Data and E-Learning Part 1: The Gold Mine

Inside your Learning Management System (LMS) there is a gold mine just waiting to be exploited. This gold mine could help you to improve your learners’ online learning experience. It could help you to track and improve your learners’ successful completion of their training. It can even allow you to get more out of your LMS. And what is in this gold mine? Data. Lots and lots of data.

This article begins a series where we will examine the data analysis process. We will show you how to tap into the gold mine within your LMS to improve learner outcomes and make the most of your LMS. We will examine in this first article why you would want to use data, introduce the data analysis process, and look at what data can tell us.

Why use data?

Data, when collected and analysed appropriately, can tell you a lot about your learners and their interaction with the LMS. The right data helps you to answer questions you have regarding learner engagement, performance, and the quality of what you deliver through your LMS. It substantiates the impressions you have and can also draw your attention to issues you were not aware of.

Data can provide you with an overall picture of your learners. This could be demographic data such as gender, location, age or ethnicity. It can tell you about your courses such as how many users are enrolled into a given course and what the average completion length for a course is. Data can also help you to understand the performance of students and to identify those that may need further support.

Sometimes data will provide you with a reality check. Are users logging in as often as you think they are? Do most of your users reside locally or are they spread across the globe? What percentage of users really complete a course? Data can be used to answer these questions and many others that you may come up with.

The data analysis process

You can use data to answer the questions you have by following a data analysis process. We will explore this process with relation to Learning Management Systems in this article series. The data analysis process involves the following steps:

• Determine the questions you wish to answer
• Determine the data required to answer these questions
• Extract the required data from the LMS
• Clean the extracted data
• Analyse the extracted data
• Interpret the results.

Throughout this article series we will be using Moodle for our examples and examining data from a Moodle test site. Many of the principles will be the same regardless of which LMS you are using. The data available and how you extract it may differ, but the general principles discussed in this article series will still apply.

What can data tell you?

What your data can tell you will depend on how the dataset is structured, what data it contains, and how well you are able to analyse the data. Below is a snippet from an example dataset that tracks students enrolled into various courses. What could this data tell you? Pause for a moment and jot down some ideas.

From this dataset you could determine the following:

• Which courses a specific student is enrolled into
• The number of students enrolled into each course
• Which courses have the most students enrolled
• Which courses have the most active students
• How much of a given course a given student has completed
• Students that are yet to start a given course, or any course.

Some of the items above you could determine just by filtering the data in Excel. Some of the items would need further calculations to group the data or to tally up different rows. These calculations can be done using software such as Python and we will look at this type of analysis in a future article.

You can use data to answer questions you have regarding your learners, your courses and your LMS. You can extract the desired data from the LMS and work through the data analysis process to answer the questions you have posed. Throughout this article series we will look at how you can tap into the gold mine of data within your LMS through following the data analysis process. In the next article in this series we will look at the different types of data you could extract from your LMS and how they can be used.

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Jeff Mitchell

P&C Reporting and Data Analyst at Fisher & Paykel Appliances

Jeff is passionate about the role of learning and development, and has a specific interest in how people and organisations can be developed in order to achieve their potential. Jeff has a keen interest in information technology and specifically data analysis and the e-learning space.

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