Training Evaluation Models

🍱 How To Design Effective Dashboard UX (+ Figma Kits). With practical techniques to drive accurate decisions with the right data. 🤔 Business decisions need reliable insights to support them. ✅ Good dashboards deliver relevant and unbiased insights. ✅ They require clean, well-organized, well-formatted data. ✅ Often packed in a tight grid, with little whitespace (if any). 🚫 Scrolling is inefficient in dashboards: makes comparing hard. ✅ Start with the audience and decisions they need to make. ✅ Study where, when and how the dashboard will be used. ✅ Study what metrics/data would support user’s decisions. ✅ Explore how to aggregate, organize and filter this data. ✅ More data → more filters/views, less data → single values. 🚫 Simpler ≠ better: match user expertise when choosing charts. ✅ Prioritize metrics: key insights → top left, rest → bottom right. ✅ Then set layout density: open, table, grouped or schematic. ✅ Add customizable presets, layouts, views + guides, videos. ✅ Next, sketch dashboards on paper, get feedback, iterate. When designing dashboards, the most damaging thing we can do is to oversimplify a complex domain, or mislead the audience. Our data must be complete and unbiased, our insights accurate and up-to-date, and our UI must match users’ varying levels of data literacy. Dashboard value is measured by useful actions it prompts. So invest most of the design time scrutinizing metrics needed to drive relevant insights. Bring data owners and developers early in the process. You will need their support to find sources, but also clean, verify, aggregate, organize and filter data. Good questions to ask: 🧭 What decisions do you want to be more informed on? (Purpose) 😤 What’s the hardest thing about these decisions? (Frustrations) 📊 Describe how you are making these decisions? (Sources) 🗃️ What data helps you make these decisions? (Metrics) 🧠 How much detail is needed for each metric? (Data literacy) 🚀 How often will you be using this dashboard? (Value) 🎲 What constraints should we know about? (Risks) And, most importantly, test dashboards repeatedly with actual users. Choose key tasks and see how successful users are. It won’t be right at first, but once you get beyond 80% success rate, your users might never leave your dashboard again. ✤ Dashboard Patterns + Figma Kits: Data Dashboards UX: https://lnkd.in/eticxU-N 👍 dYdX: https://lnkd.in/eUBScaHp 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush: https://lnkd.in/dUgWtwnu 👍 UKO: https://lnkd.in/eNFv2p_a 👍 Wireframing Kit: https://lnkd.in/esqRdDyi 👍 [continues in comments ↓]

Kirkpatrick is often criticized. But rarely fully understood. Let's change this 👇 The model is simple. It describes four levels of evaluating learning impact: Level 1 — Reaction How participants experience the learning. Level 2 — Learning What knowledge and skills they acquire. Level 3 — Behavior How their on-the-job behavior changes. Level 4 — Results What organizational outcomes improve. That’s it. Four levels. And yet, it is frequently dismissed as outdated or simplistic. Why? Because we often treat it as a measurement checklist, instead of a design framework. Kirkpatrick is not just about evaluating training. It’s about thinking in cause-and-effect logic. Instead of asking, “Was the training good?” we should be asking a sequence of strategic questions. When designing: – What business outcome must change? – What behavior must shift to deliver that outcome? – What knowledge and skills are required? – What learning experience will enable mastery? And when evaluating: – How did participants evaluate the experience? – How well did they acquire the knowledge and skills? – How did behavior change at work? – What changed in the targeted business indicators? Planning must start from the top (Results). Measurement must begin from the bottom (Reaction). Think forward. Measure backward. Of course, the model has nuances - leading and lagging indicators, performance environment, manager accountability, isolation factors. But beneath the complexity lies a simple and powerful logic. The pyramid is not a hierarchy of surveys. It’s a chain of impact. That’s why I created this visual, to show the model not as theory, but as a practical thinking framework. How do you approach Kirkpatrick in your projects? #designforclarity #LearningAndDevelopment #InstructionalDesign #LearningStrategy #Kirkpatrick #LearningImpact #LXD #CorporateLearning

There are 1.1M credentials but our latest research finds that only 12% offer significant wage gain earners wouldn’t have otherwise gotten. The Burning Glass Institute is launching the Credential Value Index to show which ones work, evaluating the outcomes from 23,000 non-degree credentials from over 2,000 providers, including every certification in America—from Coursera digital marketing certificates to OSHA certifications. To see whether they actually deliver for workers, we analyzed how each changed the course of the careers of 7 million people who had earned them. While only 1 in 3 credentials meet a minimum threshold vs. counterfactual peers for either boosting wages, facilitating career changes, or moving people up within their field, we still found 8,000 credentials that really move the needle for workers—often in ways that are transformative. The top decile of credentials yields annual wage gains of nearly $5,000 vs. counterfactual peers, increases by 7x vs. bottom credentials the chances of switching jobs into an aligned career, and boosts by 17x the probability of an earner’s getting promoted within their current field. We found wide variances in outcomes even for the same credential across named providers–and across the portfolio of credential offerings of even high-reputation providers. That says that learners can’t just trust brands and they can’t just trust that a credential will help just because it’s in a high-paying field. Instead, they need real data to help them make informed decisions. Our goal in this work is practical: to put these evaluations in the hands of workers and learners, employers, education institutions & training providers, and policymakers. The Credential Value Index–available through our Navigator site available on https://lnkd.in/e_BTX9bs –makes all 23,000 evaluations accessible to the public, with easy-to-understand metrics of performance, comparisons with other credentials, and helpful context, like which roles earners find themselves working in, which employers they’re working for, and which skills they master along the way. Our research is summarized in an American Enterprise Institute working paper which I coauthored with AEI senior fellow Mark Schneider and Burning Glass Institute colleagues Shrinidhi Rao, Scott Spitze, and Debbie Wasden. You can find it on https://lnkd.in/ezynMA-v. I want to express my deep thanks to Ellie Bertani, Matt Zieger, and the GitLab Foundation for all they have done to support this initiative. I am grateful for your partnership. And a big thank you to Patti Constantakis and Sean Murphy at Walmart for the opportunity to test this framework in a real-world laboratory. Finally, the Credential Value Index builds on a close partnership with Jobs for the Future (JFF). Many thanks to Maria Flynn, Stephen Yadzinski, and their terrific team. #education #careers #highereducation #learning #skills

(PBIX Available) Why this simple chart works better than 90% of dashboards I see? Not because it's fancy. Because every single detail has a purpose. There are 6 intentional design choices. Let me break it down. 1. The insight is IN the title "3 out of 5 goals achieved this week". Before anyone looks at a single bar, they know: - What they're looking at - The success rate (3/5) - The time period (this week) 2. You can scan this in under 3 seconds and immediately know: "Good, good, bad, good." 3. Data Labels on the Bars: Every bar shows its exact value (100, 130, 150, etc.). Why label when you have an axis? Because: - Precision matters for action (teachers need exact scores) - It eliminates squinting at the axis - It makes the chart self-contained The axis provides scale. The labels provide exactness. Both serve a purpose. 4. The vertical dotted reference line at 115 isn't decoration- it's the goal threshold. Notice three things: - It's clearly labeled ("Goal: 115") - It's positioned where the eye naturally looks (right side) - It instantly divides performance into "met goal" vs "didn't meet goal" Without that line, you'd have to mentally calculate whether 130 is good. With it? Instant understanding. 5. Minimal Color Palette: No rainbow bars. No gradient fills. Just gray bars with color-coded outcomes. Everything is gray except: - Green checkmarks (success) - Red X's (failure) - The goal line (dark gray, neutral) 6. The Layout Hierarchy: - Student selector (who am I looking at?) - Weekly summary (how did they do?) - Daily breakdown (where specifically?) Each level answers a question. That's not accident- that's intentional information architecture. The Lesson: This chart works because someone asked: - What decision does this support? (Teacher identifying struggling days) - What's the 3-second takeaway?  (3/5 goals met) - What cognitive load can I remove? (Use Icons, labels, reference line) Most dashboards fail because they show data. Great dashboards support decisions. Download the PBIX here: https://lnkd.in/gEfYdQU9 Love this? #TheVisualBreakdown series drops every other day with a new chart deconstruction. Follow + hit the bell icon so you don't miss the next one.

The question of how to measure skills is one that educators have grappled with for years. Often, it’s meant relying on proxy metrics to define success. Hours spent learning. Qualifications gained. Important, but still improveable. Yes, completion rates matter. But they encourage you to limit who gets access to learning based on who is likely to complete, rather than who can benefit. And if you’re an employer waiting to the end of a programme to find out if you’ve got ROI, then you should demand better. The fundamental question for any leadership team: is this investment of time and money delivering a tangible return to the business? So in addition to that, at Multiverse, we’ve shifted the focus from time spent learning to value created. Our quarterly impact numbers are grounded in the actual work our apprentices do. Every project submitted on the Multiverse platform represents someone applying new skills to a real challenge in their organisation. That's what we measure, and that's what we report. In 2026 so far, our apprentices have reported monthly ROI of: - 325,000 hours of time saved  - £240 million in saved or avoided costs - £40 million in increased revenue In a world where every budget line is being scrutinised, “we think it's working” isn't good enough. This is the data I come back to when I want to know whether we're actually delivering on that. Real outcomes, from real apprentices, doing real work. And if you're a customer, we'll show you exactly what this looks like for your organisation. If you can't demonstrate the direct return on your talent development spend, you're essentially guessing. We think you deserve better than that. Ultimately, this is what true accountability looks like in skills development. We are proving that when you equip your workforce with the right technical tools, the result is a measurable and scalable surge in productivity.

Most teams polish visuals. Few design the thinking. That’s why dashboards often look fine — but explain nothing. Try this flow instead: 1) Structure metrics – map relationships, drivers, and shared definitions. 2) Define purpose – clarify what decisions it supports. 3) Build & format – choose charts that mirror logic. 4) Add context – if-then prompts, comparisons, slices, thresholds. 5) Maintain & evolve – track usage, prune, update. Pretty dashboards inform. Logical dashboards explain. Save this for your next redesign.

𝐓𝐡𝐞 𝐒𝐞𝐜𝐫𝐞𝐭 𝐭𝐨 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐓𝐡𝐚𝐭 𝐀𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐖𝐨𝐫𝐤𝐬? 𝐒𝐭𝐚𝐫𝐭 𝐚𝐭 𝐭𝐡𝐞 𝐄𝐧𝐝. 🏁 I used to think my job as an L&D professional started with a syllabus. I was wrong. Recently, I was tasked with building a learning solution for our Talent Acquisition (TA) team. The goal wasn’t just to "train recruiters"—it was to solve a business problem. Instead of looking at what they needed to know (Level 2), I started with what the business needed to achieve (Kirkpatrick Level 4). The "Reverse" Approach I didn’t start with slides. I started by analyzing Voice of the Customer (VOC) survey results, focusing on various metrics from both Hiring Managers and Candidates. Working Backwards: ✅ Level 4 (Results): I defined the business KPI. ✅ Level 3 (Behavior): Based on the VOC metrics, I identified the specific actions recruiters needed to change—specifically around "Precision Intake" and "Candidate Experience Management." ✅ Level 2 & 1 (Learning & Reaction): Only then did I design the actual training content that addressed those specific behavior gaps. The Result? The training didn't feel like a chore; it felt like a solution. Because I built it based on the actual metrics revealed in the VOC surveys, the TA team saw immediate value, and the business saw a measurable shift in hiring efficiency. The Lesson: If you want your learning solutions to be more than just "check-the-box" exercises, stop asking "What should we teach?" and start asking "What does the data say I need to solve?" How do you use VOC data to shape your enablement programs? 👇 #LearningAndDevelopment #InstructionalDesign #TalentAcquisition #KirkpatrickModel #Enablement #DataDrivenLD #BusinessImpact

Measuring the ROI of Virtual Behavioral Training Investing in behavioral training is not just about cost—it’s about measurable impact. The real question organizations must ask is: Does the training deliver a return on investment (ROI) in terms of improved retention, productivity, and leadership effectiveness? In our previous analysis, the total cost of a two-day virtual behavioral training for 60 mid-level managers was ₹19,63,000. Now, let’s calculate the potential ROI based on key business outcomes. 1. ROI Formula The standard formula for training ROI: ROI (%) = {Monetary Benefits} - {Training Cost}/ {Training Cost} * 100 2. Business Impact Assumptions To estimate the monetary benefits, we consider three key areas: A) Reduction in Attrition Average attrition for mid-level managers: 15% annually Assumed reduction in attrition due to training: 3 percentage points Average cost of replacing a manager (hiring, onboarding, productivity loss): ₹15,00,000 per manager Retention improvement: 60 managers × 3% = 1.8 managers saved {Cost Savings from Reduced Attrition} = 1.8*15,00,000 = ₹27,00,000 B) Increased Promotions & Internal Mobility Assumed impact: 5% increase in internal promotions Cost of hiring an external manager: ₹20,00,000 (recruitment, ramp-up, lost productivity) Savings from internal promotion: 60 × 5% = 3 managers promoted {Cost Savings from Internal Promotions} = 3* 20,00,000 = ₹60,00,000 C) Productivity Gains from Behavioral Improvement Behavioral training enhances leadership, communication, and decision-making, leading to improved productivity. Assumed productivity increase: 2% per manager Average annual contribution per manager (₹30L salary, assuming 3× salary as productivity value): ₹90,00,000 Total productivity gain per manager: ₹90,00,000 × 2% = ₹1,80,000 Total impact: ₹1,80,000 × 60 managers = ₹1,08,00,000 3. Total Monetary Benefit Benefit Area and Financial Impact Reduction in Attrition 27,00,000 Increased Internal Promotions 60,00,000 Productivity Gains 1,08,00,000 Total Benefits 1,95,00,000 4. ROI Calculation ROI (%) = {1,95,00,000 - 19,63,000}/{19,63,000} * 100 ROI = {1,75,37,000}/{19,63,000} * 100 ROI = 892% 5. Strategic Takeaways: Why This Matters High ROI Justifies Investment: An 892% ROI confirms that investing in behavioral training yields substantial business value. Retention and Internal Mobility Drive Cost Savings: Avoiding attrition and promoting from within reduces hiring costs significantly. Productivity Gains Create Long-Term Impact: Even small behavioral shifts in leadership and decision-making lead to tangible business outcomes. By linking training costs to measurable business benefits, organizations can move beyond cost discussions to strategic impact measurement—ensuring learning investments drive organizational growth. Would love to hear from others.

A problem with the Kirkpatrick taxonomy (not a model, not a theory) of evaluating instruction is that by its very design it is evaluation by autopsy: We may know a program didn't work, but not what went wrong or how to fix it. Practitioners looking for other ideas might want to take a look at Robert Brinkerhoff, who in eyeing the idea of training as a process rather than an event said: "Evaluating a training program is like evaluating the wedding instead of the marriage." His success case method is a wonderful substitute or, if you must, supplement to, Kirkpatrick. And consider, too, work from Daniel Stufflebeam's CIPP model, that looks at an entire program from context to inputs to organizational support to outcomes and on to transferability. As a practitioner are you trying to prove results, or drive improvement? More: https://lnkd.in/eFWkR-5J

📌 Most Dashboards Fail Because of Bad UX Here’s the hard truth: You can have the cleanest data and the most advanced models… But if your dashboard is confusing, cluttered, or hard to navigate? Nobody will use it. BI isn’t just about data. It’s about experience. Dashboards are in fact UX products and should be treated that way. Great dashboards don’t just “show data.” They guide attention. Simplify decisions. Reduce friction. And just like any great product, they follow strong UX principles: → Clear layout → Logical flow → Minimal cognitive load → Built for the user, not the developer Let’s break down the 3 dashboard principles that make this possible 👇 1️⃣ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐖𝐢𝐭𝐡 𝐭𝐡𝐞 𝐄𝐧𝐝 𝐔𝐬𝐞𝐫 𝐢𝐧 𝐌𝐢𝐧𝐝 This is where most dashboards go wrong. They’re built from a technical perspective and not a business one. Before touching a single chart, ask: → Who is this dashboard for? → What do they care about? → What action do they need to take from it? → What single question should this dashboard answer? If a dashboard tries to do everything for everyone, it ends up doing nothing for anyone. Treat your dashboard like a product. Build it around one user persona and one decision-making flow. 2️⃣ 𝐆𝐮𝐢𝐝𝐞 𝐭𝐡𝐞 𝐄𝐲𝐞 𝐰𝐢𝐭𝐡 𝐚 𝐂𝐥𝐞𝐚𝐫 𝐋𝐚𝐲𝐨𝐮𝐭 A great dashboard feels effortless to use. You don’t need to explain how to read it because it guides the user by design. Here’s how to do it: 1) Follow a natural reading pattern (top-left to bottom-right) 2) Use consistent spacing, alignment, and visual hierarchy 3) Group related charts and KPIs together 4) Avoid visual noise (limit to 5–7 key visuals per view) Think of your dashboard like a story It should unfold logically and lead the user to an insight without them having to look for it. 3️⃣ 𝐔𝐬𝐞 𝐭𝐡𝐞 𝐑𝐢𝐠𝐡𝐭 𝐕𝐢𝐬𝐮𝐚𝐥 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐉𝐨𝐛 Just because you can use a radar chart or sunburst doesn't mean you should. The best dashboards use simple, familiar visuals that communicate clearly. Here’s a cheat sheet I use: ⤷ To show progress or results → Use Scorecards or KPIs ⤷ To show trends over time → Line Charts or Area Charts ⤷ To compare parts of a whole → Pie Charts or Bar Charts ⤷ To analyze distributions → Histograms or Bell Curves ⤷ To show multivariate complexity → Heatmaps, Bubble Charts, or Pivot Tables Here what you need to remember is prioritizing clarity over creativity. Your dashboard isn’t a dribble a piece of art. It’s a decision tool. The bottom line is: Dashboards aren’t “data displays.” They’re interfaces for decision-making. And just like a product interface, design is everything. ☑ Good UX = Faster insights ☑ Good flow = Higher adoption ☑ Good visuals = Better decisions Build with purpose. Structure with clarity. Design for people. That’s how Business Intelligence becomes actual business impact. #DataStrategy #BusinessIntelligence #DataAnalytics