Unlock LMS hidden information
Online courses generate a multitude of data, but few educators effectively draw this data. The models are hidden in each learning management system (LMS) which reveal how students learn, commit and succeed. However, most of the course conceptions are based on hypotheses rather than evidence. This article explores how the exploration of educational data can discover these hidden models and transform them into usable information. Using methods based on data aligned with established learning theories, such as the Moore Inquiry Community (COI), educators can transform their course design approach, going from reactive adjustments to proactive and based on evidence.
Why data is important in online learning
The LMS data is more than just recording of clicks – it is a window on how learners are committed, where they fight and what keeps them motivated. By analyzing this data, educational designers can discover models that influence students' success. For example, interaction with the content of the course, such as access to readings and videos, has become the strongest predictor of student performance in my research.
Theoretical foundations: Moore Investigation Community and Interaction Framework
This approach is based on two fundamental theories: the framework of the community of survey (COI), developed by Garrison, et al. (2000) and the Moore interaction framework (1989). The COI frame highlights three essential types of basic interaction for significant learning:
- Social presence
Interactions that build a feeling of community among learners. - Educational presence
Actions of the instructor that guide, facilitate and support learning. - Cognitive presence:
Learning commitment with the content of the course, leading to critical thinking.
Moore's interaction framework also highlights three essential types of interaction for distance education:
- Interaction of the learner's content
Direct commitment with learning equipment. - Learning-instructor interaction
Comments, advice and support of educators. - Learning-learning interaction
Communication and collaboration by peers.
By aligning LMS data analysis with these frameworks, educational designers can diagnose what types of interaction thrive and which are lacking, providing a clear path for improving prices.
Practical data exploration techniques for educators
Learning clustering
Use the K-Means clustering to group students according to their interaction models. This helps to identify learners with a balanced and low commitment, allowing targeted support.
Predictive modeling
Applying classification algorithms to predict which behaviors are most strongly in correlation with success, with the interaction of content showing the most substantial impact.
Trends analysis
Follow the weekly engagement data to identify when learners tend to disengage and introduce interventions at the right time.
Example of the real world: how data exploration has transformed a higher education course
In my research on an online higher education program, I applied the K-Means clustering to identify three learners profiles: high-engagement, balanced and low commitment students. Balanced learners obtained the greatest satisfaction and performance. Predictive modeling also revealed that the interaction frequent with the content of the courses and the participation in online discussions were among the most important predictors of success.
In addition, the analysis has shown that students who have returned to specific readings or to review video conferences have demonstrated higher retention and performance. This idea led to the introduction of periodic reminders for essential readings and a mid-term examination module.
3 usable design principles
1. Design for the three types of interaction
Align courses on the framework of the survey community (COI):
- For the cognitive presence (learner's content), include interactive video conferences, self-assessment quizs and case studies in the real world.
- For teaching the presence (learner-instructor), maintain coherent announcements, provide personalized comments and questions and answers.
- For social presence (learning, with income), facilitate peer discussions, group projects and peer exam activities.
2. Monitor LMS data every week
Configure a clear data exam routine:
- Use LMS dashboards to monitor weekly engagement measures, including access to content, participation of the discussion and the completion of the quiz.
- Configure automated alerts for low activity, targeting students who have not accessed key modules.
- Use first information on data to identify learners at risk and provide targeted boost or reminders.
3. ilete according to data
Make data -based adjustments throughout the life cycle:
- After each course, analyze the data to identify the most engaging and less engaging activities.
- Experience with different content formats (videos, infographics, podcasts) to see what improves engagement.
- Examine and update assessments regularly to maintain alignment with the objectives of the course and the needs of learners.
Conclusion
Exploration of educational data is not only for data scientists. Educational designers can use these techniques to make data -oriented decisions, improve prices design, increase engagement and improve learning results. Start by exploring your LMS data, allowing him to reveal the learner's behaviors and inform your course design strategies.
By aligning your analysis on the framework of the survey community (IO) and the Moore interaction framework, you get a clear objective to assess the quality of your course design. Do students engage with the content (cognitive presence)? Do they interact with instructors (educational presence) or peers (social presence)? Data can answer these questions and guide targeted improvements.
When educators make decisions based on data, they go from reactive education to proactive and adaptive education. This improves not only the results of learners, but also promotes a culture of continuous improvement in online education. Educational designers who take advantage of data on data do not only design courses – they design better learning experiences.
At Learnopoly, Finn has championed a mission to deliver unbiased, in-depth reviews of online courses that empower learners to make well-informed decisions. With over a decade of experience in financial services, he has honed his expertise in strategic partnerships and business development, cultivating both a sharp analytical perspective and a collaborative spirit. A lifelong learner, Finn’s commitment to creating a trusted guide for online education was ignited by a frustrating encounter with biased course reviews.
