Educational Program Assessment

“Show outcomes, not outputs!” I’ve given (and received) this feedback more times than I can count while helping organizations tell their impact stories. And listen, it’s technically right…but it can also feel completely unfair. We love to say things like: ✅ 100 teachers trained ✅ 10,000 learners reached ✅ 500 handwashing stations installed But funders (and most payers) want to know: 𝘞𝘩𝘢𝘵 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 𝘤𝘩𝘢𝘯𝘨𝘦𝘥 𝘣𝘦𝘤𝘢𝘶𝘴𝘦 𝘰𝘧 𝘢𝘭𝘭 𝘵𝘩𝘢𝘵? That’s the outcomes vs outputs gap: ➡️ Output: 100 teachers trained ➡️ Outcome: Teachers who received training scored 15% higher on evaluations than those who didn’t The second tells a story of change. But measuring outcomes can be 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲. It’s easy to count the number of people who showed up. It’s costly to prove their lives got better because of it. And that creates a brutal inequality. Well-funded organizations with substantial M&E budgets continue to win. Meanwhile, incredible community-led organizations get sidelined for not having “evidence”- even when the change is happening right in front of us. So what can organizations with limited resources do? 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗲𝘅𝗶𝘀𝘁𝗶𝗻𝗴 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵: That study from Daystar University showing teacher training improved learning by 10% in India? Use it. If your intervention is similar, cite their methodology and results as supporting evidence. 𝗗𝗲𝘀𝗶𝗴𝗻 𝘀𝗶𝗺𝗽𝗹𝗲𝗿 𝘀𝘁𝘂𝗱𝗶𝗲𝘀: Baseline and end-line surveys aren't perfect, but they're better than nothing. Self-reported confidence levels have limitations, but "85% of teachers reported feeling significantly more confident in their teaching abilities," tells a story. 𝗣𝗮𝗿𝘁𝗻𝗲𝗿 𝘄𝗶𝘁𝗵 𝗹𝗼𝗰𝗮𝗹 𝗶𝗻𝘀𝘁𝗶𝘁𝘂𝘁𝗶𝗼𝗻𝘀: Universities need research projects. Find one studying similar interventions and collaborate. Share costs, share data, share credit. 𝗨𝘀𝗲 𝗽𝗿𝗼𝘅𝘆 𝗶𝗻𝗱𝗶𝗰𝗮𝘁𝗼𝗿𝘀: Can't afford a 5-year longitudinal study? Track intermediate outcomes that research shows correlate with long-term impact. 𝗧𝗿𝘆 𝗽𝗮𝗿𝘁𝗶𝗰𝗶𝗽𝗮𝘁𝗼𝗿𝘆 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻: Let beneficiaries help design and conduct evaluations. It's cost-effective and often reveals insights that traditional methods miss. For example, train teachers to interview each other about your training program. And funders? Y’all have homework too. Some are already offering evaluation support (bless you). But let’s make it the rule, not the exception. What if 10-15% of every grant was earmarked for outcome measurement? What if we moved beyond gold-standard-only thinking? 𝗟𝗮𝗰𝗸 𝗼𝗳 𝗮 𝗰𝗲𝗿𝘁𝗮𝗶𝗻 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗲𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗺𝗲𝗮𝗻 “𝗻𝗼𝘁 𝗶𝗺𝗽𝗮𝗰𝘁𝗳𝘂𝗹”. We need outcomes. But we also need equity. How are you navigating this tension? What creative ways have you used to show impact without burning out your team or budget? #internationaldevelopment #FundingAfrica #fundraising #NonprofitLeadership #nonprofitafrica

A prior post that raised the concept of computational legal thinking now has me reflecting further on legal education. Let me share an idea about teaching that might help bridge theory and practice. Consider how we traditionally teach case analysis: Students learn to identify key facts, spot relevant legal principles, and reason through precedent. Now imagine augmenting each step with technological understanding. For instance, when teaching statutory interpretation, we could pair traditional close reading with lessons on how language models process legal text. This creates natural opportunities to discuss both the power and limitations of computational analysis. This reminds me of how medical schools transformed their curriculum when imaging technology advanced. They didn't just teach doctors to read X-rays - they taught them to integrate visual data with patient symptoms and medical knowledge to make better diagnoses. Similarly, we need to teach lawyers to weave together computational insights with traditional legal reasoning. Here's what this might look like in practice: Instead of just having students brief cases, we could have them compare their analysis with AI-generated case summaries. The goal isn't to show which is "better," but to help students understand how different analytical approaches complement each other. They learn to ask: What did the AI catch that I missed? What contextual nuances did I grasp that the AI overlooked? For assessment, we might evaluate students not just on their conclusions, but on their ability to articulate their reasoning process: 1) How did they combine computational tools with traditional legal analysis? 2) What made them trust or question automated insights? When did they rely more heavily on human judgment? I would say that the goal is to develop lawyers who see technology as as an integral part of their analytical framework while maintaining legal acumen defines the legal profession. #legaltech #innovation #law #business #learning

No State Achieves 'Daksh': What the PGI 2.0 Really Tells Us About School Education in India The just-released Performance Grading Index (PGI) 2.0 report is a mirror held up to the deep structure of India’s school education system. While the report highlights Chandigarh’s “distinction” with a score of 703/1000, the more telling story lies in what no one achieved: not a single state or UT has reached the top three performance bands; Daksh, Utkarsh, or Atti-Uttam. In other words, no public system in India has yet created a school education model that can be held up as fully exemplary across learning outcomes, governance, equity, and teacher training. What’s worth acknowledging is the narrowing gap between top and bottom performers, from 51% in 2017–18 to 42% in 2023–24. Bihar, Meghalaya, Mizoram, Nagaland, and Arunachal Pradesh still rank lowest, yet many are showing year-on-year gains, indicating incremental system-level changes. However, this closing gap should not lull us into complacency. It is narrowing, yes, but from a dismally low baseline. Deconstructing the PGI: More than a Metric The PGI evaluates performance using 57 indicators across six domains: learning outcomes, access, infrastructure, equity, governance, and teacher education. These indicators are ambitious and aligned in spirit with the aspirations of the National Education Policy 2020. But three critical concerns emerge: 1. Equity and Governance Remain Chronic Weak Spots: While some states like Kerala have shown strength in teacher education (scoring 91.4/100), others falter significantly in equity-related outcomes and administrative processes. For example, West Bengal and Tamil Nadu posted steep declines in 2023–24, reflecting slippages in governance. This aligns with past research (e.g., Ramachandran et al., 2017) that shows how systemic governance failures; vacancies, poor accountability mechanisms, and data manipulation, deeply affect learning. 2. Improvement ≠ Transformation: The best-performing regions still hover within the middle-tier band of Prachesta-1. This suggests that while incremental reforms and mission-mode programs (such as PM SHRI or Samagra Shiksha) are helping states move upward, we are yet to see transformative change that can enable even a single state to reach the Daksh level. This is a sobering reminder that policy ambition needs to be matched by implementation architecture. 3. Data Quality and Context Sensitivity: PGI relies heavily on administrative data. But does this data reflect ground realities? Are teacher training sessions counted as completed even if they are online, short-term, or poorly attended? Is infrastructure being measured in terms of quality or mere presence? Without contextual and qualitative validation, we risk mistaking form for substance. #SchoolEducation #EduPolicy #PGI2024 #NEP2020 #TeacherTraining #Governance #PublicEducation #EducationReform #LearningOutcomes #SystemChange #EvidenceBasedPolicy

"I've had good feedback after my degree show, but I have no idea how to start a business." I hear variations of this statement all too often from young designers. Fashion school graduates enter the industry starry-eyed, inspired by the success stories of Galliano, McQueen, and the Antwerp Six. While undeniably talented, many lack crucial business acumen. My Observations: 🪡 Fashion programs rightfully focus on creativity and design but often neglect essential business skills. 🪡 Students graduate with significant debt and unrealistic expectations about starting their own brands. 🪡 So, there's a disconnect between the romantic notion of being a fashion designer and the industry's harsh realities. What are the solutions? ✂️ Curriculum Integration: Incorporate courses on market dynamics, financial management, and entrepreneurship principles into fashion programs. ✂️ Incubators and Accelerators: Establish programs tailored for budding fashion entrepreneurs, providing mentorship, resources, and support to transform innovative ideas into viable businesses. ✂️ Industry Connections: Organize networking events, workshops, and seminars where students can connect with professionals, potential collaborators, and investors. We have to close the gap between creativity and commerce in fashion education. I don't believe fashion schools intentionally set graduates up for failure, but we can do more to integrate them into the industry and nurture their talent. Without new talent, fashion's future dims. I believe equipping our next generation of designers with artistic vision and business savvy will ensure a more sustainable and innovative future for the industry. What are your thoughts on preparing fashion graduates for the business world? Let's discuss 👇 #FashionEducation #BusinessOfFashion #EntrepreneurialDesign PS 💫 Follow me for unfiltered insights on the business of fashion, beauty, and style.

Since we’re on the methodology train, I wanted to highlight something that sometimes gets misused or misunderstood in education research—mixed methods designs. It’s not about slapping qualitative and quantitative together—it’s about systematically integrating them to answer complex research questions. When done well, mixed methods research allows us to capture depth, mechanisms, context, and “causality” in ways single-method approaches can’t. Here’s a visual I’ve designed that summarizes five common types of mixed methods designs: 1. Concurrent Design – QUAL and QUAN are collected at the same time and analyzed together. 2. Sequential Design – One follows the other, like QUAL then QUAN (or vice versa). 3. Embedded Design – One method supports the other (often a smaller QUAL inside a larger QUAN). 4. Transformative Design – Framed by a theoretical lens, often for equity or social justice. 5. Multiphase Design – A series of studies and data collection methods that build on each other over time. In program evaluation, mixed methods are essential. Quantitative data helps us track outcomes and trends, while qualitative insights bring meaning to the numbers. Together, they create a richer, more actionable picture—whether we’re evaluating learning gains, implementation fidelity, or systems change. Especially in education, where context really matters, mixed methods allow evaluators to ask and answer the right questions with greater nuance. #researchmethodology #evaluation

Measuring Research and Innovation Outputs Research and Innovation are key drivers of progress in academia, leading to new discoveries, technologies, and ways of thinking that can have a profound impact on the world. However, measuring the research and innovation capacity and output of a university can be a complex challenge. What metrics should be used, and how can universities effectively track and assess their research and innovative activities? One important factor to consider is research productivity. The number and quality of publications, patents, and other intellectual property generated by a university's faculty can be a strong indicator of innovative thinking and problem-solving. Citation impact, or how frequently a university's research is referenced by others in the field, is another useful metric. Universities can also track the commercialization of their innovations, such as the number of startup companies spun out or licensing deals made. Beyond traditional research outputs, universities should also look at more holistic measures. This could include the number of interdisciplinary collaborations, number and quality of doctoral programs, number and quality of international conferences, number and quality of international academic partnerships, joint publications, quality of research labs, amount of internal funding, the diversity of research topics and methodologies, the speed of knowledge transfer to real-world applications, and the university's ability to attract top talent and external funding (from industry and research funding agencies) for innovative initiatives. Student-led projects, hackathons, and entrepreneurship programs are other important indicators of a culture of innovation. In addition to academic impact through publications and citations, the social, economic, health, environmental, and quality of life impact should also be measured. Qualitative assessments can supplement quantitative metrics. Interviews, case studies, and peer reviews can provide valuable insights into the quality, creativity, and impact of a university's innovations. Gathering feedback from industry partners, community stakeholders, and other external collaborators can also shed light on the university's ability to drive meaningful change. Ultimately, a multifaceted approach is needed to accurately gauge a university's research and innovative capacity. By tracking a balanced set of quantitative and qualitative measures, institutions can identify their strengths, pinpoint areas for improvement, and ensure they are delivering on their mission to advance knowledge and positively transform society. At ADU, Research and Innovation is led by my esteemed colleague Professor Montasir Qasymeh and all the above measures are taken into account when measuring our research and innovation outputs. Please provide your views if I have missed any important measures. #Research #Innovation #ADU Hamad Odhabi Khulud Abdallah Abu Dhabi University

Are we all doomed by using ChatGPT? I read the MIT paper. And here’s what I walked away with. The good. ✅ ChatGPT is great at lowering friction. LLM users experienced less frustration and effort. The cognitive load was significantly lower, particularly the extraneous and germane load. In plain terms? It’s easy to use and helps people “get it done.” ✅ It boosts productivity by 60%. People completed the same writing task faster and with more output. ChatGPT helped with structure, grammar, and transitions. ✅ Essays scored well. Even when the LLM group showed low brain engagement, their essays often scored high—both by AI graders and human teachers. Especially strong in surface-level linguistic traits: composition, fluency, polish. ✅ It’s useful for high-structure tasks. Participants used it to scaffold, translate, rephrase, and smooth out language. Those who used ChatGPT after brain-only writing actually showed re-engaged brain activity, especially in visual and memory networks. And here is the bad ... ❌ Their brains? Checked out. EEG data showed that the LLM group had the lowest cognitive engagement:less alpha, beta, theta connectivity. Less thinking, less strategizing, less integrating. ❌ They couldn’t remember what they wrote. 83% of ChatGPT users couldn’t quote their own writing minutes after the session. Brain-only users? Nearly all could. ❌ They didn’t feel ownership. Most LLM users said the essay didn’t feel like it was theirs. In the brain-only group, 16 of 18 felt full ownership. ❌ They got lazy. By session 3, many LLM users were copy-pasting straight from ChatGPT with minimal editing. The essays became more templated, less original. The default ChatGPT voice took over. ❌ Echo chambers got worse. LLM users stuck to narrow vocab, repeated the same named entities, and explored fewer topic angles: e.g., "homelessness" dominated all Philanthropy essays. LLMs reinforced biases subtly but consistently. ❌ Strategic thinking atrophied. Compared to the Search group—who had to read, evaluate, and synthesize—LLM users showed lower memory recall, reduced schema building, and less topic exploration. ❌ And in Session 4? The impact lingered. Those who switched from LLM to brain-only didn’t bounce back. Their brains stayed underactivated. Neural patterns stayed low. They forgot how to think deeply, even when ChatGPT was taken away. So, are we doomed by using AI tools? The bottom line: we are accumulating something the researchers call cognitive debt. And just like credit cards, it feels fine… until it’s not. Though ChatGPT is not evil. ChatGPT cannot be your strategy. ChatGPT is a tool. But every time we use it to bypass thinking instead of supporting it? We’re outsourcing what makes us… us. So here’s the question: If we keep relying on ChatGPT to write, plan, and decide ... What happens when we need to remember how to do it ourselves?

Let’s be honest about fashion sustainability. Organic cotton and recycled polyester won’t save the planet. Because the core problem isn’t the material. It’s the system. The real crisis starts with deadstock — the mountains of unsold garments that end up dumped in landfills or shipped to poor African countries as waste disguised as aid. And what causes all this overproduction? Not just fast fashion. But poor fit. 🔸 Designers don’t understand how to translate feedback from real fittings. 🔸 Pattern makers work in isolation from design intent. 🔸 Fabric is wasted because of inefficient cutting and miscommunication. The biggest sustainability issue in fashion is not the fiber. It’s the disconnect between design, pattern, and production. Until fashion education teaches: • Fit analysis • Pattern logic • Cutting layout strategy • Communication between departments …we are not solving anything. You can use all the recycled materials you want — but if you’re producing garments that won’t be worn, it’s still waste. Sustainability starts in the sample room, not just the sourcing department. Let’s teach designers to build smarter — from the inside out. #NeoEgyptianDesign #FashionAsBusiness #TeachFitNotTrends #CreativeEntrepreneurship #FashionEducation #BuildNotJustCreate

In recent months and years, extraordinary attention has been paid to the risks of diversity, equity, and inclusion, such as lawsuits by anti-DEI activist groups, executive orders, and social media campaigns. But what about the risks on the other side? Earlier this year, in the immediate aftermath of Donald Trump's anti-DEI executive orders, the Meltzer Center for Diversity, Inclusion, and Belonging at NYU School of Law and Catalyst Inc. fielded a survey of 2,500 U.S. employees and leaders (c-suite and legal) in medium and large organizations with active DEI programs. We wanted to get behind the headlines and understand how people on the ground were actually navigating the current legal and political environment. When we reviewed the data, one big theme immediately emerged: the risks of *retreat*. Whether you look at it from a talent perspective, a financial perspective, a legal perspective, or a reputational perspective, we found ample data to suggest that retreating from initiatives that promote fairness and equal opportunity in the workplace creates its own significant risks. These risks need to be factored into an organization's DEI strategy. Take these examples: ⏺️ 68% of c-suite leaders and 65% of legal leaders said moving away from DEI would create more legal risk for their organization. ⏺️ 64% of the c-suite and 62% of legal leaders said there was greater risk of litigation alleging discrimination from traditional plaintiffs (e.g., people of color, women, LGBTQ+ people) than non-traditional plaintiffs (i.e., members of dominant or majority groups). This new report, which I coauthored with Alix Pollack, Tara Van Bommel, PhD., Christina Joseph, and Kenji Yoshino, helps leaders benchmark their DEI strategy against that of organizational peers, and serves as a playbook to help them navigate this tricky terrain. Please read it at the link below and let me know what you think! https://lnkd.in/gefJahjN

Thinking about pursuing a PhD? Choose your program wisely. (What questions to ask ⬇️) When I studied how to evaluate doctoral programs in corporate strategy, I discovered that the best programs weren’t just those with big names or reputations. They excelled in three key areas: 🔹 Influence: are faculty actively publishing in the top journals in your field? Influence matters because it shapes the conversation you’ll be entering. 🔹 Internal Collaboration: do professors write with each other and with students? Strong internal networks mean you’ll have more opportunities to co-author and learn. 🔹 External Collaboration: does the school have meaningful partnerships beyond its walls? Programs that engage across institutions open doors for broader impact. If you’re selecting a program, don’t just ask, “What’s the school’s ranking?” Ask instead: - Who are the faculty publishing with, and how often? - Do students appear as co-authors? - How connected is this program to the wider academic community? A doctoral journey is more than coursework; it’s about joining a scholarly community. Choose the one where collaboration and influence will set you up not just to graduate, but to lead. As a result of my research, I presented a paper at the Administrative Sciences Association of Canada (1992) Conference on how school influence, internal collaboration, and external collaboration shape the strength of doctoral programs in strategy. I was awarded a Best Paper award for this research. #Phd #PhdProgram #academia #corporatestrategy #phdstudies