Using Mayer's Principles to Improve Video Learning
- petersonkaitlinm
- Apr 12
- 4 min read
Updated: Apr 14
Introduction
Have you ever created a video that just felt flat or less engaging? Maybe you received feedback from learners who were having trouble following along or understanding certain topics, but you weren’t quite sure why. In this blog, we’ll explore how using an evaluative rubric (with the help of Mayer’s 12 Principles of Multimedia Learning) can take your videos from "blah" to impactful. I’ll share how I did just that by evaluating and improving one of my videos using a structured rubric.
The Original Video
Let’s start with the culprit, my original video.
I created a video tutorial on a component of the Campus Solution's Student Information System (SIS) called What-If Reports. The video was a simple screencast with text-to-voice AI narration that walked users through how to locate and use the What-If Reports feature in SIS. What-If Reports help academic advisors and students explore how a student’s current coursework applies to other majors, providing a more informed way to determine what major to choose. I had always felt that something was off with this original video. The narration seemed fine, but the screencast was fairly small, and I relied only on my mouse to point out specific elements on the screen.
Creating an Evaluation Rubric
To make improvements to this video, I began exploring Mayer’s 12 Principles of Multimedia Learning, a framework that examines elements such as narration, modality, pacing, use of graphics and text, redundancy, and signaling.
Using these principles as a foundation, I developed a rubric with nine evaluation criteria:
Content
Clarity
Distractions
Multimedia use
Cues
Redundancy
Engagement
Visual quality
Overall impact
I then used AI (specifically ChatGPT) to generate feedback on my video using the rubric. I uploaded the video as an MP4 and the rubric as a PDF, asking for feedback along with a score of 1, 3, or 5 for each category.
Here are the results, with my original score being a 33 out of 45 possible points.
Areas for Improvement
After reviewing the feedback, several areas stood out for improvement:
Improving transitions between ideas to better segment information
Enhancing signaling and highlighting of key ideas. As shared by Sung & Mayer (2012), "highlighting essential information helps guide the learner’s attention” (p. 5).
Adjusting areas where text and narration overlapped too closely
Changing the tone of the AI voice to be more conversational and natural
Improving visual quality to enhance understanding
Applying the Improvements
In my revised video, I made several intentional changes:
Pacing & Segmenting: I added more pauses between narration to give viewers time to process what was being shown. In the original version, narration moved too quickly, limiting understanding.
Signaling: I added arrows and highlights to draw attention to key information and guide viewer focus.
Redundancy: I confirmed that redundancy was minimal, as text overlays were not heavily used. As noted by Farkish et al., (2022), “We observed that signaling and coherence were the most effective principles. Regarding the redundancy principle we saw a negative effect” (p. 1624). I added two slides with text at the beginning and end of the video to emphasize specific content, but made sure they did not compete with other graphics or visuals.
Narration: I changed the AI voice to sound more conversational and human-like.
Reflection: I added a moment for reflection at the end of the new video. As Mayer (2021) notes, “Learning by explaining can be effective when it causes students to reflect on what they are learning” (p. 10). The reflection asked advisors to consider how they would use What If Reports in their own advising practice.
Visual Quality: I improved the screencast by zooming in, making on-screen elements easier to see.
Here is the improved version of the video:
Reevaluation
I submitted the new video in MP4 form with the same Video Evaluation Rubric to ChatGPT again to evaluate the changes and see if my score could increase. My score increased from 33/45 to 43/45, with ChatGPT noting improvements to the pacing of content and narration, a better flow, highlights adding better direction for the learners, the change in the AI voice made it feel more conversational, and overall improvement in engagement. One area that remained the same was redundancy due to the addition of the intro and outro slides.
Here is the updated rubric with scoring from ChatGPT:
What I Gained
Using a rubric grounded in Mayer’s 12 Principles of Multimedia Learning provides a structured and research-informed way to evaluate and improve video tutorials. This approach helped me more intentionally assess elements such as modality, pacing, use of graphics and cues, and narration. One of the biggest takeaways from this process is that impactful videos do not need to be overly complex. In fact, research shows that “students learn better when extraneous material is excluded rather than included” (Mayer, 2001). Keeping videos more simple enhances the learning experience by reducing unnecessary visuals, text, and animations that can distract from key content. By prioritizing clarity and learning objectives, video tutorials become more purposeful, accessible, and engaging.
References
Farkish, A., Bosaghzadeh, A., Amiri, S. H., & Ebrahimpour, R. (2022). Evaluating the
effects of educational multimedia design principles on cognitive load using EEG
signal analysis. Journal Name, Volume(Issue), page range. https://doi.org/xxxxx
Mayer, R. E. (2001). Coherence Principle. In Multimedia Learning (pp. 113–133).
chapter, Cambridge: Cambridge University Press.
Mayer, R. E. (2021). Evidence-based principles for how to design effective instructional
videos. Journal of Applied Research in Memory and Cognition, 10(2), 229–240.
Sung, E., & Mayer, R. E. (2012). When graphics improve liking but not learning from
online lessons. Computers in Human Behavior, 28(5), 1618–1625.
Comments