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How can you use analytics to optimize your LMS?

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Analytics can help you improve your learning management system (LMS) by providing insights into how learners interact with your courses, content, and activities. By collecting and analyzing data from various sources, you can measure the effectiveness of your LMS design, pedagogy, and engagement strategies, and make informed decisions to enhance the learning experience and outcomes. In this article, we will explore some ways you can use analytics to optimize your LMS.

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1 Define your goals and metrics

Before you dive into the data, you need to have a clear idea of what you want to achieve with your LMS and how you will measure your progress. Depending on your context and objectives, you may want to focus on different aspects of your LMS, such as learner satisfaction, retention, completion, performance, or behavior. You also need to identify the key indicators and metrics that will help you track and evaluate these aspects, such as course ratings, dropout rates, grades, or time spent. Having a well-defined framework of goals and metrics will help you align your analytics with your LMS strategy and priorities.

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Matthew Harrington

Product Manager at Cambridge Spark
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Unless you can define exactly what makes your learning programme a success, your analytics are basically useless to you. Data without purpose is background noise. For each and every data point that you're focused on, you need to look at why you want and need that data, and plan how it will be used. This isn't to say you can't find a use for extra data later, but coming at "Why" as early as humanly possible will help you to ensure you're collecting the data you actually need, and will highlight gaps in the fine-grain details. Sure, you want to know your dropout rate, but unless you can identify why people drop out from the data, it's just adding to your pile of questions, rather than giving you answers.

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2 Collect and integrate data

The next step is to collect and integrate data from various sources that can provide valuable information about your LMS and your learners. This data may come from built-in reports, dashboards, or analytics modules, or from external plugins or applications. Common sources include course content and activities, learner profiles and feedback, learning outcomes and assessments, social and collaborative learning, and external sources. It's important to ensure that the data is reliable, relevant, and secure, and that you comply with any ethical and legal requirements. The more data you can collect and integrate, the more comprehensive and holistic your analytics will be.

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3 Analyze and visualize data

Once you have collected and integrated your data, you need to analyze and visualize it to extract meaningful insights and patterns. Depending on the type and complexity of your data and the questions you want to answer, you can use various methods and tools to process, filter, aggregate, and segment your data. For instance, descriptive analytics can help summarize and present your data in a simple way, such as tables, charts, graphs, or maps. This can help answer questions such as what happened or how many. Meanwhile, diagnostic analytics can explore and explain your data in more depth; this is useful for understanding why something happened or what are the factors. Predictive analytics can forecast and anticipate future events or scenarios; this is helpful for answering what will happen or how likely. Finally, prescriptive analytics can recommend and optimize your data for the best outcomes; this is useful for figuring out what should happen or how to achieve it.

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Matthew Harrington

Product Manager at Cambridge Spark
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Visualisations are all about providing what your audience needs to know. Certain visualisations are definitely useful for identifying trends and patterns, but at the end of the day the primary audience for most visualisations are people who aren't neck-deep in your data. Consider what exactly the questions people looking at your pretty charts and graphs should be asking, and answer them before they even ask the question.

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4 Act and evaluate

The final step is to act and evaluate based on your analytics. You can use your insights and recommendations to improve your LMS design, pedagogy, and engagement, and to address any issues or gaps that you identified. You can also use your analytics to monitor and measure the impact and effectiveness of your actions, and to adjust and refine your goals and metrics as needed. By acting and evaluating based on your analytics, you can create a continuous cycle of improvement and optimization for your LMS and your learners.

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Matthew Harrington

Product Manager at Cambridge Spark
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It takes a lot to admit that your assumptions are wrong, but that's the point of science - to reassess and check your assumptions when empirical data is presented. Data science is no different. Make sure that before you act that you check your biases. Does the data *actually* say what you think it does, or are you simply projecting what you want to see? Large organisations are especially prone to this, and it's important to be OK with being proven wrong if you want your decisions to truly be data-driven.

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5 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How can you use analytics to optimize your LMS?

1

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3

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5

1 Define your goals and metrics

2 Collect and integrate data

3 Analyze and visualize data

4 Act and evaluate

5 Here’s what else to consider

Digital Learning

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How can you use analytics to optimize your LMS?

1

2

3

4

5

1 Define your goals and metrics

2 Collect and integrate data

3 Analyze and visualize data

4 Act and evaluate

5 Here’s what else to consider

Digital Learning

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