The last decade has seen a dizzying amount of change in most facets of work and life, brought about by breakthroughs in computing and software engineering. One area that many feel has missed out on much of this is education – but this is a common misunderstanding. While some claim that teaching and learning styles have barely altered, the much needed ‘digitisation’ of learning connectivity and content has now stabilised, and recent reports show some fascinating bits of technology that could forever alter the very perception of learning. Here we’ll briefly look at five.
Gamification
Simply put, gamification is the application of game-design elements and principles in non-game contexts. In regards to education the goal is to maximise engagement and motivation in order to achieve learning objectives. This usually involves structural changes to the grading or reward process of learning. While there are many ways about this, a common method that has emerged amongst proponents of gamification for younger learners is in using ‘experience points’ as opposed to numerical or categorical grades. Other methods involve writing course structures in the format of narratives and the use of more frequent micro-rewards. Duolingo and Mathletics are popular commercial examples of this push. Industry experts put the potential benefit rating of gamification as high and the maturity as adolescent.
Affective Computing
Further away on the horizon, affective computing refers to systems and technologies that are able to sense, understand, analyse and synthesise the emotional place of a user, and respond appropriately. This field of study goes much broader than simply education, although in that context, the main benefits have been linked by studies in the importance of context-specific moods on learning outcomes. It’s obvious that we learn better when we are happy and relaxed, but its not widely appreciated just how much of a difference mood has on particular learning sessions (a lot). The early work being done here is primarily involved with facial recognition – MIT recently developed a ‘mood meter’ which rated the overall mood on campus by aggregate smiles. The effect of affective computing on society could be incredible, however it’s very early days yet. Industry experts put the benefit rating of affective computing at moderate and the maturity at emerging.
Adaptive Learning
Perhaps best seen as the brain to affective learning’s heart, adaptive learning is a method that involves computers dynamically altering the presentation and pathways within a learning set based on responses by the learner. A simple example would be a school-aged learning whom is capable in all subjects except english. While completing her homework at night (through the adaptive learning system) the learner is proportionally shown and tested more commonly on english learning goals in order to make up for this deficit. Drilling down further, suppose the learner only struggle with some particular aspects of english, with a moderate grasp on grammar, but poor spelling – again the system will adapt to fix this issue. Suppose still that the learner only has particular phonetic issues. Coupled with breakthroughs in pedagogy and psychology, many hope adaptive learning will also be able to spot links between strong and weak spots in a learner’s development and give a highly relevant prognosis about the learner’s capabilities. The way in which the system adapts will lean heavily on aggregate data of many learners, and the adaptations will most likely be determined through large-scale split testing, a method drawn from the field of analytics. Industry experts put the benefit rating as transformational and the maturity as emerging.
Personal Analytics
While many are excited by developments in large-scale data sets, across broad numbers of people, a huge amount of change may just come from focusing inwards and tracking micro information about individuals. The goal of personal analytics is simply to apply the scientific method (through the observation and analysis power of computers) to better oneself. While we all know that eating healthy will make us feel better, being told unequivocally that eating exactly a certain combination of foods once daily will have enormous benefits for our particular genetic makeup would be much more of a motivator. In an educational sense, perhaps answers as simple as ‘your eyes read better on darker backgrounds’, or ‘you take in 244% more information by listening to your textbook in an audiobook format’, will be all the difference on people achieving their best learning outcomes. While these solutions sound exciting, many are already worried about the potential for exploitation by businesses (especially advertisers), and the potential privacy issues. Industry experts put the benefit rating of personal analytics at high and the maturity at emerging.
Tin Can API
The Tin Can API (xAPI) is a disruptive technology that is attempting to recategorised many established learning softwares with a primary focus on recording micro learning activities, wherever and however it is they occur. Philosophically, the Tin Can API was born from the idea that at least 70% of learning occurs informally – outside of a school, university, or course setting. For example, perhaps a marketing graduate goes to work for an advertising agency and finds themselves in the art department. Thrown in the deep end, the graduate, after many weeks, becomes proficient at graphics design through the help of thousands of google searches, minute-long how-to videos, and through simply trial and error within the software. The graduate continues this method and eventually becomes the most proficient designer at the company. Essentially none of this is ever recorded, and to the world the graduate is still just a marketing graduate. Perhaps at their next job a small amount of these learned skills are required, amongst others – while experience will certainly mean a lot, a more detailed picture of what it is precisely the learner has learnt will assist both the learner and the recruiter in finding best-match. Industry experts put the benefit rating of the Tin Can API at moderate and the maturity at embryonic.