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Peter L. Lim

Any chance of an economic recovery in 2001

Knowing what skills your people have and what they need is an essential part of any upskilling and reskilling strategy. According to Bridge-sponsored research, it's never been more important, as 77 percent of HR professionals and business leaders consider talent development a top priority over the next two years. However, despite talent development’s importance, survey ﬁndings also reveal that less than half of HR teams currently match learning to future talent requirements or assess employee needs and gaps in knowledge.Let’s take a look at how developing a skills matrix can keep your talent management strategy in step with learner needs and business demands, as well as how to use AI-powered upskilling software to personalize your L&D programs.What Is a Skills Matrix?Think of a skills matrix as an inventory of your employees’ current abilities. By evaluating these skills and competencies and presenting them in a table, you can easily monitor skills gaps, ﬁnd your strongest performers, and ensure your people are equipped to undertake a project or succeed in their roles.A skills matrix is useful for quickly identifying and comparing people’s competencies and experience, allocating resources, and targeting L&D programs.The Beneﬁts of Using a Skills MatrixSome of the beneﬁts of a skills matrix include:1. Finding and Filling Employee Skills GapsKnowing what skills are required to drive organizational success means you can organize your talent and prioritize training programs based on demand.Mapping your workforce’s skills, capabilities, and experience levels makes it easy to spot any skills gaps across teams or departments, and then direct them toward learning and development opportunities they need.2. Assessing Employee PerformanceConnecting the work your people do to organizational objectives with a set of measurable goals reveals your strongest performers and the capabilities that drive organizational success.A robust skills framework can help your people understand what’s expected of them, showing them the areas in which they make the most impact and providing actionable steps for improvement.3. Incentivizing Learning and DevelopmentBeyond delivering mandatory training, a skills matrix can help identify employees’ strengths and career goals and connect them to meaningful learning opportunities. When your employees develop and grow, everyone beneﬁts. LinkedIn’s Workplace Learning Report ﬁndings show that people are four times more likely to engage in training when it’s connected to their career goals. And 87 percent of L&D pros say they show business value by helping employees gain skills to move into different internal roles.4. Workforce Planning and Risk MitigationAssigning tasks is easy when it’s clear who possesses the needed skills and competencies and who’s most experienced to undertake projects. With a view of skillsets across the organization, you can mobilize talent most effectively and tap into otherwise hidden potential. Additionally, you can guard against future disruption and anticipate changing needs by identifying high-potential talent who can step into business-critical roles and prioritize training programs for in-demand skills.How to Build an Employee Skills MatrixAssessing employees at scale requires a system for collecting, categorizing, and organizing these skills. Follow the steps below to build a skills matrix for your organization.Step One: Identify the Required SkillsWhether your matrix is focused across a team, department, or the entire organization, you must be clear about the essential skills each person needs to perform in their role.Start by compiling these skills and competencies with leaders and key stakeholders across departments, including both soft and hard, or role-speciﬁc. You could also review your company’s competency framework to identify core values and behaviors that your people should possess—but remember to keep the list speciﬁc and only include what’s relevant.It’s likely that some of these skills and competencies will be the same for everybody in the organization, while others will be more technical and role-speciﬁc.Step Two: Create a Scoring System to Evaluate SkillsOnce you’ve gathered a list of skills and competencies, you’ll need a way to rank and categorize proﬁciency and map it onto your matrix.A popular way to score and manage skills mastery is using a Likert scale. For example:ScoreRatingDescription1LimitedThe employee has limited knowledge or understanding of the skill or hasn’t yet had the chance to apply it.2BasicThe employee demonstrates the essential components of the competency or skill with the ability to develop a greater competency in a reasonable timeframe.3WorkingThe employee demonstrates experience using the skill at a necessary level to advance organizational objectives.4SeasonedThe employee effectively applies the skill in practice in most circumstances.5ExpertThe employee specializes in this area, showing consistent, superior application of the skill, even in unanticipated circumstances.For an accurate picture of employee proﬁciency, consider collecting and combining several sources of data, such as: Skills assessments Manager and peer reviews Employee self-evaluationsStep Three: Consider Employees’ Career GoalsAlthough including employees’ career goals and the skills they want to develop is optional, doing so can inform development plans and direct people to meaningful L&D opportunities.To uncover this insight, partner with your people managers. By making career and skills development a staple of regular manager-employee discussions, you’ll understand what motivates people and what skills they want to learn. You can use this insight to ﬁnd opportunities that develop these skills aligned with organizational goals.Step Four: Compile Skills Data in a TableAn Excel spreadsheet is a popular format for mapping skills data, and this sheet will give you a visualization of each employee’s proﬁciency level.You’ll plot this information using two axes, with employee names on one and a list of skills on the other. Then, you’ll populate the sheet using the data you’ve collected.Once you’ve built your matrix, you’ll have an overview of each person’s abilities, strengths, and weaknesses. You can sort these skills into categories based on tasks and projects, ﬁlter to ﬁnd gaps, and use them to inform employee development plans.Step Five: Keep Skills Matrices Up to DateUp-to-date skills data is a must for tracking growth over time and prioritizing skills needs. To get the most out of a skills matrix, you’ll need to review and update your data regularly to reﬂect your people’s current skills, proﬁciencies, and certiﬁcations.How Dedicated Employee Upskilling Software Can Streamline Skills AssessmentsHere are some ways an all-in-one learning, development, and upskilling platform like Bridge streamlines creating and managing your skills matrix and keeps your data accurate and up to date.1. Creating a Single Source of Truth for Employee SkillsA dynamic skills taxonomy is the foundation of any skills strategy. However, gathering data to present an in-the-moment picture of your people’s skills takes time, and skills inventories quickly become outdated as employees continue to learn and grow.An LMS allows you to swiftly identify, group, and measure the skills within your organization, giving you a transparent and agile framework from which to build.Using a learning, development, and upskilling platform with AI to combine your existing employee data with external labor market data gives you an accurate, ever-evolving, and easy-to-visualize view of competitive in-demand skills and where they exist in your organization.2. Assessing Employee Skills and CompetenciesOnce you know what skills your people have and what’s required, you’ll need a way to ﬁll any gaps with training and development and measure learner growth.An L&D platform gives you a single destination to conduct performance conversations, record employee one-on-ones, and share skills feedback and recognition. With AI, you can take this a step further by helping people identify the skills they need to grow and recommending competencies based on their roles.3. Personalizing Employee DevelopmentMapping skills using an AI-driven taxonomy keeps learning targeted andgrowth-oriented with minimal administrative input. Once learners know what they want to develop, you make their growth actionable and measurable by connecting them to employee development plans and assigning tasks and training.However, formal training programs are only one way to strengthen your talent pool and close skills gaps. You can also harness these skills and insights and direct learners toward gigs, projects, and mentorship opportunities that put these skills into practice.

How to Create an Effective Skills Matrix With the Help of AI-Based Skills Suggestions

In January, OpenAI’s conversational platform, ChatGPT-3, became the fastest-growing app in internet history. This is just one indication of growing curiosity about GPT (generative pre-trained transformer) and other chatbots in the past few weeks. As one of the first learning professionals to see the potential of conversational AI and one of the few designing the applications for years, I’m excited to see the recent attention paid to this engaging technology.In my book, AI in Talent Development, I outline several ways chatbots can be used as part of a robust learning experience, and ChatGPT-3, the third version in OpenAI’s ChatGPT series, demonstrates most of them. When ATD asked me to write a post on this subject, I struggled to find the value I could add to the exploding conversation. I decided to do what people say I do best: Give you the bottom line and point the way towards practical applications. Let’s start with the basics.How ChatGPT-3 WorksChatGPT operates on an autoregressive model. It’s not as complicated as it may sound. A transformer is a program, model, or algorithm that converts code into something else—in this case, human language. The autoregressive part means the software uses prior events to predict the most likely future events, which is what your brain does all the time. It allows ChatGPT and similar applications to simulate a conversation between two humans. Learn the two keys to making an application like this work:1. Natural Language ProgrammingNatural Language Processing (NLP) allows the bot to recognize input through human speech or writing, eliminating the need for menus or code to engage with it. This same ability enables the bot to reply through text. Before the development of NLP, most chatbots were menu-driven, and the bot responded by selecting the best fit from a database of pre-written options. Conversational AI represents a new way for humans to use computers; it’s a user interface requiring less hardware and prior knowledge than any previous UIs.!CAUTION! Don’t confuse Natural Language Processing with the pseudo-science term using the same acronym. Neural Linguistic Programming is widely disproved and an invalid practice.2. Massive Training DatasetsLike any neural network (including your own), AI models must be effectively trained using a technique called deep learning. Quite frankly, the stunning capabilities of ChatGPT-3 are nothing new. As David Chalmers points out, there is no new technology here. There are many bots with similar abilities, and I described them in detail in 2020. How this bot is trained and how well this approach works is innovative. First, Open AI used the largest training dataset, combining a database containing much of the internet, a huge library of books, and Wikipedia.Then, after refining the bot in-house, Open AI released it free to the world. As we all started to play with it, we were training it further for them. Think of it as a crowd-sourced training effort. The sheer size of the training database resulted in what everyone always expected: Impressive results that can mimic human expression so well it is hard to tell a machine generated it. However, there continues to be an underlying problem: Chatbots are only as good as their training (like us humans). If you consider the source of GPT’s training, you can see why it sometimes makes mistakes.What Can We Do With ChatGPT-3?Because this product is still being explored, there may be new use cases by the time this post publishes. Below are a few applications that can save time and make your work as a learning professional more efficient and productive.Write ContentIf you ask the bot to write a paragraph introducing a subject, it comes back almost instantly with plausible, grammatically correct text. Because its training included unvalidated sources like the internet and Wikipedia, some of the content it produces must be corrected. Still, you can get excellent first drafts and refine your content from there.Summarize DocumentsLooking for a “Reader’s Digest” version of a book or article? Paste the text into a prompt to ChatGPT, and it returns a nice summary that should hit most of the highlights.Write and Troubleshoot Computer CodeBecause ChatGPT was built to analyze and produce output in human language, it is also great at computer languages. Programmers are using it to troubleshoot code. It can even generate new code if you ask it to develop a new capability.Translate DocumentsFar more effective than other translators out there, ChatGPT can take a website or article and translate it into another language.Write and Grade QuizzesYou can feed your content into GPT and ask it to generate a quiz with answers. Or, give it a general subject, like world history, and it will do the same. Feed your student’s quiz responses into GPT, and it will grade them (even essays!).Create GamesI asked GPT to create a game teaching sixth-grade students about the solar system, and the bot came back with a fun adventure through space.Improve Your WritingI didn’t do this for this article, but I could have asked GPT to review my draft and suggest improvements.Conduct ResearchI asked the bot to provide a list of current peer-reviewed studies on chatbot use in education using APA style and received a properly formatted list.Why Does ChatGPT Have Some People Worried?While most of the comments about GPT are positive, you will find a few educators deeply concerned about this new tool’s capabilities.Mimicking Human WritingTeachers are worried about GPT’s ability to write plausible text reading like a human created it and write in a known author’s style. A student could ask the bot to write a paper in the same style as one of their previous works, and it would be hard to tell the GPT paper wasn’t an original student effort. But, again, this mimicking ability is nothing new. AI has generated art in the style of great artists for years. ChatGPT applies the same predictive approach to language.ChatGPT Makes MistakesAnother concern is the bot’s ability to present false information so convincingly that some users forget to validate the response with research. For example, I asked the bot to calculate the mean of five numbers, and the result was interesting. I received a detailed explanation of the statistical definition of “mean” and how to calculate it, which I did NOT request. However, the bot added the five numbers incorrectly.To be fair, ChatGPT wasn’t built to perform math problems. But it didn’t tell me that or report the result might be incorrect. Like any willing assistant, it did its best to provide an answer. If I had been a student relying on GPT’s help, I would have been wrong. The reason for most of these mistakes is the same reason that it is such a stunning improvement in the chatbot experience: It’s primary sources of information are the internet and Wikipedia.I asked Vince Han, one of the pioneers in building chatbots for learning, to sum up the challenges presented by GPT’s new capabilities, and here’s his response:“One of the effects of ChatGPT & increased quality in Artificial Intelligence in general is shifting the skills users need. For example, most users have developed a skill for how to search which is knowing how to craft keywords and how to sift through search results. Now, people will need the skill of discernment, or learning how to decipher truth and views from a chatbot that provides what reads like a definitive answer. So, from a performance support perspective, yes, one can now get an immediate response to a question but how accurate and relevant that response is will be up to them to determine. “As educators, we must now confront a problem that has been boiling for decades—the decline in critical thinking across most domains, demographics, and workgroups. In my opinion, ChatGPT makes this gap more visible and increases the urgency for closing it quickly.Closing ThoughtsBecause Microsoft owns the license for the exclusive use of the model, we’ve all been working for them, revealing exciting use cases that may find their way into AI-enabled Microsoft products. For now, the platform consists of both free and professional versions. It’s an excellent way to begin learning about the promise of AI and our place alongside it. As machine learning expert Santiago Pino says, “AI will not replace you. A person using AI will.”Where Can You Learn More?There are so many articles about this subject right now that I’ve limited myself to a few favorites:Under the Hood of Chat GPT With Myra RoldanHow ChatGPT3 Impacts the Future of L&D in an AI WorldChatGPT Has Educators Scrambling to Keep UpWhat Is ChatGPT and How to Teach With ItWhat Is ChatGPT and Why Does It Matter?What Is ChatGPT? Is It the Beginning of the End?Ger’s Learning Notes #51 - ChatGPTFor more information and resources, Check out ATD's AI Resource page.

ChatGPT-3 Has Everyone Talking

L&D professionals spend much of their time developing formal learning programs. Yet, in today’s work environment where geographically dispersed workforces may limit in-person gatherings, many budgets are constrained, and the pace of business is increasing, you need to be intentional about fostering experiences and peer interactions to keep learning going. Communities of practice can help you do just that. In this issue of TD at Work, Maggie Romanovich highlights how CoPs work. Further, she: Reviews different peer learning methodsDiscusses methods of creating, maintaining, and encouraging CoPsOutlines best practices for successful CoPs.Tools & Resources included in this issue are a CoP worksheet, meeting discussion planning template, and results communication template. 

Expand Employee Learning With Communities of Practice

December 2022 - TD Magazine

Reduce the friction that is preventing learners from engaging with your learning.Businesses spend billions of dollars trying to understand consumer behavior, resulting in marketing strategies that aim to get consumers to buy their products. However, in those companies, many L&D functions are not leveraging the same insights and strategies to reach their learners. Treat your learning solutions like products, and minimize the friction between the solutions and learners. One way to do so is using BJ Fogg's behavior model to evaluate your learning initiatives.Identify the frictionThe Fogg behavior model looks at three conditions that must be present for someone to engage in a behavior: motivation, ability, and a prompt. When one of them is absent or diminished, so is the likelihood that the desired behavior will occur.For example, let's say you have a missed call on your cell phone. Why didn't you answer the phone? Maybe you heard it ring, looked down and saw who was calling, and decided you didn't want to talk to that person right now. You heard the ring—that was the prompt. Nothing was keeping your from answering the call (that is to say, you had the ability). You just didn't want to—you didn't have the motivation to engage in the behavior.Now let's say the phone rings, and you're expecting the call and want to take it. But you can't find your phone, or it's in another room and you can't get there in time. Again, you heard the ring (the prompt), and this time you wanted to take the call (motivation), but you weren't able to get to the phone in time (ability).Finally, let's say you're expecting the call, and your phone is right next to you. However, the ringer is set to silent. The phone rings, you want to answer and are able to do so, but you never hear the ring—you received no prompt to answer it.That's how Fogg's behavior model works. The presence or absence of any of the variables can contribute directly to the likelihood that you'll engage in a given behavior.What if we look at learner behavior through that lens? You've created a great learning initiative and spent a lot of time and money on the learning ecosystem. However, you don't seem to be getting the traction you're hoping for. Here are some questions to consider:Are learners aware of the learning solution? Are there prompts that alert learners to its availability?Are learners motivated? Do they want to engage in learning?Are they able to engage with the learning solution?Is there anything preventing them or making it more difficult for them to access it?If you have opportunities in any of those areas, then you have friction between learners and the learning solution. Friction, in this context, is the absence of prompts or is anything that impedes motivation or ability when it comes to a learning initiative.PromptsIf learners don't know about your learning initiative, it may as well not exist. These days, people are inundated with prompts—pop-ups on smartphones, ads on social media, digital billboards, and the like. How can we ensure that learners are prompted to our initiatives? The traditional methods are static and push prompts.A static prompt is one that is in a fixed physical location, such as a poster in a breakroom. It works fine when your audience tends to be isolated in one particular location. But if someone is not in that exact location, they won't see it.A push prompt is much more targeted. It entails identifying your audience members and sending a prompt to them, such as an email reminder that a course is available. However, you often can't customize that prompt by audience member—everyone gets the same message. And you can't time the prompt so that it reaches an individual right when they need it most. Thus, there are some limitations to what we have traditionally used as prompts in our learning ecosystems.This is an area we should pay close attention to because it has the potential to change rapidly. Imagine if we could activate a prompt for a specific learner at a specific time and in a specific location with a learning solution that is designed precisely for the activity that they are engaging in in that moment? It would be similar to finding an exclusive offer from Starbucks popping up on your phone whenever you're within 50 yards of a store.MotivationMotivation should be the easiest variable in this equation. If your initiative meets a learning need, learners should be inherently motivated to engage with it. However, that isn't always the case. In my experience, there are two major reasons. The first is that the solution does not meet a learning need (which is a subject for another article). The second, and more egregious, reason is that learning solutions get bogged down with demotivators that dilute learners' eagerness.Given the massive increase in video use for learning, there are some lessons to be learned from how people engage with YouTube and how we think about the videos and other learning initiatives we create.I often use the example of having a hole in my drywall and turning to YouTube to see how to fix it. Not surprisingly, there are numerous options from which to choose. We've all become accustomed to using filters to narrow down internet search results, although we often do our own visual filtering first. For example, I can take a quick look at a results list on YouTube for drywall repair and immediately rule out, or at least deprioritize, some of the videos. The first thing I'll look at is time. I expect that this should be a relatively simple task, so I'm not looking for a long video. The shorter the better.Likewise, learners have an expectation of how simple or complex a task is and at least some sense of how long it should take to learn. If they see that a video is 30 minutes long, and their perception of the task doesn't line up with that amount of time, you may lose them right away.I start playing a six-minute video from the list. After a lengthy introduction, a bio of the host, and several examples that don't apply to what I'm looking for, the host eventually starts the process of explaining how to execute the repair. It took over one minute to get there. That is an informal measurement I call "time to learning"—or how long it takes before I get to the actual learning. Time to learning is especially important when considering whether our goal is knowledge retention or knowledge acquisition. In other words, are learners in this for the long haul, or do they just need a quick one-off fix that they will rarely if ever need again?When looking at learning from a knowledge retention standpoint, time to learning is slightly less important. We know we're going to get deep, and participants learning about the instructor's credibility can be helpful to set the tone. But if we seek knowledge acquisition, or disposable knowledge, time to learning matters a lot—especially if there are several options from which to choose.For example, imagine I need to generate a report for my manager, who is out sick. I've never done the task before, and it's highly unlikely that I'll ever have to do it again. After some digging, I find a few videos that will help me figure out what to do. However, the videos don't cover only the report I need to generate; they cover all the reports in the entire program and explain where the data from those reports comes from and why they're important to the business.Are those bad things for me to know? Of course not. But they're not relevant to what I need to do right now. Getting what I need is like searching for that how-to needle in a haystack of video instruction. I may resort to other options, such as trial and error (risky) or asking a colleague (now two of us are wasting time trying to figure this out).Going back to the drywall video: I stuck with it for more than a minute only to see how long it would take for the purposes of this example. If I had a real need, I doubt I would have held out for more than 10–15 seconds.We can and should apply the same lessons to our video content libraries and consider where we are adding friction that we should remove. In most cases, those are demotivators that don't reflect the quality of our instructional design yet may deter learners from ever finding that out.AbilityAnother go-to source for information in people's nonwork lives is Google. Here's what happens if I search for "how to repair drywall"—let's count the clicks: I open the browser to Google and type the search term, then click Search. One click. What I see first are a few links to associated terms like "water damage" and "peeling" that enable me to refine my search. Below that is a frame from a video already queued to a specific timecode that is likely to address my need. Below that are links to a few more videos, all with descriptions and timecodes of key moments in the videos. Next up is the "People also ask" section, which displays several ways that others have posed their question that may be more in alignment with my need. And finally, I get a list of results that I would normally expect to see on Google. All from one click.Now think about your learners and, more importantly, your learning ecosystem. Most likely your version of Google is some sort of learning management system or learning experience platform. How many clicks does it take learners to get there? Once someone arrives at your version of Google, how many of the features described above does it have? How easy is it for learners to find what they need?Many of us are presumably still in a situation where learners must go through six, eight, or even 10-plus clicks to find what they need. Number of clicks and speed to solution are ways we can assess learners' ability to engage in the desired behavior.Nobody wants to click through a never-ending menu. Individuals have been conditioned that they shouldn't have to, and they are far more likely to give up somewhere along the way than they are to reach the end.Other ability barriers include bandwidth and hardware. Sometimes our instructional design comes with ability barriers. If you have motivated learners who know about your initiatives, make it as easy as possible for them to access and engage with learning.Enhance learning's impactThis article has numerous questions you can ask about any given learning solution. It's going to be difficult to get satisfactory answers to all of them. But what you can do is identify an area that you believe may be causing friction for your learners and is something that you can influence.In other words, we all want better bandwidth, but you probably have an extremely limited ability to influence that yourself. You may have a better chance implementing some prompts for learners that can move the needle. Or you may be able to reduce the number of clicks required to access content with some adjustments to your internal websites or portals. Start small by looking for areas to reduce friction with existing learning solutions, and then incorporate the ideas into the design phase of future ones.If you treat your learning program like a product and view your learning ecosystem as a mechanism for reaching your audience and providing that product, it may change your perspective.

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