Learning is expensive, takes a long time and the results can vary. Those characteristics have made it a prominent subject for research in different scientific fields, particularly IT (Information Technology).
Elearning has been trying for years now to complement the way we learn to make it more effective and measurable. The result now being that there are a number of tools that help create interactive courses, standardize the learning process and/or inject informal elements to otherwise formal learning processes.
Still, the potential is much greater. In this post we will describe a few ongoing elearning trends and a view on learning’s distant future.
Ongoing learning trends include things like microlearning, gamification and personalization.
Microlearning focuses on the design of microlearning activities through micro-steps in digital media environments, which already is a daily reality for today’s knowledge workers. These activities can be incorporated into a learner’s daily routines. Unlike “traditional” elearning approaches, microlearning often tends towards push technology through push media, which reduces the cognitive load on the learners. Therefore, the selection of microlearning objects and also pace and timing of microlearning activities are of importance for didactical designs. Microlearning is an important paradigm shift that avoids the need to have separate learning sessions since the learning process is embedded in the daily routine of the end-user. It is also perfectly suited for mobile devices where long courses can be overkill.
Gamification is the use of game thinking and game mechanics in a non-game context to engage users and solve problems. There is an ongoing debate over whether games can lead to learning. Currently we have a number of strong proofs that support the use of games for learning under carefully selected and well executed educational contexts.
Personalized Learning is the tailoring of pedagogy, curriculum and learning environments to meet the needs and aspirations of individual learners. Personalization is broader than just individualization or differentiation in that it affords the learner a degree of choice about what is learned, when it is learned and how it is learned. This may not indicate unlimited choice since learners will still have targets to be met. However, it may provide learners the opportunity to learn in ways that suit their individual learning styles and multiple intelligences.
The above trends are very hot topics and we can safely predict that they will lead to a new breed of efficient learning tools soon. But what about the distant future of learning? How learning will happen in say 100 years? It is very difficult to predict the future. It is difficult to predict even what will happen next year. Still, scientific evidence lends us some idea as to where learning is heading.
In a well-known scene from The Matrix, Neo (played by Keanu Reeves) lies down in a high-tech dentist’s chair and straps on a wild array of electrodes, downloading a series of martial arts training programs into his brain. Afterward, he opens his eyes and speaks the words geeks have been quoting ever since: “I know Kung Fu.”
This type of automatic learning might sound like a dystopian future for many but it is where we are heading. And despite the ethical questions that may arise, the benefits could be substantial at multiple levels if used properly.
Think of a person watching a computer screen and having his or her brain patterns modified to match those of a high-performing athlete or modified to recuperate from an accident or disease. Though preliminary, thanks to recent research by Brown University neuroscientist Takeo Watanabe, such possibilities may exist in the future.
The details can be overwhelming but here’s how it works: you pick a task that requires high performance from your visual cortex such as catching a ball. Then you go find someone who’s a pro at catching a ball, stick them in an fMRI machine, and record what’s going on in their brain whilst they visualize catching a ball. Now you’ve got your ball-catching program, and you’re ready to learn. Next step: put yourself into the fMRI machine, and rig it to induce that pro ball-catching imagery that you recorded earlier in your brain using neurofeedback. You don’t even have to be paying attention while this is going on. Your brain, though, becomes familiar with that pattern – which is essentially what learning is: the brain becoming familiar with new patterns. Play that pattern back enough times and you will improve at whatever activity the pattern is associated with.
This isn’t just conjecture – researchers involved have shown that this fMRI pattern playback can in fact “cause long-lasting improvement in tasks that require visual performance.” In theory, a type of automated learning is a potential outcome.
The bottom line
Learning consumes enormous resources yet it is essential for our survival and prosperity. As with any challenging problem that potentially has huge impact, we can expect disruptive innovations to make it happen at a fraction of the cost or time it currently requires. The learning evolution is just awakening and we can expect exciting things along its path.