Learning analytics involves the collection and analysis of data on learners and learning processes to improve outcomes through quantitative tools. It serves as a valuable resource for developing pedagogical approaches and optimizing resource allocation. For learning and development (L&D) teams, learning analytics can significantly enhance both learning outcomes and operational transparency.
However, learning analytics has primarily been designed for academic settings, with limited adaptation for the corporate environment. When applied to corporate training, learning analytics tools can often face challenges, making it difficult to gather reliable data.
This article explores the challenges L&D teams may encounter when implementing data-driven tools to assess and develop training initiatives. It also offers actionable solutions, including adopting appropriate technologies and adjusting workflows to overcome these obstacles in day-to-day operations.
Barriers of Learning Analytics in Corporate Training
Let’s review three setbacks to using learning analytics in corporate training:
1. Competencies are practical.
The skills taught in corporate training programs are often highly practical, requiring a blended approach to learning. Learners may engage in a combination of digital lectures, online training modules, hands-on experience and mentoring sessions with senior staff, among other methods.
It can be challenging to make learning visible and assess progress in a way that is easily quantifiable, as required by learning analytics processes. Traditional assessment methods like quizzes, exams and multiple-choice tasks may not fully capture the breadth of learning. Instead, L&D professionals must find ways to record and quantify learning processes and outcomes in ways that are both meaningful and measurable.
2. Learning goals are individualized.
L&D programs are typically designed for competent adults already established in the workforce. These programs are often customized to meet the specific needs of individual employees, considering their backgrounds and experience.
While standardized online courses offer undeniable value due to their cost-efficiency and scalability, there is also significant demand for tailored learning aimed at small groups or individual learners.
However, customizing content and goals for specific cases can complicate the learning analytics process. Even when digital platforms are used to deliver these courses, individual objectives influence the types of data generated. As a result, it can become challenging to compare learners or identify patterns when each case is unique.
3. Learning takes place in real environments.
L&D programs differ from academic courses. Formal education often exists in relative isolation from “real” working life. L&D programs, on the other hand, are always closely integrated into a company’s workflow. The line between learning new skills and performing regular work tasks can be blurred.
A significant portion of workplace learning may occur outside digital learning platforms. Employees often acquire new skills in real-world settings — on the shop floor, in the field, working with clients or through countless other on-the-job experiences.
This can limit opportunities to naturally document learning. Learning analytics typically relies on data automatically collected by an LMS or other digital tools. In corporate learning, however, there may simply be less actionable data available for L&D teams to work with.
Overcoming Barriers in Learning Analytics
To accumulate reliable and comparable data despite these challenges, it is essential to design workflows that effectively supervise learning and use appropriate tools to monitor learners.
1. Provide a workflow for documenting learning.
When learning primarily occurs in the classroom, versus a learning management system (LMS), L&D teams must be proactive about tracking learners’ progress using previously defined metrics, or key performance indicators (KPIs). One of the most practical approaches is to establish a process for learners to record their progress. This can be done through learning diaries or periodic progress reports.
It may also be necessary to both require and actively encourage the documentation of learning. Clearly communicate the value learners gain from reflecting on and documenting their progress, such as improved learning outcomes and forming a personal portfolio. Learners can be more likely to invest effort in these tasks when they view them as valuable tools rather than burdensome obligations.
Ensure that documenting learning does not become an excessive burden. Reporting should be time-efficient to avoid diminishing motivation. Using mobile devices or capturing progress through photos, videos and audio recordings can make the process easier and more accessible.
2. Leverage personalized learning pathways.
Even when learning goals and methods are tailored to individuals, a formal process can still be established. Learning pathways make individual progress more transparent and enable comparisons between learners, even when goals and methods are customized to specific needs.
Learning pathways are curated sequences of study modules designed for specific groups or individuals. They allow for the creation of personalized learning programs while also providing a transparent and measurable framework to monitor progress.
By using learning pathways, L&D teams can deliver content that meets individual needs while simultaneously collecting valuable data on learner progress. This data can be used to identify challenges and evaluate the overall impact of the program.
3. Use appropriate tools for monitoring learning.
For learning analytics to be effective in developing training programs and monitoring learners, it is essential to centralize training processes in one platform.
Learning analytics rely on a systematic and automated process for data collection. While tools like Excel, email and messaging apps may suffice in smaller organizations, they are not suitable for scalable analytics. Long-term data collection requires an automated, structured tool for collecting and analyzing data.
L&D teams must leverage technology to manage training programs. A centralized platform enables data collection when learners document their progress, submit reports, take quizzes, complete online courses or receive feedback. Technology is crucial to address challenges posed by deskless work, hybrid models and on-the-job learning.
Be Prepared to Lead with Data
Corporate training can present unique challenges for learning analytics, given that most modern learning environments and management systems are designed with different audiences in mind. However, well-structured workflow and the right tools can help overcome these obstacles. Creating such a workflow and fostering genuine engagement among workers requires an active approach from the L&D coordinator.
