In e-learning, you must be able to monitor each learner’s progress in real time. Tracking video engagement is crucial for e-learning success. Video analytics for e-learning enable you to collect and analyze data. When you understand how learners interact with video content, you can identify areas for improving your strategies to enhance learning outcomes. Whether you’re an online course creator, L & D professional, or EdTech decision maker, you need to use data-driven insights to enhance your video content.
Key video metrics to track in e-Learning
Learner engagement metrics are critical in evaluating the effectiveness of e-learning. It isn’t just about collecting data but about interpreting the metrics to gain actionable insights.
- Completion rates: Are learners finishing the videos? It is important to investigate why this is the case if completion rates are low.
- Engagement heatmaps: Engagement heatmaps visually represent how viewers interact with a video. Warmer colors show high engagement and cooler colors represent low engagement.
- Drop-off points: These help you to understand where learners lose interest. If you can pinpoint where students tend to drop off or struggle, you can intervene promptly.
- Quiz and interaction data: Regular Quizzes can help you measure learner understanding throughout a course. For example, a language training course could include quizzes at the end of each module. Interaction data gives you feedback on learner engagement.
On the Cincopa video hosting platform, advanced analytics offer in-depth engagement data and interaction tracking. A live feed gives you detailed information about every learner who watches a video. A video heatmap shows you how learners interact with videos. These insights help you to optimize video content and improve learner outcomes.
How video analytics help improve course design
Aligning video analytics with specific learning outcomes can help to guide course design.
1. Identify content that needs improvement
Looking at video completion rates helps you to identify content that needs improving. A low completion rate may indicate that a video is too long. You may have to cut it shorter and break it down into smaller units. Low completion rates could also indicate technical problems with the video player or internet connection.
By tracking metrics like watch time, skipping, and rewatches, you can pinpoint sections you may need to clarify or simplify. Pausing and rewinding can indicate areas where learners may need further context or additional explanations.
When you see which parts of a video hold the viewer’s attention the most on a heatmap you know these parts are effective. You can improve areas of low interest by making them more interactive. For example, you may decide to introduce a call-to-action or a quiz.
If a specific module has a high drop-off rate, a challenging concept or poorly explained section may need revision.
2. Adapt learning materials based on learner behavior
Insights into learner behavior can help you to adapt learning materials. You can gain these insights by looking at the performance of quizzes, engagement with interactive video elements, etc.
Learner behavior can help you to segment learners into groups. Some may be flying through the materials and others may be struggling. Knowing this can help you to intervene and offer support to help struggling learners and to give quick learners supplementary material.
Video-based training analytics can help you to improve corporate training materials. For example, you can add subtitles to videos to increase their accessibility and ensure non-English speaking learners and those with hearing problems can understand them.
Learners can benefit from live streaming of lectures and video-on-demand (VOD). If they miss a live lecture, they can always watch it later. They can access video-on-demand in video libraries whenever they need it and watch when it suits them.
3. Personalize learning experiences with data insights
To optimize learning experiences, data insights are invaluable. Analyzing data helps to identify patterns, trends, and key insights into how learners learn. This can help you to create learning experiences that are right for every learner. Every click learners make and quiz they complete gives you the information you can use.
- Predictive analytics can help you to anticipate learning outcomes so you can personalize your interventions.
- Adaptive learning technologies are able to adjust content based on real-time learner data.
- AI-powered systems can enable one-on-one interactions so you can provide personalized explanations and adapt to different learning styles.
Best practices for using video analytics effectively
Set clear learning objectives for each video: What do you want learners to know how to do after completing a video? Your objectives help you to focus your video content. For example, in a video on workplace bullying, you may want learners to know the best ways to respond to bullies after watching the video. A clear objective allows you to evaluate whether the video achieves its purpose or not.
Regularly review analytics and adjust content: E-learning performance tracking needs to be continuous. You need to keep refining your video content based on analytics. A/B testing provides an easy way to try out different versions of course material so you can see what works best.
Combine qualitative and quantitative insights: Qualitative insights come from learner feedback. Analytics help you to track feedback via surveys, polls, discussion boards, video messaging, etc. Acquiring and responding to feedback helps to create a more dynamic learning environment. Quantitative insights come from online course data. Identifying trends in engagement and feedback could lead to improvements in course content and structure.
Case study: How video analytics were able to transform an online course
EduNext is an online learning technology provider. One of its flagship courses “Data Science for Beginners” had a low completion rate of just over 40% despite the fact that many learners enrolled. The dropout rate was also high after the first two modules. The company wanted to understand what the reason was for the issues so they could improve engagement rates and course completion.
EduNext used analytics to extract insights from their LMS data, user insights, and feedback surveys. They looked specifically at drop-off points, engagement metrics, and performance trends.
Findings and insights
- Module three had the highest drop-off rate. It was a complex module about statistics with few interactive elements.
- Videos of more than 12 minutes had lower completion rates. Drop-offs happened after about six minutes.
- Low activity in forums corresponded to low quiz scores.
- Learners with prior programming experience had higher completion rates.
Implementing solutions
- Module three was redesigned by simplifying statistical concepts and using real-world scenarios.
- Longer videos were made into bite-sized videos of about five minutes each with more interactive elements.
- Students had to post in discussion forums before completing quizzes and peer mentoring groups were introduced.
- Optional pre-courses were designed for non-programmers.
Results
After six months course completion went up to 76%. Video watch times improved and the new short format resulted in an 18% higher retention rate. Engagement in forums grew and this improved quiz scores. Learners with no coding background increased quiz pass rates by 35%.
Conclusion
If you don’t use video analytics, you will miss out on opportunities to personalize your online courses in ways that resonate with learners. With data insights from analytics, you can make data-driven improvements to enhance e-learning effectiveness. On Cincopa’s home page, you can find out more about its advanced video analytics.
Try out a free trial and see how using video analytics can help you to continuously improve your e-learning and keep it relevant and effective.
