{"id":603,"date":"2016-01-11T02:20:11","date_gmt":"2016-01-11T02:20:11","guid":{"rendered":"https:\/\/edtune.com\/blog\/?p=603"},"modified":"2016-01-11T02:20:11","modified_gmt":"2016-01-11T02:20:11","slug":"surviving-big-data-in-education","status":"publish","type":"post","link":"https:\/\/edtune.com\/blog\/2016\/01\/surviving-big-data-in-education\/","title":{"rendered":"Surviving Big Data in Education"},"content":{"rendered":"

How Important is Data in Education?<\/h1>\n

The speed of change in the world of data is astounding. It can be overwhelming. In education, new apps and methods proliferate. Test data abound. Even if one were to disregard the sheer quantity of information added each day, our psychological\u00a0ability to integrate all the data\u00a0diminishes as\u00a0the quantity of information increases. It is an inverse relationship that can slowly erode our sense of agency and control.<\/p>\n

\"Big<\/a>

Juggling big data in education<\/p><\/div>\n

Big Data hopes to ride into\u00a0the rescue by providing analysis of the vast troves of assessment and demographic data available on student learning.<\/p>\n

The educational data\u00a0tools can be extremely useful in unearthing patterns, but only if (1) we ask the right questions about student learning, and (2) we then follow it up with effective\u00a0instruction.<\/p>\n

The ultimate goal of data\u00a0tools in education is to provide a superior learning experience. To that end it is critical to understand that data\u00a0analysis is only as good as the validity<\/strong> of its underlying constructs.\u00a0One such construct is:\u00a0student achievement = learning<\/strong>.<\/p>\n

The underlying premise is that learning can be adequately assessed through various tests. The sum total of these performance measures are labeled “achievement<\/strong>.” This is where it gets sticky because of the desire to make tests\u00a0objective. As a result, they are reductive. They test the lowest common denominator of recalled knowledge, typically with multiple choice questions that have one right answer.<\/p>\n

If one asks the broader question\u00a0–\u00a0“Can the student apply<\/strong> what he or she knows?” – then the assessment of learning can yield\u00a0a significantly different answer. This is the fundamental thinking that underlies inquiry into any taxonomy of knowledge. The most well known is Bloom’s Taxonomy of the Cognitive Domain<\/a><\/strong>. The revised Bloom’s Taxonomy looks something like this:<\/p>\n

\"Bloom's<\/p>\n

Algorithms have a human starting point<\/h2>\n

Who provides the data concepts that underlie the algorithm? Where does educational purpose originate, and as teaching professionals, do we have the right to challenge the relevance or importance of those concepts?<\/p>\n

When we look at the sources for these data algorithms, we find multiple categories:<\/p>\n