Data-Driven Education Insights

The Inconvenient Truth About Education Elite educational institutions often present themselves as neutral pathways to opportunity, yet their underlying structures tend to reproduce existing social and economic hierarchies. The dominant meritocratic narrative suggests that access is determined by talent and effort. In reality, access is shaped long before admissions by early exposure to enriched learning environments, private tutoring, and high‑quality preparatory schools. These advantages correlate strongly with household income and parental educational attainment. As a result, admissions processes frequently reward accumulated privilege rather than isolate innate ability. Entrance examinations are widely regarded as objective assessments, but they largely measure the long‑term effects of unequal resource distribution. By the time students reach the testing stage, disparities in nutrition, literacy development, school quality, and parental availability have already influenced their academic trajectories. The exam functions as a symbolic equalizer that obscures the structural inequities embedded in the educational pipeline. Policymakers often rely on this symbolism to justify existing systems, despite consistent evidence that opportunity gaps emerge years before formal schooling begins. For individuals who succeed within this architecture, achievement reflects both genuine effort and the presence of enabling conditions that many students never experience. These conditions include stable households, functional schools, psychological safety, and access to mentors who can translate potential into performance. Many equally capable individuals are excluded from the competition long before selection occurs. Their absence is not a reflection of lower ability but of systemic barriers that restrict participation. A policy‑informed response requires interventions across multiple stages of the educational pipeline. Early childhood programs must be expanded to ensure that foundational skills are not determined by socioeconomic status. Public investment in teacher quality, school infrastructure, and community‑based learning resources can reduce disparities in basic education. Admissions processes should incorporate contextual indicators that recognize structural disadvantage rather than relying solely on standardized tests. Targeted scholarships, mentoring programs, and bridge curricula can support high‑potential students who lack preparatory advantages. Without such reforms, elite education will continue to reproduce inequality while maintaining the appearance of fairness.

“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.

Children spend just 190 days of the year in school. That leaves 175 days where learning is shaped elsewhere. Yet our education policy debates often treat schools as though they operate in isolation. Attainment gaps, literacy development, attendance, behaviour and aspiration are not solely the product of what happens between 9am and 3pm. Positive outcomes are influenced by: • home stability • access to books • youth provision • community safety • parental confidence • cultural capital • enrichment opportunities If we are serious about raising standards and narrowing gaps, policy cannot stop at the school gates. With such little time being spent in school we need to be innovative about how, when and where we educate our young people. Funding decisions around youth hubs, libraries, early years support, family services and community provision are not peripheral to education policy, they are central to it. We cannot demand that schools compensate for structural disadvantage in 190 days a year while reducing the infrastructure that supports children in the other 175 days. Education reform must move beyond classroom reform. Outcomes are shaped by ecosystems, not institutions alone.

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.

🚀 Can teaching students “how to learn” actually change how they engage with their coursework? In this study published in the British Journal of Educational Technology, we used over 257,000 online learning “clicks” from biology students to track how their study habits evolved. Researchers moved beyond simply counting clicks—they mapped patterns of engagement, like how regularly students moved between different resources (quizzes, notes, calendars). Key findings: Students who received a short “science of learning to learn” training showed more organized, regular study patterns—and kept them up all semester. This regularity (think: consistent, purposeful learning routines) was a strong predictor of final grades—above and beyond just how much students clicked. Complexity-based network analysis offers powerful, AI-ready ways to monitor and support student self-regulated learning in real time. 💡 The big idea: Success isn’t just about what you study—it’s about building adaptive, organized habits you can sustain. https://lnkd.in/er9mmBfa

I am pleased to share our new publication, "Navigating centralized admissions: The role of parental preferences in school segregation in Chile," recently published in the International Journal of Educational Research (co-authored with Macarena Kutscher). https://lnkd.in/gAyJ8iTR The question we investigated: Why doesn't equal access lead to equal outcomes in school choice? In 2015, Chile enacted the Ley de Inclusión, eliminating school screening practices—no more entrance exams, parent interviews, or income verification. Every family gained equal access through a centralized, algorithm-based system. Key objective: reduce school segregation. The result: Recent evidence by Kutscher and Urzua found minimal impact on integration. Our paper confirms and extends these findings. We analyzed 133,000+ prekindergarten applications to understand why equal access hasn't translated into more integrated schools. By examining families' rank-ordered school choices using discrete choice models, we uncovered systematic differences in how low-SES families navigate school selection. Key findings: Low-income families systematically choose different schools—not because of barriers, but due to distinct preferences: 🔹 They prioritize safety, climate, and belonging over test scores 🔹 They're significantly less likely to apply to high-SES schools 🔹 They strongly favor schools with fewer violent incidents and lower discrimination 🔹 They avoid previously selective schools, even when entitled to fee waivers 🔹 Distance matters far more—they're much less willing to travel The deeper story: Disadvantaged families seek schools where their children will feel welcomed and safe. They rely on observable signals—student behavior, familiar environments, community connections. These choices reflect legitimate concerns about belonging, but may also reflect information gaps about school quality. What this means for policy: Simply removing barriers isn't enough. Effective centralized choice systems need: ✓ Comprehensive information on both academic quality AND school climate ✓ Clear data on safety, inclusiveness, and well-being ✓ Better platform design—parents often spend only minutes applying ✓ Personalized guidance, not just generic rankings ✓ Explicit explanation of how matching algorithms work The opportunity: Pioneering work by Jishnu Das and colleagues in Pakistan and Chris Neilson and colleagues in Chile demonstrated that targeted information interventions can dramatically improve parental choices. We've replicated these approaches in Haiti, Ecuador, and Peru with similar findings. We're now testing these insights on choice platforms in Recife, Brazil, with promising early results. The welfare gains from improving school access for disadvantaged students are substantial. This research points toward specific design features that could help centralized choice systems deliver on their promise of integration.

How can we bridge the gap between academia and policymaking to create more effective public policies? This report provides actionable recommendations on improving academic-policy engagement. Key recommendations include: 🔶 Proactive Support: Universities and policy institutes should actively provide information and resources to aid academic engagement with policymaking. Effective signposting to these resources is also essential. 🔶 Recognition in Academic Frameworks: Institutions need to acknowledge policy engagement within workload models and career progression frameworks. This is frequently highlighted during research impact training and is crucial to get right. 🔶 Tailored Guidance: Policymakers should create specific resources for academics to navigate policy engagement opportunities. 🔶 Addressing Geographic Disparities: Mechanisms should be developed to increase engagement with universities outside London and the South East. 🔶 Sustained Engagement: Continuous interactions between policymakers and academics should be facilitated, considering the workload implications. 🔶 Case Studies and Transparency: Publicly accessible case studies of successful academic-policy engagements and transparent use of research evidence are essential. There is wide agreement that engagement between academia and policymakers is a positive step, but it can be challenging to implement. Ensuring that research effectively informs decision-making is key. #AcademicEngagement #PolicyMaking #ResearchImpact #HigherEducation #PublicPolicy #KnowledgeExchange #EvidenceBasedPolicy #AcademicResearch

More Than Algorithms: How Hybrid Tutoring Is Rewiring Learning Equity Improving Student Learning with Hybrid Human-AI Tutoring is a three-site, quasi-experimental study led by Carnegie Mellon University, exploring the effectiveness of a hybrid tutoring model that combines AI-driven adaptive math software with human tutoring. Conducted in 3 urban, low-income U.S. middle schools, the intervention was designed to enhance learning for students historically underserved in math. The study evaluates outcomes among over 500 students—Black & Latinx—revealing that hybrid tutoring significantly increases student engagement and learning progress, particularly for students below grade level. At a fraction of the cost of traditional high-dosage tutoring, this model offers a scalable, equity-oriented solution to pandemic-era learning gaps [The cost of the hybrid human-AI tutoring intervention was reported as: Average cost per student: ~$700 USD/year] 5 Key Takeaways: 1. Hybrid Human-AI Tutoring Boosts Engagement and Progress: Students in the hybrid model showed statistically significant increases in time spent on task, lessons completed, and proficiency gains compared to students using math software alone. 2. Equity Gains: Hybrid Tutoring Reaches Students Who Need It Most: Students below grade level benefitted more from hybrid human-AI tutoring than their on-grade peers. AI-informed tutors were more likely to engage struggling students, even those who did not actively seek help. This suggests the model helps overcome systemic help-seeking disparities and redirects support toward the most underserved learners, advancing equity. 3. Teacher Support Helps Learning—But May Reinforce Inequities Without Guidance: While the presence of math teachers during EdTech sessions led to improved outcomes overall, these gains more benefited higher-achieving students. Teachers, without AI guidance, tended to respond more to students who actively asked for help. Hybrid tutoring systems equipped with dashboards can correct this imbalance by proactively identifying and prioritizing students in greater need. 4. Lower Tutor-to-Student Ratios Improve Impact: At one study site, reducing the tutor-to-student ratio from 1:8 to 1:4 significantly increased the number of learning modules completed per hour. This highlights how maintaining manageable group sizes is essential for maximizing personalized learning is key. 5. Quasi-Experimental Methods Offer Rapid, Useful Evidence—But Broader Validation is Needed: The study demonstrates how rapid-cycle quasi-experiments can provide timely and actionable insights into what works and for whom. Thomas, D. R., Lin, J., Gatz, E., Gurung, A., Gupta, S., Norberg, K., ... & Koedinger, K. R. (2024, March). Improving student learning with hybrid human-AI tutoring: A three-study quasi-experimental investigation. In Proceedings of the 14th Learning Analytics and Knowledge Conference (pp. 404-415). https://lnkd.in/eHyKtW7p

You ran the data meeting on Friday. Everyone nodded. Nothing changed on Monday. Here's what really happened. Data was collected. The team discussed the data. But nobody decided 𝙝𝙤𝙬 𝙩𝙤 𝙩𝙚𝙖𝙘𝙝 𝙙𝙞𝙛𝙛𝙚𝙧𝙚𝙣𝙩𝙡𝙮. Here's the problem: we've confused 𝘤𝘰𝘭𝘭𝘦𝘤𝘵𝘪𝘯𝘨 data with 𝘶𝘴𝘪𝘯𝘨 it. Data without a clear instructional response isn't a system. It's a filing cabinet. So what does acting on data actually look like? After your next assessment, before your data meeting, ask your team one question: "𝗕𝗮𝘀𝗲𝗱 𝗼𝗻 𝘁𝗵𝗶𝘀 𝗱𝗮𝘁𝗮, 𝘄𝗵𝗮𝘁 𝗮𝗿𝗲 𝘄𝗲 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 𝗮𝗻𝗱 𝗵𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆 𝗻𝗲𝘅𝘁 𝘁𝗶𝗺𝗲?" Not re-teaching the same lesson. Not moving on and hoping it clicks. 𝗛𝗼𝘄 𝗮𝗿𝗲 𝘄𝗲 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵𝗶𝗻𝗴 𝗶𝘁 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁𝗹𝘆? Here's a simple three-step protocol to make that question actionable: 𝗦𝘁𝗲𝗽 𝟭: 𝗡𝗮𝗺𝗲 𝘁𝗵𝗲 𝗺𝗶𝘀𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝗺𝗶𝘀𝘁𝗮𝗸𝗲. Don't stop at "students got question 4 wrong." Ask why. Was it a procedural error? A conceptual gap? A language barrier? The misconception tells you how to respond. The mistake only tells you something went wrong. 𝗦𝘁𝗲𝗽 𝟮: 𝗠𝗮𝘁𝗰𝗵 𝘁𝗵𝗲 𝗶𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝗶𝗼𝗻𝗮𝗹 𝗺𝗼𝘃𝗲 𝘁𝗼 𝘁𝗵𝗲 𝗺𝗶𝘀𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻. If students have a conceptual gap, teachers should use the CRA model (Concrete, Representational, Abstract) as a guide. Start with manipulatives or real-world context, move to visuals, then rebuild the abstract. If it's procedural, slow down the steps and make student thinking as visible as possible. The response has to match the root cause, not just re-cover the content. 𝗦𝘁𝗲𝗽 𝟯: 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 𝗮𝗻𝗱 𝗮𝘀𝘀𝗶𝗴𝗻 𝗼𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 𝗯𝗲𝗳𝗼𝗿𝗲 𝗹𝗲𝗮𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗼𝗼𝗺. Every instructional response needs a name attached to it. Who is trying what, in which class, by when and what does that instruction actually look like? Without ownership, the plan dies in the meeting. 𝗗𝗮𝘁𝗮 𝗺𝗲𝗲𝘁𝗶𝗻𝗴𝘀 𝘀𝗵𝗼𝘂𝗹𝗱 𝗲𝗻𝗱 𝘄𝗶𝘁𝗵 𝗮 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗽𝗹𝗮𝗻, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗮𝗹𝗸𝗶𝗻𝗴 𝗽𝗼𝗶𝗻𝘁. ♻️ If this idea resonates, repost to help school leaders and math teams turn data into action, not just conversation. 📧 If you're interested in more practical strategies like this, I'm launching a new newsletter called The 3-1-4, where I share practical strategies for improving math instruction and leadership. The first issue goes out on Pi Day (March 14). Link in the comments. _______________________________ Hi, I'm Dwight Williams. A proud first-gen everything, and I help schools and districts strengthen math instruction through coaching, curriculum support, and data-informed systems that drive student confidence and achievement. 👍🏿 Like | 🔔 Follow | 💬 Comment | 🔁 Repost

Yesterday’s Office of the Auditor General of Canada / Bureau du vérificateur général du Canada report on Canada’s International Student Program highlights a critical reality: stronger system supports for international students are not optional-they are essential. This is not simply about volumes or growth. It points to deeper challenges in accountability, coordination, and the integrity of information. When responsibility for ensuring clear, accurate information is diffused, the result is confusion and increased vulnerability for international students. We welcome the Auditor General’s findings and value the opportunity WES had to contribute to the review. Recently, WES released a seven‑part research series to examine systemic challenges in the information network that international students rely on. Our report highlights key issues such as information gaps, inconsistent policy messaging, unregulated agents, and the growing impact of AI-driven misinformation - while outlining practical solutions focused recommendations for policymakers, intermediaries, and post-secondary institutions. International students play a pivotal role in shaping Canada’s global education standing and reinforce our reputation as a welcoming destination - but only if student voice and protection are treated as core policy outcomes, not afterthoughts.  #IRCC #GAC #AGreport #WESResearch #InternationalStudents #IntlStudents #CdnPoli #CdnImm Read WES’s Trust Through Information series: https://lnkd.in/erxx_byQ