How lms analytics can really help you understand the skills gap

What lms analytics really means for the skills gap

Why skills gaps are hard to see without the right lens

Most organisations feel the skills gap long before they can describe it. Projects slow down, quality drops, and teams struggle to adapt to new tools or processes. Yet on paper, the workforce often looks well trained. People attend training programs, complete courses in the learning management system, and tick all the compliance boxes.

The problem is simple ; traditional training reporting focuses on activity, not capability. Learning management systems were originally built to deliver and track training, not to give deep insights into what people can actually do. Completion rates, attendance lists, and basic scores tell you who showed up and who clicked through the content, but not whether the learning outcomes match the skills your organisation really needs.

This is where lms analytics and learning analytics change the game. When you treat your lms as a data driven skills radar instead of just a course library, you start to see patterns in learner engagement, performance, and learning paths that reveal where skills are strong, where they are fragile, and where they are missing entirely.

From course tracking to skills intelligence

Modern learning management systems collect a huge amount of data about learners and their learning experiences. Every course enrolment, quiz attempt, video view, and learning path decision leaves a trace. On its own, each data point is small. Together, they form a detailed picture of how people learn, where they struggle, and how that connects to performance on the job.

Lms analytics is the practice of turning this raw lms reporting into meaningful insights about skills. Instead of asking only “How many people completed this course ?”, you start asking :

• Which courses and learning programs are most strongly linked to better performance in key roles ?
• Where do learners consistently drop out or repeat content, and what does that say about the difficulty or relevance of the material ?
• Which teams or locations show low engagement with critical training programs that support strategic priorities ?
• How do different learning paths affect learner engagement and long term learning outcomes ?

When you connect these questions to your workforce planning and talent processes, your lms becomes more than a management system for training. It becomes part of a broader ecosystem of talent management systems that enhance workforce capabilities. The same analytics learning approach that helps you understand skills gaps can also support recruitment, internal mobility, and succession planning.

What “lms analytics” really means in practice

In practical terms, lms analytics is about using reporting analytics and performance metrics from your learning management system to answer three core questions about the skills gap :

• What skills do we think we are building ? This is the intent behind your training programs, course catalog, and learning paths. It is usually documented in course descriptions, competency frameworks, and program objectives.
• What skills are learners actually developing ? This shows up in engagement patterns, assessment results, course completion behaviour, and how learners move through content over time.
• What skills are we missing for real work performance ? This is where you connect lms data to business performance, quality metrics, or role expectations to see whether learning experiences translate into real capability.

To do this well, you need more than basic lms reporting. You need to look at how metrics like completion rates, time spent in course content, assessment scores, and repeat attempts interact with each other. You also need to consider the context of each learner : their role, seniority, location, and previous learning history.

For example, if a critical safety course shows high course completion but low assessment scores on the first attempt, that may signal a surface level learning culture where people rush through content just to meet deadlines. If a leadership program shows moderate completion but very high engagement and strong follow up performance, that may indicate a smaller but more meaningful learning experience that truly builds skills.

How analytics connects learning to real performance

The real value of lms analytics for the skills gap comes when you connect learning data to performance data. On their own, learning metrics tell you how people interact with courses. When you link them to job performance, project outcomes, or quality indicators, they start to reveal which learning programs actually help close the skills gap.

Some organisations do this by mapping specific courses and learning paths to defined skills or competencies, then tracking how learners who complete those paths perform over time. Others integrate their learning management system with performance management systems or operational dashboards, so they can see whether improvements in learner engagement and course completion align with better business results.

In both cases, the goal is the same : move from counting training activities to understanding which learning experiences build the capabilities your organisation needs. This shift is essential if you want to design training programs that are truly data driven and focused on closing real skills gaps, not just filling calendars.

Why this matters for your learning culture

Using lms analytics to understand the skills gap is not only a technical exercise. It also shapes your learning culture. When leaders and learning teams rely on evidence from analytics learning rather than assumptions, they send a clear message : learning is not just about attendance, it is about impact.

This mindset encourages better conversations with managers and learners about what training is needed, which courses are genuinely useful, and how learning experiences should evolve. It also helps you identify where your learning programs are misaligned with real work, so you can adjust content, formats, and learning paths instead of simply adding more courses.

In the next parts of this article, we will look at how your existing lms data is already highlighting hidden skills gaps, which specific metrics signal trouble, and how to move from raw reporting to practical actions that improve learner engagement and workforce capability.

The hidden skills gap your lms data is already showing

The skills gap you do not see in the classroom

Most organisations look at their learning management system as a record of who finished which course. That is useful, but it hides a deeper story. Your lms analytics already contains signals of a skills gap that never shows up in a simple completion report.

When you start treating your lms as a real learning management system, not just a course library, the data becomes a mirror of how people actually learn, struggle and perform. The hidden gap is often not about access to training programs, but about whether learners can turn that training into real performance on the job.

Patterns in learner behaviour that reveal hidden gaps

Learning analytics is powerful because it tracks behaviour over time, not just outcomes. Several patterns in your lms reporting quietly point to skills problems long before they show up in performance reviews.

• High enrolment, low completion rates : When many learners start a course but few finish, it often means the content is too complex, poorly structured, or not clearly linked to their role. The skills gap here is about relevance and support, not motivation alone.
• Repeated attempts on the same assessments : If analytics show multiple quiz attempts with only small score improvements, learners may be memorising answers instead of building real skills. This is a gap in understanding, not just knowledge recall.
• Long time spent on specific modules : Unusually long time on certain lessons can signal that the concepts are difficult or that prerequisite skills are missing. This is where learning paths and scaffolding become essential.
• Short, rushed learning experiences : Very fast completion of complex courses can be a red flag. Learners may be clicking through content to satisfy compliance, leaving the underlying skills gap untouched.
• Drop off at the same point in a course : If many learners disengage at the same lesson, your analytics learning data is telling you that this part of the training is not working. The gap may be in instructional design, not just learner effort.

These patterns are often visible in standard reporting analytics dashboards, but they are rarely interpreted as signals of a skills gap. Instead, they are treated as simple engagement metrics. Reading them differently is where data driven learning really starts.

Engagement metrics that mask capability issues

High engagement in a learning management system can be misleading. A learner who logs in frequently, opens many courses and completes a lot of content may still lack the skills the organisation needs.

Some common examples :

• High course completion with low on the job performance : If performance metrics from your management systems do not improve after training programs, your lms analytics is showing a hidden gap between knowledge and application.
• Activity without progression : Learners may take many short, easy courses that do not move them along a meaningful learning path. The data shows activity, but not growth.
• Mandatory training spikes : Engagement peaks around compliance deadlines can hide the fact that voluntary learning is low. This suggests a weak learning culture and a gap in intrinsic motivation to build new skills.

To uncover the real skills gap, you need to connect lms reporting with other data sources, such as quality metrics, productivity indicators or customer feedback. When learning outcomes do not align with business outcomes, the gap becomes visible.

Where your current reporting quietly points to risk

Even basic lms reporting can highlight risk areas if you know what to look for. These are some of the most common warning signs hidden in everyday reports :

• Teams with consistently lower completion rates : This may indicate that managers are not supporting learning, or that the training content does not match their real work context.
• New hires struggling with onboarding courses : If new employees take much longer to complete onboarding training or fail key assessments, your organisation may face a future capability gap.
• Critical courses with low learner engagement : When strategic or safety related courses show low engagement, the risk is not only a skills gap but also potential compliance or operational issues.

These signals become even more powerful when combined with external frameworks and registries. For example, understanding how your internal skills data aligns with national standards or registries can reveal where your workforce is falling behind broader market expectations. Resources that explain how to work with these systems, such as guidance on the national skill registry login process, can help you benchmark your internal analytics against external requirements.

Turning hidden signals into actionable insights

The real value of lms analytics is not in collecting more data, but in asking better questions of the data you already have. Instead of only tracking course completion, consider how each metric relates to actual skills and performance.

Some practical questions to ask your learning management data :

• Which courses show high completion but no measurable change in performance metrics ?
• Where do learners spend the most time, and does that match the skills we consider critical ?
• Which learning programs lead to sustained learner engagement over time, not just one off spikes ?
• How do different roles or teams move through learning paths, and where do they stall ?

By reframing your analytics around these questions, you move from simple reporting to real time insight. This shift prepares you to connect the dots between raw lms data, learning experiences and the skills outcomes your organisation actually needs.

Independent research on learning analytics and learning management systems consistently highlights this point : the organisations that benefit most are those that interpret metrics in context, combine them with operational data and use them to guide continuous improvement in training programs. In other words, the hidden skills gap is visible, but only if you are willing to look beyond surface level reports.

Key lms analytics metrics that signal a skills gap

Reading the signals in your LMS metrics

Most learning management systems are full of numbers, charts, and reporting dashboards. But only a handful of metrics really help you spot a skills gap before it turns into a performance problem. The goal is not to track everything ; it is to focus on the analytics that connect learning experiences to real outcomes in your training programs.

Below are key learning analytics signals that often reveal where learners are struggling, where your content is misaligned with skills needs, and where your learning culture is not supporting growth.

1. Course completion patterns that do not match business needs

Completion rates are usually the first metric people look at in an LMS. On their own, they are not enough to judge skills. But patterns in course completion can tell you a lot about the skills gap.

• Low completion in critical skills courses : If courses linked to priority skills have poor completion rates, you may have a motivation, relevance, or design issue that is blocking skill development.
• High completion, low performance : When completion is high but on the job performance or assessment scores stay low, it suggests the training content is not building the right capabilities.
• Uneven completion across teams : If one team completes a cybersecurity course at 90 percent and another at 40 percent, you likely have a localized skills gap and possibly a management or support issue.

Use your LMS reporting to segment completion data by role, team, region, and seniority. This makes the gap visible where it actually matters, instead of hiding it in an overall average.

2. Learner engagement and time on task

Engagement metrics are not just vanity numbers. They are early warning signs that your learning programs are not connecting with learners, or that the learning experience is too hard, too easy, or simply irrelevant.

• Time spent vs. expected time : If learners spend far less time than expected on a course, they may be skipping content or rushing through. If they spend far more time, the material may be too complex or unclear, which often signals a deeper skills gap.
• Drop off points : Look at where learners stop a course. Consistent drop off at the same module often means that specific topic is a barrier skill.
• Interaction with learning content : Clicks on additional resources, practice activities, or virtual labs can show where learners feel they need extra support.

Modern learning management systems can provide real time analytics on engagement. When you see engagement collapse around a specific concept, you are probably looking at a skills gap that needs targeted support, maybe through more practice based learning experiences such as hands on virtual labs.

3. Assessment scores and attempts

Assessment data is one of the most direct ways to connect learning analytics to skills. But it needs to be interpreted carefully.

• Item level performance : Instead of only tracking final scores, look at which questions or tasks learners consistently miss. Group these items by skill or competency to see where the real gap lies.
• Number of attempts : Many attempts to pass a quiz or certification can indicate that the underlying skill is weak, even if the final score is technically a pass.
• Pre and post training comparison : Comparing pre course and post course assessments shows whether the training program is actually closing the gap or just moving learners through a learning path.

When assessment metrics are mapped to a clear skills framework, they become powerful indicators of where your learning programs are working and where they are not.

4. Learning paths and progression through programs

Learning paths are supposed to guide the learner journey from beginner to proficient. The way learners move through these paths is a strong signal of skills readiness.

• Where learners stall : If many learners start a path but do not progress past a certain course, that stage likely represents a difficult skill jump.
• Time to progress : Very long gaps between courses in a path can mean learners are not confident enough to move on, or that managers are not reinforcing the learning.
• Skipped or bypassed modules : When optional modules that cover foundational skills are consistently skipped, you may see gaps later in more advanced training.

Analytics learning tools inside your LMS or learning management system can show progression in real time. Combined with performance data from other management systems, this helps you see whether your designed learning paths actually support the skills your organization needs.

5. Correlating learning data with performance metrics

The strongest signals of a skills gap appear when you connect LMS analytics with real performance metrics. This is where learning analytics moves from simple reporting to data driven decision making.

• Training vs. on the job outcomes : Compare course completion and assessment scores with KPIs such as quality, productivity, or customer satisfaction. If training is completed but outcomes do not improve, the skills gap is still open.
• Role based comparisons : Look at high performers in a role and analyze their learning history. Differences between their learning programs and those of average performers can highlight missing skills.
• Time to proficiency : Track how long it takes new hires or newly promoted staff to reach expected performance. Long times to proficiency often reveal gaps in onboarding or role specific training.

This type of reporting analytics usually requires integration between your learning management system and other business systems. But once it is in place, it turns your LMS reporting into a powerful skills management tool.

6. Signals from learner feedback and behavior

Not all useful metrics are numeric. Qualitative data from learners can reveal skills issues that pure numbers miss.

• Feedback comments : Repeated comments about unclear explanations, missing examples, or unrealistic scenarios often point to content that does not match real work skills.
• Search behavior : What learners search for inside the LMS can show where they feel unprepared. Frequent searches for the same topic suggest a perceived skills gap.
• Voluntary enrollment : Courses that learners choose to take without being required can highlight emerging skills needs that are not yet in formal training programs.

Combining this behavioral data with more traditional metrics like completion rates and assessment scores gives you a fuller picture of learner engagement and skills readiness.

7. Turning metrics into meaningful skills signals

On their own, each metric is just a piece of information. The real value comes when you connect them to build a story about your learners, your content, and your learning culture.

• Low completion in a critical course + high time on task + poor assessment scores = a deep skills gap and probably a design issue in the course.
• High completion + good assessment scores + no change in performance metrics = a misalignment between training content and real work skills.
• Strong engagement + steady progression through learning paths + improved outcomes = a learning experience that is genuinely closing the skills gap.

When you treat LMS analytics as a way to understand people, not just courses, you move from basic reporting to real skills insights. That is the foundation you need before you start redesigning training programs or investing in new learning programs to close the gap.

From raw lms data to real skills insights

Turning scattered LMS numbers into a clear skills picture

Most learning management systems are full of numbers, but very light on meaning. Completion rates, time spent in courses, quiz scores, engagement graphs ; they all look like analytics, yet they do not automatically tell you where the real skills gap is. The shift from raw LMS data to real skills insights starts when you connect these metrics to roles, tasks, and learning outcomes that matter for your organisation.

A practical way to do this is to treat your LMS as a learning analytics hub, not just a course delivery tool. That means you do not only track what learners click, but you map each course, module, and assessment to specific skills or competencies. When you do that consistently, every piece of reporting becomes a signal about skills, not just about training programs.

Map courses and content to a clear skills framework

The foundation of useful analytics learning is a skills framework. Without it, your LMS reporting will stay at the level of generic training metrics. With it, you can translate course performance into skills performance.

• Define the skills your roles actually need ; use job descriptions, performance reviews, and operational KPIs as your starting point.
• Tag each course and learning path in the learning management system with one or more of these skills, not just with topics or departments.
• Align assessments so that quiz questions, assignments, and practical tasks are explicitly linked to the same skills tags.

Once this mapping is in place, your LMS analytics can show, for example, that learners in a specific role consistently struggle with content tagged as “data analysis” or “customer communication”. Completion rates and scores stop being abstract numbers and become direct indicators of where the skills gap is widening or closing.

Combine multiple metrics to avoid misleading signals

Single metrics rarely tell the full story. A high course completion rate might look good in a dashboard, but if learner engagement is low and assessment scores are weak, the underlying skill is probably not improving. To move from raw data to real insights, you need to look at combinations of metrics across your learning programs.

For example, for a critical training program, you might track together :

• Course completion and completion rates by role, team, and location.
• Time spent in key modules compared with expected time.
• Assessment performance on questions tied to priority skills.
• Learner engagement signals such as logins, return visits, and interaction with practice activities.

When these analytics are viewed together, patterns emerge. Short time on task plus high completion and low scores often means learners are rushing through content without building real capability. High engagement and strong scores, but only in some teams, can highlight where local management support is helping learning culture and where it is missing.

Use reporting analytics to compare skills across roles and teams

Modern learning management systems usually allow you to slice LMS reporting by role, department, region, or manager. This is where data driven analysis becomes powerful for skills management. Instead of asking “Is this course working ?”, you can ask “For which learners is this course improving performance, and where is the skills gap still open ?”.

Useful comparisons include :

• Role based views ; compare analytics for the same course across different job roles to see where learning experiences are not translating into the same outcomes.
• Team or site comparisons ; identify teams with strong learner engagement and high scores, and those with low participation or weak performance.
• New hires vs experienced staff ; check whether learning paths are closing the skills gap faster for new learners or whether they are mainly used as compliance training.

These comparisons turn generic LMS data into targeted insights that can guide management decisions about where to invest more training, coaching, or content redesign.

Connect LMS analytics with performance and workplace data

To really understand whether learning is closing the skills gap, you need to go beyond the learning management system itself. LMS analytics show what happens inside courses ; performance data shows what happens on the job. When you connect the two, you can see which learning experiences actually change behaviour and results.

Typical connections include :

• Linking course completion and assessment scores with sales, quality, safety, or customer metrics.
• Comparing time to proficiency for learners who follow recommended learning paths versus those who do not.
• Tracking whether training programs launched to address a known skills gap are followed by measurable improvements in operational KPIs.

Even simple correlations, handled carefully, can reveal which courses and learning programs are driving real performance improvements and which are just adding to content overload.

Use real time dashboards to spot emerging gaps early

Skills gaps are not static. New tools, regulations, and processes create fresh demands on learners all the time. Real time or near real time reporting analytics in your LMS can help you detect emerging gaps before they show up as serious performance problems.

For example, when you launch a new system or process, you can monitor :

• Enrollment and completion in the related learning paths within the first days and weeks.
• Drop off points in the course where many learners stop or repeat modules.
• Assessment questions with unusually low success rates that may indicate confusing content or a deeper skills issue.

By acting on these signals quickly, learning management teams can adjust content, provide extra support, or involve local management before the skills gap affects customers or operations.

Translate insights into concrete actions for learners and managers

Analytics only matter if they lead to better learning experiences and stronger skills. Once you have identified patterns in your LMS data, the next step is to turn them into targeted actions for learners, managers, and learning teams.

Some practical moves include :

• Adaptive learning paths ; use analytics to recommend extra modules or practice activities to learners who show weak performance on specific skills.
• Manager friendly reports ; share simple dashboards that highlight where their teams are behind on critical training programs and where coaching might help.
• Content improvement cycles ; use low engagement or poor assessment results as triggers to review and redesign course content, not just to push reminders.

Over time, this cycle of measurement, insight, and action builds a stronger learning culture. Learners see that data is used to help them, not just to track them. Managers see that learning analytics can support real performance management. And the organisation moves closer to a data driven approach to closing the skills gap, instead of relying on assumptions or one off training initiatives.

Common mistakes when using lms analytics to judge skills

Overtrusting course completion rates

One of the most common mistakes in learning analytics is treating course completion as proof of competence. Completion rates are useful, but they mainly show that learners finished a course, not that they can apply the skills in real situations.

In many learning management systems, reporting focuses heavily on completion status and time spent. This can create a false sense of security. A training program can show 95% course completion and still leave a serious skills gap on the job.

To avoid this, combine completion metrics with:

• Assessment performance before and after the course
• Real time practice tasks or simulations linked to learning outcomes
• On the job performance data from other management systems
• Follow up checks weeks or months after training

Completion is a starting point in lms analytics, not the final answer. Treat it as one signal among many in your analytics learning toolkit.

Confusing engagement with mastery

High learner engagement looks good in dashboards, but it does not always mean skills are improving. A course can be popular, interactive, and highly rated, while still failing to close the skills gap that matters to your organization.

Typical engagement metrics in lms reporting include:

• Logins and active sessions
• Clicks and page views on content
• Time spent in courses or learning paths
• Discussion forum activity

These metrics help you understand the learning experience, but they do not automatically translate into better performance. A learner can spend a lot of time in a course because they are confused, not because they are mastering the material.

To reduce this risk, connect engagement data with:

• Task based assessments that mirror real work
• Performance metrics from your management system or HR tools
• Clear learning outcomes that can be measured beyond the lms

When engagement and performance move together, you can be more confident that your learning programs are closing the skills gap.

Relying only on averages and ignoring outliers

Another frequent mistake in analytics is focusing on averages and overlooking the spread of results. Average scores, average completion time, or average satisfaction can hide serious problems in specific teams, roles, or locations.

For example, an average course completion rate of 80% might look acceptable. But if you break the data down by department, you might find one group at 98% and another at 40%. The skills gap is not evenly distributed, and your learning management system can reveal that if you look beyond the top line numbers.

To avoid this trap, use your lms analytics to:

• Segment learners by role, team, region, or seniority
• Compare performance and engagement across these segments
• Identify outliers where training programs are clearly not working

This more detailed reporting analytics approach helps you target support where it is really needed, instead of designing generic learning experiences for everyone.

Ignoring context outside the learning management system

Skills do not exist only inside your lms. A common error is to treat lms data as the full picture, without connecting it to other sources of information about learner performance and real work.

For example, if a course shows strong engagement and high scores, but operational metrics such as quality, safety, or customer satisfaction are not improving, there is a disconnect. The learning content might be misaligned with what people actually need on the job.

Stronger insights come from combining lms reporting with:

• Performance data from business systems
• Feedback from managers on observable behavior change
• Surveys that ask learners how confident they feel applying new skills
• Time to proficiency for new hires or people moving into new roles

When you integrate these sources, your analytics become more data driven and more closely tied to real outcomes, not just learning activity.

Misreading time and progress signals

Time based metrics in learning analytics are easy to misinterpret. More time in a course is not always better, and fast completion is not always a sign of mastery.

Some typical misreadings include:

• Assuming long time on page means deep understanding, when it may indicate confusion or distraction
• Assuming very short completion times mean high skill, when they may reflect skimming or guessing
• Reading linear progress through learning paths as proof that the learner followed the intended journey

To use time and progress data more effectively, combine them with:

• Question level analytics that show where learners struggle
• Attempts and retries on key assessments
• Patterns of drop off or re entry into courses

This gives you a more realistic view of how learners move through your learning programs and where the skills gap is actually appearing.

Overlooking qualitative feedback from learners and managers

Many organizations rely almost entirely on quantitative metrics from their learning management systems and forget to collect qualitative feedback. This is a missed opportunity, because numbers alone rarely explain why a skills gap persists.

Useful qualitative inputs include:

• Short surveys after key courses asking what felt useful or confusing
• Manager feedback on whether behavior changed after training
• Open comments on learning experiences and learning culture
• Interviews or focus groups with learners in critical roles

When you combine this feedback with your analytics, you can see not only where performance is weak, but also what might be blocking progress. This helps you adjust content, learning paths, and support in a more targeted way.

Using static reports instead of real time monitoring

Another mistake is treating lms reporting as a one time exercise. Many teams run quarterly or annual reports on training programs and then wait for the next cycle. In fast changing environments, this is too slow to respond to emerging skills gaps.

Modern management systems and lms analytics tools allow for more real time monitoring of learner engagement, course completion, and assessment performance. When you use these capabilities, you can:

• Spot early signs of low engagement in critical courses
• Identify groups that are falling behind in mandatory training
• Adjust learning paths while programs are still running

This ongoing view supports a more adaptive learning culture, where training programs evolve in response to what the data is telling you, not just at fixed reporting dates.

Focusing on isolated courses instead of end to end learning paths

Finally, a frequent error is to analyze each course in isolation, without looking at the full learning experience. Skills are usually built across multiple courses, resources, and on the job practice, not in a single module.

If you only track metrics at the course level, you may miss where learners actually drop off or lose confidence. For example, they might complete the first two modules of a path but never reach the advanced content where the real skills gap is addressed.

To avoid this, use your learning management system to:

• Map complete learning paths for key roles
• Track progression across all related courses and programs
• Analyze where learners stall, repeat, or disengage along the path

This broader view of learning programs makes it easier to see which parts of the journey are truly helping to close the skills gap and which parts need redesign.

Practical steps to use lms analytics to close the skills gap

Turn analytics into a focused skills strategy

Using lms analytics to close the skills gap starts with a clear focus. You already have a lot of learning data in your learning management system. The risk is to drown in metrics instead of acting on them.

Begin by choosing 1 or 2 priority skills that matter most for business performance. Then align your analytics learning work around them.

• Map which courses and learning programs are supposed to build these skills
• List the key metrics that show progress : course completion, assessment scores, time in content, learner engagement
• Decide what “good” looks like for each metric, so you can spot gaps in real time

This turns your lms reporting from generic dashboards into a data driven skills strategy.

Build simple dashboards that expose the gap

You do not need complex reporting analytics to get real insights. What you need is clarity. Create a small set of dashboards in your learning management system that directly answer skills questions.

For each priority skill, build views such as :

• Who needs this skill : target roles, teams, locations
• Who is actually enrolled in the related training programs and courses
• Completion rates and drop off points for each course
• Assessment results linked to the skill, not just to the course
• Engagement patterns : time spent, repeat visits, interaction with content

When these views are easy to access, managers can use lms analytics in their regular performance and talent discussions, not only during annual reviews.

Connect learning analytics with business performance

To really close the skills gap, learning analytics must connect to real outcomes. That means linking your lms data with performance data from other management systems.

In practice, this can look like :

• Comparing learner engagement and completion rates with sales, quality, or customer metrics
• Tracking whether teams that finish specific learning paths improve their performance faster
• Using reporting analytics to see if new learning programs reduce error rates or rework

Even simple correlations can help you decide which courses are worth expanding and which content needs to be redesigned or retired.

Redesign learning experiences based on real data

Once you see where learners struggle, use those insights to improve the learning experience. Your lms analytics will often show patterns such as high drop off at a certain module or low scores on a specific topic.

Use that information to :

• Shorten or split long courses into smaller learning paths
• Replace dense content with more practical, scenario based activities
• Add quick knowledge checks earlier in the course to reinforce key concepts
• Offer alternative formats for the same topic, such as video, job aids, or short practice tasks

Then monitor the same metrics again. If completion rates and learner engagement improve, you know the redesign is working. This creates a continuous improvement loop for your training programs.

Give managers usable reporting, not raw data

Managers play a central role in closing the skills gap, but they rarely have time to dig into complex lms reporting. Make it easy for them.

Provide simple, action oriented reports that show for each learner :

• Required courses and learning programs for their role
• Current completion status and due dates
• Key skills linked to each course
• Recent performance on assessments or practical tasks

Encourage managers to use these reports in regular check ins. The goal is to turn learning analytics into everyday management, not a separate activity.

Use real time signals to trigger support

Modern management systems and lms analytics tools can provide real time signals about learner progress. Use these signals to offer timely support instead of waiting for end of course reporting.

For example, you can set up alerts when :

• A learner fails the same assessment multiple times
• Engagement drops sharply after a specific module
• Critical compliance or safety courses are overdue

These alerts can trigger concrete actions : a short coaching session, a reminder from the manager, or access to additional practice content. This kind of targeted help keeps learners moving along their learning paths and reduces the risk of persistent skills gaps.

Measure learning outcomes, not just activity

To close the skills gap, you need to move beyond counting logins and course completions. Activity metrics are useful, but they do not prove that skills have improved.

Strengthen your analytics by adding outcome focused measures such as :

• Before and after assessments tied to specific skills
• On the job observations or checklists completed by supervisors
• Simple performance indicators linked to the learning experience, such as fewer errors or faster task completion

When you combine these outcomes with your lms analytics, you get a more accurate picture of whether your learning programs are truly closing the gap.

Embed a learning culture around data

Finally, using analytics to close the skills gap is not a one time project. It is part of building a learning culture where data is used to support people, not to blame them.

Some practical habits that help :

• Share key metrics and insights with learners, so they can track their own progress
• Discuss learning outcomes in team meetings, alongside other performance metrics
• Invite feedback on courses and learning experiences, then show how you act on that feedback
• Review lms reporting regularly to decide which training programs to expand, adjust, or stop

Over time, this approach turns your learning management system into a central tool for workforce development. The skills gap becomes visible, measurable, and most importantly, manageable.