Originally published November 16, 2018
Does your organization want to report on data coming from Brightspace? There are of course reports available in Brightspace, and they should be your first destination. If your needs can be met by those, fantastic! You may have a more unique reporting need though, and therefore you will need to extract, analyze, and display the data to get the output needed. The details will be different for every organization, but the following steps provide a general roadmap for the process of reporting using Brightspace data.
First and foremost, determine what you want to measure and set clearly defined metrics.
To be successful and not be overwhelmed by the amount of data available, you should have clearly defined metrics you are targeting. This step is by far the most important for an effective reporting process. Don’t aim to collect data for the sake of data, it is far more efficient to only collect the data you need to make actionable reports. For example, if you desire to measure outcomes across and within departments, you might decide to collect an average final grade from each course offering. However, if you would like to compare outcomes of students with similar course histories, you would need to collect course offering histories and final grades at the individual level. In both cases you are measuring ‘Outcomes’ but the specifics of the data collected is quite different and each set of data would be ineffective for addressing the other need.
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Determine how frequently you need updated data, and where the data is located in Brightspace.
Once you have your metrics, then identify where the data is you will want to use. Also important is to know how frequently you will need the data refreshed, this will inform what datasets are available and what processes fit best. For example, if you wanted to do an end of semester report comparing engagement between courses, you could use a manual data download process from the Data Hub UI since you only need it once a semester.
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Determine what skill sets your team has. Confirm these match with the planned data wrangling process.
Some methods of data access and processing require specific skill sets. For example, if you want to use the Brightspace datasets, but don’t have individuals on your team that can work with APIs, that could hamper the ability to set up automated downloads of datasets. We find that typical skill sets needed for working with Brightspace datasets include: APIs, database creation and structures, querying, and data visualization.
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Make sure you understand the format, structure, and content of the data you are using.
It is important to confirm these details about the data so there can be confidence in the end result and any aggregation that happens in the process. For example, if you are trying to flag individuals who have spent less than an hour viewing content in a week, it is important to know if the units for that data point are in minutes or seconds.
Set up a method for acquiring the data.
Will the (long-term) data acquisition be fully automatic, or will some manual work be needed – for example tweaking the courses pulled from the advanced data sets every semester? Will you be using the full data sets only or also the differentials? This is an area where the metrics targeted, freshness needed, and skills available will really drive the solution.
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Figure out where your data will reside. If needed, build a database to hold your data.
The specific solution chosen here will really be a reflection of the skill sets you have available and the organization’s need and related processes. For instance, you might want to combine data from Brightspace with data from other systems for a more holistic picture of your metrics which would require you to store them both in a database you manage. We find the important questions to answer include: Will Brightspace data be combined with other data sets? How long will it need to be retained? Who/what tools will need access to this location?
Analyze the data, build reports, deliver your outputs to end users
Depending on your reporting process, if end users are the ones doing the analysis and report building, the handoff to them will happen before analysis of the data. This step may be as simple as running a query and pasting the results into Excel, or as complex as a custom widget that brings the data back into Brightspace so it is easily accessible to users. How will your users be accessing the report? Many organizations will access the data with a BI tool. Make sure everyone has the correct accounts and permissions and any automation is set up.
Dig deeper and start the process again!
The best reports give actionable insights and lead to different and/or more nuanced lines of inquiry. Sometimes we find that processes need to be changed to report on the exact metrics desired which might only become apparent after the first reports are used. Reporting is an iterative process as your goals and metrics change – keep in mind that access to new data might change your reporting possibilities. Stay on top of our new releases related to data here.
Need further help working with data? Stuck on any of the above steps? Lacking one of the skill sets needed? D2L provides flexible levels of Learning Analytics Engagement services that can assist with any or all steps in the process detailed in this post. If you are interested, please contact your D2L Customer Success representative.