Part 4 of our 4 part series on big data in learning.

Big data has led to big changes in statistics. One could argue that our existing paradigm has been rendered impotent in many cases by big data.

Historically it has been difficult and largely impossible to gather all data in a target audience or domain. We were taught that sampling data is the way to go, and then to avoid selection and sample bias, we avoid self-selection or snowball samples and randomize. All of this assumed scarcity of data.

Part 3 of our 4 part series on big data in learning.

Learning data can be harvested at ten different levels. The included inverted pyramid shows a hierarchy of levels from which data can be harvested. Note that it moves through different categories, but, in general, it describes the move upwards towards big data, as each level does, potentially, include those below.

Part 2 of our 4 part series on big data in learning.

Let’s start with a dose of reality: education and training have always coveted data. But in any honest appraisal of this data collection, we have to admit that it is largely the wrong data. There has historically been too much focus on start and end point data. All dull inputs and outputs. What we need to focus on is the cognitive improvement of the learner. Included are five examples of data, mostly superficial, that account for the vast bulk of the data collected in education and training.

Part 1 of our 4 part series on big data in learning.

Big Data, at all sorts of levels in learning, reveals secrets we never imagined we could discover. It reveals things to you, the user, searcher, buyer, and learner. It also reveals things about you to the seller, ad vendors, tech giants, and educational institutions. Big data is now big business, where megabytes mean megabucks. Given that less than 2% of all information is now non-digital, it is clear where the data mining will unearth its treasure—online. As we do more online, searching, buying, selling, communicating, dating, banking, socializing, and learning, we create more and more data that provides fuel for algorithms that improve with big numbers. The more you feed these algorithms, the more useful they become.