Author: trishbriere

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When Institutional Knowledge Walks Out the Door

Organizations lose institutional knowledge every day. Loss of critical knowledge could be caused by retirement, turnover, or even worse, restructuring. The reason doesn’t change the result: critical knowledge disappears, and the people left behind scramble to recreate what was already known.

I’ve witnessed this happen recently in federal agencies and universities, as well as in the corporate world during my time in finance, back in the early 2000s. The universal scenario works like this: A subject matter expert leaves. Their replacement arrives weeks or months later to discover that the documentation is outdated, the processes aren’t written down, and/or the informal networks that made things work are no longer in place. Anyone who has been in the workforce for a length of time has seen it.

The Knowledge Management Gap

Most organizations treat knowledge management as a documentation problem. Create better SOPs. Maintain more detailed process guides. Build comprehensive wikis.

But documentation alone doesn’t capture institutional knowledge. It doesn’t capture the why behind decisions or explain which workarounds exist because of legitimate constraints versus which ones exist because “that’s how we’ve always done it.” It doesn’t preserve the relationships between systems, stakeholders, and strategic priorities.

Real knowledge management requires understanding that knowledge exists in three forms:

Explicit knowledge lives in documents, databases, and formal training materials. This is what most KM systems capture.

Tacit knowledge resides in people’s minds; it is the experience-based insights that experts utilize without conscious thought. This is harder to transfer but equally critical.

Embedded knowledge lives in processes, culture, and organizational relationships. This is nearly impossible to document, but devastating to lose.

What Actually Works

Organizations that maintain knowledge through transition do three things differently:

They build redundancy into expertise. Critical knowledge doesn’t live in one person’s head. Multiple people understand key systems, maintain important relationships, and can execute essential processes. This isn’t inefficient, it’s resilient. As a former university colleague once taught me, there should never be a single point of failure.

They create learning pathways, not just documentation. New team members don’t just read manuals. Instead, they work alongside experienced colleagues, participate in communities of practice, and have structured opportunities to ask questions and make sense of what they’re learning. (Something my current organization does well.)

They treat knowledge transfer as continuous work, not crisis response. Knowledge management isn’t something you do when someone gives notice. It’s built into regular operations—shadowing rotations, cross-training, documented decision-making processes, and accessible repositories that people actually use. It also doesn’t hurt to have a team focused on knowledge management. (Shout out to the KM team!)

The Real Cost of Lost Knowledge

When institutional knowledge leaves the organization, it’s not just efficiency that is lost; it’s also the expertise and experience that are lost. Organizations lose the ability to learn from past mistakes. They repeat failures that were already solved. Decisions are made without understanding the historical context.

In learning and development specifically, lost institutional knowledge means:

  • Training programs that don’t connect to organizational strategy because the people who understood both are gone
  • Assessment practices that measure the wrong things because no one remembers why current metrics were chosen
  • Technology implementations that ignore lessons from previous failures
  • Competency frameworks that drift from business needs because the connection isn’t documented

The cost shows up months later, in small inefficiencies and repeated mistakes that compound over time.

Building Resilient Knowledge Systems

Effective knowledge management isn’t about preventing people from leaving. It’s about building systems that can absorb transitions without losing critical capabilities.

That means:

  • Documenting decisions, not just processes. Capture why choices were made, what alternatives were considered, and what constraints influenced the outcome.
  • Creating apprenticeship opportunities. Pair experienced staff with newer team members on real projects, not just training exercises.
  • Building accessible knowledge repositories. Systems people actually use, organized around how work gets done, not how information is categorized.
  • Maintaining communities of practice. Regular opportunities for people to share insights, solve problems together, and build collective understanding.
  • Making knowledge sharing part of performance expectations. Reward people for developing others, not just for individual expertise.

What This Means for Learning Leaders

For those of us in learning and development, knowledge management is central, not peripheral, to our work. We’re responsible for building organizational capability. That includes the capability to preserve and transfer what we know.

During times of transition, that responsibility becomes more visible. The question isn’t whether knowledge will be lost. The question is whether we’ve built systems that can minimize that loss and accelerate recovery.

The organizations that navigate transitions successfully aren’t the ones that prevent change. They’re the ones who treat knowledge management as an essential and continuous infrastructure, worth the investment even when things are stable.

What knowledge management practices have you seen work (or fail spectacularly) during organizational transitions? The approaches that work in stable times may not work under pressure.

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Should This Course Be Virtual? Stop Converting Courses Blindly

In my current role, we are considering the necessity of virtual learning and how to implement it effectively. Is the question we should be asking: “Can this be delivered virtually?” OR “Should this be delivered virtually?” The difference boils down to virtual learning strategy. The first question assumes that technology determines training decisions. The second recognizes that strategic learning design should drive technology choices.

How many times have you seen conversion projects fail, or worse, just done poorly because teams skipped the strategic assessment phase? Does your organization focus on capabilities over learning effectiveness? If you measure technical feasibility instead of performance impact, you may end up with courses that technically work but don’t actually improve job performance.

Strategic Assessment Changes Everything

An effective virtual learning strategy starts with four questions that have nothing to do with technology:

  1. Does this content address an actual performance gap? The real problem could be workflow design, resource availability, or even policy. Virtual training (or any kind of training) won’t fix a problem that is not caused by a knowledge or skills gap.
  2. Will virtual delivery enhance or limit learning transfer? Context matters. Consider whether your content works better with self-paced reflection, live virtual interaction, or immediate hands-on application. Different formats serve different learning transfer needs.
  3. What’s the true cost comparison? Beyond platform fees and development time, consider instructor preparation, learner technology support, and the hidden costs of reduced engagement or learning effectiveness. (To name a few)
  4. How does this align with the organizational learning strategy? Random course conversions create inconsistent learner experiences. Strategic conversion builds systematic virtual learning capabilities.

Virtual learning effectiveness research:

https://trainingindustry.com/wiki/remote-learning/virtual-instructor-led-training-vilt

Weighing the Pros and Cons: Actual Training Versus Virtual Training – Brandon Hall Group

Design Decisions Drive Technology Needs

Once you know what learning outcomes you’re trying to achieve, technology decisions become much clearer. Need high-stakes skill practice? Look for platforms with robust simulation capabilities. Focusing on knowledge transfer? Simple video conferencing might be perfect. Using the technology-first (then adapting content to fit) approach could result in unnecessary constraints that could compromise learning effectiveness. The ultimate square peg-round hole problem.

Resource Allocation as Strategic Capability

A successful virtual learning initiative would treat conversion as an organizational capability, rather than focusing solely on individual course development. It should invest in instructor preparation, virtual content design standards, and quality assurance protocols that improve every subsequent virtual course. This perspective changes resource allocation decisions. Instead of evaluating each course conversion separately, organizations can build systematic virtual learning excellence that creates competitive advantages in talent development and operational efficiency.

Implementation Lessons from Government Training

Government training environments reveal what works under real constraints. Limited budgets, security requirements, global time zones, and mandatory compliance create conditions where strategic thinking isn’t optional. Organizations succeed by focusing on design principles before technology. They build instructor competency alongside content conversion. And they measure performance impact, not just engagement and satisfaction metrics. But most importantly, they recognize that virtual learning excellence requires different approaches than traditional training, not just digital versions of existing training.

Questions that Lead to Better Decisions

Instead of asking whether content can be delivered virtually, try these strategic questions:

  • What performance outcomes are we trying to achieve, and how will we measure success?
  • How does this conversion support broader organizational learning goals?
  • What instructor development and support systems need to be in place?
  • How will we ensure learning transfer to actual job performance?
  • What’s our plan for continuous improvement based on performance data?

These questions lead to conversion decisions that create lasting organizational value rather than short-term technology adoption. Because the goal isn’t to convert everything to virtual delivery. It’s to make strategic decisions about where virtual learning adds genuine value while building systematic capabilities that improve training effectiveness across the organization.

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The Patience Paradox in Skill Development: What Recovery Teaches About Sustainable Learning Strategy

After my foot injury at the end of July, I figured I’d be back to normal in a couple of weeks. Maybe three. Then the surgeon mentioned six weeks of non-weight bearing. Then September became the target. Now we’re looking at early November. Each timeline adjustment has been humbling. I keep learning that healing doesn’t follow my schedule.

I find this metaphoric to corporate learning strategy, where we design fixed timelines and expect uniform progression. We design training like sprints when a sustainable learning strategy actually works like rehabilitation.

The Recovery Reality Check

You may think this is a reach, but it really isn’t. Physical recovery forces you to confront uncomfortable truths about progress. You can’t rush healing by working harder or longer, nor skip stages. And you can’t will your way past biological timelines. As I discussed in my post about accessible virtual learning, managing limitations while learning creates competing cognitive demands that we often overlook in training design.

In physical therapy, some days you nail the exercises. Other days, the same movement that felt easy yesterday feels impossible today. This isn’t failure – it’s how adaptation actually works. Most days, you see linear progression with lessening pain and increasing mobility, but then there are days that you just cannot move.

In learning and development, we often design as if skill acquisition follows a straight line. Complete Module 1, master Module 2, demonstrate competency, and check the box. When learners struggle or plateau, we assume they need more content or more practice. What if they need more time?

The Sprint Learning Trap (not to be confused with scrum)

Corporate learning strategy loves efficiency. Accelerated programs. Intensive bootcamps. “Learn leadership in three days.” We optimize for speed and completion, not retention and application. This sprint mentality creates several problems:

Cognitive overload: Cramming complex skills into compressed timeframes overwhelms working memory. Learners may complete the program but struggle to transfer knowledge to actual work situations.

Superficial mastery: Quick wins in controlled learning environments don’t always translate to messy real-world application. Skills that seem solid in training modules often crumble under workplace pressure.

Burnout and dropout: Intensive programs work for some learners but exclude others who need different pacing or processing time. We lose people who might excel with sustainable approaches.

False completion: Finishing a program isn’t the same as developing competency. But our metrics often conflate the two.

What Recovery Teaches About Skill Building

Physical therapy operates on principles that directly apply to learning design:

Gradual progression: You start with basic movements and slowly add complexity, resistance, or range of motion. Each stage builds genuine capacity for the next.

Plateau acceptance: Improvement isn’t constant. Sometimes you maintain current capability while your body integrates new patterns. These plateaus aren’t stagnation, they’re consolidation.

Individual variation: Everyone heals at different rates. Good therapists adjust timelines and approaches based on individual response, not standardized schedules.

Multiple modalities: Recovery combines different approaches – strengthening, stretching, balance work, movement pattern practice. Complex skills require varied practice contexts.

Long-term perspective: The goal isn’t just returning to previous function but building resilience against future injury. Sustainable learning should prevent skill decay and support continued growth.

Recovery-Informed Learning Strategy

What would corporate learning strategy look like if we designed it more like rehabilitation?

Spaced practice over cramming: Distribute skill practice across weeks or months instead of intensive multi-day sessions. This supports memory consolidation and real-world application.

Plateau recognition: Build explicit reflection points where learners assess current competency without pressure to advance. Sometimes maintaining skills while integrating them with other capabilities is progress.

Adaptive pacing: Offer multiple pathways through learning objectives. Some learners need more repetition, others need varied contexts, some need additional foundational work.

Integration time: Schedule buffer periods where learners apply new skills in low-stakes situations before formal assessment or high-pressure implementation.

Maintenance planning: Include strategies for maintaining skills over time, not just initial acquisition. What will prevent skill decay six months after training? Check my resources page for tools that support long-term skill retention. (Coming Soon)

The Patience Paradox

Here’s the paradox: Sustainable learning approaches often appear slower initially but create faster long-term results.

Learners who rush through leadership development may complete programs quickly but struggle with actual management challenges. Those who take time to practice difficult conversations, reflect on feedback, and gradually build confidence often become more effective leaders sooner.

Recovery-informed learning strategy requires patience from learners, managers, and L&D teams. It means resisting the pressure to show immediate results in favor of building genuine capability.

Practical Implementation

Start small. Choose one program where you can experiment with recovery-informed principles:

  • Extend timelines: Add two weeks to a one-week program and use the extra time for practice and integration.
  • Build in plateaus: Create explicit “maintenance” periods where learners practice current skills without adding new complexity.
  • Offer multiple paths: Provide options for learners who need different pacing or approaches.
  • Measure differently: Track skill retention at 30, 60, and 90 days, not just immediate completion.

Beyond Individual Learning

Recovery-informed approaches also apply to organizational change. Teams recovering from restructures, leaders adapting to new roles, or organizations implementing new processes all benefit from rehabilitation principles. Sustainable change happens in stages. It requires patience with setbacks. It demands individual adaptation within systematic approaches.

The question isn’t whether we can afford to slow down our learning programs. It’s whether we can afford not to build a more sustainable learning strategy. True skill development, like physical recovery, can’t be rushed. But when we respect the natural rhythms of learning, we build capabilities that last.

References

Brown, P. C., Roediger, H. L., & McDaniel, M. A. (2014). Make it stick: The science of successful learning. Belknap Press.

Ericsson, A., & Pool, R. (2016). Peak: Secrets from the new science of expertise. Houghton Mifflin Harcourt.

Bjork, R. A. (1994). Memory and metamemory considerations in the training of human beings. In J. Metcalfe & A. Shimamura (Eds.), Metacognition: Knowing about knowing (pp. 185-205). MIT Press.

Helpful Resources for Recovery-Informed Learning

L&D Professionals Managing Recovery:

Sustainable Learning Approaches:

Federal Learning Professionals:

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The Feedback Loop Problem in Learning Design

Have you ever wondered if learning feedback systems are measuring the wrong things at the wrong time through the wrong channels? We ask about satisfaction after sessions end, assess knowledge weeks after content delivery (for instructor-led training), and evaluate application performance months later, when it’s more challenging to make improvements.

Meanwhile, learners send us immediate, actionable feedback signals constantly. The problem isn’t that we lack feedback from our learners, it’s that feedback loops are structured mroe like post-mortems than navigation systems.

What Direct Feedback Actually Looks Like

At six weeks into recovering from foot surgery, I’m learning what direct feedback systems look like. Physical limitations create immediate, unfiltered information about what’s working and what isn’t. Pain signals problems instantly. Energy levels indicate capacity in real-time. Range of motion shows progress directly. This feedback isn’t delayed or filtered through satisfaction surveys. It’s connected immediately to decisions that matter, and it informs the next action rather than evaluating the last one.

Learning design rarely operates this way. In my post about completion rates, I explored what we should measure beyond compliance metrics. But recovery is revealing a deeper issue: It’s not just what we measure, but how disconnected our feedback loops are from the learning process itself. (For an alternative explanation of feedback loops take a look at this post by Terry Heick.)

The Communication Delay Problem

Research on virtual instructor-led training effectiveness shows that systematic design approaches outperform technology features. But systematic design depends on feedback loops that can actually inform design decisions. Yet most learning feedback comes too late to improve the experience. We measure engagement after sessions end, assess comprehension days or weeks after content delivery, and track application months later. By the time we get usable information, the learning context has passed.

The rub – learners provide immediate feedback constantly. The hesitation before answering questions. How they rephrase concepts back to us. Whether they return to specific resources or bookmark particular sections. These are real-time signals about what is or isn’t working. The question here is: Have we designed to capture and respond?

Beyond Satisfaction: Performance-Connected Feedback

The indicators I identified in earlier posts (voluntary re-engagement, sophisticated questioning, cross-contextual application) are all direct performance signals. So how do we build systems that respond to them during learning? The feedback my foot is giving to me is immediate and connected to outcomes that matter. Learning feedback should also be connected this directly to learning outcomes. Let’s measure retention instead of reaction, skill transfer instead of satisfaction.

The Social Acceptability Filter

Sounds simple, so what’s stopping us? One barrier to direct learning feedback is social acceptability. Physical feedback doesn’t care about politeness. Pain tells you to stop regardless of social considerations. (Even though I was embarrassed to be stuck on the ground in public a half mile from my office when I broke my foot, I wasn’t going to stand up.) But in learning environments, social filters can make honest feedback feel risky.

People say they “got it” when they’re confused. They may rate sessions positively to avoid seeming difficult or nod along when they’re lost because questioning feels like admitting inadequacy. (I mean, we’ve all been there.) But what if we designed learning environments where direct feedback felt as natural as physical feedback? Where confusion was treated as valuable naviagation data rather than failure.

Building Learning Feedback Systems: Navigation Systems Instead of Evaluation Systems

Consider a feedback system that functions like navigation tools. Tools that help us adjust course while traveling, not evaluate the trip after you’ve arrived. (And let’s please not mention building the airplane while flying. Maybe I’ll share my thoughts on that another time.) Instead of post-session surveys, we could build in real-time comprehension checks that actually inform content delivery. Instead of delayed knowledge assessments, we could track application attempts that reveal gaps while there’s still time to address them. This isn’t about more technology or complex analytics. It’s about designing feedback loops that connect directly to learning decisions and respond quickly enough to improve the experience while it’s happening.

The goal isn’t perfect measurement. Don’t get me wrong. I still value the Kirkpatrick levels and they have their place. But we need to build feedback systems that actually improve learning while there’s still time to make adjustments. What direct learning signals have you noticed that current systems typically ignore? When you’ve experienced learning environments with good feedback loops, what made them different? I’d love to hear about the gaps you’ve observed and what signals you trust instead. Share your thoughts at the Coffee Corner or on LinkedIn.

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What My Injury Teaches Us About Accessible Virtual Learning

Three hours into my first day back at work after foot surgery, my concentration began to falter. Not from the work itself, but from managing pain while trying to focus. I estimate that I’m at about 15% mobility, and I’m discovering what we miss when we design virtual learning for the “standard” participant.

Basic tasks I took for granted, adjusting my chair, getting coffee, and even the trip to the restroom, now require planning and drain the energy I need for learning. The pain creates cognitive load that competes with focus. My temporary setup isn’t ideal, but requesting equipment modifications seems excessive for a short-term situation.

This is what many learners experience on a daily basis. Not temporarily, but permanently. Yet too often, virtual learning design assumes participants can sit comfortably for extended periods, access materials effortlessly, and maintain consistent energy throughout sessions.

However, what’s fascinating about virtual learning, when designed thoughtfully, is that it can be more accessible than any classroom ever was. The problem isn’t the technology, it’s that we design for ideal conditions instead of real people.

The Cognitive Load We Ignore

Managing physical limitations while learning creates what Universal Design for Learning (UDL) calls competing cognitive demands. When comfort requires active management, there’s less mental capacity for content processing. When basic navigation is challenging, engagement strategies require a complete rethink.

Section 508 compliance and WCAG guidelines address technical accessibility—screen readers, keyboard navigation, and color contrast. These are essential foundations. However, they overlook a deeper challenge: designing for variable cognitive capacity and fluctuating energy levels.

From my experience, I’ve seen accessibility often treated as a compliance checkbox rather than a design opportunity. We ensure captions are available and call it accessible. We provide alternative formats and consider it inclusive. However, real accessibility means designing learning experiences that cater to individuals managing pain, fatigue, attention challenges, or competing demands.

Universal Design Principles in Practice

The best virtual learning design doesn’t just accommodate differences—it’s built around them. UDL’s principle of multiple means of engagement becomes critical when learners have varying energy levels throughout a session. Multiple means of representation matter when processing capacity fluctuates. Multiple means of action and expression allow participation despite physical limitations.

Simple design decisions make massive differences. Shorter content segments benefit everyone, not just those managing attention challenges. Flexible participation options cater to both individuals with chronic fatigue and busy managers joining between meetings. Downloadable materials help learners with unreliable internet and participants who process information better offline. But we rarely think this way. Much of the virtual learning I’ve encountered still follows the classroom model: fixed timing, consistent energy expectations, and assumption of optimal conditions.

The Infrastructure Reality

Accessibility isn’t just about individual accommodations; it’s about making assumptions about infrastructure. Many training materials assume high bandwidth and large file downloads, which are not feasible for learners in remote locations or those with limited access to technology. We design for state-of-the-art setups while participants join from phones with spotty connections.

Language accessibility often gets overlooked entirely. Enabling captions and speaking more slowly accommodates English language learners, but how often do we consider whether our content is culturally accessible or uses language that assumes specific cultural contexts?

The Temporary Accommodation Dilemma

Here’s what my current situation illuminated: the hesitation to request accommodations for “temporary” needs. It feels excessive to modify everything for a few weeks. But this mindset creates barriers for anyone whose needs feel uncertain or fluctuating. How many learners skip virtual sessions because their current setup isn’t quite right, but it feels like too much trouble to request changes? How many participate but struggle because we haven’t designed for their reality?

Designing for Real People

Accessible virtual learning design starts with questioning our assumptions. Can learners really sit comfortably for 90 minutes? Do they have quiet, private spaces? Can they easily access materials while managing other demands? Is our engagement timing realistic for variable energy levels?

The answers to these questions reshape everything. Instead of hour-long segments, we design 15-20 minute modules. Or, instead of requiring constant participation, we offer multiple engagement options. Instead of assuming optimal setups, we plan for suboptimal conditions. This isn’t just good accessibility practice, it’s good learning design. When we design for people managing limitations, we create better experiences for everyone.

Beyond Compliance to Effectiveness

Section 508 and WCAG provide crucial technical baselines, but accessible virtual learning requires thinking beyond compliance. It means designing for cognitive load management, energy fluctuation, and the reality that many participants are managing competing demands while trying to learn.

The question isn’t whether our platform meets accessibility standards. It’s whether our learning design actually works for people navigating real constraints. What assumptions about learner capabilities are built into your virtual training design? How might designing for limitations create better learning experiences for everyone?

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What Makes Virtual Instructor Led Training Actually Work?

A meta-analysis of 69 studies comparing virtual instructor-led training to traditional classroom learning found that 66.7% of the studies showed no significant difference in effectiveness. Not better, not worse, just equivalent. We’re not talking about self-paced e-learning here. This research focused on synchronous, instructor-led sessions conducted through video conferencing platforms and live virtual classrooms, featuring real-time interaction.

The differentiator wasn’t the platform or engagement features. It was the design quality and how systematically organizations approached conversion from in-person to virtual delivery. Are we asking the wrong questions when planning virtual instructor-led training? Instead of “What platform should we use?” maybe it’s “How do we restructure content for virtual environments with live instruction?”

This systematic approach makes a difference in practice. The Defense Acquisition University tripled the development of training assets while maintaining professional certification standards by focusing on systematic course conversion processes rather than technology features for their virtual classroom programs. Their success stemmed from treating virtual delivery as an instructional design challenge, rather than merely implementing technology.

Three patterns emerge from successful virtual instructor-led training:

Suitability assessment first. Some content translates well to virtual classroom formats, while other content requires hybrid approaches. Organizations that achieve good results evaluate this upfront, rather than assuming everything can be transitioned from a classroom to a virtual classroom.

Instructor development over platform training. Research suggests virtual classroom environments actually need more instructor interaction, not less. Platform training teaches buttons. Facilitation development teaches virtual engagement and attention management with live participants.

Performance focus beyond completion rates. High completion with flat performance outcomes might signal conversion problems, not success. Effective virtual classroom programs prioritize learning transfer over seat time.

Well-designed virtual instructor-led training can enhance outcomes while reducing logistical constraints. But the approach matters more than the technology.

What’s been your experience? When virtual instructor-led training works well for you, what makes the difference?

References

CTEC. (2024). Workforce training case study – Defense Acquisition University. https://www.ctec-corp.com/customers/case-studies/dau-case-study/

Defense Acquisition University. (2024). About DAU: Mission, organization, and accreditation. https://www.dau.edu/about

Woldeab, D., Yawson, R. M., & Osafo, E. (2020). A systematic meta-analytic review of thinking beyond the comparison of online versus traditional learning. e-Journal of Business Education and Scholarship of Teaching, 14(1).

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The Competency Mapping Revolution: Beyond Skills Lists

Many competency frameworks are simply skill inventories. We list what people should know, add some behavioral descriptors, and possibly add proficiency levels. Then we wonder why performance remains inconsistent across individuals who supposedly possess the same competencies. The problem isn’t that we’re measuring the wrong things; it’s that we’re thinking about competency development in the wrong way.

What We Get Wrong About Competencies

Traditional competency mapping treats expertise like a collection of discrete components. “Strategic thinking” may be defined as planning, analysis, and decision-making. “Communication” becomes writing, presenting, and listening. We create these intricate taxonomies that appear impressive in frameworks but often fail when people attempt to apply them.

Real competency isn’t additive. You don’t get strategic thinking by combining planning skills with analytical thinking. You get it by repeatedly navigating complex situations where multiple valid options exist, stakeholders have competing interests, and incomplete information forces judgment calls.

Confusion About Competencies

Part of the problem is that “competency mapping” is used to describe different things by different people. Some people use it to mean curriculum mapping, which involves aligning courses with job requirements or KSAs. Others think it means creating competency frameworks with lists and proficiency levels. These aren’t the same thing as mapping how expertise actually develops and transfers to performance.

When we confuse the tool (frameworks) or the process (curriculum alignment) with the real work (understanding how people develop expertise in context), we end up with solutions that look comprehensive but don’t actually improve performance.

Context Changes Everything

Here’s what’s missing from most competency conversations: context isn’t background noise, it’s the whole point. Strategic thinking in federal environments means navigating stakeholder politics, compliance requirements, and mission constraints simultaneously. That’s not the same skill as strategic thinking in a startup, where you can pivot quickly and break things.

The competencies that matter aren’t just what people can do – they’re what people can do within the specific constraints, pressures, and expectations of their actual work environment.

What Competency Mapping Actually Reveals

When you map competencies the way work actually happens, patterns emerge that skills lists miss entirely. You begin to see how expertise develops through repeated exposure to authentic challenges, rather than through training modules that teach components in isolation.

You also discover that the most valuable competencies are often the ones we never thought to name. The ability to recognize when standard procedures won’t work. The judgment to know which stakeholder concerns are worth addressing and which are distractions. The skill of translating complex requirements into actionable guidance for people who don’t live in your technical world.

The Questions That Matter

Instead of asking “What should people know how to do?” try asking:

What are the situations where good performers consistently succeed and others struggle? What makes those situations challenging? What do successful people notice or prioritize that others miss?

When people fail at tasks they theoretically have the skills for, what’s actually happening? What contextual factors are they not accounting for?

What does expertise look like when things don’t go according to plan?

These questions reveal competencies that cannot be captured in skills inventories. They point toward the kind of learning experiences that actually prepare people for the complexity of real work.

Moving Beyond Lists

The competency mapping revolution isn’t about better taxonomies or more sophisticated measurement tools. It’s about recognizing that competency development happens through authentic practice in realistic contexts, not through decomposed skills training.

This doesn’t mean throwing out frameworks entirely. It means using them as starting points for deeper conversations about what expertise actually looks like in practice, rather than as endpoints that define what people need to know.

The question isn’t whether learners can demonstrate strategic thinking on an assessment. It’s whether they can navigate the messy, ambiguous, politically complex situations where strategic thinking actually matters.

And that’s a very different kind of mapping problem.

Resources for Implementation

If you’re dealing with competency confusion in your organization, I’ve created three templates that distinguish between competency frameworks, curriculum mapping, and competency mapping approaches. Each addresses different organizational needs:

They’re designed to help teams understand which tool fits their actual needs and when to use each approach. The goal isn’t more sophisticated measurement – it’s clearer thinking about what we’re actually trying to develop.

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Design for Transfer: Why Training Fails at Application

Estimated reading time: 4 minutes

The biggest challenge in corporate learning may be the gap between what people learn in training and what they apply when needed.

Someone completes conflict resolution training with perfect scores, then still sends that passive-aggressive email when tensions rise. A manager masters delegation frameworks in a workshop but continues micromanaging their team. New hires excel at compliance training but often repeat the same mistakes their predecessors made.

The problem here isn’t a lack of motivation or memory. It’s a transfer problem.

The Transfer Challenge

Learning transfer refers to the ability to apply knowledge and skills in contexts that differ from where they were initially learned. This seems to be one of the persistent challenges in instructional design. Transfer appears to be rare and complex, and doesn’t happen automatically just because learning occurred.

This pattern frequently appears in learning environments. Participants can master problem-solving techniques in training but fail to recognize when those same techniques apply to similar problems in their work. Students learn mathematical concepts thoroughly but struggle to use them in word problems. Employees demonstrate skills perfectly in controlled practice but struggle when real situations introduce complexity and pressure.

This isn’t because the original learning was ineffective. People genuinely acquired new knowledge and skills. The breakdown happens in the gap between acquisition and application.

Why Training Context Matters

Most corporate training occurs in artificial environments: conference rooms removed from daily work pressures, online modules completed during dedicated learning time, and workshops where the primary task is learning. These contexts are designed for focused attention and reduced cognitive load, which makes sense for initial skill acquisition.

However, work often occurs in complex contexts with competing priorities, time constraints, emotional stress, and organizational politics. The psychological and environmental cues that support learning in training frequently don’t exist when application is needed.

When someone learns project management techniques in a quiet workshop, they’re building neural pathways associated with that calm, reflective environment. Later, when they need those skills during a heated stakeholder meeting with unrealistic deadlines, the context is so different that the learned behaviors may not automatically activate.

This context dependency isn’t a design flaw. It’s how human learning works. Skills and knowledge tend to become associated with the situations where they’re developed. If we want to transfer learning to work contexts, we should design for it intentionally.

What Makes Transfer More Likely

Here are a few approaches that support transfer:

Varied practice contexts. Instead of practicing skills in one standardized scenario, learners need exposure to multiple situations where the same principles apply. A negotiation workshop is more effective when participants practice with diverse personality types, stakeholder levels, and organizational dynamics, rather than relying on a single script.

Authentic problems. The closer practice problems mirror real work challenges—with all their complexity and ambiguity—the more likely it is that skills transfer. Sanitized case studies with transparent, correct answers don’t prepare people for situations where the right answer is not immediately apparent.

Metacognitive awareness. People need to understand not just how to do something, but when to do it and why it works. Making the underlying principles explicit helps learners recognize new situations where those principles might apply.

Spacing and interleaving. Skills practiced over time, combined with other skills, transfer more effectively than skills learned intensively in isolation. A leadership program that spans several months, with mixed challenges, works better than a week-long deep dive on a single technique.

Real consequences. Practice with actual stakes – even small ones – creates different neural pathways than consequence-free simulation. When possible, building real-world applications during the learning process increases the odds of transfer.

The Organizational Side

Individual learning design only goes so far. Organizations often undermine transfer through systems that reward behaviors different from those taught in training. Someone learns collaborative leadership but works in a culture that only recognizes individual achievement. People master quality improvement methods but face pressure to prioritize speed over thoroughness.

Environmental cues play a significant role in the transfer process. If the workplace doesn’t provide reminders, prompts, or opportunities to use new skills, they fade quickly. Job aids, checklists, peer coaching, and manager support all influence whether learning becomes performance.

The most sophisticated learning design can’t overcome organizational contexts that actively discourage the behaviors being taught.

Designing Backwards from Application

What if we started with the moment of application and designed backwards? Instead of asking “What should people know?” we could ask “What specific situation will require this knowledge, and how can we make the learning context mirror that situation?”

This may result in more complex learning experiences with increased variables, ambiguity, and frustration. It means longer development timelines and more complex facilitation. However, it also means a higher likelihood that the learning will transfer to performance.

Not every skill requires this level of design intensity, but those that matter most to organizational outcomes probably do. The question becomes: are we designing learning experiences or performance interventions?

What situations have you seen where people learned something but couldn’t apply it when it mattered? I’m curious about the contextual factors that seem to prevent transfer—and what conditions you’ve noticed that make application more likely.

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The Engagement Trap: What Learning Analytics Actually Predict (And What They Miss)

Estimated reading time: 4 minutes

Last week, I wrote about completion rates and whether they tell us anything meaningful about learning. I also considered a middle ground with “engagement metrics”. Time spent in modules, quiz attempts, discussion participation, and resource downloads. These feel more sophisticated than simple completion tracking.

But are we still measuring the wrong things?

The Engagement Mirage

Turning to engagement isn’t random; it’s rooted in decades of education research. Student engagement theory has established clear connections between engagement and learning outcomes. Students who participate in high-impact practices, interact with faculty, collaborate with peers, and dedicate time to academic activities exhibit better learning gains and higher retention rates.

These findings are robust and have shaped our understanding of effective learning environments. The problem isn’t the theory – it’s how we’ve translated engagement from education to corporate learning analytics.

Student engagement research measures meaningful participation, including contributing to class discussions, collaborating on projects, seeking help from instructors, and connecting coursework to real-world experiences. Corporate learning analytics measure platform interactions, including clicks, timestamps, module completions, and quiz attempts.

We kept the concept of engagement but changed what we’re actually measuring.

Engagement analytics reveal what people do with our content, rather than what our content does for them. Someone can spend twenty minutes on a page, attempt a quiz three times, and download every resource, but if they can’t apply the concepts next Tuesday when they need them, was that engagement meaningful?

This raises questions about what high engagement actually indicates. Are learners deeply processing content, or are they struggling with confusing materials? When someone spends minimal time accessing specific resources and moving on, does that signal disengagement or efficiency?

The disconnect between engagement metrics and learning outcomes suggests we might be measuring the wrong things entirely.

What Analytics Predict

The appeal of engagement metrics is theoretically sound. Self-Determination Theory suggests that intrinsic motivation (genuine interest and engagement) yields better learning outcomes than external compliance. Time-on-task research in education has shown correlations between the time spent and achievement. So measuring engagement feels like we’re measuring motivation and effort.

But we’re conflating different types of engagement. There are three types of engagement: cognitive engagement (deep thinking about the content), behavioral engagement (interacting with materials), and emotional engagement (interest and enjoyment). Most learning analytics capture behavioral engagement, such as clicks, scrolls, and submissions, while assuming it indicates the cognitive engagement that drives learning.

Most engagement metrics predict user experience satisfaction, not learning transfer. High time-on-task might indicate content that’s genuinely compelling, or content that’s confusing and requires multiple passes to understand. Quiz retakes could demonstrate determination to master the material, or they could reveal poorly written questions that trick people into incorrect answers.

Participation in discussions often correlates with personality traits and comfort with interaction (especially online) rather than learning depth. Some people think out loud; others process internally. We’re measuring communication preferences, not comprehension.

Return visits are more promising, especially when people revisit specific resources rather than browsing randomly. That suggests they’ve identified something helpful enough to reference again. But even then, we don’t know if they’re applying what they’re accessing.

What’s Missing from the Data

Cognitive Load Theory adds another wrinkle: high engagement might indicate cognitive overload. When learners struggle with extraneous cognitive load, such as navigating confusing interfaces, processing unnecessary information, or managing complex systems, they exhibit high behavioral engagement; yet, their actual learning suffers.

The most efficient learning often looks disengaged in our analytics. Someone who quickly finds the specific information they need, applies it successfully, and moves on appears to have low engagement. Meanwhile, someone wrestling with poorly designed content shows high engagement metrics while learning less effectively.

Engagement analytics capture everything except the thing we care most about: whether someone can do something differently because of the learning experience. They tell us about platform behavior, not performance change.

Transfer theory, how learning moves from the training context to the work context, is what we should be measuring. However, transfer is difficult to track, occurs over time, and depends on factors beyond the training itself. So, we default to engagement metrics because they’re available and immediate, even though they measure the wrong thing.

The gap between engagement and application is where most learning efforts fail. Someone can engage deeply with conflict resolution content but still avoid difficult conversations. They can master project management frameworks in theory, but continue missing deadlines in practice. The analytics will show successful engagement; the work will show no improvement.

Objective evidence of learning occurs in contexts that we rarely measure: the decision someone makes differently, the question they ask that they wouldn’t have thought to ask before, or the moment they recognize a pattern and respond more effectively than they would have previously.

Better Questions to Ask

Instead of asking “Are people engaging with our content?” what if we asked “Are people performing differently because of our content?”

Instead of measuring time spent, what if we tracked time saved, the moments when someone solved a problem faster because of something they learned?

These measures aren’t perfect either, but they point us toward outcomes rather than activities. They’re harder to capture in dashboards, but they might reveal more about whether our learning experiences truly matter.

What gaps have you noticed between engagement scores and actual performance change? I’m curious about the times when your analytics looked promising but the results felt disappointing – or when the data suggested low engagement but you saw real improvement in people’s work.

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Beyond Completion Rates: What Tells Us Learning Happened

We celebrate high completion rates in our training programs. They feel like success, clean percentages that stakeholders understand and LMS systems track easily. However, do completion rates reveal anything meaningful about learning?

People click through entire modules without absorbing the content, just checking compliance boxes. Think about the last time you completed that annual mandatory training. Then ask your colleagues about the last time they finished theirs. In other instances, learners dive deep into the content, apply it immediately, but never officially “complete” the course because they got what they needed and moved on to solve real problems. Unless, of course, completion is required.

The gap between completion and learning remains. Are we measuring the right things?

Why We Love Completion Rates

Completion rates are seductive because they’re measurable. Your LMS generates clean reports with metrics that allow leadership to track progress, and everyone feels confident about their training investments. They solve the “how do we know people did the training” question. But completion rates measure compliance, not capability. They tell us someone reached the end of content, not whether they can do anything different because of it.

The actual evidence of learning is evident in job performance, as evidenced by someone making better decisions, solving problems they couldn’t handle before, or improving their work as a result of training. However, measuring performance change is complex, time-consuming, and often depends on factors beyond the training itself. So, we default to completion rates because they’re available, maybe not because they’re meaningful.

Signs That Learning Happened

If completion rates don’t tell us about learning, what does? Learning science points to several indicators that someone has genuinely absorbed and internalized new knowledge or skills:

Voluntary re-engagement with content. When people return to specific resources or bookmark particular sections, they’ve identified something valuable enough to revisit. This self-directed behavior suggests the content addressed real needs.

Evolution in questioning. Early in learning, people ask procedural questions: “How do I do this?” As understanding develops, questions become more sophisticated: “What if I did this differently?” or “How does this connect to that other concept?” The complexity of questions often reflects the depth of learning.

Cross-contextual application. Real learning transfers across situations. Someone who applies a conflict resolution technique from leadership training to a family situation has moved beyond surface-level memorization to genuine understanding.

Spontaneous knowledge-seeking. When learners voluntarily explore related resources or seek out additional learning opportunities, it indicates that the initial learning has created momentum for continued growth.

Teaching or explaining to others. Perhaps the strongest indicator of learning is when someone can introduce the concept to others or reference it naturally in conversations. This demonstrates both understanding and integration.

The Middle Ground: Better Indicators

These engagement patterns aren’t proof of learning either – they’re more like conditions that make learning transfer more likely. Someone who never engages deeply with content is unlikely to apply it. But deep engagement doesn’t guarantee application – it just creates better odds.

Return visits, time spent, and bookmarking behaviors are still analytics, not evidence. They tell us more about content quality and learner intent than completion rates do, but they’re not the same as watching someone solve problems they couldn’t solve before.

Think of engagement analytics as leading indicators rather than evidence of learning. They might predict whether learning transfer is possible, but they can’t tell us whether it actually happened. There’s more to explore about what these signals predict and what they miss entirely. More on that next week.

Questions I Ponder

What would happen if we designed learning experiences assuming most people won’t “complete” them traditionally? What if we optimized for immediate usefulness rather than comprehensive coverage?

How might we track performance improvements without creating burdensome measurement systems? And what would it mean to define training success based on application rather than completion?

I don’t have neat answers yet. However, I believe that our current metrics may be addressing the wrong problem.

Small Experiments Worth Trying

Instead of abandoning completion tracking entirely, what if we supplemented it with human-sized indicators?

Track what people actually use. Most LMS platforms already capture return visits and content engagement – we might just need to look at this data differently.

Ask simple application questions. Not immediately after training, but 30 days later: “Did you use this?” The answers might be revealing.

Notice skill demonstrations in real work. Look for evidence that people are doing things differently, rather than relying solely on assessment scores.

Pay attention to what people share. When someone references training content in a meeting or forwards a resource to a colleague, that’s application happening naturally.

The goal isn’t perfect measurement – it’s a better understanding of whether our learning experiences actually help people perform their jobs more effectively.

What patterns have you noticed between completion and actual learning? I invite you to share the gaps you’ve observed and what signals you trust instead. Converse with me at the Coffee Corner or on LinkedIn.

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