Two of the hottest developments in education at the moment are the MOOCs (Massively Open Online Courses) and Big Data.
Some have argued that both are currently at the “Peak of Inflated Expectations” in the Hype Cycle. Expectations are indeed high, maybe rightly so. Imagine my pleasure this weekend when I discovered the course about “Big Data in Education” at the MOOC Coursera by Ryan Baker from Columbia University. I registered immediately. What a great way to kill two birds with one stone! I can experience one of the leading MOOCs first hand and learn more about big data in education at the same time.
MOOCs emerged from the Open Educational Resources movement in the second half of the noughties. Leading players include edX, Coursera and Udacity, which are all well-funded and have excellent connections to world-class institutions. Two things in particular excite me about them: opening up access and improving quality in education.
Access to world-class education has historically been restricted to the happy few. However, anyone (with access to the internet) can take a course on a MOOC, unrestricted by price, the requirement to commit to several years of full time study, geography, or capped class-sizes. As Time put it. ”MOOCs open the door to the Ivy League for the Masses.” Imagine the possibilities that his will give to improve the life chances of individuals across the globe. And also the benefits to society as a whole of broad access to exceptionally good education.
The quality of education can also be boosted by the success of the MOOCs. Competition should play its part in raising teaching standards and spurring innovation. Everyone should get access to the most talented professors with the highest quality content and best teaching methods, leading to a focus on excellent teaching and the weeding out of mediocrity. And the ability to mine the data created through participation in the MOOCs should bring new insights in teaching and learning that can drive further improvements in quality and efficiency.
In my opinion, insights derived from big data will eventually transform education through personalisation. By this I mean the tailoring of pedagogy, curriculum and learning support to the needs and aspirations of the individual. I believe this will help learners to achieve better outcomes, in more efficient ways. And about subjects that both play to their strengths and support the development of their core life skills. Big data will be a core ingredient in that transformation.
Sanoma Learning (I am employed by Sanoma Group) is predominantly active in K-12 markets in Europe at this time. The amount of data available in K-12 education today is limited, and the insights offered rather poor. One reason for this is the still low availability of technology in schools (typically of the order of one device per 5-10 pupils) and the lack of any platform with real scale in collecting, analysing and providing insights from data. This will probably improve significantly in the coming years as schools take further strides in adopting technology.
Three sorts of data particularly interest me in this coming transformation journey: inferred student data, inferred content data and system-wide data. Put another way: how do students, content and education systems perform, why is that so, and what can we do to improve that performance? I believe the next generation of Learning will be engineered from the insights derived from the interplay between these three datasets. The promise is significant, although given the sometimes slow pace of change in education, I think it will be a long journey.
Passion for learning
I consider myself lucky to have been born with a passion for learning. I think technology will enable teachers (who are central to achieving success in learning) to transform education for the better. I’m excited about participating in this program on Big Data in Education at Coursera. Typically about 90% those who start a course on a MOOC drop out along the way. I hope I won’t be one of them. I’m curious to try it. Anyone care to join me?