Training Evaluation Models

Formative assessment is like a compass—it is ongoing, diagnostic, and designed to guide learning. It provides students with timely, actionable feedback during the learning process so they can improve before reaching the final destination. Examples include quizzes, think-pair-share, drafts, reflections, or teacher-student conferences. Formative assessments help identify misconceptions, adjust teaching strategies, and personalise support. In this way, they build student confidence and competence incrementally. Summative assessment is more like a snapshot—it evaluates what students have achieved at the end of an instructional period. It measures mastery against learning outcomes and is used to judge the effectiveness of instruction. Examples include final exams, projects, performances, or standardised tests. While summative assessments do not provide direct guidance during the learning process, they reflect the culmination of all the formative learning and feedback that came before.

🍱 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 representative tasks and see how successful users are. It won’t be right the first time, 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/d6yvKS6G 👍 Ethr: https://lnkd.in/eSTzcN7V Orange: https://lnkd.in/ewBJZcgC 👍 Semrush Charts + Tables: https://lnkd.in/dnDRtG32 👍 UI Charts: https://lnkd.in/eJkyB6zS UKO: https://lnkd.in/ehvcSnuV 👍 Wireframes: https://lnkd.in/e-m3VQqs 👍 [continues in comments]

I noticed a major gap in healthcare training. Students spend years studying textbooks, yet many feel unprepared when faced with real patient care. The lack of repeated hands-on experience makes it difficult to build confidence and make critical decisions under pressure. I spoke with professionals who wished they had realistic training before stepping into high-stakes situations. That’s where the potential of MR (mixed reality) in medical education thrives. MR allows trainees to practice complex procedures in a safe, controlled environment. It helps them build confidence, improve decision-making, and experience emergency scenarios without real-world consequences. Unlike traditional simulation labs, MR training is accessible anytime, anywhere, making high-quality education more affordable and scalable. It is not just improving medical training; it is shaping the future of healthcare by preparing professionals with the skills they need before they ever step into a patients’ room. The question is no longer whether MR will transform healthcare education but whether institutions are ready to adopt it. Key Benefits of VR in Healthcare Training ✹ Provides hands-on, immersive training in a risk-free environment ✹ Enhances critical thinking and decision-making under pressure ✹ Reduces training costs while improving accessibility for students and professionals ✹ Allows for remote learning, making high-quality medical training more scalable #VRinHealthcare #MedicalTraining #ImmersiveLearning #MixedReality #MR #FutureOfMedicine #AIinEducation VRpatients

Please, stop building AI agents. If you want to succeed as an AI engineer, you need to get this: Building autonomous agents isn’t just about prompting clever instructions. It’s an operational marathon. And if you’re not thinking in LLMOps layers, Your agents will break at scale. Here’s the Ops Blueprint you need: Core LLMOps Layers: The Foundation 1. The Data Layer This is the bedrock of LLM systems. Every retrieval, Every response, Every plan your agent makes Relies on the quality of your data. From sourcing → cleaning → chunking → embedding → indexing This pipeline determines how relevant and accurate your context is. Garbage in, garbage out is a 𝘭𝘢𝘸. 2. Prompt Lifecycle Management Prompts aren’t static text; They’re evolving artefacts. You need registries, versioning systems, and testing workflows to operationalise prompt changes. Treat them as code: modular, testable, and traceable. 3. Model Specialisation & Serving Fine-tuning isn't always required, but when it is, you need pipelines that can train on your domain-specific data while managing drift and cost. More broadly, this layer covers how your models (fine-tuned or base) are deployed, scaled, and served efficiently via inference endpoints, often with caching and routing logic. 4. Monitoring & Guardrails The mission control layer. Track usage, latency, token consumption, hallucinations, and behavioural anomalies. Integrate feedback loops to continuously improve both performance and safety. The Agentic Upgrade: Advanced Ops for Autonomy 1. Agent Orchestration & Tooling Autonomous agents are planners and doers. This layer governs how they decompose goals, sequence actions, and interact with external tools and APIs to complete tasks. 2. State & Memory Management Agents need 𝘴𝘩𝘰𝘳𝘵-𝘵𝘦𝘳𝘮 𝘴𝘵𝘢𝘵𝘦 (e.g. current task status) and 𝘭𝘰𝘯𝘨-𝘵𝘦𝘳𝘮 𝘮𝘦𝘮𝘰𝘳𝘺(e.g. persistent knowledge or preferences). Operationalising this involves orchestrating the entire memory layer. 3. Multi-Layer Evaluation You can’t rely on pass/fail metrics anymore. Agents need evaluation across reasoning chains, tool usage, response coherence, and task success rates. Operationalising evaluation = continuous QA pipelines + human-in-the-loop reviews + synthetic testing. Resources to get you started: Data Engineering: • https://lnkd.in/g9RyKh4P • https://lnkd.in/ggfHUGet • https://lnkd.in/ghUWTxc7 LLMOps: • https://lnkd.in/gkxqUAVs • https://lnkd.in/gfWhes7d • https://lnkd.in/gS_tA8hJ Prompt Lifecycle: • https://lnkd.in/g2RmYfKh LLM Observability: • https://lnkd.in/gHyEpgBJ ♻️ Reposting this helps everyone in your network upskill

*** 🚨 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?

Explaining the Evaluation method LLM-as-a-Judge (LLMaaJ). Token-based metrics like BLEU or ROUGE are still useful for structured tasks like translation or summarization. But for open-ended answers, RAG copilots, or complex enterprise prompts, they often miss the bigger picture. That’s where LLMaaJ changes the game. 𝗪𝗵𝗮𝘁 𝗶𝘀 𝗶𝘁? You use a powerful LLM as an evaluator, not a generator. It’s given: - The original question - The generated answer - And the retrieved context or gold answer 𝗧𝗵𝗲𝗻 𝗶𝘁 𝗮𝘀𝘀𝗲𝘀𝘀𝗲𝘀: ✅ Faithfulness to the source ✅ Factual accuracy ✅ Semantic alignment—even if phrased differently 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: LLMaaJ captures what traditional metrics can’t. It understands paraphrasing. It flags hallucinations. It mirrors human judgment, which is critical when deploying GenAI systems in the enterprise. 𝗖𝗼𝗺𝗺𝗼𝗻 𝗟𝗟𝗠𝗮𝗮𝗝-𝗯𝗮𝘀𝗲𝗱 𝗺𝗲𝘁𝗿𝗶𝗰𝘀: - Answer correctness - Answer faithfulness - Coherence, tone, and even reasoning quality 📌 If you’re building enterprise-grade copilots or RAG workflows, LLMaaJ is how you scale QA beyond manual reviews. To put LLMaaJ into practice, check out EvalAssist; a new tool from IBM Research. It offers a web-based UI to streamline LLM evaluations: - Refine your criteria iteratively using Unitxt - Generate structured evaluations - Export as Jupyter notebooks to scale effortlessly A powerful way to bring LLM-as-a-Judge into your QA stack. - Get Started guide: https://lnkd.in/g4QP3-Ue - Demo Site: https://lnkd.in/gUSrV65s - Github Repo: https://lnkd.in/gPVEQRtv - Whitepapers: https://lnkd.in/gnHi6SeW

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.

As organizations strive for excellence, the challenge often lies not in making improvements, but in maintaining them. Once we achieve a certain level of quality or efficiency, how do we ensure these standards continue to be met, or even surpassed, over time? This is where the role of management systems needs to be understood. A robust management system is crucial for sustaining improvements. It's not just about the changes we implement, but how we embed them into our daily, weekly, and monthly routines. Annual reviews play a vital role, but the essence of sustaining improvements lies in the regular, smaller checks – those daily and weekly habits that keep us on track. 👉 Daily Gemba walks and check-ins or "huddles" can help teams stay aligned with their goals and quickly address any deviations. Adopting 2 Second Lean can help drive small everyday incremental improvements. (See previous posts on these topics and I also recommend Paul Akers book "2 Second Lean"). 👉 Weekly team meetings and cross-departmental process review meetings are key to increasing and sustaining improvements. These are most effective when they include a focus on not just planning but also performance metrics, reviews and lessons learned. Don't forget about 1:1's either- it's important to have regular individual check-ins. 👉 Monthly and bi-monthly evaluations provide opportunity for a comprehensive review of processes and a look at longer-term trends and effects. Regular Kaizen events ensure everyone is involved in CI discussions, process mapping, root- cause analysis and decision-making. In addition to this, it's important to offer ongoing training in your chosen quality management methodology. 👉 Annual reviews and audits are crucial for a deep dive into the year's performance, reassessing objectives, and setting new goals. I also recommend including an organizational health check at least once a year, and including succession planning in the strategic focus. Every organization has responsibility for setting up management systems. They are structured activities that everyone must be aware of and involved in. Each organization's management systems may look different, and that's ok! It is evident that leaders plays a pivotal role in this process. Managers and leaders need to be vigilant, proactive, and adaptable. They are responsible for fostering a culture of continuous improvement, where feedback is encouraged and acted upon. Maintaining high standards is a dynamic process. It requires a combination of robust management systems, consistent practices, strong leadership, and the integration of methodologies like Lean and Agile. By focusing on these elements, organizations can not only achieve excellence but sustain it in the long run. #continuousimprovement #lean #leanmanagement #agilemethodologies #managementsystem #leadership #management

Measuring Success: How Competency-Based Assessments Can Accelerate Your Leadership If it’s you who feels stuck in your career despite putting in the effort. To help you gain measurable progress, one can use competency-based assessments to track skills development over time. 💢Why Competency-Based Assessments Matter: They provide measurable insights into where you stand, which areas you need improvement, and how to create a focused growth plan. This clarity can break through #career stagnation and ensure continuous development. 💡 Key Action Points: ⚜️Take Competency-Based Assessments: Track your skills and performance against defined standards. ⚜️Review Metrics Regularly: Ensure you’re making continuous progress in key areas. ⚜️Act on Feedback: Focus on areas that need development and take actionable steps for growth. 💢Recommended Assessments for Leadership Growth: For leaders looking to transition from Team Leader (TL) to Assistant Manager (AM) roles, here are some assessments that can help: 💥Hogan Leadership Assessment – Measures leadership potential, strengths, and areas for development. 💥Emotional Intelligence (EQ-i 2.0) – Evaluates emotional intelligence, crucial for leadership and collaboration. 💥DISC Personality Assessment – Focuses on behavior and communication styles, helping leaders understand team dynamics and improve collaboration. 💥Gallup CliftonStrengths – Identifies your top strengths and how to leverage them for leadership growth. 💥360-Degree Feedback Assessment – A holistic approach that gathers feedback from peers, managers, and subordinates to give you a well-rounded view of your leadership abilities. By using these tools, leaders can see where they excel and where they need development, providing a clear path toward promotion and career growth. Start tracking your progress with these competency-based assessments and unlock your full potential. #CompetencyAssessment #LeadershipGrowth #CareerDevelopment #LeadershipSkills

You’re not alone if you’ve noticed that, despite the time and resources invested, the DEI training programs in your organization aren’t delivering the impact you expected. The reality is, success isn’t just determined by the commitment of the participants —it’s heavily influenced also by how the program is structured and delivered. There are key signs to watch for that may suggest your DEI program is like a broken ladder, making it difficult for employees to climb toward meaningful change Here are 8 common pitfalls to watch out for, and what you can do to ensure the DEI trainings in your organization make a lasting impact: ❌ Single-session workshops ✅ Effective DEI programs involve spaced learning, delivered over time to allow for deeper understanding and lasting impact ❌ Same content for people in different roles  ✅ Does the training feel generic, like it’s meant for everyone but relevant to no one? A good DEI program should be tailored to specific roles and the needs of your group. ❌ Focusing on compliance and what not to do ✅ The focus should be on modeling inclusive behaviors and showing what to do in real situations and how to incorporate them into daily work ❌ Copy-pasting training content from global DEI programs ✅ If it feels like the examples or exercises don’t really apply to your workplace, the content may have been copy-pasted from global programs. Check how the material has been adjusted to reflect your specific organization’s culture and challenges. ❌ Run by passionate DEI advocates with no facilitation experience ✅ A passionate facilitator is great, but they should also know how to manage group dynamics and keep discussions productive. Pay attention to whether the facilitator is able to navigate complex conversations and make the space feel safe for everyone. ❌ Raising awareness without driving behavioral change ✅ DEI training should focus on translating awareness into concrete actions that people can start practicing immediately. ❌ Ignoring pushback and concerns ✅ A DEI training that shies away from tough conversations might miss real issues. Good training fosters open dialogue, allowing participants to voice concerns and discuss challenges openly. ❌ No follow-up or next steps ✅ A truly impactful program provides follow-up phases for implementation, ensuring the lessons learned are integrated and built upon. By paying attention to these aspects, you can transform the DEI training program into one that delivers meaningful, lasting change. Do any of these issues resonate with you? I’d love to hear your thoughts!