Learning analytics in eLearning

Companies are increasingly relying on digital learning platforms to offer their employees flexible and customized learning opportunities. But how can they ensure that these learning opportunities are actually effective and deliver the desired results? This is where learning analytics comes in. Learning analytics refers to the collection, analysis, and evaluation of data generated during the learning process. These state-of-the-art analysis tools can give you valuable insights into your employees’ learning behavior so you can continuously improve your learning strategies.

In this article, you’ll find out exactly what learning analytics is, how it works. and what benefits it holds for your company. We’ll also introduce important standards such as SCORM, xAPI, and cmi5—which play a central role in data collection and analysis in eLearning—and provide you with a step-by-step guide on how to get learning analytics up and running at your company.

Imagine you could understand exactly how your employees learn, what content they understand particularly well, and where they need support. This is precisely what learning analytics enables you to do. But what exactly is it?

Learning analytics is the systematic collection and analysis of data generated during the learning process. This data comes from various sources, such as online courses, learning management systems, and interactive learning platforms. Learning analytics can help you identify patterns and trends in your employees’ learning behavior and use them to optimize your training strategies.

The history of learning analytics goes back to the earliest days of digital training technologies. With the advent of the internet and advanced analysis tools, it has become possible to gain ever more detailed insights into learning processes. Unlike traditional training analytics, which is often based on surveys and test results, learning analytics offers continuous and data-driven monitoring of learning progress.

The process behind learning analytics may seem complex at first glance, but it can be broken down into three main steps: Data collection, data analysis, and data interpretation. Let’s take a closer look:

With just three steps—data collection, data analysis, and data interpretation—learning analytics becomes a powerful tool that helps you make the learning processes in your company more efficient and targeted.

Here’s an example: Imagine the learning analytics indicates that many of your employees are having difficulties with a particular course module. You could then take targeted measures, such as offering additional training or adapting the learning materials, to counteract these challenges.

To successfully implement learning analytics in your company, it is important to first familiarize yourself with the current standards. These standards help you record and analyze data consistently. The most well-known are SCORM, xAPI, and cmi5. 

eLearning standards transmit data relating to the learning process between the learning content and learning management systems. They ensure that two different systems can communicate with each other and speak the same language. For example, they allow an online course to send and receive information to and from the learning management system. Among other things, this includes information on progress and learning outcomes. They store learner’s current status making it easy for them to resume courses at any time.

SCORM, short for Sharable Content Object Reference Model, is a widely used eLearning standard that governs the development and delivery of learning content. SCORM ensures that digital learning objects function independently of the learning platform or learning management system (LMS). This is achieved through a series of technical specifications that define how learning content should be packaged and run, and how it communicates with the LMS.

SCORM enables you to record a wide range of data on users’ learning behavior and progress. Learning analytics can cover the following aspects:

• Course utilization: Which courses are the learners attending, how often, and for how long?
• Progress and completion rates: How far have the learners progressed in the course, and have they completed it?
• Test results: Results of tests and quizzes taken as part of the course.
• Interactions: Interactions with learning content, such as watching videos, clicking on links, and completing modules.
• Time spent: The time spent on individual learning activities or modules.
• Failed attempts: Number of attempts and failed attempts at tests or tasks.

You’ll need the following technologies and tools to collect and analyze this data:

• Learning management system (LMS): A SCORM-compliant LMS can collect and store data relating to learning activities. Examples of SCORM-compatible LMSs include Knowledgeworker Share, eFront, and Cornerstone.

• SCORM authoring tools: You can use these tools to create SCORM-compliant learning content. Examples include Knowledgeworker Create, Articulate 360, and Lectora.

• Database and reporting tools: The SCORM data you collect must be stored and analyzed in a database. Relational databases such as MySQL and PostgreSQL, and reporting tools such as Tableau and Microsoft Power BI, can be used for this.

• Learning record store (LRS): An LRS stores learning activities and interactions recorded using the Experience API (xAPI) standard. It can also import SCORM data and combine it with other data sources.

xAPI, short for Experience API, and also known as Tin Can API, is a modern eLearning standard that facilitates the capture and exchange of learning experiences. In contrast to older standards such as SCORM, which are limited to interactions within an LMS, xAPI enables you to track learning activities in a variety of contexts and environments. This includes formal learning content, social interactions, simulation-based training, mobile learning applications, and even real-world, offline learning activities. xAPI uses “statements” in the format “subject, verb, object” (e.g. “John completed a quiz”) to document learning activities and store them in a learning record store (LRS).

xAPI enables you to record extensive data on your users’ learning behavior and learning progress. Learning analytics can cover the following aspects:

• Course usage and interactions: Detailed records of which learning activities users perform, which media they view, which tasks they complete, and which modules they attend.
• Progress and completion rates: How far learners have progressed in their learning process, and whether they have completed certain courses or modules.
• Test results and performance: Results of tests and quizzes, including detailed data on correct and incorrect answers, response times, and failed attempts.
• Time spent: Detailed records of the time spent on individual learning activities, modules, and courses.
• Learning outside the LMS: Records of learning activities that take place outside the LMS, such as attending workshops and conferences, reading articles, or watching videos

You’ll need the following technologies and tools to collect and analyze this data:

• Learning record store (LRS): The central element of the xAPI infrastructure. An LRS stores the statements generated by xAPI so that they can be queried and analyzed later. Examples of LRSs include Learning Locker, Watershed, and Grassblade.

• Learning platforms and systems: Modern LMSs and other learning platforms that support xAPI.

• xAPI authoring tools: These tools enable you to create xAPI-enabled learning content. Examples include Knowledgeworker Create, Articulate 360, and Lectora.

• Database and reporting tools: The data stored in an LRS can be put into in databases such as MySQL and MongoDB and analyzed using reporting tools such as Tableau and Microsoft Power BI, or special learning analytics platforms.

• Integration platforms: Tools and platforms that combine xAPI data with data from other sources to facilitate a more comprehensive analysis. Examples include Zapier, MuleSoft, and Apache NiFi.

Combined with these technologies, xAPI offers a flexible and powerful solution for capturing and analyzing learning experiences that goes far beyond the options offered by traditional eLearning standards. It gives you a more in-depth analysis of learning behavior, and supports you with developing personalized and effective learning strategies.

LRS Learning Locker dashboards are a well-known reporting tool that is also used by a global wholesaler to evaluate the learning activities of its employees and customers. An individual reporting system for a learner group (country level) might look like this:

cmi5 is an eLearning standard that combines the strengths of both SCORM and xAPI to create a comprehensive and flexible solution for delivering and tracking learning content. cmi5 is designed to overcome the limitations of SCORM and take advantage of xAPI, while providing the necessary specifications for interoperability and management of courses within a learning management system (LMS). cmi5 defines a structured method for managing courses and sessions, which is particularly beneficial for formal learning environments.

cmi5 can be used to collect detailed data on users’ learning behavior and progress. Learning analytics can cover the following aspects:

• Course usage and interactions: Detailed tracking of which courses and modules learners attend and how they interact with the content.
• Progress and completion rates: Monitoring how far learners have progressed in their courses and whether they have successfully completed them.
• Test results and performance: Detailed records of test and quiz results, including the number of attempts, scores achieved, and reaction times.
• Time spent: Measures the time learners spend on different learning activities and modules.
• Session management: Tracking and management of learning sessions, including starting, pausing, and ending sessions.
• Competence development: Documentation of the acquisition and application of skills and competences through various learning activities.

You’ll need the following technologies and tools to collect and analyze this data:

• Learning record store (LRS): cmi5 uses xAPI for data transfer and storage. An LRS stores the xAPI statements generated by cmi5 so that they can be queried and analyzed later. Examples of LRSs include Learning Locker, Watershed, and Grassblade.

• cmi5-compatible LMSs: LMSs that support the cmi5 standard—such as Moodle with the corresponding plugins—and other modern LMSs that have implemented cmi5.

• cmi5 authoring tools: Tools for creating cmi5-compliant learning content that generates xAPI statements. Examples include Knowledgeworker Create, Articulate 360, and Lectora.

• Database and reporting tools: The data stored in an LRS can be put into in databases such as MySQL and MongoDB and analyzed using reporting tools such as Tableau and Microsoft Power BI, or special learning analytics platforms.

• Integration platforms: Tools and platforms that combine cmi5 data with data from other sources to facilitate a more comprehensive analysis. Examples include Zapier, Mulesoft, and Apache NiFi.

By incorporating these technologies, cmi5 offers a powerful and flexible solution for capturing and analyzing learning experiences that combines the strengths of SCORM and xAPI. It gives you a more detailed and precise analysis of learning behavior, and supports you with developing more personalized and effective learning strategies.

Using these standards ensures that you capture accurate and consistent data about your employees’ learning activities. This is critical when it comes to making informed decisions about your eLearning strategies and maximizing learning outcomes. By implementing SCORM, xAPI, and cmi5, you can ensure that your learning content is effective and that your employees are supported as constructively as possible. These standards provide you with the tools to efficiently manage and continuously improve your eLearning portfolio.

Implementing learning analytics in your company can seem like a challenge at first glance, but with a structured approach you can make the process run smoothly. Here are the most important steps for successfully introducing learning analytics:

Before you start the implementation, you should clearly define your goals and requirements. Think about which questions you want to be able to answer with the help of learning analytics, and which goals you are pursuing. This could include improving course quality, increasing completion rates, or optimizing learning resources.

Determine requirements

Identify the main problems and challenges your company faces with regard to eLearning. This could be low course uptake, low completion rates, or difficulties in assessing learning effectiveness. Conduct a survey or interviews with your employees to gain a better understanding of their needs and expectations.

Define objectives

Formulate specific, measurable, achievable, relevant and time-bound (SMART) goals. For example: “Increase completion rates in online courses by 20% within the next six months” or “Improve employee satisfaction with training programs by 15%”.
Prioritize your goals based on their importance and the expected benefits for your company.
 

There are a variety of tools and platforms that support learning analytics. Select the technologies that best suit your needs. Ensure they are compatible with your existing systems and that they offer the option of integrating standards such as SCORM, xAPI, and cmi5. A thorough evaluation and, if necessary, a pilot phase can help you find the best solution.

Research and select technologies

• Create a list of potential tools and platforms that support learning analytics.
• Ensure that the tools are compatible with your existing systems and support SCORM, xAPI, and cmi5. This ensures that you will be able to capture and analyze data seamlessly down the line.

Tip: Use independent eLearning providers such as chemmedia AG, who offer various learning technologies and can advise you. You’ll not only get the bundle that perfectly meets your needs, but also benefit from the provider’s many years of experience and comprehensive services.

Evaluation and pilot phase

• Carry out a thorough evaluation of the tools in question. Consider factors such as user-friendliness, range of functions, scalability, and support.
• Start a pilot phase with a small group of users to test the selected tools in practice. Gather feedback and evaluate whether the tools meet your requirements and support your goals.

Implement the selected technologies

• Develop a detailed implementation plan that includes all the necessary steps and responsibilities. Consider aspects such as data migration, system integration, training, and communication measures.
• Work closely with your IT department and the providers of the selected tools to ensure a smooth implementation.
• Plan training for your employees to ensure they can use the new tools effectively. Ensure that support and resources are available to address any questions or issues.

By carefully planning and carrying out the needs analysis and selecting suitable technologies, you’ll lay the foundations for the successful implementation of learning analytics in your company. These steps will help you optimize your employees’ learning processes and sustainably boost their learning outcomes.
 

When collecting and analyzing learning data, protecting your employees’ privacy is of the utmost importance. Ensure that all applicable data protection regulations are complied with. Inform your employees transparently about what data is collected and how it is used. Develop guidelines that ensure their data is handled ethically.

Learning analytics is an ongoing process. Regularly monitor the data you collect and the results of your analyses. Continuously adapt your strategies and measures to achieve the greatest possible benefit. Gather feedback from your employees and use it to improve your approaches.

The introduction of learning analytics can involve some technical and organizational hurdles. One common problem is the integration of new technologies into existing systems. Ensure that your IT department is closely involved in the process and provides technical support. Another challenge can be employee resistance to new methods. Transparent communication and training are crucial here.

• Take small steps: Start with a pilot project to gain initial experience and make adjustments before introducing learning analytics throughout the entire company.
• Involve your employees: Involve your employees in the process right from the start. Inform them of the benefits and train them in how to use the new tools.
• Ensure you have data protection-compliant processes in place: Inform your employees transparently about what data is collected and how it is used.
• Monitor the process regularly and adapt it: Use dashboards and reports to react quickly to changes and recognize new trends.
• Foster a data-driven culture: To realize the full potential of learning analytics, it is important to foster a data-driven culture in your company. Make sure your employees understand the importance of collecting and analyzing learning data and encourage them to actively use this data themselves.
• Work transparently: Be transparent in how you handle the data you collect. Inform your employees about what data is collected and how it is used. This creates trust and acceptance. Open communication about the goals and benefits of learning analytics helps to dispel concerns and increase participation.
• Integrate learning analytics into your business strategy: Ensure that learning analytics is an integral part of your business strategy. The findings from the analyses should be incorporated into your decision-making processes and help you achieve your strategic goals. This might include planning training measures, developing new products, or improving customer service.
• Offer training and support: This ensures they can use the tools and technologies effectively, and may take the form of workshops, online tutorials, or individual coaching sessions.