Training Feedback Mechanisms

A lot of trainers run a great exercise… and then waste the learning moment that follows. The debrief is where performance improvement actually happens. But too often we get generic reflections: “Yeah, that was good” or “Interesting exercise.” None of that helps anyone perform better back on the job. A simple tool I use in almost every session, face-to-face or virtual, is the Feedback Grid. It structures the debrief so delegates can evaluate the outcomes of an exercise, not just how it felt. Here’s exactly how to use it straight after an activity: 1. Set up the 4 quadrants before the exercise Worked Well (+) Needs Change (Δ) Questions (?) New Ideas (💡) By having it visible from the start, delegates know there will be a structured review, not a free-for-all discussion. 2. Immediately after the exercise, ask individuals to add notes Give everyone 2–3 minutes to jot down their thoughts in each category. This stops dominant voices from setting the tone and gives you a broader view of what actually happened. In a virtual room, this is as simple as shared online sticky notes. Face-to-face, use flipcharts or a whiteboard. 3. Analyse the activity, not the activity’s “vibe” This is where most trainers go wrong. We’re not asking whether they “liked” the exercise. We’re capturing what the exercise showed about their skills, behaviours, and decision-making. Examples might include: Worked Well: “Clearer roles helped us move faster.” Needs Change: “We didn’t communicate early enough.” Questions: “How do we apply this under time pressure?” New Ideas: “Create a decision checklist before starting.” These are performance insights, not opinions. 4. Turn the grid into next-step actions Once patterns emerge, summarise 2–3 practical actions they can take into the workplace. This is where the ROI sits. The exercise becomes a rehearsal, and the grid becomes the bridge to real work. 5. Keep the pace tight A structured debrief shouldn’t drag. Five to eight minutes is enough to turn a simple exercise into a meaningful learning moment. When used properly, the Feedback Grid transforms exercises from “fun activities” into performance diagnostics. That’s the whole point of training, to improve what people do, not what they think about the training. What do you use for this? -------------------- Follow me at Sean McPheat for more L&D content and then hit the 🔔 button to stay updated on my future posts. ♻️ Save for later and repost to help others. 📄 Download a high-res PDF of this & 250 other infographics at: https://lnkd.in/eWPjAjV7

Most LLMs sound smart. Very few can reason through a problem. Pretraining gives them language. But logical thinking, alignment, and decision-making come after. If you're working on models that need to follow steps, justify answers, or act in real environments, fine-tuning is not enough. The challenge? The best techniques for post-training are buried across papers, half-documented repos, and scattered benchmarks. To help with that, the Awesome-LLM-Post-training GitHub repo brings it all together. Inside, you'll find: ✅ Survey papers on reasoning, RL, and LLM alignment ✅ Techniques like RLHF & Reward modeling ✅ Planning methods like Tree of Thoughts & Self-play ✅ Libraries including trl, veRL, and LLaMA-Factory ✅ Benchmarks for math, agents, and code generation This isn't just a reading list. It's a practical guide. Here’s how to use it based on your role: 🔬 For researchers 🔸 Track the latest work in LLM reasoning and alignment 🔸 Speed up your literature reviews by topic 🔸 Select benchmarks for evaluating new models 🔸 Identify gaps in the current research landscape 💻 For developers and practitioners 🔸 Apply techniques like RLHF or Reward Models 🔸 Use libraries like trl & LLaMA-Factory to build faster 🔸 Test your models with real-world benchmarks 🔸 Contribute code or research to improve the repo 📘 For students and newcomers 🔸 Start with survey papers for the big picture 🔸 Use each section as a guided learning path 🔸 Connect theory to practice with code examples 🔸 Explore applications & benchmarks for project ideas If you're working on post-training or want your models to do more than predict the next word, this is the place to start. ♻️ Repost this to save someone 100+ hours of research ➕ Follow me, Sairam, for more hands-on AI resources

I lead the product team for model customization at AWS, and the question I get most often is some version of: "we know post-training matters, but where do we actually start?" So I made the walkthrough I wish existed. It's a YouTube series on post-training open-weight LLMs (SFT, DPO, RLVR, RLAIF, multi-turn RL), taught the way I'd explain it to a colleague, with the theory and the hands-on code side by side. Everything runs on SageMaker AI, and I use a fictional fintech support agent as the running example so the numbers are real, not toy. The first three are live:     → When to Post-Train an LLM (and When Not To): because sometimes the answer is you don't need to.  → How Fine-Tuning Actually Works: Reading the Loss Curve: what the model is really doing on each step, and how to read a training run instead of just hoping it goes down.  → Fine-Tune an LLM on SageMaker AI: SFT End to End: a full run, eval to training to eval again, with the real scorecard. I show what the model gets wrong, and how you'd spot a training run going sideways. More coming, DPO is next. If you're working through this yourself, I'd like to hear what's tripping you up. That feedback shapes both the videos and the product. https://lnkd.in/gchGjthv     #MachineLearning #LLMs #finetuning

Corporate training often feels like throwing seeds onto concrete. We mandate attendance, deliver information in a single format, and expect immediate growth. For neurodivergent professionals, standardized assessments rarely measure actual competency. They simply measure the ability to take a standardized test. Dr. Kirkpatrick developed a renowned model to evaluate training across four sequential levels: Reaction, Learning, Behavior, and Results. It is a brilliant clinical framework. But if we want it to work for a neurodiverse ecosystem, we must change how we measure growth at every level. Here are 10 neuro-inclusive ways to assess learning, mapped to the Kirkpatrick Model: 1/ Pre-Learning Reality: Live information dumps overwhelm working memory. Practice: Send reading materials 48 hours early so participants can process at their own pace. 2/ Advance Inquiry Reality: Spontaneous Q&A triggers anxiety and limits participation. Practice: Allow the team to submit questions anonymously before the live session. 3/ Regulation Pauses (Level 1) Reality: Long blocks of forced attention drain executive function. Practice: Mandate five minute biological processing breaks every 45 minutes to stretch, stim, or regulate. 4/ Multi Modal Anchors (Level 2) Reality: Auditory lectures fail visual and kinesthetic learners. Practice: Provide options. Let them watch a live demonstration, read a case study, or review a video. 5/ Structured Breakouts (Level 2) Reality: Unstructured group work creates heavy social ambiguity. Practice: Provide a strict, written rubric for peer roleplay so expectations are perfectly clear. 6/ Collaborative Polling (Level 2) Reality: Timed, silent quizzes spike cortisol and block recall. Practice: Use live polls or collaborative quizzes where small groups talk out answers before submitting. 7/ Flexible Demonstration (Level 2) Reality: Written tests do not equal practical mastery. Practice: Let employees choose to prove competency via a written summary, audio reflection, or practical demonstration. 8/ Implementation Maps (Level 3) Reality: Information without a plan quickly withers. Practice: Give participants time at the end to write down exactly how they plan to apply the new skill. 9/ Supervisor Support (Level 3) Reality: Managers often do not know how to support new habits. Practice: Provide supervisors with exact questions to check on the new skill without micromanaging. 10/ Reverse Cultivation (Level 4) Reality: We often train for skills the current environment does not support. Practice: Define the final organizational result first. Work backward to ensure the ecosystem allows that new behavior to survive. We must stop blaming the individual when the system is too rigid. By diversifying how we assess learning, we give every mind a fair chance to grow. How does your organization currently measure if a training was successful?

This paper is a helpful reminder that occupational performance in tactical populations is not predicted by one single fitness quality or one universal test. Orr and colleagues found that aerobic capacity, muscular strength, muscular endurance, power, anaerobic capacity, and agility were all associated with occupational task performance across military, law enforcement, and firefighter populations. Their conclusion was not that one assessment should replace another, but that a wide range of fitness assessments may be needed because the physical demands of tactical work are broad and highly specific to the environment and role. That finding matters because job performance is not just “fitness” in a general sense. A firefighter, police officer, soldier, or other tactical professional may need to run, carry, drag, lift, climb, change direction, sustain work, and repeat high intensity efforts under load. The paper highlights that occupationally specific tasks, such as casualty drags, loaded carries, stair climbs, manual handling tasks, and job simulation tests, can provide valuable insight into whether fitness qualities are transferring toward real occupational demands. This is one reason I am an advocate for a tiered approach to assessment. We need to isolate specific physical capacities first, such as strength, aerobic capacity, upper body endurance, power, and anaerobic capacity, so we understand what qualities are present, deficient, or responding to training. Then we need task specific assessments that move closer to the demands of the sport, job, or operational environment. Finally, when possible, we should monitor performance in the job itself. All three tiers matter. If we skip the first tier and only look at simulations or job outcomes, we may see the result, but we lose the ability to understand the mechanism, training application, and training efficacy.

5,800 course completions in 30 days 🥳 Amazing! But... What does that even mean? Did anyone actually learn anything? As an instructional designer, part of your role SHOULD be measuring impact. Did the learning solution you built matter? Did it help someone do their job better, quicker, with more efficiency, empathy, and enthusiasm? In this L&D world, there's endless talk about measuring success. Some say it's impossible... It's not. Enter the Impact Quadrant. With measureable data + time, you CAN track the success of your initiatives. But you've got to have a process in place to do it. Here are some ideas: 1. Quick Wins (Short-Term + Quantitative) → “Immediate Data Wins” How to track: ➡️ Course completion rates ➡️ Pre/post-test scores ➡️ Training attendance records ➡️ Immediate survey ratings (e.g., “Was this training helpful?”) 📣 Why it matters: Provides fast, measurable proof that the initiative is working. 2. Big Wins (Long-Term + Quantitative) → “Sustained Success” How to track: ➡️ Retention rates of trained employees via follow-up knowledge checks ➡️ Compliance scores over time ➡️ Reduction in errors/incidents ➡️ Job performance metrics (e.g., productivity increase, customer satisfaction) 📣 Why it matters: Demonstrates lasting impact with hard data. 3. Early Signals (Short-Term + Qualitative) → “Small Signs of Change” How to track: ➡️ Learner feedback (open-ended survey responses) ➡️ Documented manager observations ➡️ Engagement levels in discussions or forums ➡️ Behavioral changes noticed soon after training 📣 Why it matters: Captures immediate, anecdotal evidence of success. 4. Cultural Shift (Long-Term + Qualitative) → “Lasting Change” Tracking Methods: ➡️ Long-term learner sentiment surveys ➡️ Leadership feedback on workplace culture shifts ➡️ Self-reported confidence and behavior changes ➡️ Adoption of continuous learning mindset (e.g., employees seeking more training) 📣 Why it matters: Proves deep, lasting change that numbers alone can’t capture. If you’re only tracking one type of impact, you’re leaving insights—and results—on the table. The best instructional design hits all four quadrants: quick wins, sustained success, early signals, and lasting change. Which ones are you measuring? #PerformanceImprovement #InstructionalDesign #Data #Science #DataScience #LearningandDevelopment

Stop measuring attendance and start measuring impact. We have analyzed, designed, developed, and implemented. Now comes the moment of truth: Evaluation. In the traditional ADDIE model, this phase is often reduced to "smile sheets." We ask learners if they liked the course, if the room was cold, or if the instructor was engaging. We gather data that tells us how they felt, but rarely how they will perform. In ADDIE 2.0, AI turns Evaluation into business intelligence. We no longer have to rely on manual surveys or disjointed spreadsheets. AI tools can ingest vast amounts of unstructured data—from chat logs to open-text survey responses—and identify patterns that a human eye might miss. It bridges the gap between "learning" and "doing." Here are three ways to revolutionize your Evaluation phase today: ✅ Ditch the 1-5 scale for sentiment analysis. Stop looking at average scores. Take all your open-text feedback and run it through a Large Language Model (LLM). Ask it to identify the top three friction points and the top three "aha!" moments. You will get a nuanced report on learner sentiment that goes far beyond a simple satisfaction score. ✅ Correlate learning with performance. This used to require a data scientist. Now you can upload anonymized training completion data alongside sales or productivity metrics into a tool like ChatGPT’s Data Analyst or Microsoft Copilot. Ask it to find correlations. Did the reps who completed the negotiation module actually close more deals next quarter? AI can help you prove that link. ✅ Automate the "Forgetting Curve" check. Evaluation should not end when the course closes. Configure an AI agent or chatbot to message learners 30 days later. Have it ask a simple question: "How have you used the negotiation framework this month?" The AI can collect and categorize these real-world stories, giving you qualitative evidence of behavior change. Why does this matter to the C-Suite? ROI. When you can show that a learning intervention directly correlates with a 15% increase in efficiency or revenue, L&D stops being a cost center and starts being a strategic partner. AI gives you the evidence you need to defend your budget and prove your value. Series Wrap-Up: We have walked through the entire ADDIE model. Analysis: Using data to find the real gaps. Design: Blueprinting faster with AI assistants. Development: Generating assets at scale. Implementation: Personalizing the delivery. Evaluation: Measuring real-world impact. The ADDIE model is not dead. It just got a massive upgrade. I want to hear from you: Which phase of the new ADDIE do you think offers the biggest opportunity for your team? Let’s discuss in the comments. -------- Resources: Kirkpatrick Model vs. Phillips ROI Methodology in the Age of AI, "The AI-Enabled Learning Leader," xAPI and Learning Analytics. -------- #ADDIE #LearningAndDevelopment #AIinLearning #PerformanceSupport #InstructionalDesign

Following up on my post on training transfer, here's the breakdown of the four critical factors you need to consider:  1. Analyze the Work Environment: Before training begins, identify barriers to applying new skills. Are there policies that block implementation? Will supervisors actively support transfer of learning? What about resource availability? I've seen cases where existing approval processes made it impossible for trained staff to use new skills. Also consider workplace stressors—being understaffed, hierarchy issues, or team dynamics can prevent even well-trained employees from performing. If decision-making under stress is critical, train under realistic pressure conditions. 2. Understand Your Learners: Develop diverse personas based on experience levels, prior knowledge, and cultural backgrounds. A novice needs a completely different pathway than an expert. If behavior change efforts have failed before, dig into why—more training may not be the answer. Use pre-tests, learner interviews, or interviews with SMEs in direct contact with learners in case you can't reach the learners to uncover the real barriers. 3. Design Skills-Based Experiences: Tie learning directly to real tasks using frameworks like Cathy Moore's Action Mapping and Richard Clark's Cognitive Task Analysis. Go beyond observable actions to uncover invisible cognitive processes and decision-making strategies. Create scenario-based assessments, demonstrations, or role-plays that test application, not just recall. Use spaced repetition for mastery and provide job aids like task-centric checklists for post-training support. 4. Measure Learning Effectiveness and Transfer: Start your design with evaluation metrics, but don't stop at course completion. Follow up 2-3 months after training to measure if learning was actually applied and identify any barriers preventing transfer. Interview with SMEs in direct contact with learners in case you can't reach the learners. #trainingeffectiveness #trainingevaluation #trainingdesign #trainingtransfer #learninganddevelopment

Most training evaluations ask the wrong question. “Did you like the course?” But instructional designers care about something else. Did job performance improve? Because the goal of training isn’t satisfaction. It’s performance. Good evaluation looks for evidence of change in the workplace. Here’s how designers measure it. First, they track performance metrics. Did key numbers improve after training? Sales conversions. Error rates. Customer satisfaction. Second, they measure skills with assessments. Not memorization. Real decisions. Simulations. Scenario responses. Third, they look for behavior change. Are people actually using the new skills? Following the new process? Adopting the new tools? Finally, they examine business outcomes. Higher productivity. Fewer mistakes. Better service. 𝐁𝐞𝐜𝐚𝐮𝐬𝐞 𝐠𝐨𝐨𝐝 𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐝𝐨𝐞𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐭𝐞𝐚𝐜𝐡. 𝐈𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐢𝐧𝐬𝐢𝐝𝐞 𝐭𝐡𝐞 𝐨𝐫𝐠𝐚𝐧𝐢𝐳𝐚𝐭𝐢𝐨𝐧.

🤔 How Do You Actually Measure Learning That Matters? After analyzing hundreds of evaluation approaches through the Learnexus network of L&D experts, here's what actually works (and what just creates busywork). The Uncomfortable Truth: "Most training evaluations just measure completion, not competence," shares an L&D Director who transformed their measurement approach. Here's what actually shows impact: The Scenario-Based Framework "We stopped asking multiple choice questions and started presenting real situations," notes a Senior ID whose retention rates increased 60%. What Actually Works: → Decision-based assessments → Real-world application tasks → Progressive challenge levels → Performance simulations The Three-Point Check Strategy: "We measure three things: knowledge, application, and business impact." The Winning Formula: - Immediate comprehension - 30-day application check - 90-day impact review - Manager feedback loop The Behavior Change Tracker: "Traditional assessments told us what people knew. Our new approach shows us what they do differently." Key Components: → Pre/post behavior observations → Action learning projects → Peer feedback mechanisms → Performance analytics 🎯 Game-Changing Metrics: "Instead of training scores, we now track: - Problem-solving success rates - Reduced error rates - Time to competency - Support ticket reduction" From our conversations with thousands of L&D professionals, we've learned that meaningful evaluation isn't about perfect scores - it's about practical application. Practical Implementation: - Build real-world scenarios - Track behavioral changes - Measure business impact - Create feedback loops Expert Insight: "One client saved $700,000 annually in support costs because we measured the right things and could show exactly where training needed adjustment." #InstructionalDesign #CorporateTraining #LearningAndDevelopment #eLearning #LXDesign #TrainingDevelopment #LearningStrategy