As schools are forced to move to online learning, effective eLearning has become extremely important to students everywhere.  The classroom has obvious benefits that cannot be recreated online, but there are more subtle things that students may be missing out on that only they can articulate.  Instructors are doing their best and will continue to provide lessons, but they have no way of knowing if their new methods of teaching are working for students.

Using learner feedback is a huge part of ensuring that both the instructor and the students are able to get the most out of their time in class.  The best way to find out what your instructors and students are struggling with is to ask them!  Feedback from both groups is key to ensuring that both instructors and students feel supported and capable of adjusting to this new learning environment is to collect their feedback and analyze it. 

A small organization with small class sizes would be easy to go through and an organization should review all of the feedback manually to make sure that each person's voice is heard.  A large organization has large scale difficulties when it comes to adjusting and will need to focus their efforts on the hottest issues first.

This is where AI & Analytics come in to help.  AI agents can go through millions of bodies of text in a short period of time and are trained to identify themes.  In large organizations you will likely have hundreds or thousands of responses to each survey.  Because AI-powered Topic Agents can go through so many free-text responses it allows an organization more opportunities to track sentiment in the student body and faculty. 

We recommend issuing the same survey repeatedly throughout the semester to track how the organization is handling the adjustments and how people respond to improvements that are made based on the feedback surveys. 

Based on a 15 week schedule our recommended survey schedule would be:

Week 1 To learn about the key issues getting set up in the eLearning systems
Week 3 To learn about first impressions from using the new systems
Week 7 To learn about how people feel about the course after midterms, including if students feel they are prepared and have the resources available to study for and perform well on midterms, and if the instructors felt prepared in giving the midterms and how to prevent cheating
Week 11 To learn about how students feel after midterms and getting close to finals and if they feel that their instructor adjusted appropriately to ensure success in finals
Week 15 End of course evaluation to learn the lasting impression of the course, including overall course material, instructor performance, and effectiveness of the eLearning platform


Having a short ongoing survey throughout the semester will help you track how both students and instructors are adjusting to new learning methods and how they are responding to changes made to help the process go more smoothly.  AI is the key to tracking this large volume of data and ensuring that every voice is heard and the largest issues are addressed swiftly and completely. 

Working with QAA has allowed us to expand our knowledge of learner analytics and how our long history of working with customer experience survey feedback can be applied to help instructors pass down knowledge to the next generation.

If you want to learn more about our solution for learner analytics, click here to download a whitepaper we co-authored with QAA.


Click here to see what we are working on with QAA!


Check out the learner analytics dashboard

Topics: artificial intelligence, text analytics, QAA, Survey Analytics, Learner Analytics

Devin Marsh

Written by Devin Marsh

Bintel Director of Operations