Measuring Employee Training Effectiveness

Interesting paper to stick your teeth into if you're an L&D, concerned with learning transfer. 💡 The authors reviewed 71 studies to build the so-called COMPASS model, which combines two well-established models: The COM-B model (Capability, Opportunity, Motivation = Behaviour) And Baldwin & Ford's training transfer framework. In a nutshell: The COMPASS model focuses on three key components that influence soft skills transfer: 1️⃣ Trainee characteristics (e.g. prior experience, motivation, and self-efficacy) 2️⃣ Training features (e.g. content relevance, design, delivery, and support) 3️⃣ Work environment (e.g. manager support, team norms, and org culture) The research identified 69 factors influencing behaviour transfer. 🟢 The ones with favourable evidence of impact: On-the-job training Relevance of training Time-spaced training Micro-learning Pre-training materials Training assessment Trainer effectiveness/credibility Multiple instructional methods Use of technology Workshops Goal-setting Mentoring/coaching/supervision 🔵 The ones with emerging evidence of impact: Community of practice Personalization Variability and increasing complexity Facilitation or assistance Feedback Group assignment Observation of others Reflection Role play Lots to chew on, and Sejaal Tilwani made a little overview, including some practice recommendations, in the latest Learning Brief Newsletter: https://lnkd.in/eMrniWs6

Pre-assessment methods help trainers understand trainees' baseline knowledge and skills before starting a training program. Here are various types of pre-assessment methods along with examples for each: 1. Quizzes and Tests Multiple-Choice Questions (MCQs): Assess specific knowledge areas with questions offering several possible answers. Example: "Which of the following is a primary key feature in relational databases?" True/False Questions: Quickly gauge understanding of basic concepts. Example: "True or False: The Earth orbits around the Sun." Short Answer Questions: Require brief, written responses to test knowledge recall. Example: "What is the capital of France?" Essay Questions: Assess deeper understanding and the ability to articulate thoughts. Example: "Explain the impact of globalization on local economies." 2. Surveys and Questionnaires Likert Scale Surveys: Measure attitudes or perceptions with scales (e.g., 1-5, Example: "Rate your confidence in using Microsoft Excel: 1 (Not confident) to 5 (Very confident)." Self-Assessment Surveys: Trainees evaluate their own skills and knowledge. Example: "How would you rate your proficiency in programming languages? (Beginner, Intermediate, Advanced)" Open-Ended Questions: Gain insights into trainees’ thoughts and experiences. Example: "What are your main goals for this training program?" 3. Practical Tasks and Simulations Hands-On Exercises: Assign tasks that mimic real-world scenarios relevant to the training. Example: "Create a simple budget spreadsheet using Microsoft Excel." Role-Playing Scenarios: Simulate situations trainees might encounter. Example: "Role-play a customer service interaction to resolve a complaint." Problem-Solving Activities: Assess critical thinking and problem-solving skills. Example: "Solve this case study on supply chain management challenges." 4. Interviews and Discussions Structured Interviews: Ask standardized questions to each trainee to compare responses. Example: "Describe a time when you successfully managed a team project." Unstructured Interviews: Allow for open-ended conversation to explore trainee experiences. Example: "Tell me about your experience with project management." Focus Group Discussions: Facilitate group discussions to gather diverse perspectives. Example: "Discuss as a group the challenges you face in your current roles." 5. Skill Assessments and Competency Tests Technical Skill Tests: Evaluate specific technical abilities required for the training. Example: "Complete a coding challenge in Python." Competency-Based Assessments: Measure specific competencies related to job roles. Example: "Complete a leadership assessment to evaluate your management skills." #training #trainthetrainer

*** 🚨 Discussion Piece 🚨 *** Is it Time to Move Beyond Kirkpatrick & Phillips for Measuring L&D Effectiveness? Did you know organisations spend billions on Learning & Development (L&D), yet only 10%-40% of that investment actually translates into lasting behavioral change? (Kirwan, 2024) As Brinkerhoff vividly puts it, "training today yields about an ounce of value for every pound of resources invested." 1️⃣ Limitations of Popular Models: Kirkpatrick's four-level evaluation and Phillips' ROI approach are widely used, but both neglect critical factors like learner motivation, workplace support, and learning transfer conditions. 2️⃣ Importance of Formative Evaluation: Evaluating the learning environment, individual motivations, and training design helps to significantly improve L&D outcomes, rather than simply measuring after-the-fact results. 3️⃣ A Comprehensive Evaluation Model: Kirwan proposes a holistic "learning effectiveness audit," which integrates inputs, workplace factors, and measurable outcomes, including Return on Expectations (ROE), for more practical insights. Why This Matters: Relying exclusively on traditional, outcome-focused evaluation methods may give a false sense of achievement, missing out on opportunities for meaningful improvement. Adopting a balanced, formative-summative approach could ensure that billions invested in L&D truly drive organisational success. Is your organisation still relying solely on Kirkpatrick or Phillips—or are you ready to evolve your L&D evaluation strategy?

Training a Large Language Model (LLM) involves more than just scaling up data and compute. It requires a disciplined approach across multiple layers of the ML lifecycle to ensure performance, efficiency, safety, and adaptability. This visual framework outlines eight critical pillars necessary for successful LLM training, each with a defined workflow to guide implementation: 𝟭. 𝗛𝗶𝗴𝗵-𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗗𝗮𝘁𝗮 𝗖𝘂𝗿𝗮𝘁𝗶𝗼𝗻: Use diverse, clean, and domain-relevant datasets. Deduplicate, normalize, filter low-quality samples, and tokenize effectively before formatting for training. 𝟮. 𝗦𝗰𝗮𝗹𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Design efficient preprocessing pipelines—tokenization consistency, padding, caching, and batch streaming to GPU must be optimized for scale. 𝟯. 𝗠𝗼𝗱𝗲𝗹 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗗𝗲𝘀𝗶𝗴𝗻: Select architectures based on task requirements. Configure embeddings, attention heads, and regularization, and then conduct mock tests to validate the architectural choices. 𝟰. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗦𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 and 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Ensure convergence using techniques such as FP16 precision, gradient clipping, batch size tuning, and adaptive learning rate scheduling. Loss monitoring and checkpointing are crucial for long-running processes. 𝟱. 𝗖𝗼𝗺𝗽𝘂𝘁𝗲 & 𝗠𝗲𝗺𝗼𝗿𝘆 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Leverage distributed training, efficient attention mechanisms, and pipeline parallelism. Profile usage, compress checkpoints, and enable auto-resume for robustness. 𝟲. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 & 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗶𝗼𝗻: Regularly evaluate using defined metrics and baseline comparisons. Test with few-shot prompts, review model outputs, and track performance metrics to prevent drift and overfitting. 𝟳. 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗮𝗻𝗱 𝗦𝗮𝗳𝗲𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀: Mitigate model risks by applying adversarial testing, output filtering, decoding constraints, and incorporating user feedback. Audit results to ensure responsible outputs. 🔸 𝟴. 𝗙𝗶𝗻𝗲-𝗧𝘂𝗻𝗶𝗻𝗴 & 𝗗𝗼𝗺𝗮𝗶𝗻 𝗔𝗱𝗮𝗽𝘁𝗮𝘁𝗶𝗼𝗻: Adapt models for specific domains using techniques like LoRA/PEFT and controlled learning rates. Monitor overfitting, evaluate continuously, and deploy with confidence. These principles form a unified blueprint for building robust, efficient, and production-ready LLMs—whether training from scratch or adapting pre-trained models.

We have a retention problem in corporate learning. Despite 98% of companies implementing eLearning and billions invested in training platforms, employees forget 90% of what they learn within a week. The issue isn't lack of content—it's that we're still designing learning like academic courses instead of performance support. After analyzing what separates effective L&D content from the training that gets completed but never applied, I've identified 7 key principles that actually drive behavior change in the workplace. The shift required: Stop teaching skills in isolation. Start solving real performance problems. Your employees don't need another module about "communication best practices." They need to know exactly what to say when a client meeting derails or how to handle 47 "urgent" requests when they're already at capacity. The companies getting this right aren't just seeing higher completion rates—they're seeing measurable performance improvements and 30-50% better retention rates. Full breakdown in the article below, including a practical implementation framework for transforming your L&D approach from information delivery to performance improvement. What's been your experience with learning content that actually sticks versus training that gets forgotten immediately?

How do you help SDRs track what skills to develop? Here’s what I’ve seen the best managers do: Build a sales competency framework Here’s how it works: First, define 3-4 core skill categories you think all reps need to build in order to do the next role up e.g. Account Executive or Senior SDR As an example, this might look like: 1. Sales Technique 2. Sales Operations 3. Commercial Acumen 4. Leadership & Stakeholder Management Under each category, list out the specific skill competencies expected e.g. Objection Handling, Pain Discovery Then, build a Google Sheet scorecard and for each competency, ask the rep to score themselves from 1-5 For each skill, encourage them to document example(s) of how they’ve demonstrated it Review this with them to modify any numbers, and identify 1-2 skills at a time to focus on developing For the target skills identified, give them a specific list of improvement actions to take in order to grow those skills Create a regular career development ritual to revisit this scorecard e.g. one a month and reflect on progress + define focus for the month ahead The beauty is twofold: 1. This keeps reps accountable in consistently developing desired skills 2. This also helps reps feel clarity in what they need to demonstrate to progress to the next level Curious to hear how different tech sales teams out there approach this! #sdr #bdr

❗ Only 12% of employees apply new skills learned in L&D programs to their jobs (HBR).  ❗ Are you confident that your Learning and Development initiatives are part of that 12%? And do you have the data to back it up?  ❗ L&D professionals who can track the business results of their programs report having a higher satisfaction with their services, more executive support and continued and increased resources for L&D investments.    Learning is always specific to each employee and requires personal context. Evaluating training effectiveness shows you how useful your current training offerings are and how you can improve them in the future. What’s more, effective training leads to higher employee performance and satisfaction, boosts team morale, and increases your return on investment (ROI). As a business, you’re investing valuable resources in your training programs, so it’s imperative that you regularly identify what’s working, what’s not, why, and how to keep improving. To identify the Right Employee Training Metrics for Your Training Program, here are a few important pointers: ✅ Consult with key stakeholders – before development, on the metrics they care about. Make sure to use your L&D expertise to inform your collaboration. ✅Avoid using L&D jargon when collaborating with stakeholders – Modify your language to suit the audience. ✅Determine the value of measuring the effectiveness of a training program. It takes effort to evaluate training effectiveness, and those that support key strategic outcomes should be the focus of your training metrics. ✅Avoid highlighting low-level metrics, such as enrollment and completion rates. 9 Examples of Commonly Used Training Metrics and L&D Metrics 📌 Completion Rates: The percentage of employees who successfully complete the training program. 📌Knowledge Retention: Measured through pre- and post-training assessments to evaluate how much information participants have retained. 📌Skill Improvement: Assessed through practical tests or simulations to determine how effectively the training has improved specific skills. 📌Behavioral Changes: Observing changes in employee behavior in the workplace that can be attributed to the training. 📌Employee Engagement: Employee feedback and surveys post-training to assess their engagement and satisfaction with the training. 📌Return on Investment (ROI): Calculating the financial return on investment from the training, considering costs vs. benefits. 📌Application of Skills: Evaluating how effectively employees are applying new skills or knowledge in their day-to-day work. 📌Training Cost per Employee: Calculating the total cost of training per participant. 📌Employee Turnover Rates: Assessing whether the training has an impact on employee retention and turnover rates. Let's discuss in comments which training metrics are you using and your experience of using it. #MeetaMeraki #Trainingeffectiveness

Various aspects to measure to what extent the employees are agile to learning in an organization-------------- To measure the extent of employees' learning agility in an organization, several aspects and metrics can be evaluated. Here are the key areas to focus on: Key Aspects to Measure Learning Agility Assessment Tools: Utilize scientifically validated learning agility assessments, such as the Korn Ferry Learning Agility Tool or the Mettl Learning Agility Assessment. These tools evaluate various traits associated with learning agility, including adaptability, curiosity, and problem-solving skills. Learning Preferences: Identify individual learning styles and preferences through assessments that analyze how employees prefer to acquire new skills (e.g., self-learning, classroom training, mentorship). Performance Metrics: Monitor performance indicators such as time-to-competency in new roles or tasks, and the speed at which employees can adapt to changes in processes or technologies. This can provide insights into their learning agility in real-world scenarios. Feedback Mechanisms: Implement regular feedback loops where employees receive constructive feedback on their adaptability and learning efforts. This can include peer reviews, manager evaluations, and self-assessments. Training Participation and Outcomes: Track participation rates in training programs and subsequent application of learned skills on the job. Evaluate whether employees are able to transfer knowledge effectively into their roles and how this impacts team performance. Engagement in Continuous Learning: Measure engagement levels in continuous learning initiatives, such as workshops, online courses, and cross-training opportunities. High engagement may indicate a proactive approach to learning. Problem-Solving Abilities: Assess employees' ability to solve complex problems by presenting them with real-life challenges and evaluating their responses and solutions. This can be indicative of their capacity to learn from experiences. Adaptability to Change: Evaluate how quickly employees adjust to changes within the organization, such as new technologies or shifts in strategy. This can be assessed through surveys or direct observation during transitions. Retention of Knowledge: Assess how well employees retain information over time through follow-up assessments after training sessions or workshops. This helps gauge both initial learning and long-term retention capabilities. Collaboration and Knowledge Sharing: Measure participation in collaborative projects and knowledge-sharing initiatives within teams. Employees who actively engage in sharing insights and learning from one another typically demonstrate higher learning agility. By focusing on these aspects, organizations can gain a comprehensive understanding of their employees' learning agility levels, which is crucial for fostering a culture of continuous improvement and adaptability.

“What gets measured gets managed.” — Peter F. Drucker This oft-cited axiom by the father of modern management, is more than a business truism. It is a call to intentional governance—a reminder that what we choose to observe becomes a reflection of our strategic priorities. Nowhere is this principle more consequential or more underleveraged than in the realm of TD Too often L&D strategies are guided by intuition, anecdote, or calendar-based rituals rather than data. We invest in leadership journeys, behavioral modules & capability academies yet struggle to articulate the causal link between our initiatives & business performance. This is not a measurement failure. It is a measurement avoidance. As TD professional & a current doctoral candidate in Business Administration—I have come to realize that without a robust architecture of metrics, we risk reducing learning to a “feel-good” function rather than a force multiplier. In business, that which is not measured becomes invisible. And invisibility breeds irrelevance. To be treated as strategic, TD must learn to speak the language of the business—a language steeped in data, outcomes & evidence. This means: Linking learning interventions to capability uplift Measuring behavioral change, not just completion Tying development to talent retention, engagement & readiness Correlating leadership programs with succession pipeline health Moving beyond vanity metrics toward business-aligned KPIs “We cannot improve what we cannot see, & we cannot defend what we cannot quantify.” My research in Business Administration has further clarified: organizations are systems of interdependencies & measurement is the currency that makes those systems intelligible. When we quantify talent outcomes—be it through ROI models, capability indices, or predictive analytics—we are not just measuring learning. We are codifying value. We are translating soft skills into hard currency—an act that elevates L&D from operational to strategic, from reactive to anticipatory. The goal is not to reduce people to numbers. It is to ensure that people strategies earn their rightful seat at the strategic table. If we measure engagement, it improves. If we measure manager effectiveness, it strengthens. If we measure internal mobility, it accelerates. Measurement doesn’t dilute the human experience—it amplifies our ability to serve it with clarity, consistency, and conviction. A Call to TD Leaders If TD is to be the engine of agility, innovation, & culture—then it must also be the custodian of strategic measurement. Let us embrace: Data literacy as a core L&D competency KPIs that resonate beyond HR dashboards A mindset that sees evaluation not as audit, but as advocacy Because what gets measured doesn’t just get managed—it gets the respect, resources, and relevance it deserves. #TalentDevelopment #PeterDrucker #LearningMetrics #StrategicHR #DBA #HumanCapital #CapabilityBuilding #WorkforceStrategy #DoctoralResearch #Future