Data-Driven Education Insights

A data culture cannot be mandated; it emerges (or not).   The promises of a data-driven organization are compelling: teams making insightful decisions, innovations sparked by analytics, and a competitive edge sharpened by information. Unfortunately, like with many transformative changes, organizations often seek shortcuts to become data-driven—installing gleaming dashboards, hiring data teams, and declaring data a core part of the strategy, but failing to address data culture. Without addressing the underlying cultural currents, however, most technical and structural changes will amount to little more than "data theater". Data is only as valuable as the questions we ask and the actions we take. The effective use of data therefore requires a culture where curiosity is nurtured, failure is tolerated, and insights are acted upon. Such a culture can neither be conjured overnight nor installed via executive decree. It is the byproduct of shared experiences, incentives, and social norms. Attempting to impose it is akin to demanding trust or commanding creativity—it misunderstands the nature of human psychology and organizational dynamics. You don’t become a data powerhouse merely by ordering employees to think quantitatively or by explaining the importance of data. Instead, an ecosystem must be created where data informs decisions at every level. Invest in training, but not just in technical skills and data literacy, but also in critical thinking. Patience and persistence trump urgency. It’s vital that leaders lead by example. Employees absorb the data-driven ethos not through mandates, but through daily experiences. When leaders model data-driven behaviors—seeking evidence, questioning assumptions, rewarding analytical thinking—they set the tone. It's the difference between proclamation and embodiment. The path forward is clear: Stop wishing for and demanding data culture. Rather, focus on building conditions where it can naturally emerge. It's daily actions, not tools and declarations, that determine whether data becomes part of your organization's DNA.

Hey #highered leaders - if you're still using static pivot tables to inform strategy, this post is for you ⤵ Take a peak at the below screenshot. This example, which shows two "paired predictors", is just one way you can turn data into action: 📈 ▶ The top right quadrant are “high achievers”. They have a high GPA + high credit earn ratio. These students might simply receive a message of encouragement. ▶ The top left quadrant are “strivers”. They have lower GPAs, but higher credits earned. These students might receive a nudge related to maximizing their use of available academic resources. ▶ The bottom right quadrant are “setbacks”. They have higher overall GPA, likely from good grades in their early coursework, but are earning fewer credits towards graduation requirements in key courses in their major. These students should probably receive messaging about the need for high-touch interaction with their advisors to stay on track and not lose their early momentum. ▶ The students in the bottom left quadrant are in "survival mode”. They are below average in both areas. These students are probably due for some real human-to-human conversation to better understand their needs. They may need in-depth intervention, with accompanied supports for finding the most successful path towards goals that match the students’ strengths and interests. You may consider nudging and re-nudging them throughout a term. ⤵ There's so many more examples of how Civitas Learning partners are disaggregating data to close equity gaps. If you're curious to learn more, let's connect 💌 #studentsuccessanalytics

𝗧𝗵𝗲 𝗠𝗲𝘁𝗮𝗺𝗼𝗿𝗽𝗵𝗼𝘀𝗶𝘀 𝗼𝗳 𝗮 𝗗𝗮𝘁𝗮-𝗗𝗿𝗶𝘃𝗲𝗻 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻 Organizations today are on a transformational journey to become fully data-driven. It’s not a sprint; it’s a deliberate progression. One that evolves through clear stages, just like guiding an “elephant” to sit, stand, walk, run, and eventually fly. 𝗦𝗶𝘁 – 𝗧𝗵𝗲 𝗗𝗮𝘁𝗮 𝗗𝗮𝗿𝗸𝗻𝗲𝘀𝘀  𝗪𝗵𝗲𝗿𝗲 𝗜𝗻𝘀𝘁𝗶𝗻𝗰𝘁 𝗠𝗲𝗲𝘁𝘀 𝗜𝗴𝗻𝗼𝗿𝗮𝗻𝗰𝗲 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸: Your organization is essentially data-blind, navigating by gut feelings and legacy practices. 𝗠𝗮𝘁𝘂𝗿𝗶𝘁𝘆 𝗠𝗲𝘁𝗲𝗿: Low across talent, strategy, technology, and data. 𝗦𝘂𝗿𝘃𝗶𝘃𝗮𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: • Embrace radical honesty about your data limitations. • Conduct a brutally honest capability audit. DCAM could be one of the frameworks for assessment 𝗠𝗶𝘀𝘀𝗶𝗼𝗻: Lay the groundwork by identifying gaps. 𝗦𝘁𝗮𝗻𝗱 – 𝗟𝗼𝗰𝗮𝗹𝗶𝘇𝗲𝗱 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗦𝗰𝗮𝘁𝘁𝗲𝗿𝗲𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗘𝗺𝗲𝗿𝗴𝗶𝗻𝗴 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲: Isolated data islands begin to form, with sporadic analytical outposts 𝗠𝗮𝘁𝘂𝗿𝗶𝘁𝘆 𝗠𝗲𝘁𝗲𝗿: Low-Medium. Like a startup finding its first breakthrough 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗮𝗰𝘁𝗶𝗰𝘀:  • Build a data and analytics team. • Design an organizational structure that breaks down traditional silos 𝗠𝗶𝘀𝘀𝗶𝗼𝗻: Connect the islands, build bridges of insight 𝗪𝗮𝗹𝗸 – 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗔𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 𝗨𝗻𝗰𝗵𝗮𝗿𝘁𝗲𝗱 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲: You've glimpsed the potential but lack the full expedition map 𝗠𝗮𝘁𝘂𝗿𝗶𝘁𝘆 𝗠𝗲𝘁𝗲𝗿: Medium • Strategy, talent, and technology improve, but analytics capability lags. • Data is shared, but execution remains inconsistent. 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝗧𝗮𝗰𝘁𝗶𝗰𝘀: • Democratize data across organizational boundaries. • Craft a digital strategy that's both ambitious and executable 𝗠𝗶𝘀𝘀𝗶𝗼𝗻: Align strategy with execution. 𝗥𝘂𝗻 – 𝗧𝗵𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗠𝗼𝗺𝗲𝗻𝘁𝘂𝗺  𝗦𝗰𝗮𝗹𝗶𝗻𝗴 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀, 𝗔𝗺𝗽𝗹𝗶𝗳𝘆𝗶𝗻𝗴 𝗜𝗺𝗽𝗮𝗰𝘁 𝗟𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲: Robust foundations, ready to accelerate 𝗠𝗮𝘁𝘂𝗿𝗶𝘁𝘆 𝗠𝗲𝘁𝗲𝗿: Medium-High – your data engine is warming up 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸: • Embed data-driven decision-making into organizational DNA • Develop comprehensive monitoring and feedback loops 𝗠𝗶𝘀𝘀𝗶𝗼𝗻: Move from basic analytics to enterprise-wide impact. 𝗙𝗹𝘆 – 𝗧𝗵𝗲 𝗖𝗼𝗴𝗻𝗶𝘁𝗶𝘃𝗲 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 (𝗗𝗮𝘁𝗮 𝗗𝗿𝗶𝘃𝗲𝗻) 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱, 𝗜𝗻𝘀𝗶𝗴𝗵𝘁-𝗗𝗿𝗶𝘃𝗲𝗻, 𝗙𝘂𝘁𝘂𝗿𝗲-𝗥𝗲𝗮𝗱𝘆 𝗘𝗹𝗲𝘃𝗮𝘁𝗶𝗼𝗻: Advanced analytics, intelligent automation, predictive prowess 𝗠𝗮𝘁𝘂𝗿𝗶𝘁𝘆 𝗠𝗲𝘁𝗲𝗿: High-Octane , you're not just running, you're soaring 𝗣𝗹𝗮𝘆𝗯𝗼𝗼𝗸: • Integrate AI as a strategic partner, not just a tool • Create self-evolving systems that learn and adapt 𝗠𝗶𝘀𝘀𝗶𝗼𝗻: Achieve full-scale, data-driven transformation with AI and automation.

SELFIE Success Stories: Empowering Schools Digitally #SELFIE stands for "Self-reflection on Effective Learning by Fostering Innovation through Educational Technology." It is an online self-assessment tool developed by the European Commission to help schools reflect on their use of digital technologies and support their digital transformation. Schools use SELFIE to assess their digital readiness, identify areas for improvement, and develop strategies for integrating digital technologies effectively into teaching and learning practices. It allows schools to self-assess their digital capabilities, policies, and practices, providing valuable insights for decision-making and planning . SELFIE helps countries by providing aggregated data at the regional or national level, which can be used for policy monitoring and refinement. Ministries can use the data to understand the digital readiness of schools, identify trends, and tailor policies and support mechanisms to enhance digital transformation in education . SELFIE serves as a valuable tool for schools and countries to assess their digital readiness, drive strategic planning for digital transformation, and foster a culture of self-reflection and collective responsibility in the use of digital technologies in education. .... Schools are using the results from SELFIE in various ways to drive their digital transformation and improve their use of digital technologies in education. Some common ways in which schools are using the results include: 1. Identifying Strengths and Weaknesses: Schools use the SELFIE results to identify their strengths and weaknesses in the use of digital technologies. This helps them understand areas where they are excelling and areas that need improvement. 2. Setting Priorities: After analyzing the SELFIE report, schools use the information as an anchor for discussion to set priorities for their digital technology integration efforts. This allows schools to focus on specific areas that require attention and improvement. 3. Developing Action Plans: Schools use the SELFIE results to develop action plans for integrating digital technologies effectively into teaching and learning practices. The insights from SELFIE help schools make informed decisions and plan strategic initiatives for digital transformation. 4. Engaging in Collective Reflection: Schools engage in collective reflection with all stakeholders within the school community based on the SELFIE results. This collaborative approach allows schools to involve teachers, students, and school leaders in discussions about digital technology use and fosters a culture of shared responsibility for digital transformation . 5. Monitoring Progress: Schools use the SELFIE results as a baseline for monitoring progress in their digital transformation journey. By regularly assessing their digital readiness and comparing results over time, schools can track improvements and make adjustments to their strategies as needed.

🚀 Unlocking the Potential of Data in Education: From Data-Driven to Data-Informed 📊✨ Do you think you're ready to elevate your approach to school improvement? My latest article dives into the often blurred lines between "data-driven" and "data-informed" decision-making and their profound educational implications. 🔍 Key Highlights: Data-Driven vs. Data-Informed: Understand the distinct differences and why it matters. Five-Level Hierarchy: Learn the stages from basic data collection to integrating R&D for innovation. Practical Examples: Real-world scenarios from schools and districts that illustrate each level. 📚 Levels of Transition: Data Collection and Basic Analysis: Reactive decision-making based on primary data. Descriptive Analytics: Identifying trends to inform improvement. Diagnostic Analytics: Understanding the root causes of trends and issues. Predictive Analytics: Forecasting outcomes for proactive planning. Prescriptive Analytics and R&D Integration: Driving innovation through evidence-based strategies. 👩🏫 Transformative Practices: Discover how transitioning to a data-informed approach can revolutionize school improvement, leading to more strategic, proactive, and innovative solutions. Dive into the full article to explore how these transformative practices can set the foundation for continuous educational growth and excellence. #Education #SchoolImprovement #DataDriven #DataInformed #Innovation #R&D #Analytics #EducationalLeadership #ContinuousImprovement

In schools today, we’re surrounded by a plethora of data - from assessments and observations to a variety of dashboards and feedback loops. But data only matters if it informs what we do next. That’s why here at American International School of Guangzhou we’ve developed the 𝐅𝐀𝐂𝐓𝐒 𝐃𝐚𝐭𝐚 𝐏𝐫𝐨𝐭𝐨𝐜𝐨𝐥 – a structured process designed to help teams move from data collection to meaningful action. FACTS guides us to: 🔎 𝐅𝐨𝐜𝐮𝐬 on the data that matters most 📊 𝐀𝐧𝐚𝐥𝐲𝐳𝐞 insights and gaps 🎉 𝐂𝐞𝐥𝐞𝐛𝐫𝐚𝐭𝐞 successes and positive trends 🎯 𝐓𝐚𝐫𝐠𝐞𝐭 strategies and interventions 🚀 Define clear 𝐒𝐭𝐞𝐩𝐬 for action and accountability We’ve recently rolled this out with faculty, middle leaders, senior leadership - as well as with our Operations Team. All with the goal of shifting the way we talk about and act on data across the whole school. A strong data protocol matters because it: * 𝐄𝐧𝐬𝐮𝐫𝐞𝐬 𝐮𝐧𝐢𝐟𝐨𝐫𝐦𝐢𝐭𝐲 – establishing consistent guidelines and vocabulary, keeping coherence across departments and educators. * 𝐂𝐮𝐥𝐭𝐢𝐯𝐚𝐭𝐞𝐬 𝐭𝐞𝐚𝐦𝐰𝐨𝐫𝐤 – giving staff a shared approach that elevates teaching and learning collaboratively. * 𝐅𝐚𝐜𝐢𝐥𝐢𝐭𝐚𝐭𝐞𝐬 𝐢𝐧𝐟𝐨𝐫𝐦𝐞𝐝 𝐜𝐡𝐨𝐢𝐜𝐞𝐬 – empowering decision-makers to rely on dependable data and implement strategies that truly improve student learning. Just as importantly, a protocol helps us 𝐝𝐞𝐟𝐢𝐧𝐞 𝐭𝐡𝐞 𝐯𝐚𝐥𝐮𝐞 𝐨𝐟 𝐝𝐚𝐭𝐚 itself: Does it suit our needs? Are there important data points missing? Can we find a way to access them? Having vast amounts of data is one thing - having useful data is another. A protocol like FACTS ensures we make that distinction quickly and clearly. We’ve also dedicated significant time to our school improvement plan: our 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐯𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤. FACTS helps us implement, monitor, and analyse its impact with greater clarity. By running the full protocol, we ensure every data dive is structured, organised, and results in actionable steps - not just endless exploration. And beyond the walls of our classrooms and offices, data also helps us 𝐞𝐧𝐠𝐚𝐠𝐞 𝐨𝐮𝐫 𝐰𝐢𝐝𝐞𝐫 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 - celebrating successes, building trust, and showing the impact of our collective efforts. Ultimately, regardless of the protocol you use, the true value lies in the cycle itself - structured, collaborative, and action-driven. It’s this cycle that turns information into impact, ensuring data is never for its own sake, but always driving improvement, strengthening our community, and helping every student thrive.

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.

A few years ago, I worked with an online education platform facing challenges with student engagement. While they had a significant number of users enrolling in courses, they struggled with low participation rates in course discussions and activities, leading to a decline in course completion rates. The platform needed to identify the causes behind low engagement and implement strategies to encourage more active participation. Improving Student Engagement Using Data Analytics 1️⃣ Analyzing Engagement Data We began by analyzing user interaction data, focusing on metrics such as time spent on the platform, participation in discussions, video completion rates, and quiz scores. Using SQL, we aggregated the data to identify patterns and pinpoint where students were losing interest. SELECT student_id, course_id, AVG(time_spent) AS avg_time_spent, COUNT(discussion_post_id) AS posts_made, AVG(quiz_score) AS avg_quiz_score FROM student_activity GROUP BY student_id, course_id; 🔹 Insight: We identified that students who interacted with course discussions and quizzes had higher completion rates, while others dropped off quickly. 2️⃣ Building a Predictive Model We then created a predictive model to determine which students were at risk of disengaging based on their activity patterns. The model incorporated features such as time spent on the platform, participation in discussions, and progress through the course material. # Pseudocode for Predictive Model def predict_student_engagement(student_data): model = train_engagement_model(student_data) predictions = model.predict(student_data) return predictions 🔹 Insight: This model helped us flag students who were likely to disengage early, allowing for timely interventions. 3️⃣ Implementing Engagement Strategies Based on insights from the model, we implemented strategies such as sending personalized emails with reminders, offering incentives for completing activities, and increasing interaction opportunities through live Q&A sessions. # Pseudocode for Engagement Follow-Up def send_engagement_reminder(student_data): if model.predict(student_data) == 'at_risk': send_email_reminder(student_data) 🔹 Insight: Personalized engagement and incentives led to an increase in student participation. Challenges Faced Identifying meaningful engagement metrics that were predictive of success. Finding the right balance between engaging students without overwhelming them. Business Impact ✔ Student engagement improved, leading to higher completion rates. ✔ Retention rates increased, as more students continued with courses. ✔ Revenue grew, driven by more active and satisfied students. Key Takeaway: By analyzing user activity and leveraging predictive analytics, businesses can identify disengaged customers early and implement strategies to improve engagement and retention.

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

📣 New article in Nature Human Behaviour (Nature Portfolio) by me & Stefan Dercon: "Mind the gap between education policy and practice." We often pay close attention to policy making --> we need as much attention on policy **implementation** https://lnkd.in/eizinxxs Largest gaps in Africa. Bright spots in Latin America. Not correlated w/ GDP per capita necessarily, similar to patterns in learning outcomes. In a companion empirical component, we analyze policy-practice gaps further by policy, setting, & gap type: https://lnkd.in/eEna5Xch. We use detailed data on education policy plans from government officials and household-level data on services received in practice, building on impressive data generation efforts by The World Bank UNESCO UNICEF. Lots more work on this area is needed. The What Works Hub for Global Education (What Works Hub for Global Education), an ambitious new center combining researchers, policymakers, practitioners, & donors, will aim to understand & close policy-practice gaps to advance implementation science in education. The What Works Hub for Global Education is supported by a seed £30M investment by Foreign, Commonwealth and Development Office, including a consortium led by Blavatnik School of Government, University of Oxford + additional investments in Strategic Partners including the The World Bank, International Institute for Educational Planning (IIEP-UNESCO) The Learning Generation Initiative UNICEF Innocenti Global Education Evidence Advisory Panel (GEEAP) Building Evidence in Education (BE²) British Council, and continues to grow and expand with support from the Bill & Melinda Gates Foundation Jacobs Foundation - more to join the movement soon!