I recently gave a talk titled “When Big Data Meets Big Education”, together with Mr. Tu Zipei, California-based author of several best-selling books on Big Data. The following is part of what I shared:
In an interview (June 20, 2014) by Bloomberg TV, ventures capitalist Rory Stirling claimed that education will be changed “beyond recognition” by big data in the next ten years. Big data can change admissions, budgeting, marketing, student services to ensure that university resources go where they are most needed.
Large amount of data is now collected with the use of personal technologies. It is especially popular in electronic governance, online commerce, and banking. “Big data”, or data in general, should also be a driver for change in the design of learning experiences. Data will help us to see patterns which then could lead to instructional improvement or innovations, which in turn will help with the advance of learning.
With the ubiquitous uses of mobile devices and learning management systems, it is now possible to gather student data to enlighten educators about the learning processes as well as the learning outcomes. Technology enables educators to check such data as access frequency, page view information, click history, and time spent on particular tasks. Such data can be a rich mine for educators to find out how they are doing and where improvements can be made.
Some data may be simple, yet useful. For instance, teachers in online programs have found from user analytics that many adult students with jobs access their courses before 8 o’clock in the morning or after 5 in the afternoon. With such knowledge, teachers will set the release time to be, say, four in the morning, which would allow early risers extra time to work on their learning tasks. When group collaborations are involved, most people tend to “hang out” later in the evening. Such data, simple and beautiful, would provide opportunity for instructional interventions.
It is also possible to use technology tools to understand when student access certain information, how they access it, for how long at a time, and how that relates to learning outcomes. Such data can be used to optimize teaching for future semesters, if not during the rest of the semester.
Teachers can also use test statistics to find out student familiarity with content. If more than half of the class have chosen the wrong answers for a particular question, it would be wise to go over certain content again to make sure that all students, or the great majority of them in class, achieve mastery. Services such as Turnitin have accumulated a large and ever-expanding database of writing, which can be used to check plagiarism and understand other kinds of writing behavior.
Teachers should also be able to use data to individualize learning to really implement individualized learning. With rich data from students, and the technology for branched learning, it is now possible to custom-make the teaching experience for different students without having to involve teachers in the logistics of managing it, since most branching will be done automatically through selective release mechanisms.
Data or big data may seem to be realms for information technology professionals, instead of teachers and administrators. As a matter of fact, gathering data does not have to be prohibitively “high-tech”. Low-tech methods such as using cards marked with A,B,C or D in the classroom, or paper surveys, can also help us gather useful data to understand what is going on in student minds. That being said, teachers and administrators should make improvements in their ability to make data-driven decisions instead of shooting in the dark. Educators’ skill portfolio should include the abilities to capture, read, analyze, communicate and utilize data. In the meantime, it is necessary to develop talent who can bridge the skill gap when deeper data management expertise is required.