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Brent D. Peterson

CompetencyBased Curricula

Here are five tips to help managers successfully provide L&D that both improves employees’ professional and personal lives and helps their organization stay productive and profitable.

Employees Crave Personal L&D—But Employers Aren’t Responding

From the moment we’re born, human beings are constantly learning. Throughout our lives, we encounter various learning formats that can make the process easier or more difficult. The key is to combine the tools we have at our disposal to increase knowledge retention without losing student motivation.Before the 1990s, business training was associated with combinations of texts and face-to-face examinations. However, the emergence of e-learning has offered a new option to encourage remote learning online. But is this the solution that HR professionals require to help employees achieve the results the organization needs?In Search of the SolutionWith the rise of e-learning, many companies began to transition their training to the online realm. Unfortunately, the results remained the same: Training was still perceived as boring by employees as it was limited to course material in text, image, and video format. Being 100 percent digital was not enough.Conversely, training that’s effective stands out for being able to keep employees engaged and motivated throughout the course, as well as providing practical content that’s applicable to their lives. It should also keep up with the trends and provide innovative formats in-line with technological advances.Ultimately, neither employees nor managers were getting the training results they wanted with the digital formats being implemented over the course of 2020.Gamelearn’s Contradictions of Corporate Training 2021 report delves into the root cause of this problem, comparing perspectives between more than 1,500 HR and L&D professionals and employees. Fifty-two percent of professionals believe that simulations of real-life situations would improve their online courses’ efficiency and attractiveness to employees. Gamification also stands out for almost 40 percent of HR professionals when it comes to engaging students in their training.When employees were asked which format they prefer for their training, the results are in-line with the modern technologies and techniques being implemented today. Forty-five percent of employees claimed to want to learn skills by playing video games and 39 percent asked for more elements of gamification within their course material. Both are highly related, as gamification elements are one of the core pillars when it comes to creating a video game.If both HR professionals and employees agree on the kind of training formats and techniques they want, why aren’t employees receiving the training they expect?Game-Based Learning: The Solution Everyone NeedsTechnology advances at the same time as employees’ preferences and needs. That’s why the teams responsible for talent development must stay attentive and keep up with the trends. The most observant have already realized that methodologies such as game-based learning meet not only their training and business objectives but also their employees’ expectations.Game-based learning is based on three main points:● Gamification and storytelling that’s attractive to professionals and appeals to their sense of competition● Top-quality and useful content that teaches all the skills employees need to thrive in their jobs, and their daily lives● Real-life simulations with personalized feedback which allow learners to practice useful skills in realistic but safe environmentsAll these factors work together to boost students’ engagement and motivation, increasing retention, and ensure they learn practical and valuable skills.Now that you know what employees expect, will you choose the training that satisfies everyone’s needs?

Why Aren’t Employees Getting the Corporate Training They Expect?

Marla designs help documents for the software applications her company produces. She sits across from Sean who develops monthly training webinars. Marla would like to “move up” to building instruction like Sean does, but Sean tells Marla that he thinks that the work that Marla does is actually more useful for the company’s users.We could argue the merits of documentation versus training, but there’s always the question of what each user needs at each moment of time. Context is everything.The science of learning is a science that works to understand how we learn and how we can improve learning and instruction. But to be fair, humans have been learning on their own since, well, forever.For instance, if Corinne wants to know how to do something, she often asks the person next to her and then gets on with her job. Yan mentors Morey and they meet most Tuesday afternoons. Marlena uses self-study materials and is going to take the certification exam in less than three weeks.So why do numerous disciplines (neuroscience, psychology, education, computer science, and others) expend enormous energy and resources to figure out how we learn and how to best build instruction? Can’t we just leave people alone?We Learn All the TimeAn interesting study by D.W. Livingstone shows that the majority of workplace learning happens not in formal settings but in informal ones. Numerous studies, including those by Livingstone, Jay Cross, Jane Hart and others, show informal learning as a proportion of all workplace learning at greater than 70 percent.If you haven’t already looked into informal learning, you should, because training professionals need to understand it more. Saul Carliner’s book, Informal Learning Basics, on the subject is a good primer, as are writings by Jay Cross and Jane Hart.You might think that the definitions of learning and instruction are self-explanatory, but actually they aren’t so simple. And they help us see exactly what we are trying to do.Learning, DefinedThere are many definitions of learning. A favorite comes from De Houwer:  “Learning is changes in behavior that result from experience.”Some definitions don’t require changes in behavior, only changes in knowledge. For me, this doesn’t cut it. If you can’t do what you are learning, the learning isn’t quite cooked yet. For example, if you can’t use what you are learning, you are on your way but not there yet.Applied learning requires knowledge and experience with the range of possibilities, of course. As my colleague Catherine Lombardozzi reminded me, people need deep knowledge, not only to follow a script. So if a customer service agent is following a script for handling returns, but she cannot handle the variety of returns, she only knows how to follow a script. She hasn’t really learned the whole returns process.One definition of learning discusses how learning requires the ability to correct errors. Now this is truly deep learning. One of the biggest problems people have is correcting things that go wrong. Misconceptions and mistakes are a big problem in learning. So when defining learning, we may want to determine how far into expertise we are talking about. Are they proficient (competent or skilled) or are they expert?Why Build Instruction?People can and do learn on their own. But we also know that when there are very specific things we want people to be able to do, there are efficient ways to help them. If learning occurs when we interact with our environment, then instruction is the creation of special environments specifically to promote learning.Dr. Richard Mayer, professor of psychology at the University of California, in his book Applying the Science of Learning reminds us we are trying to change behavior as a result of instruction and there are methods to do so that are more effective than others (that’s what the science of learning and instruction are about).This view has very far reaching implications. It means that if we are building “instruction,” we have to impact what people do. That’s a high bar. Although it is may not be practical to expect that everything we do reach that high bar, we ought to reach that bar often.So that leads to two question that we should ask ourselves:How do we infer (assume changes in) learning after instruction?How do we determine if the change has made it from instruction to the job?We will answer these critical questions soon.This is article 1 of Science of Learning 101, and I very much want your comments. Is this helpful to you? Does it answer your questions? Your comments can change the course of this series.ReferencesCross, J. (2007). Informal learning: Rediscovering the Natural Pathways that inspire innovation andPerformance. San Francisco: Pfeiffer.De Houwer, J., Barnes-Holmes, D. & Moors, A (2013). What is learning? On the nature and merits of a functional definition of learning. Psychonomic bulletin & review.Livingstone, D.W. (2001). Adults’ informal learning: Definitions, findings, gaps and future research. NALL Working Paper 21-20010. Centre for the Study of Education and Work, Ontario Institute for the Study of Education, University of Toronto. Retrieved from  Mayer, R. E. (2011). Applying the science of learning. Upper Saddle River, NJ: Pearson.

Science of Learning 101: What’s the Point of Instruction?

“Next learning challenge.” That’s how many organizations are approaching today’s pace of change. AI, automation, skill gaps, and evolving roles—these forces are transforming how work gets done. They’re also reshaping expectations on how people learn, grow, and deliver results. Training isn’t just about checking the box anymore. It’s about building the right skills at the right time and making sure learning fits into the flow of modern work.Traditional corporate learning models weren’t built for this. They assume time, predictability, and static roles. But today’s workforce needs something different: scalable programs that can flex across departments, keep up with job changes, and prove their value.Here’s how teams are making it happen.1. Start with skills that matterThe volume of training isn’t the problem. Misalignment is.Forward-thinking L&D teams are moving away from generic content libraries and toward skill-based learning pathways that reflect real roles and business outcomes. These aren’t static curricula; they’re responsive frameworks that evolve as workforce needs shift. They’re also using AI-powered insights to identify skills gaps and personalize learning for each role. That means learners aren’t just getting access to content—they’re getting the right content, aligned to their role, their pace, and their path forward.By defining clear role expectations and aligning learning to real outcomes, organizations are closing capability gaps faster, with less guesswork. Employees know what good looks like. Teams track what’s working. And leadership sees a direct line between training and performance.This shift doesn't just benefit learners. It aligns the entire organization around measurable growth.2. Design for the way people actually workThe average learner doesn’t have an hour. Sometimes, they don’t even have ten minutes. They’re juggling meetings, deadlines, and often caregiving or side gigs—wearing multiple hats across work and life.That’s why successful programs are modular, mobile, and adaptive. Think short bursts of targeted content, surfaced when they’re needed most. Think mobile access that works as well on a shop floor as it does in an office.Cohort-based models bring accountability. Self-paced tools offer flexibility. Personalized learning plans support growth at the right speed. And embedded support, like job aids and nudges, ensures training translates into action.This isn’t about attention spans. It’s about fitting into a learner’s reality.3. Make progress measurable and meaningfulThere’s growing pressure to prove the value of learning, not just through activity, but through outcomes.That’s why many teams are turning to platforms and processes that show skill development in motion. Real-time dashboards track proficiency. Hierarchical data structures connect performance back to business goals. Leaders get clarity on where to invest and why it’s working.This visibility isn’t just about optics. It empowers smarter decisions, faster pivots, and long-term alignment between talent strategy and company performance. Teams can track learning outcomes and engagement in real time, using those insights to guide future investments and improve program effectiveness.When outcomes are clearly linked to learning, it becomes a powerful lever for growth.4. Streamline the delivery, not just the contentScaling effective learning across teams, departments, and geographies takes more than good intentions. It takes infrastructure.Disconnected systems, manual tracking, siloed LMS tools, and duplicative processes waste time and erode consistency. Many teams are replacing them with unified ecosystems that connect learning, HR, and performance tools. This cuts down on friction and frees up capacity for strategy.With smart integrations and automation, it becomes easier to launch, track, and optimize programs without getting buried in logistics.The result? Less admin. More impact.Redefine what workforce learning looks likeThis isn’t about tweaking old programs. It’s about building systems that grow with your organization, designing learning that adapts to how people work today, and tying development directly to results that matter.The stakes are high. But so is the opportunity.Right now, teams across industries are helping define what modern workforce learning can be. And they’re setting the pace for what’s next.

4 Ways to Rethink Workforce Learning for Agility and Impact

Training alone isn’t enough to scale great leadership. New managers especially need just-in-time support. That’s where AI comes in.Far from replacing leaders, artificial intelligence is becoming a quiet partner in helping them communicate clearly, make better decisions, and support their teams more consistently. For learning and development professionals, the opportunity isn’t just to teach leaders about AI. It’s to help them use it.Here are four practical ways AI is already transforming leadership development:1. AI as a Communication Assistant Leaders spend much of their day writing—emails, evaluations, strategy documents, team updates, and more. Generative AI tools like ChatGPT and Microsoft Copilot can help draft first versions, suggest more inclusive or motivating language, and adjust tone for different audiences.When I using AI to write, start with a brain dump—what you want to say, who it’s for, tone, length, even your favorite humorists. The more prompt you offer, the better the draft.AI tools are especially helpful for newer managers still finding their leadership voice. They act like training wheels—supporting clarity and consistency until the leader gains more confidence.2. Smarter Meetings With AI Note-Takers AI-powered assistants like Otter.ai or Fireflies.ai can transcribe meetings, generate key takeaways, and highlight action items. These tools let leaders focus on discussion while capturing a searchable, shareable record—reducing confusion and boosting accountability.For L&D professionals, introducing these tools into leadership programs models good meeting hygiene—and provides practical, immediate value.3. Personalized Coaching in the Flow of Work Coaching has impact—but it’s expensive and often limited to executives. AI chatbots and simulators make practice accessible to everyone. Imagine a manager prepping to give difficult feedback. They could prompt an AI coach: “Help me practice a conversation with an employee who’s missing deadlines,” and walk through a simulation with real-time feedback.This kind of practice can dramatically reduce the anxiety that often keeps leaders from having the conversations that matter most.4. Insightful Decision Support AI dashboards and people analytics can help leaders make smarter, faster decisions. Predictive models can flag burnout risk, highlight skills gaps, or recommend peer mentors. Tools like Microsoft Viva Insights and Culture Amp offer dashboards that help managers spot trends and support their teams with data-informed decisions.What L&D Teams Can Do Now Here are a few ways to help leaders benefit from AI:Introduce secure AI tools into programs—like notetakers or roleplay scripts. Teach prompt basics. Show examples that leaders can apply immediately. Encourage experimentation with a 30-day “try one new use” challenge. Emphasize ethics. Reinforce that AI should enhance—not replace—human connection. Leadership isn’t getting simpler. And AI reminds us that most decisions aren’t right vs. wrong—they’re balancing good vs. good. That’s why paradox-aware training is essential. In the Leadership Progress Cycle, we help leaders navigate tensions like these with confidence and clarity. Learn more at:leadershipprogresscycle.com/.

The Leadership Multiplier: How AI Can Elevate Managers and Their Teams

At the beginning of the past two years, I’ve written a post on important points I think you should know. In 2021 I mentioned the importance of context, emotion, and learning to learn. In 2022 I discussed some of the newer cognitive science models of cognition—including distributed, social, and embodied cognition—as well as the role of mental models. This year, I’d like to focus on a trend that we should extinguish: the belief that we are formal, logical, reasoning beings.The cognitive revolution transcended the view of behavioral psychology; we couldn’t talk about what went on in the brain. While many valuable insights were generated by that scientific paradigm, including the role of performance objectives, the move to thinking we could isolate necessary components provided further advances. Results that have stood the test of time—the recognition of sensory, working, and long-term memory as well as core cognitive processes like attention, elaboration, and retrieval—were identified.We tend to adopt existing models as frameworks for thinking about the brain, and the cognitive revolution was no exception. For example, consider the computer as a general-purpose information processing system. It made sense to adopt that model to reflect our abilities to process information and complete a variety of tasks. Thus, the dominant framework for thinking about thinking was to view our brains as formal, logical, reasoning machines.Such models, with some assumptions like mapping the world into formal representations, accomplished impressive tasks. Systems built upon that model could break down problems into components and solve them. They could even learn. Artificial intelligence (AI) advanced as a result. So-called “expert systems” were built upon interviews with the best practitioners. Sadly, these models failed in practical situations, leading to an AI winter. This was a disconcerting time, when behavior naturally observed in humans did not reflect these symbolic systems.The Postcognitive RevolutionOne of the most impressive capabilities of our thinking is the ability to organize the world into flexible frameworks. What David Rumelhart called schemas, Marvin Minsky called frames, and Roger Schank called scripts all reflect our ability to infer knowledge about the world. Thus, we had representations such as wheels that scaled up to vehicles but differently for automobiles versus bicycles. Symbolic logic struggles with this, however. Attempts were made to adapt, such as Lofti Zadeh’s Fuzzy Logic and Douglas Hofstedter’s Slipnets, but suffered in practice.An alternative framework emerged that could be characterized as “subsymbolic,” operating below the symbolic level. John Holland, Keith Holyoak, Richard Nisbett, and Paul Thagard created a genetic algorithm approach in Induction, which borrowed mutation from biology, but wasn’t aligned with neuroscience. Rumelhart, along with Jay McClelland, proposed connectionist models instead in their two-volume set Parallel Distributed Processing with a neuroscientific inspiration. These models were found to exhibit rational behavior similar to humans. They represented the same types of behaviors, including errors, that were empirically obtained from psychology research.These neural net models created not only a psychological revolution, capturing elements of cognition, but also a renewed basis for artificial intelligence, underpinning much of today’s work in machine learning.A Cognitive LegacyOne small legacy of the formal, logical, reasoning model of cognition, however, seems to linger. It appears that many of our practices in learning and development (and business in general), still seem to be based on this idea. This is a potential problem.If we’re formal logical beings, there are several things we should be able to assume. For one, if we give people new information, they’ll appropriately adjust their behavior. Moreover, the person with the greatest access to information should be the one to make a decision, and that decision will be the best one possible. We see this manifest in several ways.For one, our training frequently resembles the presentation of new information with an expectation of behavior change. Too often, we see bullet points of information extracted from (and even presented by) experts. After this, there’s an expectation that we’ll do something differently. Similarly with annual or quarterly reviews. People need feedback at the appropriate time and support for change. We don’t just change when we wish to; it takes time and practice.We also see decisions made by individuals without consultation. Folks will decree a new approach because it’s been determined by the person at the appropriate decision point. Yet the evidence suggests that we get better outcomes when we bring more people into the decision-making process, such as in Keith Sawyer’s Group Genius. When we follow practices that help us suitably explore a problem individually, and then collectively brainstorm solutions, test, and adapt, the outcomes are better.A Problematic LegacySadly, there is also evidence that this approach to work isn’t working. We see it in the outcomes of learning investments, and when we evaluate organizational success as an outcome of our approaches, we see that more informed ones work better.In the 2012 Harvard Business School working paper The Great Training Robbery, Michael Beer, Magnus Finnstrom, and Derek Schrader cite metanalyses that suggest only 10 percent of training investment has an impact. There may be multiple causes for this, but it does suggest that we’re wasting money with our approaches. The Serious eLearning Manifesto suggests that much of traditional learning is based on information presentation, so it’s safe to say that we’re not doing as well as we could.Similarly, efforts like Laura Overton’s Toward Maturity and Lauri Bassi’s Human Capital Capability Scorecard suggest that organizations do better when they’re better aligned with how people think, work, and learn. Organizational performance reliably improves with better employee approaches.Overall, the continued use of ineffective practices undermines our organizational success. We need to effectively achieve our organizational goals, which includes ensuring we can optimally execute what we know we need to do and continually innovate when we have to invent or adapt.Moving ForwardWhat should you do differently?Truly understand how your brain actually operates. Know the basics of the human information processing loop and extensions for learning and performance.Avoid the myths that continue to hinder our industry.Conduct deep needs analyses to find important performance gaps and their root causes. There are many causes of problems besides a lack of knowledge or skills. Stop thinking that if you give people knowledge, behavior will change.Leverage external resources. Decision making is best done collectively and iteratively. To do otherwise is to minimize your potential.Move from legacy practices to evidence-based practices. There are vested interests that continue to exist based on perpetuating approaches that aren’t effective. Many products that are quite lucrative—profiling instruments and development programs —aren’t effective.Start measuring impact. This doesn’t mean what people prefer (there’s no correlation with what people think of a program and its impact). Instead, assess the actual change in the organization. You can’t collect this yourself; you must partner with the business units that require change.Align programs to demonstrable gaps, and then measure until they’ve improved.Ultimately, L&D should be the folks that are responsible for not only optimal execution but also continual innovation. When you’re innovating (problem solving, designing, or researching), you don’t know the answer when you start, so inherently it is learning. It’s informal learning, and no one knows the answer (yet)! Still, it’s an opportunity for L&D to attain a more valuable role in the organization.To make this strategic move and start bringing evidence-based practices from formal to informal learning, take ownership of these practices within your teams. Make it safe to fail; share stories of what you tried; and learn together. Create and understand a learning microculture before taking it to the rest of the organization.Last, identify the organizational legacies that are holding you, and your organizations, back. Then, find alternatives, and implement them. When you’re free of your unexamined past, you can move forward to an evidence-based future that means better outcomes. That’s a trend I’d like to see and encourage you to undertake.

A Trend to Supersede

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While facilitating leadership development workshops at Emerson (a Fortune 125 company with 90,000 employees in 140 countries), there is an interesting comment I frequently hear: “I did not realize how much time you would give me in this workshop to think!”Is that a compliment or a complaint? I believe most people mean it as a compliment because they are surprised. Most of them haven’t had enough time to think in prior trainings.I’ve heard this comment many times, and it never takes me by surprise. In fact, I would be disappointed if I did not hear it. Most learners aren’t given adequate time to think and reflect when they attend training sessions.Far too many training programs rely on content rather than application and do not offer learners time for intentional reflection. In my 28-year learning and development career, I’ve seen too many programs approach learning by telling learners as much as possible in the time provided. Many of us in L&D call that strategy “ten pounds of training in a five-pound sack.”Fred Harburg, former chief learning officer at Fidelity Investments, sums it up well: “If there is no time for reflection, there is almost no chance for improvement.”L&D professionals want to create value for learners. One of the most valuable activities we can build into the design and delivery of our courses is intentional reflection and implementation planning. To improve our training programs, we must stop talking and pushing content, and let learners think.This represents a paradigm shift for many L&D practitioners. Spend less time on content and more time on intentional thinking and reflection.L&D thought leaders Roy Pollock, Cal Wick, and Andy Jefferson (authors of The Six Disciplines of Breakthrough Learning) popularized the term “learning scrap.” Learning scrap refers to content covered in a workshop that is never applied. Most L&D programs have a typical learning scrap rate of 85 percent. At best, most learners apply 15 percent of what’s taught in a workshop on the job. If you want to decrease learning scrap and increase learning transfer, build more deliberate thinking and reflection activities into your courses.In his classic book, Learning and Memory, Donald A. Norman writes, “To remember is to have managed three things successfully: the acquisition, the retention, and the retrieval of information. Failure to remember means failure at managing one of these three things.” I argue failure usually occurs at the retention level. If there is no reflection, there is no retrieval. If there is no reflection and retention, do not expect retrieval to follow.This issue is well-documented by Ruth C. Clark, Frank Nguyen, and John Sweller in another classic book, Efficiency in Learning. They refer to this concept as “information fatigue.” We need to give learners more time to process during a training program. The authors’ research shows a lack of time for reflection leads to cognitive overload. Overload implies a malfunction; learning is short-circuited. If a person attends a program to learn, and we cram it full of content without giving them time to think, it’s no wonder learning scrap occurs.Several years ago, I codeveloped a workshop with Michael Watkins (a former faculty member at Harvard Business School and founder of Genesis Advisers) to create a workshop based on his bestselling book, The First 90 Days. Our workshop designed intentional thinking activities that asked participants a series of questions at the end of each module. The reflection activities took between 20 and 30 minutes to complete and consisted of five or six questions. These were not simply recall or comprehension questions, but rather synthesis and evaluation questions (the higher levels of cognitive thinking).Some of the top executives who attended this workshop told me that if I had not built in deliberate time to think, their goal would have been to rush through the course so they could move on to the next thing on their list.Here are some ways to build time for reflection into your programs:
Build thinking time into the design and delivery of every learning program you create, no matter the delivery method (in person or virtual).Resist the temptation to add more content to your courses. Do not fall prey to the “myth of more.” More content at the expense of intentional time for reflection will not improve learner performance. It will diminish it.Remember, the goal is not to give the content. The goal is for learners to get it, which will only happen through the discipline of quiet reflection.If trainers are given the option to talk or let their learners think, many would choose to fill the silence with their own voices, but the greatest value trainers bring to a workshop is knowing when to stop speaking and give learners time to reflect.

Want Them to Learn? Give Them Time to Think

Upskilling and reskilling is the top priority for L&D teams worldwide, according to LinkedIn’s 2021 Workplace Learning Report.For L&D teams, a critical component of upskilling and reskilling is taking the time to check up on your learners. However, L&D teams often don’t have the resources to assess every piece of learning.In this blog post, we will share how you can use data analytics to track your learners’ progress and assess what’s working versus what’s not. You’ll start by answering these three questions:What does your learner love?Does the learning stick?Is your community still going strong?These questions explore how learners experience content, engage in practice, and find community. Learners need all three to upskill or reskill successfully.What Does Your Learner Love?Instructional designers often conceptualize courses linearly. But, that’s not how learners use the platform. They’re also not just moving through courses but through a learning ecosystem. So how are they moving through both the courses and the learning platform?Your data can show you the learner’s journey. You’ll see which content learners skip and what they come back to again and again. You’ll see which courses people take next. You’ll discover what separates successful learners from unsuccessful learners. This data can help you strategize how to help more learners upskill successfully.Suggestions for how to capitalize on what your learners love:● Use push notifications or automated emails to trigger favorable actions from your learners. Research from Proliteracy showed push notifications improved attendance by 25%.● Think like a marketer. Advertise the resources people love through emails, bulletin boards, or learning campaigns.● Create more content for specific audiences in their preferred modality. This data can help you complete your next learners’ needs analysis.Does the Learning Stick?Big data can illustrate the stickiness of learning based on the number of visits by a single user. The stickier the resource, the more often they return to it. However, more qualitative data can also be helpful. Learner surveys with open-ended questions three months later can show you if people are still using their new learning.If learners are not using their new learning, you have to ask some big questions. Do they find it irrelevant? Or, do they lack confidence?Research shows that doubtful learners may ace the test, but hesitate on the job. You must achieve mastery before you can make the right decision every time. For newly upskilled learners, this presents a big challenge. You can either wait for the experience to cure the doubt or measure learner confidence.Confident users are not just better at their jobs. They’re also more active learners. BenchPrep data shows that the most confident learners answered almost 5 times more questions. By showcasing the confidence, you also create engagement.Suggestions on how to make learning stickier:● Ask learners to rate their confidence in each answer.● Make learning stick with daily microlearning questions.● Integrate the new learning into their daily workflow.Is Your Community Still Going Strong?Often, online learning communities start out strong but eventually teeter off. Without a strong community, learners easily stop practicing their new skills.BenchPrep data shows that the average online discussion participant is 15 times more active online. This means they aren’t just using discussion boards. They’re playing games, doing practice quizzes, and consuming more learning content overall.Your online community can function as a jumping-off point for learners. They log in for the community, but end up taking a few microlearning modules. For upskilled or reskilled learners, learning communities keep the learning fresh and combat the forgetting curve.Suggestions for rejuvenating your online learning community:● Offer weekly challenges where everyone can share their solution.● At the end of a Q&A, suggest posting thoughts to a discussion board.● Use moderators to provide an expert voice.● Include live events through the Zoom integration in your LMS.Build in a Learner Check-UpLearning technology has never made it easier to check up on your learners, giving you access to the data you need right at your fingertips. In fact, you can make learner check-ups part of your process.Together, Richardson Sales Performance, a global sales training company, and BenchPrep launched Richardson Accelerate, a solution that provides various online training programs to inspire and motivate sales teams to better engage with buyers and drive results. BenchPrep’s data and analytics dashboard allows managers to monitor progress and determine where additional coaching was needed. This method illustrates the success of each learner against their personal best. Learners show progress by measuring against benchmarks from the start of the training. Everyone loves seeing learners’ skills and confidence grow. Plus, it proves the results of your instructional materials. Find out how Richardson made learner check-ins a reality using the BenchPrep platform in this case study.

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