e-learning data: how data analytics helps e-learning

The plethora of e-learning data available

e-learning data analytics - e-learning data logs - digital learning data - online course data With the digital revolution, periods such as the Covid lockdown, e-learning has become an increasingly popular way of delivering education. With it, the amount of data available from e-learning has grown exponentially. E-learning platforms such as Learning Management Systems (LMSs), Virtual Learning Environments (VLEs) and other such systems generate a plethora of data about the online courses they host, as well as the content, activities, learners, instructors and the interactions between them. This data can be of great benefit to educators and learners alike, as well as to the subject matter specialists, the content creators, the tutors, the course manager and beyond. Such benefits only happen as long as these stakeholders know how to harness that data within the boundaries set by regulations, and as long as the platforms and these courses are set up to optimise the generation of such data.

The importance of knowing what e-learning data is generated

Data can play a vital role in e-learning once it is understood. E-learning systems such as LMSs and VLEs record or generate a wide diversity of data about users (learners, instructors/tutors, and other users), courses, and interactions) so it becomes vital to know what data is needed, what data is generated, how data can be accessed (directly and indirectly) and how it can be interpreted. While not all these systems will record or generated all of these, and some integration with peripheral systems such as information systems (student registration) might be responsible for getting part of the data, understanding the sort of data that is available goes a long way towards a better grasp on what is happening in the digital learning environment,

The data recorded or generated on or via e-learning systems

The e-learning data recorded or generated on or via e-learning systems differs from one system to the other and from one digital course to another. However, the following data is often encountered:

  • Learner demographics may include the age, gender, location, and/or occupation of the learner. Sometimes, the data even provide learner preferences.
  • e-learning data analytics - e-learning data logs - digital learning data - online course dataLearner performance data tracks how well learners have performed on given activities and their scores on assessments.
  • Learner participation data tracks user engagement and contribution in discussions e.g., in fora.
  • Learning experience: Focused feedback activities such as surveys, polls, or comments, to cite a few, are as many ways to collect targeted data from learners about their learning experience.
  • Learning content access: This data tracks how often learners log in to an e-learning course, what content they access, and how long they spend on each screen.
  • Learning activity: This data tracks which learning activities learners have completed and how long they have spent on each of these.
  • Learning context data tracks the context in which learners are completing learning activities, such as the time of day, the location, the browser and the device they are using.
  • Learning environment infrastructure: e-learning systems may also generate data such as the number of users, the amount of bandwidth used, and the number of errors that occur.

How much of this e-learning data generated is available and how reliable is it?

First, not all systems generate all possible data. Secondly, not all generated data is readily available for use via a user-friendly graphical interface; sometimes, it needs to be extracted from logs. Thirdly, the amount of data available, whether readily or not, is regulated by privacy policies effective in the country where the online course is setup but also should take into account the privacy policies from the e-learners’ and other users’ countries of residence. Four, the modularisation of the e-learning content will determine the level of detail of the data analytics returned. This will also vary according to the e-learning standard used, e.g., SCORM or TinCan/xAPI. Finally (and definitely not least), the acuracy in data literacy is vital in understanding the e-learning data generated. For example, we may say that a system will track whether a learner has opened a page but not whether they have read it, unlike what some systems would allegate. We might consider that the country of access recorded for a learner’s session is their country of residence, but this might just be the headquarters of their Internet provider or a stopover. It is therefore essential to be clear what the data means so it can be used appropriately and to its full benefits.

How can we use the e-learning data thus generated?

Once you know what the data truly is (not what it appears to be), you can use different strands of it for different purposes. The main uses of e-learning data are:

  • Better understand learners and identify learner needs: By using e-learning platforms to track learner progress and engagement, e-learning professionals can identify areas where learners are struggling or need additional support, and troubleshoot issues. This information can be used to better understand the target audience for an e-learning course and to tailor the content accordingly and thus create personalized learning paths and more personalized learning experiences. Educators can also thus identify which learning resources are most popular and recommend additional resources to learners.
  • Improving course content and activities: The data collected from e-learning platforms can also be used to improve the course content itself. For example, if learners are consistently struggling with a particular concept, the course content can be revised to provide more explanation or practice opportunities. This information helps to measure the effectiveness of the online course and of learning interventions, identify areas for improvement and make informed decisions about the design and delivery of e-learning courses. For example, if learners are more likely to engage with a particular type of content or activity, the platform can be configured to provide more of that type of content. It can be used to continuously track the overall effectiveness of e-learning initiatives.  You can identify the most effective learning activities and resources and improve the overall quality of learning.
  • Optimise the learning environment: Data about the learning context can be used to troubleshoot problems with the learning environment and to ensure that it is running smoothly. It also allows relevant parties to make informed decisions about how to allocate resources and improve the overall learning experience. Knowing what devices, browser and other client tools learners use also helps to guide learners into configuring their devices to optimise their experience of the learning environment.

However, the amount of data available and the amount that can be done remain disproportional to the advances observed in popular e-learning practices.

e-learning data literacy among e-learning professionals

The importance of data in e-learning cannot be challenged. However, e-learning data literacy is disproportional to its impact on the discipline. A 2021 survey by eLearning Industry found that only 34% of e-learning professionals feel confident in their data literacy skills. The same survey found that 66% of e-learning professionals believe that data literacy is important for their job.

A 2020 study by the Society for Learning Analytics and Knowledge (LAK) found that e-learning professionals are most confident in their ability to interpret data visualizations, but they are less confident in their ability to collect and analyze data.

The LAK study also found that e-learning professionals are most likely to use data to track learner engagement and performance, but they are less likely to use data to make decisions about the design and delivery of their courses. The former fosters a reactive culture while the latter encourages a more proactive and even innovative one. In addition, this study hints at the latent belief that sissociates the effectiveness of a course and its instructional design.

These statistics suggest that there is a need for e-learning professionals to improve their data literacy skills and to fully harness e-learning data for the purposes of improving the learner experience through their engagement, their performance but also the course design. By doing so, they can use data to improve the learning experience for their learners in a 360 degree and make their work more efficient.

Challenges faced by e-learning professionals in improving their data literacy

e-Learning professionals face many challenges with e-learning data, from being stuck with the wrong tools to facing barriers to improving their data literacy:

  • Lack of time: Many e-learning professionals are busy with other tasks, so they don’t have time to learn about data literacy.
  • Wrong tools: Many instructional designers must develop the online courses with available tools which are not always the right tools for the job.
  • Outdated standards: Many online courses still use the popular SCORM standard which does not give as much data for perusal and informed decision-making.
  • Lack of resources: There are not many resources available to help e-learning professionals learn about data literacy.
  • Lack of confidence: Some e-learning professionals may feel that they are not good at math or statistics, so they may not be confident in their ability to learn about data literacy.
  • The compartmentalisation of educational roles can hinder advances in e-learning. There are still silos around responsibilities and accountabilities, which stops the seamless flow among professionals about the data, the awareness of the relevant data and the correct interpretation and application of the information it could bring to instructional designers, SMEs, educators, tutors, researchers, e-learning initiatives and groups and different level of Education.
  • The threat of Artificial intelligence (AI): The rise of Artificial intelligence (AI) is often seen as a threat and thee-learning domain isn’t an exception. The plethora of data does also mean the feeding of AI. This threat may influence the way that e-learning stakeholders enter, process or interpret data. While the rising of AI has the potential to revolutionize e-learning, it still must overcome some potential threats, including the potential displacement of educators and e-learning professionals, biases affecting data ccuracy, the lack of transparency or accountability, and seccurity and privacy linked to the vast amounts of data collected on e-learning users and its potential misuse if hacked. Clearer regulations about the role of AI and the boundaries around this would clarify things.

How can e-learning professionals improve their data literacy

Despite these challenges, there are things that e-learning professionals can do to improve their data literacy:

  • Look at e-learning from the perspective of providing decision-making insights for business strategy, course improvement, learner engagement and retention or subject matter refinement
  • Take a data literacy course: There are many online and in-person courses available that can teach e-learning professionals the basics of data literacy. My personal favourite is the one I am experiencing with Correlation One.
  • Read data literacy articles and books: There are many resources available that can help e-learning professionals learn more about data literacy. On LinkedIn, you will find a few, but my favourite is of course, is to Follow Correlation One’s Data Science for all.
  • Attend data literacy workshops and conferences: There are many events that are held each year that can help e-learning professionals learn about data literacy.
  • Find a mentor: A mentor can help e-learning professionals learn about data literacy and apply their skills to their work.

By taking these steps, e-learning professionals can improve their data literacy skills and use data to improve the learning experience for their learners.

Choosing the right e-learning tools

Choosing the right e-learning tools, e-learning standards and e-learning practices play a fundamental role in obtaining relevant data. A typical example is the widespread use of the SCORM standard in e-learning courses. xAPI, or Experience API, is a newer standard for tracking e-learning data than SCORM. It offers a number of advantages over SCORM, including:

  • Flexibility: xAPI can track learning experiences from a variety of sources, including online courses, mobile apps, and even real-world experiences and non-formal learning experiences, such as attending a conference or reading a book. This data can be used to get a complete picture of a learner’s learning journey. SCORM is limited to tracking learning experiences from online courses.
  • Extensibility: xAPI is designed to be extensible, which means that it can be adapted to track new types of learning experiences as they emerge. SCORM is a more rigid standard, and it is not as easy to adapt to new types of learning experiences.
  • Reliability: xAPI uses newer technologies than SCORM, which makes it more reliable and secure. SCORM uses older technologies, which can be less reliable and secure.
  • Interoperability: xAPI is more interoperable than SCORM, which means that it can be used with a wider range of learning platforms and tools. SCORM is less interoperable, and it can be more difficult to use with some learning platforms and tools.

Overall, xAPI is a more flexible, extensible, reliable, and interoperable standard for tracking e-learning data than SCORM. If you are looking for a standard that can track learning experiences from a variety of sources, and that can be adapted to new types of learning experiences as they emerge, then xAPI is the better choice. Looking at it from a strategic perspective, you would want to adopt a powerful standard that will provide digital courses, learners and educators with the ability to gain insights into how to faciliate positively unforgettable learning experiences, with all the success and marketing impact that this generates.

Adopting the best e-learning practices

The way that an instructional design breaks down and structures their content as well as the way that they match learning outcomes with content and learning activities also has a great deal of impact in the effectiveness of e-learning. By adopting the most effective e-learning practices based on their target audience, they create online courses in a way that informative data can be better extracted. For example, creating bite-size content individually related to the achievement of an outcome helps track the progress of a learner against that outcome and pinpoint the part(s) with which the learner is struggling when trying to achieve that outcome.

It is important to keep in mind that the expected learning outcome plays a key role. It is not just about matching activities with the correct tools but also formulating the associated content appropriately. For example, an instructional designer or an educator wanting to ensure that the learner has read and understood a piece of content can add a question at the end summing up the main takeaway. When articulating the question, they must remember that it is a test of the learner’s ability to understand and/or apply the particular related outcome of the subject, not to test their English, their spelling or their overall cleverness. Clarity and simplicity within a learning-outcome focused question will nicely round up the piece of content and give the learner a sense of achievement, a sense of another building block solidified in the erection of the building of their subject matter expertise.

Choosing the right tools

There are over 1000 Learning Management Systems in existence and a few dozens rapid e-learning tools. This makes choosing one a difficult task. For many educators in established organisations, the choice is made for them which is sometimes a relief and sometimes a pain. Either way, being given the tool before you have worked out your need makes it harder to have your goals drive the technology. There is no right answer as to how to do that, but keeping in mind your goals and not letting technology limit your imagination is a nice trick to start. Your goals will determine the data that you need to extract and this can guide the segmentation of your course and even the way you use the tools you have, whether you selected them or not.

Taking into account the limitations of data

Here are some of the challenges associated with collecting and analyzing e-learning data:

  • Data privacy: It is important to protect the privacy of learners when collecting and analyzing e-learning data. Data privacy policies must be clear and transparent and comply with the relevant regulations such as GDPR in Europe, among others. There are also policies linked to the age of the learners, their consent and more. Either way, respecting the learner’s privacy and making this transparent makes the learning environment and the organisation behind it more trustworthy. Free courses are not exempt from this.
  • Data quality: The data collected from e-learning platforms must be accurate and reliable in order to be useful. Data literacy develops critical thinking towards generated data.
  • Data interpretation: The data collected from e-learning platforms can be complex and difficult to interpret. A data literateprofessional is able to interpret the data that they are given without making it say what it doesn’t and making sure they ask themselves relevant questions about the story the data tells.

Conclusion

A wealth of data is generated in e-Learning when the right systems, best practices, useful standards are used and that strategic and educational goals drive the process. With the right mindset and expertise, this data can be used to improve the learning experience, the digital course, the subject matter and even e-learning itself. The opportunity to create more satisfying, personalised, engaging and effective learning experiences and to measure the impact of learning interventions are motivations that incites e-learning professionals to improve their data literacy adn contribute to the better understanding and practices in e-learning, despite the many challenges that they are bound to encounter.

The data collected from e-learning platforms can be a valuable resource for improving the learning experience. By carefully considering the privacy, quality, and interpretation of this data, e-learning professionals can use it to make informed decisions about how to improve their courses and programs.

While the challenges cannot be ignored, the benefits are worth the effort. Becoming data literate to make better informed decisions regarding the improvement of the course content, the proactive support of our learners, the better understanding, improvement and even personalisation of their learning experience, the contribution to improving the subject matter and more.

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