In our mooc week 2, @gsiemens presented the data cycle shown below. It represents a cycle of what you would need to do to work with and get insight from learning data, leading to understanding that should lead to an informed action. Therefore, a data loop. I will break down my understanding of the data cycle below.
Data Collection and Acquisition
This is the specific process by which the data is collected. This is very critical first step. You can start by considering what data you have, but should very quickly start to consider what data you want, but don't have. If you don't figure out how to collect it, you won't get it.
For example, do you want what time the learning activity started? Not when it was scheduled, but when it actually started. How do you get that information? Does the data have to be auto-generated or is it acceptable to have a person manually take an action to collect the data? For me, this is more trial and error then strategic thought, but I'd like to be more proactive in the future. Can we create simple, reliable data collection points and gather all the data this already being generated? I am hopeful that I can start to map some of the Internet of Things (IoT) "offerings" to this stage. Can we put a sensor in the learning environment to help collect information?
Where will you store the data. This will quickly become another "big" consideration. When you set out on an ideal journey, you want all the data to be stored in a single location. But you quickly realize that this is just not possible. For many organizations, the LMS was put into place with this purpose, but all systems have limitations... i.e. when did the class really start? 9:02, 9:15... when did the students login? Does that matter to you enough to store it in another system?
Once your in a two system world, then you are starting to into the next phase....
Data integration can get tricky, you don't want to have to keep managing the your learner demographics, but depending on your organization, even who you are measuring can be pretty fluid. Learners can enroll, not participate, enroll late, leave the learner population all together, and a million of scenarios that can drive you crazy like sharing devices, leaving in the middle of the activity... If you have a lot of data sources, this can quickly become challenging and complex.
This is were the Data Wrangling comes in. How do you start to get data that you can structure and use? So far, for me, this has not been a fun activity. It takes some patience.
Once you have integrated your data, you can begin to conduct your analysis of the what is happening in the data. In my world, you will typically have some ideas of what you want to seek out, at least initially, however once you start to (try) to answer questions, you can move into new areas, look for new trends, look for outliers, or correlations.
It is a little interesting, most best practices seem to point to some version of the scientific method, develop a hypothesis from which to work. What questions are you trying to answer? Do students who show up 15 minutes late perform as well as those who were present at the beginning of the class? As an educator, you hope not. :) Just Kidding. Of course, there are an endless number of questions and you should really spend some time writing them down, showing them to others, and try restating them differently. I also like to tell people to think about what action they are going to take when they get the answer? Kind of a "so what" test.
I am not yet that familiar with the concepts in the map above. SNA, NLP, Concept Development. I will have to do some research to see what these things can do for the analysis process.
Representation & Visualization
This process is about portraying your analysis in a why that enhances or at least explains your analysis. Can you see trending data? Can you see relationships in the data? Can you compare volume or velocity? Can you look at a map and quickly know what has happened or is happening?
This can be a very powerful moment for a group of people working together. Again, spend time looking at the story with your team, peers, or force you significant other to look at it and tell you what they think. You will be surprised at what they tell you it means.
If you can start to see these things then it is time to take action.
If you are engaged in Learning Analytics to "enhance learning" (see our original definition) then you are almost obligated to take action. What are the next steps? Based on the data that you have, what actions can be taken to enhance learning? How does the action get implemented? Knowing what we know now (through the analysis) can we take action and continue to measure the impact of that action?