Big Data & Learning Analytics

Big data has been a emerging technology for almost a decade, which begs the question--why? One of the reasons I think Big data is still so powerful is that it is no longer something "managers" or "administrators" do. Analytics is merging into roles that typically did use data (as we know it today) to make decisions in their daily roles. For an example, I work in public safety where our safety officers use analytics to prioritize inspections and manager their time. While I think the technology will continue to be important, I think the largest impact it will have on education is ensuring students learn these analytic tools, as some element of analytics is likely to be required in any job they take in the near future. With Big Data comes an increase need of data literacy, which is currently not met in most education programs.

Applying learning analytics to assessments of students (performed on a digital platform) would provide very important data to teachers and help them reshape their classes/assessments in a highly effective way. Teachers would be able to identify patterns, questions students struggle with or don't understand at all, questions that are too easy etc...

It is my experience and it is generally accepted that mixed-ability classrooms are the norm these days. As a teacher I am personally interested in differentiated instruction. It is for this reason that I am interested in personalized learning. Today the reality is that any teacher would be involved in producing individualized learning plans for students. As we read, there is little doubt about the future of personalized learning. It is the Holy Grail of teaching. Except that it is not easy to practice and becomes incredibly difficult for any teacher to do for large number of students. It is for this reason that differentiated instruction in classrooms advocated today is clearly identified as not personalized learning or individualized instruction. However, the promise of the future is that personalized learning will be a reality for the majority of students through the use of technology. In my opinion, we would have achieved the Holy Grail of Teaching. Adaptive Learning and Adaptive Learning is one way of attempting to achieve personalized learning, the Holy Grail of Teaching. Along this path we would need the tools of Learning Analytics plus in my mind, what would likely succeed is a recipe of Learning Analytics, AI and Machine Learning.

Tapping into big data and learning analytics in public (or private) education is essential at the school and district level. Individuals who have never worked in higher management may not always be able to see the big picture, especially when there are so many day to day tasks that consume our time. The power of utilizing data to find otherwise missed patterns or trends (e.g., attendance, performance, graduation rates, etc.) allows schools and districts to address issues as they arise and make well-informed, research-based decisions. The 2017 Horizon Report for K-12 Education gives an excellent summary and several examples of how learning analytics can be applied in educational settings. I believe we currently have the means to implement analytics technologies into our school systems; however, a few obstacles may include funding for public schools for some of the more advanced programs and software as well as sufficient training for staff and specialists in learning how to utilize this technology to its full potential.

My impression is that public and private research approaches to collecting and processing big data will enter the education market in meaningful ways through different routes. One early route may be the application these approaches to MOOC data, generating insights into learner behaviours in online learning environments that inform evolving designs of MOOC environments and the kinds of activities they provide. At the same time, I think learning analytics will also be applied to much smaller data sets (e.g. K-12 student behaviours when using specific LMS platforms or apps). These K-12 applications are more likely to emerge once a versatile set of analytics libraries become well established as low-cost SAAS offerings from the likes of Amazon Web Services, Microsoft Azure and Google Cloud Platform.