The Educational paradigm shift part 3: my 5 pedagogies that support change and innovation.

While I stated that no university can afford to be an island (heavily paraphrazing John Donne) in the first post on the educational paradigm, I also wrote about the challenges of providing timely courses and trainings for emerging skills in part 2. 

In this third part on the educational paradigm shift, I am gathering some pedagogies that match the challenge of providing timely learning opportunities across universities using new and tested change-friendly pedagogies. Using innovative pedagogies enable this in part. As innovative pedagogy has a responsibility to prepare citizens of the knowledge society with all its ongoing changes, emerging data streams and knowledge. 

Obviously this is not an exhaustive list, however, all of the mentioned approaches did result in benefits for projects I was involved in. I will very briefly touch on each of these approaches and than add a link to more theoretical information:

Challenge-Based Learning: an approach that uses challenges put forward by ngo's, policy makers or industry that need to be solved for societal goals (society at large). What makes this approach match the speed of innovation and emerging new content, is that it offers a way to look at a challenge, break it down into feasable or actionable steps, and gradually move towards solutions or realizations of where new challenges pop-up. The great benefit of this approach is that teachers and experts can be guides on the side, offering their expertise in solving challenges or solving part of the challenge. A great resource for this is the website from the Challenge Based Learning Institute. Multi-actor teamwork (so people working in different sectors coming together) is key for this approach, as solutions can be found in different disciplines. 

Case studies. These case studies has been at the center of Harvard business school and has been proven its usefulness over the years. They have a student guide on this as well. The benefit of using a case study approach, is that it also allows you to make informed decisions, and create real-life evaluations for the problems you are addressing. The guide also includes online cases and discussion guidelines. Teamwork is key in this approach, and again teachers are guides on the side. 

Mentoring. Any sectorial innovation, brings along the challenge of having to create new content or new processes describing these new innovations (e.g. when we moved from face-to-face to online learning, the best of us looked at other online learning experts to find best practices). Mentors can provide insights into new content, new evolutions, new skills. While innovation-rich sectors move forward rapidly (e.g. space exploration, renewable energy), it becomes difficult to know what is happening and how it feels or looks. By using experts as mentors, the old and proven approach of mentorship gets a new push forward. In each sector you have these experts and some of them are also great educators. By attracting the right experts, you can lift learners to the level of innovation rapidly (mentoring as one-on-one or small group learning). 

Data cases. With Big Data embedded in many different sectors, data saviness is becoming critical. Data saviness can have many faces: coding and manipulating data, looking and analysing data through visualisations to name but two. Once data savviness is integrated into learning, more people can integrate data analysis in their own sector and come up with new ideas (e.g. Waze happened as tracking traffic came together with map alternatives, data cases using life energy data streams are used to optimize energy consumption). Embedding or exploring data is a way to enrich learning within certain domains. 

Enlarge AI-based EdTech within approaches. The first 3 approaches listed above have a strong emphases on the human creative factor (= coming up with solutions). There is some social learning involved, learners are working collaboratively and across disciplines and sectors. But of course we are now in the AI age, which means automated results and matching based on big data analysis can now be integrated in learning in general. For instance using informal and formal EdTech tools that facilitate learning. A really nice informal bit of EdTech that I am following is https://startlearning.today/ where micro-learning is offered based on your own job profile. Another interesting project is the roll out of adaptive learning like the adaptive learning degree in biology at Arizona state. 

(Picture source: https://www.flickr.com/photos/gforsythe/ have a look, she is absolutely great!)

The Educational paradigm shift part 2: teaching emerging & ever-changing skills.

More universities are joining their efforts to meet the needs and financial demands of an increased content development in these online first learning space that has dawned in 2020. 

Some universities are sharing their future plans to inspire others, have a look at Stanford 2025 (thank you Frank Gielen for the link and the insights!). But as always with learning and teaching, it is the human factor that makes learning an inspiring success or a tedious process. One can clearly feel something is changing within education and training at large... but why do we hit a wall with our old school learning that pushes us to rethink learning overall?

What are some of the barriers for providing timely teaching?

Uncertainty concerning future skills is one of course, but these will be better addressed soon (look at the SkillCharge project where AI is enabled to screen CVs or job profiles for skills that are there and match them to emerging skills, while pointing to useful training to acquire those necessary skills). 

Keeping up with innovation. It takes time to build course content that integrates new innovations happening in industry, startups, a.o. To create an up to date course, teachers who are content experts need to collaborate with industry experts to know what is emerging in the sector and is part of the new realities within a sector. This means multi-actor learning needs to take place (= networking across disciplines and sectors), this also means such a collaboration needs to be set up (B2U, business to university collaborations). 

Use innovation reality, use data for modeling. Reality in itself is a challenge, with Big Data being integrated in many segments of society, it also means we can use this data to think forward and build anticipating models that can 'predict' or at least list a couple of future scenarios. A great person to follow and to see what can be done with forward thinking and future horizon exploration, is Bryan Alexander. He does a great remodelling excercise on where Higher Education is going.

Creating course content from scratch. One of the main barriers in creating content at a pace that reflects innovation, is the old school content creation. The most common time and cost consuming development of a course, is preparing class notes or content. It comprises of: writing the learning objectives, fixing pedagogical interactions based on the given content (e.g. discussion on a subject like ethics, to complement content that might be questionable or is a reaction to questionable processes - for instance the move to sustainable energy), creating ALL the content for a course, evaluating the course content as well as the course process. Creating course content demands time from the teacher, but also from media support and others, so this is an ideal element within the course development to adress and bring down costs. 

Same content behind multiple closed walls (breaking the university silo's) is another barrier. I wrote about this in my previous post. It is clear that if many universities build the same content, it is a non-working cost model, as all of us invest in human resources as well as in material development with the same results (well, given we all work at the same level of quality, but let's face it, offering basic quality is most of the time the most cost effective way to create anything. 

Where do we go next to address these bariers?

The way forward is to create or use teaching and learning approaches that work under these conditions: innovation, collaboration, using innovation to create new content... so in a next post (which I call part 3 addressing the current paradigm shift in education), I will list a couple of useful teaching and learning approaches that allow learners to prepare for future skills, while using the content and expertise that is only just emerging.

(picture source: http://etag.report/)

The Educational paradigm shift part 1: building a layer on top of single universities

Universities have been around for 1000 years, it is time for the next level of education, moving beyond single universities. New learning architectures are being build, a paradigm shift is happening as course development needs to be made more efficient in keeping pace with sector innovation. This means, new alliances need to be setup across universities (see further below) and new pedagogies need to be installed (see part 2 later). This demand for change has been around for 10 years, but it seems change is upon us. Will the change do us all good?

Old and still here

The university of Bologna was founded in 1088 A.D. and is considered the oldest university in the world. Oxford university was founded not long after that and gradually more universities emerged. Their model was profitable, as you can see as these universities stood the test of time. But is there model still relevant today? Do universities deliver top employees, ready for the higher end job market? No. What they can do, is deliver strong research and evidence-based results. But researchers are only a small part of everyone graduating from university. So when it comes to teaching students to work in non-university sectors, they fail. 

While jobs change, curricula seldomn adjust rapidly

This is due to the rapid change of job profiles, emerging new sectors, innovation overall... no rigid university curriculum can stay on top of rapid change. There is too much specialization involved and the capacity to change on the go as innovation reshapes a sector (e.g. AI embedding affecting all sectors).  

It is a tough goal to ensure that any student coming in for a four year learning journey, will have learned the most innovative, timely training. This becomes even more difficult if we consider the needs of new jobs, that are often situated between different 'known' disciplines (but of course AI can be used to train people for future jobs, hence using innovation to prepare for innovation). But any Utopian or Distopian believes aside, universities in their single, ivory towers are no longer ready to deliver the best employees for these changing times. 

Merging knowledge and specialize

The dominant form always wins, we know this since the Pullitzer prize winning book of Jared Diamond, called "Guns, Germs and Steel". Unfortunately, dominance is seldom the highest quality, it is simply the most agile, the best equiped. Quality always subserves quantity and technological lead (feel free to find examples, there are many! My preferred one is VHS versus Betamax). 

Single universities cannot compete with innovation. They lack speed and specialization. Engineering courses for instance, almost every big university has them.... this is clearly not an efficient use of resources nor course development. Now if a set of universities combines forces, each one of these universities can specialize in one particular area, ensure course relevance and speed due to this specialization (you don't need to update all engineering courses, only your own segment). 10 universities in the USA have started such a collaboration (see Big Ten Academic Alliance post referring to online courses). This is only the start of course, but you can see that working together will save money and human resources, while at the same time enable more rapid course development as innovations are rolled out. As part of such a university network, each partner can now focus on one particular area. 

More than graduates and beyond course degrees

Of course, this type of developing innovation-paced courses, also opens the door for more than students, as professionals will need to stay on top of their field as well. This means it is no longer about curricula and formal courses within a single or joined university to get a degree. With professional learners taking up parts of a degree course, certificatoin needs to become modular. This means certificatoin that is both formal and informal, as well as part of a micro-certification becomes necessary. Look at the Microbol European Certification program that offers a certification across universities (btw nice keynote on the subject can be seen here as part of the virtual EDEN conference September 2020). 

In the next few years many universities will merge to stay afloat and become more competitive. This also means that they will need to choose which main language they will speak with their students ... this again will lead to a more dominant educational language... if I push aside the impact on educational divides ... I guess I concur with Cheryl Crow: "A change will do U good".