Learning Analytics isn’t only about collecting data from students and analyzing the some meaning from the data to enhance learning in the future.
In order to achieve it, the process of learning analytics rely on several analytical techniques such as diagnostic analytics, descriptive analysis, prescriptive analytics and predictive analytics.
Analyzing data descriptively
Descriptive analytics is a method of statistical analysis that is employed to analyze and analyze the historical records to find patterns or to determine the meaning.
In the context of learning analytics, this is a reflection analysis of the learner’s data. It is intended to provide insights into patterns of behavior and learning outcomes in the online environment.
For instance for an online course that has an online discussion forum the descriptive analytics would be able to be used to determine the number of students who took part in the discussion or the number of times the student was on the forum for discussion.
How do descriptive analytics work?
Data Aggregation along with Data mining are two strategies employed in descriptive analytics to uncover historic data. The data is initially gathered, then processed by data aggregation in so that the information easier to manage by analysts.
Data mining is the next stage of analysis. It requires a thorough search of the data in order to discover patterns and their meaning. The patterns that are identified are then analyzed to determine the particular ways that students were interacting with the material and in their learning environments.
What can descriptive analytics tell us?
The type of data descriptive analytics may provide will depend on the capability of the learning analyst provided by the learning management system (LMS) that is being employed and the specifics of what the system is reporting specifically on.
The most common indicators that can be detected are learner engagement as well as performance. In terms of learner engagement, researchers can determine the level of participation of learners within the course, as well as how and when resources for the course were used.
Performance data gives analysts insights into how learners completed the course successfully The information may be derived from data from assignments or tests. It’s important to keep in mind that the insights gained from descriptive analysis should not be utilized to make predictions or inferences about the performance of the learner in the future.
The method of analysis is designed to provide strategic insights into the areas where students or a particular learner might have required more assistance. It may be used to help instructors enhance the learning experience by providing an understanding of what went well as well as what didn’t work in the course.
Some examples of descriptive analytics
A variety of LMS platforms as well as learning platforms provide an analytical and descriptive report to help institutions and businesses assess the performance of learners and make sure that the training objectives and goals are met.
The data from descriptive analytics will quickly reveal areas in need of improvement, whether it’s improving student engagement or the efficacy of the course’s delivery.
Here are a few instances of the ways that descriptive analytics are being applied in the area of learning analytics:
- Monitoring courses, enrollments, conformance rates,
- Recording the learning resources used and at what frequency.
- The number of times learners post on a discussion board.
- Monitoring assignment and assessment grades
- Comparison of post-test and pre-test assessments
- Examining the rate of completion of courses by the student or course
- Collating course survey results
- The length of time students took to finish a course
Benefits of descriptive analytics
When students engage in online learning and leave digital footprints behind each time they interact within the learning environment.
This means that analytical descriptive data in online learning can give insights into the behaviours of learners or performance indicator that otherwise would not be available.
Here are a few benefits to making use of this information:
- It is easy to quickly and efficiently report the return of Investment (ROI) with a report demonstrating how your performance was able to meet objectives for the business or goals set by the target.
- Find performance gaps and issues in the early stages before they cause issues.
- Find out which learners require extra support regardless of how many employees or students are.
- Find successful learners to provide an encouraging feedback or provide additional sources.
- Assess the effectiveness and value of course design and teaching resources.
Descriptive vs Predictive vs Prescriptive Analytics
Descriptive Analytics is focused solely on data from the past.
It is possible to imagine Predictive Analytics as the use of these historical data points to build statistical models which will later predict future opportunities.
Prescriptive Analytics goes Predictive Analytics an additional step and considers the possibility of future outcomes that are forecasted and then predicts the implications for the outcomes.
