Data-Driven Education Insights

There are 1.1M credentials but our latest research finds that only 12% offer significant wage gain earners wouldn’t have otherwise gotten. The Burning Glass Institute is launching the Credential Value Index to show which ones work, evaluating the outcomes from 23,000 non-degree credentials from over 2,000 providers, including every certification in America—from Coursera digital marketing certificates to OSHA certifications. To see whether they actually deliver for workers, we analyzed how each changed the course of the careers of 7 million people who had earned them. While only 1 in 3 credentials meet a minimum threshold vs. counterfactual peers for either boosting wages, facilitating career changes, or moving people up within their field, we still found 8,000 credentials that really move the needle for workers—often in ways that are transformative. The top decile of credentials yields annual wage gains of nearly $5,000 vs. counterfactual peers, increases by 7x vs. bottom credentials the chances of switching jobs into an aligned career, and boosts by 17x the probability of an earner’s getting promoted within their current field. We found wide variances in outcomes even for the same credential across named providers–and across the portfolio of credential offerings of even high-reputation providers. That says that learners can’t just trust brands and they can’t just trust that a credential will help just because it’s in a high-paying field. Instead, they need real data to help them make informed decisions. Our goal in this work is practical: to put these evaluations in the hands of workers and learners, employers, education institutions & training providers, and policymakers. The Credential Value Index–available through our Navigator site available on https://lnkd.in/e_BTX9bs –makes all 23,000 evaluations accessible to the public, with easy-to-understand metrics of performance, comparisons with other credentials, and helpful context, like which roles earners find themselves working in, which employers they’re working for, and which skills they master along the way. Our research is summarized in an American Enterprise Institute working paper which I coauthored with AEI senior fellow Mark Schneider and Burning Glass Institute colleagues Shrinidhi Rao, Scott Spitze, and Debbie Wasden. You can find it on https://lnkd.in/ezynMA-v. I want to express my deep thanks to Ellie Bertani, Matt Zieger, and the GitLab Foundation for all they have done to support this initiative. I am grateful for your partnership. And a big thank you to Patti Constantakis and Sean Murphy at Walmart for the opportunity to test this framework in a real-world laboratory. Finally, the Credential Value Index builds on a close partnership with Jobs for the Future (JFF). Many thanks to Maria Flynn, Stephen Yadzinski, and their terrific team. #education #careers #highereducation #learning #skills

“Beta dhokha dega, data nahi.” Sounds reassuring, right? But in education especially Online courses, this belief can quietly mislead us. Yes, data analytics in education helps us track logins, completion rates, drop-offs, quiz scores. It tells us what happened. But from a Behavioural Science lens, data rarely tells us WHY it happened. 📉 A learner drops out of a MOOC. Data says: Low engagement after Week 3. Behavioural reality may be: 👉 Cognitive overload 👉Loss of identity (“people like me don’t finish MOOCs”) 👉Present bias (“I’ll do it later”) 👉Lack of social accountability None of this shows up cleanly on a dashboard. When we become obsessed with metrics, we risk: Designing for completion rates, not learning Nudging clicks instead of shaping habits ❌ Treating learners as datapoints, not humans with context, emotion, and constraints In #MOOCs, more data ≠ better decisions Unless it’s paired with: 🧠 behavioural diagnostics 🧪 experimentation (A/B tests with theory) 💬 qualitative insight So maybe the wiser mantra is: “Beta bhi dhokha de sakta hai, data bhi .....agar behaviour ko samjhe bina dekha.” Data is a tool. #Behaviour is the truth behind it.

Monday’s termination of scores of Department of Education contracts includes virtually all contracts that the National Center for Education Statistics relies on for its data collection and numerous products, according to various news outlets. Without NCES products, families, communities, and decisionmakers throughout the country will be left in the dark on many aspects of our education system. NCES’s reports on the status of student learning on state-by-state and international basis are widely used by parents, administrators, and policymakers to make decisions on school programs based on what’s working and isn’t working. Students and parents use NCES resources to monitor school safety and help locate public and private schools and colleges that meet their needs. Policymakers in the private and public sector use NCES products to develop programs, allocate resources, and track the latest trends in education. States, localities, and institutions around the United States use the data to compare themselves with others on tuition, salaries, staffing, expenditures, student achievement, graduation rates, and many other measures. Businesses use NCES data to inform their recruitment and siting for new facilities. Federal, state, and local governments as well as businesses and corporations used the data to determine the supply of labor with specific skills and training. Researchers use data to study progressions from early childhood through postsecondary education and into early careers to help answer questions such as whether students’ high school academic achievement is related to college enrollment and completion. I call on the administration and Congress to immediately rectify the situation so that NCES can continue being an invaluable resource to families, communities, and policymakers who need objective and timely information to inform their decisions in the best interests of America’s students and the country’s future.

Bad data = bad decisions. The decision of the U.S. Department of Education to cancel #IPEDS trainings isn't just a budget cut—it’s a #data #quality #crisis in the making. I’ve spent the past decade as an IPEDS Educator with National Center for Education Statistics (NCES) and Association for Institutional Research (AIR)—leading workshops, creating tutorials, and supporting literally thousands of new and veteran institutional researchers. My goal has always been to help ensure accurate reporting and meaningful use of higher education data. That mission is now at serious risk. The Department has chosen not to renew AIR’s contract to provide free, expanded training on IPEDS. You may think, why should we care? Here’s why this matters: 💡 IPEDS isn’t just another bureaucratic form—it underpins nearly every dataset about enrollment, financial aid, completion, and student outcomes. 💡 Over 6,000 institutions rely on it to make decisions that support student success. 💡 Funding for institutions is based in large part on it. 💡 Search engines for students to help them find the college that best fits their needs is based on it. 💡 Higher education policy is based on it. 💡 Accreditors make determinations based on it. Institutional Research isn’t a field people typically enter on purpose. There’s no straight path. Most IR professionals are promoted from within, trained on the job, and handed massive reporting responsibilities with little preparation. That’s why these workshops matter. That’s why they’ve existed. IPEDS training has been the foundation for quality, consistency, and confidence in data collection and use. When training disappears, data quality drops. Episodes of inconsistency, misreporting, and misinterpretation aren’t theoretical—they’re inevitable, affecting policy decisions, public trust, and student impact. Let’s start asking tough questions: ❓ Who will train the next generation of data professionals? ❓ If we lose these supports now, we won’t just miss a workshop—we’ll miss an entire culture of data accountability? ❓ Who is going to ensure consistency and accuracy across institutions? ❓ Who is going to build a common language around enrollment, outcomes, and equity? ❓ Who is going to help data professionals turn compliance into insight? Now, with the Department of Education discontinuing this support, we’re risking a decline in data quality, a growing burden on institutions, and the erosion of one of the most important public datasets in higher education. The loss won’t just affect campuses. It affects policymakers. Researchers. Journalists. And ultimately, students. Because when we get education data wrong, we get education policy wrong. https://lnkd.in/eriVUF6R

In meetings, you might hear phrases like "the data speaks for itself" or "we’re just looking at the facts." These statements can give the impression that data offers a neutral view of reality. But data is never completely neutral. Here’s why. Data reflects a world shaped by existing systems of power. Disparities in education, health, and incarceration show how these systems' social structures are maintained. However, it’s common to interpret disparities in social data as individual failures or successes. For example, someone’s health is often seen as a matter of personal responsibility. Yet no matter which data metric we use—whether deprivation, income, or education—there is a strong social SYSTEM gradient. The poorer you are, or the less education you have, or the more deprived your neighbourhood, the more likely you are to die younger and sicker. This pattern holds across almost every condition or disease. It is not shaped by individuals, but institutional systems of power. So, if you share data about people and communities, you have more responsibility than you might realise. You have the power to influence your peers, government decisions, and ultimately public opinion. By explaining the conditions that shape data, we make it harder for inequities to go unnoticed. To use data responsibly, we have to recognise its dual role. Data can be a mirror that reflects inequality and a magnifier that can make it worse if misinterpreted. ---- Kia ora, I'm Kat 👋 I wrote The Data Storyteller's Handbook. My next book exposes how powerful systems like racism, sexism, and classism shape not only our world but the data we rely on every day.

Students are now getting a warning on their FAFSA: “Some of the colleges you selected show lower earnings.” Sounds like a smart move. Who doesn’t want transparency? But it’s not that simple. The Department of Education is flagging schools where median earnings, four years after graduation, are lower than for high school graduates. But the measure is based on broad institutional data, not by program. So, a strong nursing or tech credential could get penalized because other degrees at the same college don’t pay as well. It also ignores regional economies, public service jobs, and career choice. Yes, we need transparency. Yes, everyone should see value from their college investment. But we have to be careful. Measuring that value by earnings just four years out is a narrow view. It misses how long it takes some graduates to gain traction, especially those starting with fewer resources or entering lower-paying but essential careers. It also ignores lifetime earnings, which we know are significantly higher for degree holders. And it assumes a single definition of success, one where public service, regional impact, or career satisfaction don’t count if the paycheck is smaller. Good data should inform choices. But rushed signals and blunt metrics can push students away from opportunity instead of toward it. We don’t just need transparency. We need context. https://lnkd.in/gXg5ZNr3

Data matters! Did you know that 53% of countries still rely on paper-based education information systems? Or that only 63% of SDG4 indicators' datapoints are currently reported? And that 1 in 3 ministries cannot accurately locate all schools in the jurisdiction? That means millions of learners are invisible in education statistics. When data is missing, delayed, or unreliable, entire education systems struggle to plan, allocate resources, monitor progress, or respond to crises. And as countries face growing demands for timely, high-quality education data—covering inclusion, learning pathways, digital access, and financing—the gaps only grow more urgent. ✨ Imagine instead: data systems that are robust, reliable, and responsive—driving smarter decisions, fairer resource allocation, stronger accountability, and better learning for every child. This why UNESCO’s education sector has developed the EMIS Progress Assessment Tool for Transformation (EMIS-PATT). What is EMIS-PATT? EMIS-PATT is a lightweight, nationally led diagnostic and planning tool designed to help countries assess, strengthen, and strategically transform their Education Management Information Systems. Built on a holistic framework, it looks at both: The enabling environment: governance, institutional arrangements, management processes The technical system: IT architecture, data management, interoperability, and information use What makes EMIS-PATT unique is its ability to take countries from knowing the problems to planning solutions—through clear progress descriptors, prioritized actions, and sequenced, costed implementation plans. 🌍 As data needs become more complex—countries navigate rising demands with limited resources—practical tools like EMIS-PATT are essential. Reliable data isn’t a technical luxury; it’s the backbone of equitable access, improved learning outcomes, and resilient education systems. 📖 Read the EMIS-PATT Methodological Guide for Educational Transformation and let’s build education systems where every learner counts, every school is visible, and every decision is driven by strong, actionable data.   📊To learn more about UNESCO work on education data systems, visit https://lnkd.in/e2Ub8VBs

Demonstrating the value of learning is easier than you think! In a recent workshop with The Institute for Transfer Effectiveness, I demonstrated how! One workshop participant was designing safety training to help employees use Microsoft 365 strategically to prevent data breaches. She was struggling to capture the value of the program for organizational leaders to understand. I used an alignment framework that incorporates Rob Brinkerhoff’s 6 L&D value propositions and mapped out how to connect her learning program with metrics that matter to organizational leaders. Here’s what that looked like! Aligning learning activities, initiatives or programs to strategic business outcomes is like looking for the through line between disparate things: learning, human performance, departmental key performance indicators, and organizational metrics. This can feel nearly impossible. The glue that holds these seemingly disparate things together are Brinkerhoff’s 6 L&D value propositions. In the safety training example we started by identifying the most relevant value proposition for the program. In this case, it was Regulatory Requirements: a learning program designed to ensure employees are complying with industry specific rules and regulations. Then we connect the L&D value proposition (Regulatory Requirements) with the most relevant outcome for the organization. In this case, it was Net Profit. If employees are complying with industry-specific rules and regulations, this consistent practice will save the organization money in fines, lawsuits, or dealing with the unpleasant consequences of safety challenges (like a data breach). Then we must do the hard work unpacking what people will be doing to support the targeted departmental KPIs. If you’re struggling to figure out the KPIs, you’ll likely find them by asking department leaders what problem they are experiencing on a regular basis that they would like solved. In this case it was too many data breaches and too many outdated files on the server causing misinformation and inconsistent practices. I discovered that what people could be doing differently to support the desired KPIs was adhering to updated protocols on how to manage data and documents within the 365 suite. If people followed the protocols with 100% fidelity, departments would experience a reduction in data breaches. Now … we have the behaviors to target in our training program and the data to use to show the value of learning: Learning metrics: Training attendance and completion rates. Capability metrics: Percentage of fidelity to data and document protocols before and after training. KPI metrics: # of documents on the server that are outdated (being at 20% of lower), # of data breaches per department being at 1 or less annually. Organizational metric: Net Profit How will you use the 6 L&D value propositions and alignment framework to tell your learning value story? #learninganddevelopment #trainingstrategy #datastrategy

📊 KCSE 2025 Analysis: When Data Speaks, Education Transforms Behind every KCSE result is more than a grade — there’s a story, a system, and a signal. After analyzing the KCSE 2025 results, one thing is clear: data has the power to reshape education if we choose to listen to it. KCSE analysis is not just about who passed or failed. It reveals: Hidden inequalities between regions and schools Performance gaps tied to resources, teacher deployment, and learning environments Subject-level trends that signal future workforce strengths and weaknesses Early warnings for students and counties at risk of being left behind This is where data science meets policy. 🔍 With proper data analysis, governments can: Allocate teachers and resources equitably Design targeted interventions instead of blanket policies Track the real impact of curriculum reforms Predict outcomes early and act before failure happens 🎓 With the right insights, educators can: Identify struggling learners early Improve subject-specific teaching strategies Learn from high-performing schools and scale what works 📈 And for students? Data-driven education means fairer opportunities, informed decisions, and a system that supports—not surprises—you. The KCSE 2025 analysis is proof that education systems should not be run on intuition alone. They must be guided by evidence, analytics, and foresight. 💡 Data does not replace educators or policymakers — it empowers them. The future of education in Kenya and across Africa will belong to those who: Ask the right questions Trust the data Turn insights into action Let’s move from results announcement to results intelligence. #KCSE2025 #EducationData #DataScienceInEducation #EvidenceBasedPolicy #EdTech #LearningAnalytics #DataForImpact #FutureOfEducation #PolicyInnovation

📊 Data in the Classroom: Useful Only When Used Wisely In classrooms, data is not the destination—it is the starting point. Test scores, observations, student reflections, and work samples are collected every day, but data creates impact only when it is interpreted thoughtfully and acted upon intentionally. Consider how data transforms when used wisely in learning spaces 👇 🔹 Raw Data This includes marks, attendance, exit tickets, anecdotal notes, and assessment results. On its own, raw data is fragmented and often overwhelming. It tells us what happened, but not why. 🔹 Sorted Data When teachers group data—by skills, concepts, misconceptions, or learning behaviors—patterns begin to emerge. Sorting helps identify: • Common areas of difficulty • Strengths across the class • Individual learning needs This step brings clarity and focus. 🔹 Arranged Data Organizing data over time allows teachers to track progress and growth. Comparing formative and summative evidence helps answer deeper questions: • Are students improving? • Which strategies supported learning? • Who needs intervention or extension? Here, data begins to inform instructional decisions. 🔹 Presented Visually Charts, rubrics, exemplars, learning progressions, and success criteria make data accessible and transparent. When learning is visible, students can better understand where they are and where they need to go. 🔹 Explained Through a Learning Story Data becomes meaningful when placed in context. Teachers reflect on student experiences, learning strategies, and classroom conditions. This narrative explains the why behind the numbers and supports reflective teaching practice. 🔹 Actionable Data The most important stage. Wise use of data leads to: • Differentiated instruction • Targeted feedback • Reteaching or enrichment • Student goal-setting and ownership of learning ✨ In education, data is not about judgement or comparison—it is about understanding and growth. When data informs teaching, empowers learners, and guides next steps, it becomes a powerful tool for improving learning outcomes. 📌 Data is useful only when it leads to purposeful action that enhances student learning. #DataInEducation #AssessmentForLearning #StudentAgency #ReflectiveTeaching #LearningFocused #EvidenceInPractice #EducationLeadership