In the era of digital education, understanding how students interact with online content is essential for improving learning outcomes. Berj Akian's dissertation (2024) explores this by using RealEye to analyze attention and engagement during multimedia learning.
Watching Educational Videos and Measuring Learning Outcomes
Participants were shown different versions of educational videos. These videos included various multimedia signals like text highlights, images, and animations designed to catch their attention. RealEye recorded data on:
Where participants looked: Identifying which parts of the screen were most watched.
How long participants looked: Measuring how much time they spent looking at each part.
After watching the videos, participants took a test to see what they remembered. They took another test a week later to measure long-term retention
Depiction of Areas of Interest and a heatmap in one of the educational videos.
Key Findings
The data from RealEye provided important insights into how multimedia signals influenced attention and learning outcomes. Here are the key findings:
Attention Patterns
Fixation Points: The eye-tracking data showed specific areas where participants frequently fixated. These were often parts of the video with highlighted text, animations, or images.
Average Fixation Duration: On average, participants spent about 3-4 seconds on these key areas. This indicates that multimedia signals effectively captured and held their attention.
Engagement Metrics
Total Viewing Time: Participants engaged with videos that had more dynamic multimedia signals for longer periods. Videos with higher multimedia richness resulted in an average viewing time increase of 15% compared to simpler videos.
Attention Shifts: Videos with frequent but relevant attention shifts (like changing visuals or on-screen prompts) helped maintain engagement. Participants in these conditions shifted their attention approximately 30 times per minute, compared to 20 times in less dynamic videos.
Learning Outcomes
Immediate Retention: Participants who watched videos with effective multimedia signals scored higher on immediate post-tests. The average score increase was 20% compared to those who watched less engaging videos.
Delayed Retention: A week later, participants who were more engaged during the videos retained more information. The score difference between high-engagement and low-engagement groups was 15% in the delayed post-test.
Design Insights
Effective Elements: Highlighted text, animations, and strategic pauses were the most effective in capturing attention. Heatmaps showed that participants consistently focused on these elements.
Ineffective Elements: Static text and long uninterrupted speech segments were less engaging. Participants often glanced away during these parts.
Summary of Study 2 Graphs of Attention and Learning Outcomes
Implications for Education
Berj Akian’s research using RealEye provides valuable insights into how multimedia signals affect learning. By tracking students’ eye movements, the study identified which elements of online content are most engaging. The findings from this study show that understanding where students look and for how long can help improve educational content. By using eye-tracking data, educators can create materials that better capture and hold students' attention, leading to improved learning outcomes.
You can run a similar study!
Follow the steps below to start your own experiment with RealEye:
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