How Do Analytics Influence Learner Behaviour?

| 4 Min Read

Imagine having the ability to look into the minds of each of your learners, to understand what motivates them, where they need support, and why they perform the behaviours they do. Learning analytics has bed this possible by providing learning designers with a wealth of data-driven insights and metrics. By deconstructing and understanding what each metric really means, designers are able to tailor courses, drive learner engagement, and ultimately reach their learning goals. Let’s look at these key learning analytics that guides our learners and how we can use them to influence behaviours.

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Key Learning Analytics for Tracking Learner Behaviour

Tracking learner behaviour is an essential step for learning designers that are looking to optimise their course and enhance overall learner engagement. There are a number of metrics that we can look at here, including:

Regularly monitoring the average screen time of your learners offers valuable information about how engaged your learners are. This helps to gauge how much time is spent on each learning activity, where minimal time would show that the learner either already knows the content or is disinterested. On the other hand, long periods of time on a single module may show that the learner is struggling or has walked away from the device. Finding the middle ground here and ensuring that learners fall in this time period will show you that your course is engaging and that your learners are involved in its content.

Embedding quizzes, assessments, and other score-tracking tasks will give you performance scores that indicate the learner's level of comprehension and grasp of the course material. By looking deeper into the individual right/wrong questions of each task, learning designers can identify areas that require support or extra information. Where this occurs across multiple learners, designers can tailor that particular content that is causing difficulty to ensure they address future knowledge gaps.

Learner satisfaction and likeliness to recommend, otherwise known as the Net Promoter Score (NPS) reflects the overall quality and effectiveness of your course. A course that has a high NPS is at the least clear, easy to navigate, and engaging. This metric is great at informing the designer on which aspects of the course resonate positively with learners and those that may require improvement.

 

Influencing Learner Behaviour through Key Analytics

By analysing average screen time, designers can influence the learner's engagement rates so that they are consistently focused throughout the course. Looking through metrics usually reveals a number of patterns, it is this information that we find valuable. For instance, if a number of learners indicate long periods of time spent on the same module, the designer may choose to shorten the content of their course or strategically place an interactive element. Making design choices that complement the learner's preferences and experience maximises the chances of creating a consistently engaging course.

Similarly, closely monitoring learners' drop-off rates may indicate areas that require adjusting. An early drop-off rate shows that potential hurdles may exist and that the learner is confused and needs support. By analysing the data and feedback from these learners, designers can make the necessary adjustments to the course content, delivery methods, or user experience to improve retention rates. By addressing the challenges that lead to early dropouts, eLearning designers can enhance learner motivation, and ultimately improve learner outcomes.

Learner analytics are more than data points pulled from the way the learner interacts with your modules. Creating a space where learners feel comfortable voicing their options and concerns is a great way to receive first-hand feedback. Not only does feedback help designers to identify areas for improvement but also guides the NPS of their course. By actively incorporating learner feedback into the course development process, designers are able to continuously optimise both existing and future courses based on the needs and preferences of existing learners, leading to higher levels of performance.

 

The Influence of Past Course Analytics on Future Course Content

Many designers overlook the treasure trove that is past courses. The profound impact that previous experience has on the planning and development stage of a course is unmatched. With the ability to know what works and what doesn’t, using this information is a great approach when attempting to create more effective learning experiences. There are two key ways in which past course analytics can be used to address knowledge gaps and create higher interactivity levels.

By delving into the analytics, course designers can identify which interactive elements and activities resonated most with learners from the past. These insights enable the incorporation of similar engaging elements into future courses. Whether this be through quizzes, discussions, or multimedia content, adding these elements to your course enhances overall involvement and fosters a higher level of interactivity, Learners are more likely to be engaged and actively participating in a course that has been created with their best interests in mind.

Analysing quiz results and identifying common areas of difficulty or misconceptions from past courses empower course creators to refine content and zero in on these specific knowledge gaps. By focusing on the topics or concepts where learners struggled the most, future courses can provide targeted explanations, additional resources, and interactive exercises to address these challenges. This approach ensures a more comprehensive and tailored learning experience, enabling learners to overcome hurdles and achieve a deeper understanding of the subject matter.

By leveraging past course analytics, eLearning companies can continually enhance the quality and effectiveness of their offerings. The insights gained help shape future courses with greater interactivity and a focus on addressing knowledge gaps. As a result, learners benefit from engaging, personalised, and impactful learning experiences that cater to their needs and support their learning journey.

 

eLearning analytics continuously emerges as a transformative force in shaping learner behaviour and optimising course content. By drawing conclusions from insights, designers can create more impactful learning experiences that resonate with learners and drive positive learning outcomes. The future of data-driven decision-making is upon us and we, as a community of learning designers and eLearning experts, need to pave the way for better, personalised, more effective learning and it begins with analytics. With each course we make, we have the opportunity to refine, innovate, and make a lasting impact on the experiences of our future learners.

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