Faculty Development
Enhance Your Courses Using Data Analytics
Course optimization involves leveraging various types of data analytics to enhance the effectiveness and alignment of course materials, activities, and assessments with student needs and learning objectives. Descriptive analytics provides an overview of current course performance through metrics like assessment scores, engagement levels, and student feedback. Diagnostic analytics identifies potential misalignment or inefficiencies by examining the root causes behind any issues. Predictive analytics utilizes historical data to forecast future student performance and engagement, enabling proactive interventions. Finally, prescriptive analytics recommends specific actions, such as revising course content, assessments, or instructional strategies, to optimize the learning experience based on the insights gained from the other analytics types.
The set of resources below is designed to support faculty by providing options which
consider desired time commitment and depth of knowledge. These professional development
resources will help you to:
- Define the role of the four types of data analytics (descriptive, diagnostic, predictive, and prescriptive) in the process of course optimization.
- Identify appropriate data points within the learning analytics and student feedback which could facilitate course optimization
- Interrogate learning analytics data, including assessment performance, engagement metrics, attendance/participation, and student feedback to identify patterns and draw appropriate conclusions regarding course materials.
- Determine whether individual students need intervention to succeed in the course and, when necessary, identify resources available to support appropriate interventions.