We often try to imagine and discuss what education will be like in the future. In the 21st century that "future" comes very quickly. In a blink of an eye you notice that a great shift has taken place in our beliefs about effective educational practices.
The same can be said about the shift from experience-based beliefs and decisions about effective education to analytics and data-driven decision-making. This is especially important in terms of higher education as a producer of specialists to serve the world market.
As Long and Siemens mention in their article "Penetrating the Fog: Analyics in Learning and Education", big data and analytics are the most crucial factors shaping the future of higher education.
What is big data? Well, it describes the concept of abundance of information, data whose size is beyond the ability of typical software tools to store and analyze.
Nowadays, it is widely accepted that decisions should be based on data and evidence. In this context, let's try to make out the difference between academic analytics and learning analytics. Academic analytics looks at the role of data analysis at institutional, regional and international levels. Learning analytics is the measurement, collection, analysis and reporting of data about learners, the relationship between learner, content, institution and educators. It follows that the aim of learning analytics is understanding and optimizing learning. A natural question could rise now- how are we going to achieve the aforementioned goals?
Well, there are different suggestions. On the course level, we can observe how students learn by having a look at their online portfolios, logs, by analyzing their activity in social networks. It is important to mention here that we should be careful not to obstruct the privacy of the students. With the advance of technology, predictive modeling and pattern mining have also become quite popular. These help us predict future trends in learning, using data analysis to find some patterns that draw a clearer picture of how learning happens, of student behavior and the probable reasons for those. The next step would be adapting the content or pedagogical approaches based on learner behavior. In their turn, learners also adapt their learning through social interactions, mutual support. The evidence from analyitcs may help improve administrative decision-making, identify learners at risk of dropping out, and, eventually, guide reforms in higher education.
In this respect, Daphne Koller makes some really valuable points in her talk "What We're Learning from Online Education". As in many parts of the world quality education is unavailable, the task of modern technologies is to bring best quality education to as many people as possible. Nowadays, with the great number of online courses, the goal is to provide a real course experience to students. It seems that some MOOCs have accomplished this task very well. They provide interactive lectures, video watching experience, homework watching assignments. The grading has become automatized.
All these changes are a step towards active learning (irrespective of where on the globe you are), lifelong learning.
For more information on learning analytics go to:
For more information on learning analytics go to:
- Leadership and Learning Analytics
- When Learning Analytics Meet Big Data: The PAR Framework
- Using Predictive Analytics to Improve Student Success