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7 Little Changes with Your Learner Data That'll Make a Big Difference

This article addresses an audience of training managers that would like to know what data to collect for improving the learning experience; and/or if they have it, how to use that data to improve the training experience and results. LMS systems offer wide possibilities for collecting data, but not every metric that’s collected is relevant.

 

The Data Collection Conundrum

There’s an ocean of data points that many Learning Management Systems (LMS) generate. However, eLearning managers face a data collection conundrum: Turning on tracking, and collecting all possible metrics may result in a data tsunami that could drown Learning & Development (L&D) teams. On the other hand, not collecting any data, or collecting too little of it, may result in missed opportunities to analyze and improve employee training experiences.  

Ideally, as a training manager, you’d like to strike a balance between collecting enough data; collecting the right types of data; and putting that data to good use. And to do that, you must first know what your data analysis objectives are, and then identify and collect only the necessary data that’ll help you achieve those goals.

Managing learner data effectively can impact workplace culture in ways that many L&D leaders might not realize. For instance, if you currently do collect some LMS data, but those data sets don’t tell you much about training effectiveness; then collecting that data adds no value to the organization.  On the other hand, if you wish to make data-driven decisions – for instance, to expand a training program or improve training results in another – then lack of appropriate data may be a bottleneck.

Collecting and Using LMS Data

Below, we’ll discuss some interesting data sets that you might want to collect from your LMS system. We’ll also highlight how you might use them, not only to improve training results but also to enhance specific operational outcomes, such as employee onboarding, staff retention, and overall workplace efficiency and productivity.

  1. Registration: When organizations roll out a new course, there’s always a focus on “how many learners logged in on Day 1?”. Well, that is an insightful statistic to have. However, a more keenly watched data point should be the number of registrants that signed up for the course.

    Use Case: Registration numbers give HR and L&D teams an indication of the popularity (or lack thereof) of a course offering. Use low course registration numbers to either cancel or re-imagine an eLearning course.  Alternatively, higher than expected numbers may indicate a course has high demand and warrants more frequent scheduling.

  2. Progress Tracking: This data point must include more than successful course completion. Data should also include overdue courses and data points on multiple attempts at completing a course segment.

    Use Case: During the employee onboarding process, tracking whether and when new employees complete designated training can help people managers better prepare for workload assignments. Knowing how each newcomer progresses through various components of training can also give managers insight into new employees’ strengths and weaknesses, including areas of potential improvement.

  3. Test Scores and Assignment Grading: While some HR managers might think reviewing test and assignment scores may be too “into the weeds”, collecting and analyzing these data points may have a positive impact on workplace efficiency. This data may reveal insights about employee training that could lead managers to recommend proactive interventions.

    Use Case: A great example of proactively managing learner data for organizational benefit is by using test and assignment grades to determine additional training, mentorship, or coaching needs of an employee. This will go a long way to enhancing overall training effectiveness. While an employee may have passed an eLearning module, their less than stellar grades on Policy & Procedure-related tests and assignments could indicate potential compliance challenges in the future. An overall “pass” or “fail” data point will not highlight such issues.

  4. Social Interaction Data points: Many courses contain formal and informal group interaction, chat sessions, joint projects, and peer-to-peer activity. Most LMS systems track such data points as the number of session log-ins, number of posts, number of questions/comments raised, number of likes, number of followers, time spent in interactions, and time spent as a passive member. If captured, analyzed, and interpreted correctly, these stats can help shape workplace culture, make the organization’s social training strategy more efficient.

    Use Case: Social interaction data, within an LMS system, can prove vital in assessing whether an employee is a “good fit” for the organization. Such data can identify introverts, extroverts, leaders, and followers. When making employee retention, promotion, re-assignment, or task assignment decisions, managers may use this data to determine the social and cultural fit of the employee for new roles/positions.

  5. Aggregates: Too often, learning professionals focus on individual employee training data. While those are important metrics to track and analyze, aggregate analysis is also a key element of managing learner data. Collecting and analyzing data on the “big picture”, can help to drive workplace efficiency and performance.

    Use Case: During employee onboarding training, a high percentage of failures in a specific module may indicate trends about the quality or complexity of that module. Looking at an individual employee’s failure in that course may lead to a different conclusion – that the issue is with the employee, and not the course. Highlighting the issues with the course, with the help of data points, and then addressing them, brings efficiency to the onboarding process.

Collecting the right data from your LMS system is beneficial at multiple levels. Individual data helps managers assess the impact of training on an employee’s career goals; Course-related data may highlight the effectiveness (or lack of) of a course in delivering its stated learning objectives; Aggregated data helps executives make macro-level decisions, not just around training, but broader workplace efficiency.

The Right Learner Data Makes All the Difference

While LMS systems are data collection machines, not all that data is useful to every organization. Tweaking what eLearning data you collect and analyze, may not only help organizations improve training results, but can have a far-reaching impact on workplace culture and can also play a huge role in enhancing workplace efficiency.  

Instead of focusing on an excessive collection of individual data, a subtle mix of individual, course-centric, and aggregate data can go a long way to creating better learning experiences, increasing employee retention, and making the workplace more effective and efficient.