End of Course Evals and How AI Can Help

[@sovorel-EDU]

Here is the prompt that I used on my end-of-course evaluations: " I am a university professor and have my end-of-course evaluations from my students. Please analyze the evaluations and provide the following: Overview of Key Themes: Identify the most common topics, feedback points, or areas of focus mentioned by students. Sentiment Analysis: Provide a sentiment breakdown (positive, negative, neutral) add visuals if possible. Highlight specific comments that strongly reflect each sentiment category. Topic Frequency: show the most frequently mentioned words or themes in the evaluations. Standout Quotes: Identify a few impactful or representative student comments that I could use as testimonials or reflections. Please organize the response using clear headings and ensure that any visual information is easy to understand. If possible, use tables or simple graphics to illustrate patterns and trends."

[@LilethViolet]

Nice video 👍🏻

[@sovorel-EDU]

Thank you Lileth. I appreciate your comment. Yes, I was never given any instructions on properly using end-of-course evaluations when I started teaching, so I thought this video and infographic would be helpful for others.

[@rdumiak]

Great idea! Another potential data point that could be used as input both during course deliver and afterwards would be to use an AI tool to assess your presentation skills. An example (not an endorsement) would be Yoodli which allows you to upload a video of yourself doing a lecture and provides analytics on your presentation skills.

[@sovorel-EDU]

That is an awesome idea, Roman. I totally agree that using an AI observation/evaluation would be a powerful additional data point. I just did a video about using AI for video analysis (kzbin.info/www/bejne/q6Xcmnt8q9qCg5Y) through OpenAI (paid) or through Google AI Studio (free): aistudio.google.com/prompts/new_chat. I don't think I've heard of the AI tool you mentioned (Yoodle). I will have to check that out. Thank you for telling me about it. I always appreciate your great comments.

[@roxannr]

Using AI for end of course evaluations can provide specific, meaningful insight for instructors/professors. Instructional designers sometimes suggest to have as you mentioned reflections at the beginning, and I'll add middle and end of the course too. Thinking in terms of time /constraints on time, would be time saving to do all three using AI at the end of the course or separately or both? I mostly have seen instructors give the course eval only at the end of the course often due to time or just not thinking it was valuable to do anything else. Another Q for you is have you seen AI create accessible results- example- increase font sizes, types, colors, and audio recordings for downloading etc?

[@sovorel-EDU]

Great point Rox. Mid-way through course feedback can be very helpful and some research indicates that courses that use mid-way through feedback have higher participation rates for end-of-course evaluations. It also gives instructors a chance to modify the course to address issues that are brought up from the feedback. I have tried asking ChatGPT to increase the font, it says it is doing it and it seems like it is for a split second and then it reverts back to its usual size. I also asked it to create an audio recording but it says "It seems there’s a persistent issue with generating a downloadable audio file. I recommend trying a local text-to-speech tool or software like Google Text-to-Speech or Balabolka, which you can use with the text provided. Let me know if you'd like step-by-step instructions for setting that up!" It was very similar when I used Google's new Gemini 2.0. Although you can use more accessibility tools via AI vision. Thank you for your comment Rox.

[@sovorel-EDU]

Here is a link to the infographic displayed within this video dealing with End of Course Evaluations and how AI Can Help: sovorelpublishing.com/index.php/infographics/