy, communication, citizenship, etc.)

Well-Being (for teachers and students)

One idea for the future of learning? Well-being-focused systems of teaching and learning that emphasize reasoning and cognitive behaviour.

Blended Learning

This isn’t new but it’s reality and powerful and, likely, at least part of the future of education)

Transfer-By-Design

Building all learning experiences for the sole intention of students using learning outcomes right now to live better lives right now in communities relevant to them right now–and this application is driven entirely by the student (though the learning wouldn’t have to be).

Data Quality

The quality, recency, and visibility of data have to become significantly better in schools of the future. This alone makes teaching a tremendous challenge and its growth could unburden over-worked teachers–especially if you add competent artificial intelligence to the idea.

Authentic ‘Apprenticeships’

Craftsperson-based, academic, informal, human, AI, cognitive, behavioural, etc. Let students be inspired and led by more than just the teacher and parents. It takes a village, after all.

Shift From Standardization To Creativity

A shift from uniformity to non-standardized diversity (in learning models, technology, curriculum, culture, data sources, etc.)

Asynchronous + Synchronous

Smarter use of the former to support the latter. I guess this could be framed simply as ‘blended learning,’ but I’m thinking more of a focus of exactly how one might support the other rather than one merely does support the other.

Adaptivity

Adaptive academic curriculum (students learn the same things at constantly varying levels of complexity based on performance)

Informal Learning

Learning through play (see below), for example.

Self-Directed Learning

Here’s a self-directed learning model I made years ago.

Game-Based Learning

This has been going on for years–but as games and simulations improve, the potential becomes more acute, compelling, and diverse.

Gamification

I’m not sure why this isn’t more commonplace informal education. It’s one of the most powerful psychological concepts that could scale at the level education requires; I wrote about it over a decade ago–and (not very well) again in How Gamification Can Uncover The Nuance Of Learning.

‘Certificates’ (or any kind of symbol of achievement or certification) come from de-centralized ‘things’–communities or artificial intelligence instead of individual institutions, for example.

Interdependence

Everything is inherently already ‘interdependent’; this would be a shift toward honouring this in the design of all of the ‘interdependent things.’

APIs

An ‘API’ is an initialism that stands for ‘Application Programming Interface’–a framework that allows software to ‘talk’ to and function through and alongside other software. Thus, this is similar to the above–the idea that the pieces of education should be designed to work together–and then in the next concentric ring outward, the schools should work with the communities and one community with another and so on.

Mobile Learning

And here I mean actually mobile learning by mobile learners–mobile within a classroom, school, community, nation, physical and digital spaces, etc.–and this movement is authentic and improves the learning experience by making the transfer more natural, among other benefits.

Physical-Digital Transitions

See above. The difference here is that the relationship between physical and digital spaces–and the strengths and needs and citizenships within each–are emphasized by using a transition between them as an integral part of the learning process. Units or lessons could begin by asking questions like, ‘How might/will physical communities function in this lesson? How might/will digital communities function in this lesson? How can the movement between the two–where one helps shape the other in a way that benefits both–be encouraged and illuminated and authentic?

The goal is for pure ‘eLearning’ to be a very narrow niche and to constantly encourage transfer–for students to, unprompted, use knowledge to shape their life and contribute to their physical communities at home.

New Learning Spaces

A farm? Startups? Augmented and virtual reality? Community centres?

Personalized Learning

By this I mean each student learning unique content based on their unique needs via unique learning models and assessed by unique assessment forms, and so on. This is different from differentiation, where students (more or less) learn the same thing in ‘different ways.’ Synchronous, artificial intelligence/machine learning can help create fluid, personalized curriculum and curricula.

Place-Based Learning

This one appears in some of the above ideas.

Project-Based Learning

This versatile teaching and learning tool can act as a skeleton to incorporate many of the others–place-based education, personalized learning, apprenticeships, simulations, digital-physical transitions, etc.

Learning Through Play

The most natural way to learn is hugely absent in most formal education systems. Which is weird.

Challenge-Based Learning

Learning based on challenges–real-life, academic, social, personal, etc.–rather than academic standards. (Of course, it doesn’t have to be either/or.)

Simulations

As digital simulations improve, the potential for schools is compelling. But there are other ways to simulate–live role-playing historical figures, for example.

Opportunity-Based Learning

Thing challenge-based learning but framed a bit differently.

‘Blockchain Learning

De-centralized, peer-to-peer transactions, new currencies, knowledge ‘mining,’ etc. I know this one is vague but it’s been used outside of cryptocurrencies for years.

Data Streams

As opposed to data snapshots (quizzes, tests, report cards, etc.)

The Inside-Out School

I’ve always liked this idea because it seems so obvious and relatively simple to make happen.

Think-Tank Schools

Challenge-based learning, project-based learning, etc.

Transparency

This is uncommon in many schools and districts and, among other causes, has resulted in a significant loss of capacity for communication between schools, families, and communities.

 

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Miracle. Has worked as a research analyst for hightail consult limited in Accra, Ghana, and as a publishing assistant in a peer-reviewed journal for the Catholic University College of Ghana. he has also worked as a data operator, team writer, and turnitin plagiarism software evaluator for research institutes and as one of his Illustriousness’s services specializing in academic journal management and software development. He is currently working as a neural network tutor, content writer, lecturer, and consultant. Miracle Research focuses on public health technology, testing and penetration, business intelligence, content management, neural networks, transitions and trajectories, as well as image and video steganography with cryptosystems.