Educational Program Assessment

Most L&D professionals learned the Kirkpatrick Model early on. Fewer have seen it applied beyond Level 1. Here's what each level can actually look like when you put it into practice, not just the textbook definition. ✨ Level 1: Reaction 🔹 Textbook version: Did learners find the training engaging and worth their time? ✅ In practice: Instead of "Did you enjoy this session?", ask "Was this relevant to the work you do?" and "Could you apply this right away?" ✅ Metric to track: Relevance and applicability ratings, not just satisfaction scores. ✨ Level 2: Learning 🔹 Textbook version: Did learners gain the intended knowledge or skills? ✅ In practice: Replace recall-based quizzes with scenario-based checks. Can the learner apply the concept to a situation they'd actually face? ✅ Metric to track: Pre/post assessment scores on scenario-based questions, not just "did you pass the quiz." ✨ Level 3: Behavior 🔹 Textbook version: Are learners applying what they learned on the job? ✅ In practice: 30/60/90-day check-ins, manager observations, or peer feedback on whether the new behavior is showing up in real work. ✅ Metric to track: % of participants demonstrating the target behavior, based on manager or peer input, not self-reported confidence. ✨ Level 4: Results 🔹 Textbook version: Did the training impact business outcomes? ✅ In practice: Pick one business metric the program was meant to influence, before you build it, not after, and track the change. ✅ Metric to track: Movement in that specific KPI (error rates, time-to-productivity, conversion rates, retention) compared to a baseline. Most programs are measured thoroughly at Level 1 and barely at all beyond it. But Levels 3 and 4 are where the "did this actually matter" conversation happens, and they are also where L&D earns a seat at the table. Which level does your organisation measure consistently, and which one do you wish you could measure better? #LearningAndDevelopment #LnD #KirkpatrickModel #TrainingEvaluation #InstructionalDesign #LearningMeasurement #TrainingAndDevelopment #LnDStrategy

Most candidates approach interviews like an exam—listing accomplishments and reciting rehearsed answers. But top performers? They showcase how they think, problem-solve, and make decisions in real time. Why does this matter? Because hiring managers aren’t just assessing what you’ve done in the past—they’re evaluating how you’ll perform in their company. Here’s how to demonstrate strong thinking and problem-solving skills in interviews. 👇 1️⃣ Think Out Loud: Let Them In on Your Thought Process Many candidates give only the final answer, but hiring managers want to see how you got there. ✅ Break problems into logical steps. ✅ State assumptions and clarify unknowns. ✅ Weigh trade-offs before reaching a conclusion. Example (for a problem-solving question): Interviewer: “How would you improve our customer onboarding process?” ❌ Weak response: “I’d optimize the emails and add a tutorial.” ✅ Strong response: "First, I’d analyze current user behaviour—where are the biggest drop-offs? If it’s lack of clarity, I’d improve messaging. If it’s complexity, I’d test simplifying steps. Balancing efficiency with engagement would be key to reducing churn while maintaining quality onboarding." 🔹 Why this works: It shows structured thinking, data-driven decision-making, and strategic problem-solving. 2️⃣ Use a Clear Answer Framework A structured answer is easier to follow and more impactful. ✅ For behavioural questions, use STAR or CAR: ✔ Situation/Challenge – Set up the context. ✔ Action – What steps did you take? ✔ Result – What was the measurable impact? Example: Interviewer: “Tell me about a time you improved efficiency on your team.” "Our team struggled with long approval times (Situation). I introduced an automated tracking system to flag delays (Action), cutting turnaround time by 40% (Result)." 🔹 Why this works: It’s concise, clear, and focused on impact. Aim for an answer that's about 2 min long. 3️⃣ Show Adaptability: There’s No “Perfect” Answer Many interview questions don’t have a single right answer—hiring managers want to see how you adapt your thinking. ✅ Acknowledge challenges or constraints. ✅ Offer multiple solutions with pros/cons. ✅ Be open to feedback and adjust. Example (for a strategy question): Interviewer: “How would you expand our product into a new market?” "There are a few ways to approach this. We could start with a pilot launch in a single region to test demand, or we could form strategic partnerships to gain traction faster. The right approach depends on factors like budget, timeline, and market research insights." 🔹 Why this works: It shows flexibility, strategic thinking, and an ability to weigh options. Interviews Are Not Just About Your Experience—They’re About How You Think. ✔ Think out loud—explain your reasoning. ✔ Structure your answers—keep them clear and concise. ✔ Demonstrate adaptability—consider different solutions. 👉 Found this helpful? Reshare to help others master interview thinking!

A couple of weeks ago, I shared a visual on quantitative research and I was really glad to see how many of you found it useful, especially for teaching and workshops. Today I’m shifting the focus to qualitative research, a paradigm I’ve been immersed in since my master’s degree. Here is the thing: condensing the richness of qualitative research into a single visual is no easy task. It’s complex, interpretive, and philosophically layered. But I also believe this: if you can’t explain something simply, you probably haven’t understood it well enough. So here’s my attempt to explain it simply. In this new visual, I tried to put together a quick and accessible guide to help students and new researchers grasp the essentials of qualitative research. The guide includes: 1. A few widely cited definitions 2. The key characteristics (based on Merriam’s work) 3. Commonly used methods (inspired by Newman & Benz) 4. A brief on the philosophical foundation 5. And a short list of recommended readings I drew mainly from: - Merriam, S. B. (2009). Qualitative Research: A Guide to Design and Implementation - Newman & Benz (1998). Qualitative-Quantitative Research Methodology: Exploring the Interactive Continuum Feel free to use this with your students, share it in your courses, or include it in your research workshops. And since LinkedIn algorithm doesn’t like links in posts, check the comment section for a downloadable PDF version of the visual. #QualitativeResearch #EducationalResearch #ResearchMethods #EdTech #MixedMethods #GradSchool #PhDLife #EducatorsTechnology

𝗪𝗮𝗻𝘁 𝗧𝗼 𝗖𝗿𝗮𝗰𝗸 𝗡𝗩𝗜𝗗𝗜𝗔, 𝗤𝘂𝗮𝗹𝗰𝗼𝗺𝗺, 𝗜𝗻𝘁𝗲𝗹 𝗢𝗿 𝗔𝗠𝗗? 𝗦𝘁𝘂𝗱𝘆 𝗪𝗵𝗮𝘁’𝘀 𝗜𝗻 𝗧𝗵𝗲 𝗝𝗼𝗯 𝗗𝗲𝘀𝗰𝗿𝗶𝗽𝘁𝗶𝗼𝗻, 𝗡𝗼𝘁 𝗘𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴. Every company hires VLSI engineers. But they don’t interview the same way. One of the biggest mistakes I see students make is preparing one generic syllabus for every interview. That approach rarely works. Here’s what experienced candidates understand. 1. Every company builds different products. The questions usually reflect the problems that company solves. A CPU company may focus more on: • RTL • Computer Architecture • Performance • Verification An Analog company may focus more on: • CMOS • Op-Amps • PLLs • ADC/DAC • Layout A Networking company may ask about: • High-speed interfaces • Protocols • Signal Integrity • SerDes Understanding the business helps you predict the interview. 2. Learn depth instead of breadth. Many students try to study everything. Interviewers usually prefer someone who can explain 20 important topics deeply rather than someone who has memorized 200 topics superficially. 3. Read the Job Description carefully. Before every interview ask yourself: • What products does this company build? • Which team am I interviewing for? • What skills are repeatedly mentioned? • Which tools appear again and again? Your preparation should change based on those answers. 4. Build projects that match the company. Instead of building random projects: • Applying for RTL → Build synthesizable RTL projects. • Applying for Verification → Build SystemVerilog/UVM testbenches. • Applying for Physical Design → Learn RTL-to-GDSII flow and STA. • Applying for Analog → Design and simulate real circuits. Relevant projects make interviews much easier. 5. Research before you interview. Spend one hour researching the company. Learn: • Their latest products • Major customers • Recent acquisitions • Technologies they are investing in • Roles they usually hire for This often helps more than solving another 50 interview questions. 6. Communication matters. Many technically good students lose offers because they cannot explain: • Why they designed something • Design trade-offs • Debugging approach • Mistakes they made • What they learned Interviewers evaluate thinking, not just answers. Final Advice Don’t prepare for “VLSI Interviews.” Prepare for the company you’re interviewing with. That small shift can save weeks of preparation and significantly improve your chances of getting shortlisted. #VLSI #ECE #Semiconductor #ElectronicsEngineering #EmbeddedSystems #ChipDesign #GATEECE #VLSICompanies

According to you, your coding interview was great → but the result was a rejection. According to you, your solution was optimal → but the result was still a rejection. According to you, you were just one step away → but the interviewer wrote “not hire-ready.” Rejections hurt. But they hurt even more when you thought you were a no-brainer hire. Here are 13 advanced tips that have helped me and the folks I’ve mentored actually convert interviews at Juspay, Google, Atlassian & other companies. ✅ 1. Never start coding immediately, even if you know the answer. Most rejections happen not because your solution was wrong, but because you didn’t show how you got there. Take 30–60 seconds to clarify the prompt, discuss assumptions, and plan aloud. It builds trust and shows real-world problem-solving, not just memorization. ✅ 2. Always ask for input constraints. “Can the input have 10^6 elements?” → For example, this is a must-ask question. Why? Because it changes everything: → A `O(n²)` solution might be fine for small inputs, but disastrous for large ones. → It helps the interviewer know you’re thinking at scale. ✅ 3. Ask about edge cases upfront. Don’t wait to get caught later. Ask: → Can input be empty? → Are values negative? → Are duplicates allowed? This shows defensive thinking, the kind engineers need in real systems. ✅ 4. Walk through a sample input on paper, before code. Don’t just nod and say “Got it.” Say: “Let me walk through [1, 2, 3, 4] and see what output we expect.” This reveals gaps in understanding before they turn into bugs. ✅ 5. Break the silence. Talk through your thought process. Even if you’re stuck, say what you’re thinking: “I’m debating between two approaches… one is brute force, the other uses a heap.” Thinking out loud shows your instincts and trade-offs. ✅ 6. Don’t obsess over the perfect solution first. Start with a naive approach. Say: “I’ll first solve this in O(n²) to validate logic. Then we can optimize.” It shows structure and maturity, not panic and perfectionism. ✅ 7. State the time and space complexity of every approach. Don’t wait to be asked. It shows you understand cost vs performance. You can also compare alternatives: “This uses O(n) space, but we can reduce it with a tweak.” ✅ 8. Code like you’re writing for another engineer, not yourself. Use clean variable names: → ❌ `i, j, a` → ✅ `startIndex, currentSum, target` It shows empathy, and it’s what real-world developers do. Rest of the tips are in the comments ↓ —--- P.S: I am taking a Webinar on Interview Preparation for SDE roles on 31st May (3 PM IST) In this webinar, I’ll be covering. 1. How to improve your profile as a candidate 2. How to prepare a stand-out resume 3. How to apply for jobs in this market and get callbacks 4. What are the interview rounds(will discuss for SDE1, SDE2 & Senior) 5. DSA strategy to crack rounds at any company, even during the recession Register here: https://lnkd.in/g-4r2y7E Continued in comments

This fall, when I teach two very large U.S. history survey courses with 800 students, I face two choices. I can return to standard forms of assessment: in-class quizzes, proctored exams, and tightly controlled assignments designed to prevent students from using AI. Or I can try something new. I can replace those conventional assessments with authentic tasks and guided learning experiences. Students can interpret unfamiliar sources, weigh competing explanations, evaluate historical decisions, respond to objections, and revise their conclusions as new evidence appears. My goal is not simply to measure what students remember. It is to help them learn to apply historical knowledge. Working with Carnegie Mellon’s David Miller and his team at ScholarStack, along with Tim Fackler, Director of Instructional Technology at UT Austin’s College of Liberal Arts, I am building structured written and spoken activities for all 800 students. The combination is what matters. Authentic assessment asks students to do more meaningful intellectual work. Guided inquiry helps them develop the reasoning that work requires. ScholarStack makes both possible in a course of this size. This is an experiment, and I do not yet know how well it will work. But the alternative is to retreat to a model built mainly around recall, surveillance, and compliance. Students are already using AI. The question is whether instructors can design its use so that students must interpret evidence, defend their claims, respond to objections, and revise their conclusions rather than turn the work over to a machine. Read the full essay at https://lnkd.in/gYUGCmJ3

𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗰𝗮𝗺𝗲 𝗳𝗿𝗼𝗺 𝗮 𝗱𝗼𝗰𝘁𝗼𝗿𝗮𝗹 𝘀𝘁𝘂𝗱𝗲𝗻𝘁 𝗶𝗻 𝗺𝘆 𝘁𝗵𝗶𝗿𝗱 𝘄𝗲𝗲𝗸 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗾𝘂𝗮𝗹𝗶𝘁𝗮𝘁𝗶𝘃𝗲 𝗺𝗲𝘁𝗵𝗼𝗱𝘀. The student had read the assigned chapter twice. Attended every lecture. Took detailed notes and still asked: "I have all these codes. I know I'm supposed to 'analyze' them now. But what does that actually mean I 𝘥𝘰?" I started to answer with the theoretical framework. The iterative nature of analysis. The move toward interpretation. Then I stopped. Because the question wasn't about theory. It was about process. The student was staring at a spreadsheet of codes with no idea what happened next. Analysis felt like a black box; something that magically happens between "I have codes" and "I have findings". 𝗜 𝗿𝗲𝗮𝗹𝗶𝘇𝗲𝗱 𝗜'𝗱 𝗯𝗲𝗲𝗻 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗽𝗿𝗼𝗱𝘂𝗰𝗲𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝙞𝙨. That moment changed everything. Now I make the black box visible. Here's what I show them in the following table. Analysis isn't magic. It's a series of questions that move you from description to interpretation. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗹𝗲𝗮𝗿𝗻𝗲𝗱: Emerging researchers need us to show them the thinking we do automatically - the moves that became invisible to us the moment we master them. Sometimes the most powerful thing we can do is make the invisible visible. 𝗪𝗵𝗮𝘁 𝗽𝗮𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝘀𝘁𝗶𝗹𝗹 𝗳𝗲𝗲𝗹𝘀 𝗹𝗶𝗸𝗲 𝗮 𝗯𝗹𝗮𝗰𝗸 𝗯𝗼𝘅 𝘁𝗼 𝘆𝗼𝘂? 𝗟𝗲𝘁 𝗺𝗲 𝗸𝗻𝗼𝘄 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀. #QualitativeResearch #ResearchMethods #AcademicWriting #PhDLife #DataAnalysis 

I've added a couple of lectures to my YouTube channel link below - a 4 parter on interviewing and transcription, and a 3 parter on generating qualitative data beyond the interview (with a focus on qualitative surveys, vignettes and story completion after exploring a wide range of possibilities for qualitative data generation). These join the following lectures -Foundations of qualitative research 1 (a gentle intro to qual) -Foundations of qualitative research 2 (getting stuck into key concepts and theories) -Qualitative research design -Thematic analysis I'm hoping to add another lecture on Quality and reporting practices and guidelines later this (academic) year The lectures are created for a postgraduate research methods module at UWE - so as I note on YouTube there are a few references to our VLE etc! Please share these lectures with anyone who might find them useful. I know a few unis use these lectures in their research methods training and I'm very happy for them to be used this way - no permission needed! https://lnkd.in/ePR74ysD

Strategies for AI-Resilient Assessments (AI & Assessments) Over time, through professional development, collaboration, and reflection, I have been exploring what it truly means to design AI-resilient assessments, those that prioritize authentic learning, creativity, and human judgment. Through this exploration, I have identified a set of practical strategies that help ensure assessments remain meaningful and resistant to overreliance on AI tools. Here's a list of these strategies: 💎Case-Based Analysis: Provide students with unique, context-rich scenarios that require them to apply course concepts, analyze data, and propose tailored solutions. 💎Personalized Reflections: Invite students to connect theoretical concepts to their own lived experiences, learning journeys, or local contexts, aspects that AI cannot authentically replicate. 💎Project-Based Assignments: Design multi-step projects that involve planning, iteration, and self-assessment across multiple drafts and revisions. 💎Oral Presentations & Defenses: Require students to explain their reasoning verbally or respond to questions in real time, fostering live, authentic dialogue. 💎Creative Products: Encourage students to produce multimedia, design, or creative outputs, such as prototypes, simulations, or artistic works, to demonstrate their understanding in diverse ways. 💎Collaborative Work: Structure group activities that depend on negotiation, clear role assignment, and peer accountability to achieve shared goals. 💎Portfolios of Work: Ask students to compile portfolios that document their growth over time through reflections, challenges, and learning milestones. 💎Scenario-Based Problem Solving: Present open-ended or ethical dilemmas that require students to synthesize knowledge and engage in creative reasoning. 💎Stepwise Problem Tasks: Require students to show the reasoning or calculations behind each step of their work, rather than only providing the final answer. 💎Peer Teaching Assignments: Have students teach a concept, design instructional materials, or lead short lessons to deepen their understanding and mastery of the subject. And here's the revision added to the list by Heliya Ahmadi a few days later: 💎Futures-Oriented & Speculative Design Assignments: Engage students in future-oriented or speculative thinking exercises that challenge them to imagine emerging scenarios, critically evaluate the evolving role of AI, and explore new forms of agency, authorship, and ethical decision-making. You can find the revised diagram under Heliya's comment in the comment section. 🤓🙏 Reflect & share: How are you rethinking your assessment designs in light of AI’s growing presence in education? #AIinEducation #AssessmentDesign #HigherEdInnovation #InstructionalDesign #TeachingWithAI #AuthenticAssessment #LearningDesign #FacultyDevelopment #EdTech #Pedagogy #AIResilience #FutureOfLearning #EducationInnovation #StudentEngagement #AIandTeaching #DigitalPedagogy

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?