What is Learning Analytics?
Learning Analytics refers to “the measurement, collection, analysis, and reporting of information about training participants to understand and optimize the learning process and the environment in which they learn. this process is carried out”. Source: (Siemens and Gasevic, 2012)
The analysis of student interactions in an eLearning context facilitates the visualization and understanding of their behaviors in order to optimize the learning experiences, verify the efficiency of the programs and improve student performance.
5 Common Learning Analytics Mistakes You Should Avoid
1) There is No Learning Analytics Strategy Aligned with The Business Goals
For an eLearning program to be successful, it is essential that it be based on a solid strategy and aligned with the business goals.
A strategic, goal-oriented approach will help L&D leaders be much more intentional about the data they will analyze and help make training efforts easier to manage.
Without a concrete roadmap, the road to learning analytics failure is imminent. Learning Solutions Magazine used this analogy to illustrate the importance of this specific point: You wouldn't drive a car using only your rearview mirror, so don't rate a program's "success" solely by course completion or assessment metrics of learning (however important it may be); a more holistic approach will also give you side mirrors and a clearer view of the road ahead.
Making data-driven decisions, looking at the big picture, like everything from individual learning goals to strategic organizational goals will ultimately mean a truly successful training program.
The key is to start by identifying business and eLearning program goals, and then map them to data the data your platform can track. The main mistake to avoid here is collecting thousands of pieces of data that are not going to be used.
So first of all: determine the data you want to analyze and which metrics are important for the company and the training department. Then you can ask yourself: Why analyze this? What is the purpose? Align these purposes with your stakeholders and prioritize them. Ideally, this initial work is carried out together with the company's senior managers, in order to later be able to design a learning strategy in accordance with the company's objectives.
Here are some examples of metrics that should be collected and analyzed (at a minimum):
- Frequency of visits to the program
- Time spent in the course and in each module
- Time to complete a course
- Employee satisfaction with the training program
- Comments and use of different types of content
- Grades in the different evaluations
*In addition, it is recommended that the tools used to collect data allow access to numerical and visual dashboards to facilitate the monitoring of the most relevant metrics.
Tip: Use an eLearning tool that offers comprehensive learning analysis capabilities. Ideally, the tool should allow to correlate different information through different periods and even analyze multiple audiences in order to improve decision-making and find out what type of training resonates more with employees, for example.
2) Lack of Understanding and Support from Management Towards Learning Strategies
For a long time, top managers in companies distrusted corporate training programs. They did not see them as a potential way to generate money or business results but rather viewed training as an expense.
However, this perspective has started to change after COVID-19. Today, most organizations have eLearning programs and this has generated an increasing interest for organizational leaders to obtain really relevant data on the performance of eLearning programs and their impact on the business. Learning analytics has become a key source of information for decision-making.
Learning analytics are commonly expressed in charts and graphs that reflect quantitative measurements and results. In this way, a comprehensive analysis of the level of engagement, behavior, and understanding of the content can be performed.
There is a direct connection between your eLearning programs and changes in business results. And learning analytics lets you prove this! Let's look at a specific case.
Imagine that a new sales process is being implemented throughout your organization and your team creates a corresponding training program.
Then, you use learning analytics to measure your employees' understanding of the new process and their ability to apply it to their jobs.
Assuming that the sales volume of the business increased by 15%, using the data you can quantitatively demonstrate that your training program played a direct role in this success.
Having this kind of detailed information is extremely powerful in getting attention and selling the idea of eLearning to corporate leaders.
Tip: Use a learning record store (LRS), which allows you to collect data from a variety of learning activities and communication between systems, including the LMS.
Also read: 4 Signs That Your Company Needs to Implement a Learning Record Store (LRS)
3) Use of Inadequate and/or Very Complicated Tools
Using eLearning authoring tools that are not aligned with the needs of the company or the poor selection of a tool that is extremely robust and/or complicated for the team's capabilities is one of the most common mistakes we have seen in companies.
Finding a tool that can ensure fast, accurate, and detailed access to data is vital for a successful learning analytics strategy.
When it comes to analyzing large amounts of data over time, simplicity is key. Anyone on the L&D team should be able to use the information in the reports and analytics, so the data needs to be available in an accessible, simple-to-use, and easy-to-understand way. If the tool is too complicated or inefficient, the analysis can become a real burden.
For example, SHIFT has an integrated reporting and analytics suite that offers simple, detailed data analysis dashboards. By presenting accessible, adaptable, and actionable visualizations that are grouped together in meaningful ways, SHIFT provides robust reports on the past, current, and future (potential) performance of the learning ecosystem and learners to ensure powerful decision-making in the company.
4) The Lack of a Data Culture
It is clear that the data revolution is changing companies in all kinds of industries in profound ways. However, learning data analytics efforts in business have not been sufficiently consolidated. According to Mc Kinsey reports, some companies are doing amazing things; some are still struggling with the basics; and the vast majority are still overwhelmed, with executives and managers questioning the return of learning analytics initiatives.
Many times the obstacles come from the training and development team itself. There are cases where different team members resist change and refuse to give up their familiar and familiar work tools because they fear that the new tools will be difficult to use or will replace their tasks.
This is why a healthy data culture is increasingly important. All the points in favor of modernizing eLearning platforms must be thoroughly understood: such as more efficient data collection; saving time and energy; more organized, detailed and integrated information; etc.
Having an organizational culture of data can accelerate the application of learning analytics, amplify its power across departments, and radically improve decision-making.
5) Not Doing Anything With Data and Expecting Results
One of the most common mistakes we see companies make is to collect data but ignore it and take no action with it. For instance, we see sales teams brainstorm new strategies often before consulting the learning data to understand their team's existing skills gaps.
On other occasions, companies that use learning analytics believe that once they implement the strategy and have the most robust platform in place, they will magically see immediate changes in their business. This causes them to think that learning analytics is "not working" and the subsequent failed expectations make it difficult to invest in the future.
L&D teams should encourage every team in the company to adopt a data-driven mindset, where data is part of their DNA to take any decision.