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. Each learning resource is identified according to the following learning outcomes

Professional Development

Digital Learning Innovation's professional development programs include instructor-led courses, webinars, micro-learning videos, self-directed resources and more to cater to different learning preferences and time constraints. They support the mission of faculty as learning scientists and allow them to develop practical skills that can be directly applied in online, hybrid, or face-to-face classrooms.

TITLE DESCRIPTION LEARNING OUTCOMES RESOURCE TYPE AVERAGE TIME
4 Types of Student Data Analytics This infographic defines the 4 types of data analytics.
  • Define the role of the four types of data analytics (descriptive, diagnostic, predictive, and prescriptive) in the process of course optimization.
 Infographic 10 minutes
4 Types of Learning Analytics Data This infographic describes the different sources of data related to student success and engagement.
  • 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.
 Infographic 10 minutes
Course Optimization Basics Quick Guide on the Concept of Course Optimization.
  • 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. 
 Reference Guide 10 minutes