Evaluation of Training Materials

Companies spend millions on sales training. But less than 1 in 10 dollars goes to knowing if it worked. In addition, nearly 1 in 3 companies run zero formal evaluation at all. That's what the research says – and it reflects what many of us have felt in the room: ✅ We ran the training. ❓But did it actually work? As enablement professionals, we’re often caught between anecdotes and dashboards. Between sales spikes that may or may not be linked to our efforts and gut instincts that can’t hold up in a boardroom. We need to move from guesswork to genuine insight. That’s why I wrote a deep-dive on sales training evaluation: what the research says, and which models actually work in practice. --- In my new guide, I break down the five most effective models for evaluating training impact: 🔹 Kirkpatrick Model – the classic 4-level framework 🔹 Phillips ROI Model – adds ROI calculation to Kirkpatrick 🔹 New World Kirkpatrick – repositions ROI as Return on Expectations 🔹 Brinkerhoff’s Success Case Method – focuses on extremes to find truth 🔹 LTEM (Learning Transfer Evaluation Model) – the most diagnostic model out there And, I cover five honourable mentions worth exploring: 🔸 CIPP Model – evaluates context, inputs, process, and product 🔸 COM-B Model – breaks down behaviour change 🔸 6Ds – emphasises reinforcement beyond the classroom 🔸 Bersin’s Impact Measurement Framework – business-linked metrics 🔸 Anderson Model – ties training to strategic priorities Whether you're launching a new programme or defending your budget, this will give you a sharper lens and a stronger voice. --- 📌 Want access to the high-res one-pager + full guide? Comment “sales training evaluation” and I’ll DM it to you. Let’s raise the bar for what enablement can prove and improve. ✌️ #sales #salesenablement #salestraining

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 And yet, it is frequently dismissed as outdated or simplistic.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

Monitoring, evaluation, accountability and learning are essential functions for ensuring that projects remain results focused, responsive to affected people, and continuously improved through evidence and reflection. In this document, the MEAL training content is presented as a practical pathway that clarifies how teams can translate core concepts into concrete routines, tools and processes for measurement, feedback, learning and use of findings. This training package brings together the main practical components of MEAL implementation: – Core definitions of monitoring and evaluation and how they differ – Accountability and learning concepts and how they fit within MEAL – The MEAL cycle and its main phases from design to use of data – Logic models including theory of change results framework and logical framework – MEAL planning and integration into project plans calendars and budgets – MEAL plan or performance management plan and its key contents – Indicator tracking tools including performance tracking tables – Feedback and response mechanisms and how response pathways are organised – Learning planning and how learning is captured and used – Communication of MEAL information based on stakeholder needs – Evaluation planning including questions timing responsibilities and budget – Terms of reference for evaluations and required operational details – Ethical standards including consent privacy confidentiality and safety – Participation and critical thinking as cross cutting requirements in MEAL practice The document provides a structured and applied overview of how MEAL is operationalised throughout a project cycle, from clarifying results and indicators to organising data collection, analysis and reporting routines. It explains how planning tools support coherence by linking what must be measured with who does what, when, and with which resources, while ensuring that feedback mechanisms and learning processes are intentionally built into implementation. By emphasising ethics, participation and disciplined use of evidence, the training supports teams to strengthen accountability, improve programme quality and make better decisions based on reliable information.

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

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?

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.

An Open Conversation About QA Training Standards: A Call for Industry Reflection Over the past six months, I've had the privilege of reviewing over 300-400 applications for various QA positions. Whilst this experience has been incredibly insightful, it has also revealed a concerning pattern that I believe warrants an honest, industry-wide discussion. The Current Challenge After completing our rigorous interview and evaluation process, I've observed that approximately 90% of candidates who graduated from certain training centres struggle significantly when faced with practical, real-world scenarios. Whilst their CVs and portfolios appear impressive. Complete with extensive GitHub repositories and numerous projects. The reality during technical assessments tells a different story. What I've Noticed Many candidates demonstrate proficiency when responding to conventional interview questions, which suggests thorough preparation. However, when we introduce slight variations or request solutions to real scenarios, there's often a noticeable gap between their prepared responses and practical application abilities. Additionally, I've found that fundamental skills, such as proper Git usage, core programming concepts, essential technical knowledge and essential tool usage knowledge, are frequently underdeveloped, despite what their portfolios might suggest. A Broader Concern I want to be clear: my intention is not to criticise individuals seeking to better their careers. Rather, I'm concerned about a systemic issue where training centres may be inadvertently prioritising presentation over substance. When learners are provided with pre-built projects (I can show you many GitHub repositories that are the same) and coached exclusively on memorising answers, we're doing them a profound disservice. Moving Forward Together To training centres: I urge you to focus on building genuine competency. Help your trainees understand concepts deeply, encourage authentic project development, and prepare them for real problem-solving rather than rehearsed responses. To aspiring QA professionals: Invest in truly understanding your craft. Authentic skills will always outshine polished presentations in the long run. Learn to code. It is very important. To fellow recruiters: Consider implementing practical assessments that go beyond standard questions to evaluate true capability. Also assess their programming competency in a live interview. Our industry thrives when we nurture genuinely skilled professionals. Let's work together to raise standards and create meaningful learning experiences that serve everyone better. What are your thoughts on this matter? How can we collectively improve technical training standards? #QualityAssurance #TechRecruitment #TrainingStandards #ProfessionalDevelopment #TechIndustry

When creating learning materials for your training, instead of starting with content which seems the most common approach, create resources & guidance learners can use while working—job aids, quick reference guides, decision trees, checklists, templates. Things they can pull up in the moment they need help. And the truth is a perfectly polished resource that nobody opens won't improve anyone's performance. What matters is whether someone can grab your resource in the middle of their workday and get the help they need immediately. Here's how to make that happen: • Design for use: Think about the actual moment someone will reach for this resource. Are they stressed? In a hurry? Confused? Design for that reality, not for perfection. Keep it short, one page max.  • Make things as simple as possible. Strip away everything that doesn't directly help someone complete their task. • Make it practical and talk to the context, not the content. Focus on their specific situation and what they're trying to accomplish, not on teaching theory or background information. For example, if healthcare workers need to communicate with anxious patients, provide examples of things to say in specific situations—not rigid call center scripts, but natural language examples: "When a patient asks about wait times, you might say..." or "If a patient seems nervous about a procedure, try..." Give them authentic examples they can adapt to their situation. • Organize by task, not topic. If it's about learning a tool, show them how to use it—skip the chapters on its history or technical specifications. People want to know "how do I do X?" not "what is X?" • Be visual. Use diagrams, screenshots, or graphics whenever they're clearer than paragraphs of text. A good visual beats a wall of words every time. • Make it accessible and easy to find. Even the best resource won't help if people can't find it in the moment they need it. Make it accessible where they're already working—embed it in their systems, pin it to frequently used platforms, or keep it in the first place they'll search. #PerformanceFirst #LearningThatWorks #PerformanceSupport #LearningAndDevelopment

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

Are you evaluating the true impact of your learning programs? 🤔 Whether you’re new to Learning & Development or a seasoned pro, mastering evaluation models is essential for assessing the success of your L&D initiatives. Kaufman’s Five Levels of Evaluation is a powerful tool that builds on Kirkpatrick’s model but goes deeper, focusing not just on individual learners but also on organizational and societal impact. 📈 Here is a snapshot of what each level explores: 1️⃣ Input: Are we using learning resources wisely? 2️⃣ Process: Was the training delivered effectively? 3️⃣ Acquisition: Did learners absorb the right knowledge? 4️⃣ Application: Are those skills being used on the job? 5️⃣ Societal Impact: How does the training benefit the organization and even society as a whole? Unlike Kirkpatrick’s model, which ends at Results, Kaufman adds a broader societal level, pushing L&D to think about the bigger picture. 💭🌍 Understanding both models allows you to evaluate not only the learning itself but also how it contributes to wider success. How do YOU measure the effectiveness of your training? What are some of the difficulties you have with learning evaluation? Share your thoughts below! 👇
 #LearningAndDevelopment #KaufmanEvaluation #TheLnDAcademy #TrainingEvaluation