Learning analytics is the use of data to improve learning outcomes. The focus of learning analytics is to provide actionable information that can improve teaching and learning. A strategy for using learning analytics may include multiple levels such as course, department or organization. Data analytics tools are now being featured on the Blackboard Home page. For example, Blackboard quiz “Question Analysis” provides statistics on question quality and overall student performance. This data helps instructors to recognize questions that might be poor discriminators of student performance.
Learning analytics can be categorized into progressive levels of maturity:
- Descriptive — What happened?
- Diagnostic — Why did it happen?
- Predictive — What will happen?
- Prescriptive — How can we make it happen?
Most instructors will start with Descriptive analytics to find out what is happening in their course, keeping in mind that data gaps exist, and data often represents a snapshot of reality. Data gaps often lead to survivorship bias and possible equity considerations.
The initiative of data democratization includes helping instructors identify reports and dashboards that can be viewed often to validate data, make it readily available when it may be useful, and to promote data literacy.