Large data sets are generated from educational settings, for such purposes as assessment, evaluation, accreditation, regulation, etc. How should they be analyzed? Is data-driven analysis the way to go? We can use traditional database queries to extract subsets of data, but a more powerful tool is emerging to address the myriad of data, and to help analyze it in ways we haven’t been able to before.
Besides the data that gets generated from classrooms, teachers, schools, students, administrators, governments, bureaucrats and the general public, collectively the stakeholders in education, we also can find that machine data is being generated in the form of # of hits to websites, frequency of logins, downloads, search criteria, messaging interactions, social media likes, various feeds from Twitter and other social media, use of electronic devices generating location information, etc.
The challenge is how do we get a big picture of the situations we want to learn about to make decisions when there are numerous forms of data. We may choose one data set and find it was not the most appropriate for our analysis. Being able to integrate multiple sources of data to generate valuable information is what we need.
Enter Big Data and Analytics. With various tools that are available for businesses to analyze their customers, competitors, products, sales and other market conditions, educators can also find valuable information to help answer the pressing questions they face about student learning, effectiveness of pedagogies and instruction, where money is best spent to achieve the greatest return, how populations can be better served through public or private education, etc. We also see Analytics being used in sports to find the best scenarios and resources to achieve winning results (see the book/movie Moneyball, for example). Once data is collected, stored, and made available through software tools, it can be mined to find answers to these important questions.