What’s the meaning of Educational Data?

The information drum beast faster and faster

It is there, you can capture it 'real time' and it is here to stay: Big Educational Data.
But what kind of information is actually flowing in? What does this data-soup look like? And how can you apply it in your organization?
Let's assume that the infrastructure and data-collecting software your organization is using works well. It's user interface is attractive and you are one mouse click away of actual using it.  You are also aware aware that it can be used as an active tool to optimize your workspace learning and performance. Comfortable starting points right?
Unfortunately these conditions prove often not to be enough. Unknown makes unwanted.  A very limited amount of opportunities and possibilities is being used.
Hence, below some short considerations about the structure of data, learning-analytics and the application in practice.


Getting educational data into structure

Obviously something has to be done with all this data. The enormous flow of incoming learning information has to be analyzed. An example of how this works is that learning analytics software converts our online learning activities into "events". Actually you are in such an event right now! You are reading this reading this piece right here, right now. The software makes something like this from this unique event:

"You (the reader), read (verb) this item (object), today (when), on your notebook (with use of what;how) on October 9th, 2018 (date and time), in 2 minutes and 5 seconds (online presence)" .

This kind of "statements" can run into tens of thousands every hour, depending on the size of the group of learners. So how to make something meaningful of all of these statements?


Working with educational data in practice

As a learner, coach, teacher, instructor, mentor or manager we really want to do something with this learning information, which is continuously and in real time available.
Maybe you want to use it to optimize our learning process.
First thing is to develop and capture a learner profile. This profile contains information before we start a new learning experience. The intake. Think about characteristics such as age, level of education, relevant working  experience, personal learning objectives, interests, etc. On this basis a good assessment can be developed. A good learner profile offers good opportunities for a personalized learning path.
As soon as we start our learning, all learning information really begins flowing in.
Suppose you are a in role as a moderator. In this case is important to know what you and the learner want to achieve. Which indicators do you for your pedagogical interventions? Which attributes of the learning content are important? What do you need to be an optimal learning guide for your students?
- Do you want to know which assignments have been completed by whom?
- Do you want to know who got good grades?
- Do you want to know who contributed to which learning objectives and how?
- Do you want to know who did which assignments within how many time and at what level?
- Do you want to know how often someone contributed to a the development of a product?
- Do you want to measure to what extent higher learning skills are demonstrated within a certain context?
- Do you want to find the time spent on this?
Sky is the limit. It can all be found! The underlying need or question is the most important of all.
This approach is called descriptive analytics. There is also a non-descriptive approach: Machine Learning is one of them.


Machine Learning

Machine Learning  goes a step further. We enter the field of predictive analytics. We are presented content, behavior and patterns we may not have thought about before. Like relationships between different learning objectives, content and student profiles. Perhaps we didn't expect that many Accountancy students and Biology students have the same problems with a specific subject in Statistics in the curriculum. From this point you start further research and continue to optimize learning efficiency.

After good effective formulation of questions and analysis, the learning analytics software allows you  to generate visual dashboards. These will enable you in doing pedagogical interventions. Of course, these dashboards also provide the learners themselves a continuous insight into their learning process, so that it becomes more engaging to reflect on this.