Insights on Data-Driven Higher Education

One of the big pieces we need to understand—and I think it’s one of the gaps higher education has—is knowing who is in our classrooms, why they are selecting us, and what they truly need. Too often, institutions fool themselves into believing the answers they want to hear. We want to think students choose us because of a strong program or a special offering we’re proud of. The reality is often more practical. For example, when I taught non-major chemistry courses, I hoped students would select the course because of its value or my teaching. But, in reality, many students chose it because it fit their schedules or fulfilled a requirement. Using data to better understand students’ motivations and needs helps institutions provide what truly benefits them. Data allows universities to make smarter decisions about tuition models, recruitment strategies, and program offerings. By identifying the real audience—who they are, where they are, and what they need—we can align offerings with demand and deliver better outcomes. This approach also helps institutions become more efficient. It ensures the focus is on students likely to thrive and succeed within the environment. It can also guide better cost and resource management by tailoring efforts to the students most likely to benefit from the institution's offerings. For higher education to truly meet modern challenges, adopting a data-driven mindset is no longer optional. It’s essential.

📊 How can we use data science to truly improve schools? For over 50 years, education leaders have been urged to leverage data for decision-making. Yet despite massive investments in dashboards and analytics systems, research shows that the link between data use and actual improvements in student outcomes is often weak. In my new paper, “Data Science in Education Administration, Policy, and Practice”, I argue that education data science should be understood as a third core methodology in education research, alongside quantitative and qualitative traditions. Open Access Preprint: https://lnkd.in/eKYTr3i3 Key insights: 🔹 Beyond dashboards: Data science is more than reporting — it involves machine learning, visualization, and exploratory data analysis to support evidence-based improvement cycles. 🔹 Prediction matters: School leaders need accurate predictions, not just statistical model fit. Accuracy should stand alongside theory in informing decisions. 🔹 Algorithms in education must be Accurate, Accessible, Actionable, and Accountable (the “4As”). 🔹 Capacity building: We need to train educational data scientists who can both analyze data and communicate findings to policymakers, teachers, and communities. In effect, we must train people who can talk to people and talk to machines. 👉 The goal is not to replace theory, but to balance explanation with prediction — and to center human judgment, ethics, and collaboration in the process. 🔑 Key Takeaways for the Field For Practice: Schools and districts should embed data science partnerships — not just dashboards — into leadership and improvement cycles. Joint sensemaking between analysts and leaders is essential. For Research: We must expand beyond model fitting to systematically test prediction accuracy and build open, reproducible workflows that connect theory, and application. For Training: Graduate programs in education leadership and policy need roadmaps for education data science capacity building — equipping future leaders to understand, question, and apply advanced analytics responsibly. A key practice for training from Data Science is the Common Task Framework which focuses on: (a) open large-scale real-world deidentified datasets, (b) a shared culture of shared code for shared research, (c) public and open evaluation of algorithms. I’d love to hear from colleagues! Let me know what you think! Open Access Preprint: https://lnkd.in/eKYTr3i3 #EducationResearch #DataScience #EducationPolicy #SchoolLeadership #LearningAnalytics #EdTech

How Students Learn Matters as Much as What They Learn: New Insights from Learning Analytics in College Math A new study published in the Journal of Learning Analytics, led by the soon-to-be Ph.D.'d Linyu Yu, reveals that the sequence of student actions in digital learning environments—like pausing, rewinding videos, and engaging with homework—can predict academic success in college math. 🔍 Key findings: * Students who frequently pause and rewind videos tend to struggle more, signaling possible confusion or disengagement. * Sequences reflecting conscientious homework engagement are linked to higher achievement. * By aligning digital trace data with students’ real-time verbalizations, researchers validated which behaviors truly reflect self-regulated learning (SRL). 💡 Why it matters: * This multimodal approach helps educators and instructional designers move beyond “what” students did to “why” they did it—enabling targeted interventions and smarter course design, especially in flipped and active learning classrooms. 🧠 Takeaway: Digital traces + student voice = actionable insights for supporting every learner’s journey. Let’s build learning environments that respond to real student needs! Read the full article: https://lnkd.in/eRySpP73 #LearningAnalytics #SelfRegulatedLearning #HigherEd #EdTech #ActiveLearning #StudentSuccess #PsychSciSky #AcademicSky #EduSky

“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.

At American Council on Education’s annual conference, higher ed leaders prepare for what one president called an “earthquake” — and the data suggest the tremors are already underway. Three themes stood out: 1️⃣ International enrollment risk is accelerating. • New international enrollment fell 17% this fall across 828 institutions. • Proposed federal policies could cap student visas at four years, increasing uncertainty around post-graduate work pathways. • For institutions where international students represent 10–30%+ of tuition revenue, that’s not a marginal fluctuation — it’s material balance-sheet exposure. 2️⃣ AI is already reshaping student demand curves. • Computer science enrollment at Stanford is reportedly down 20% this year after a decade of growth. • Software development job postings have declined sharply since their 2022 peak, with entry-level roles particularly affected. • Students are responding to labor market signals in real time. • Higher ed must embed AI fluency across 100% of programs. AI will become horizontal infrastructure — embedded in business, healthcare, engineering, humanities. 3️⃣ The demographic cliff is measurable — but access is the bigger gap. • The U.S. college-going rate fell to 62% in 2022, down 4 percentage points from a decade earlier. • The number of high school graduates is projected to decline for more than a decade starting this year. • Yet we still have millions of adults with some college and no degree. The issue isn’t just fewer 18-year-olds. It’s operating models built around: - Full-time students - Rigid semester schedules - Limited re-entry pathways The throughline: this isn’t cyclical volatility. It’s structural reconfiguration across enrollment, curriculum, labor alignment, and delivery models — simultaneously. Institutions making incremental adjustments may stabilize. Institutions redesigning around data, flexibility, and AI-enabled models will lead. Great piece by Natalie Schwartz #HigherEducation #EnrollmentStrategy #AIinEducation #HigherEdLeadership #DemographicCliff https://lnkd.in/ggaVeEdX

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.

Data as the new oil? We need to talk about who controls the refinery. https://lnkd.in/ei2RYM9E The Financial Times piece last week on the UK's plans to monetise public data highlights something I've been discussing with educators worldwide: we're witnessing a fundamental shift in how data shapes our society, yet we're barely scratching the surface of what this means for education. The article correctly identifies data as incredibly valuable - the EU data economy is projected at €145bn this year. But here's what concerns me: whilst governments and tech giants race to extract value from our collective digital footprint, we're missing a critical opportunity to use this same data revolution to transform how we learn and teach. When I talk about AI I describe the "perfect storm" with 3 ingredients that powers modern AI: 1. Vast data sets, 2. Sophisticated algorithms, and 3. Unprecedented processing power. This storm has created tools that could revolutionise education - helping us understand how each student learns, identifying knowledge gaps in real-time, and personalising education in ways we've never imagined. And even more importantly, enabling us to understand ourselves as learners, and become ever more sophisticated in our learning capability. Yet instead of harnessing this potential, we're largely stuck debating whether students should be allowed to use ChatGPT for essays. The real question isn't whether data is valuable - it's whether we'll use it to make humans more intelligent, or allow it to make us more dependent. The article mentions Meta using AI to create personalised adverts based on user data. Imagine if we applied that same sophistication to creating personalised learning experiences or personalised feedback about our own cognitive, metacognitive and epistemic cognition. But here's the crucial point: this won't happen automatically. It requires "data stewardship" - ethical frameworks that put human development first, not profit margins. Projects such as that exploring the creation of Data Trusts at Kings College London and University of Cambridge are extremely important in this respect. We need educational leaders who understand that in this data-rich world, our students must become more critical thinkers, not less. The UK government's data strategy could be transformative for education - if we ensure that the intelligence infrastructure we build serves human flourishing, not just economic growth. Professor Rose Luckin Institute of Education, University College London Educate Ventures Research Limited #SkinnyonAIED #AI #EdTech #Edchat #Leaders #innovation #technology #Learning #Students #Teaching #Edreform  #AIinEducation #DataStewardship #EducationalLeadership #FutureOfLearning For more thoughts like this read the skinny here https://lnkd.in/gTaNTRkb https://on.ft.com/4eBAnAw https://lnkd.in/ei2RYM9E Sylvie Delacroix Neil Lawrence Jane Mann Jim Knight

📊 Are we thinking about data in the right way? The push for more and better education data in low-and-middle income countries has become almost a mantra. Dashboards, EMIS platforms, and evidence-based decision-making frameworks are everywhere. At its best, this work helps leaders move from instinct to insight, from gut feeling to grounded decisions. But Brad Olsen’s recent piece is a timely reminder that we also need to ask harder questions. Whose data counts? What counts as data? And what are we missing when we treat data use as an unqualified good? Too often, the global education community champions data as a technical solution, without fully grappling with the political, cultural, and epistemological questions that surround it. Brad calls for broader, more inclusive definitions of data; approaches that build from existing systems and local knowledge, not just impose new tools; and an honest reckoning with the limits of what data can do on its own. That doesn’t mean abandoning data. But it does mean doing the harder work of aligning evidence with power, culture, and trust. I found this a thoughtful and grounded piece, especially the call to centre the middle tier and support hybrid models that connect technical and traditional forms of knowledge. What resonates with you? Where does this analysis feel right or fall short based on your experience? Link in the comments 👇

We’ve digitized the university. But we haven’t integrated it. And students are paying the price. In a recent EDUCAUSE Review article, Elliot Felix argues that adding more tools or launching new initiatives won’t fix student success outcomes, but structural, integrative shifts will. Over the past decade, campuses have layered on platforms for advising, LMS, career services, co-curricular tracking, employer engagement, analytics, and more. While each tool solves a local problem, collectively they create fragmentation. - Students switch between systems that don’t talk to each other. - Faculty manage disconnected workflows. - Leaders struggle to see what’s actually driving retention, engagement, and career outcomes. So how do we integrate better? Elliot outlines 3 key structural shifts to do just that: 1. Blend work and learning at scale. We know work-based learning (WBL) improves student engagement and employment outcomes. Yet on most campuses, it’s still treated as an optional enrichment, not an expectation. The institutions that win will be the ones that embed applied learning directly into the curriculum as a core part of the infrastructure. “Technology,” Elliot argues, “can play a vital role in integrating work experiences into learning,” because it enables institutions to scale WBL for all students, rather than the select few who gain a traditional internship. 2. Treat data as oxygen, not exhaust. Most campuses are sitting on fragmented systems that track everything and connect nothing. If we can’t see which students are gaining applied experience… If we can’t measure skill progression… If we can’t tie learning to workforce signals… Then we’re flying blind. Data should inform early intervention, curriculum design, employer engagement strategy, and resource allocation, not just reporting cycles. 3. Tame digital sprawl. Higher Ed doesn’t have a strategy problem; it has a coordination problem. Every new tool adds complexity and creates a disconnected platform that limits the student experience. The opportunity isn’t more technology. It’s redesigning the institution around connected learning and work. This is exactly why we built Riipen the way we did. Not as a marketplace. Not as a standalone internship portal. But as infrastructure embedded in the curriculum, enabling institutions to scale WBL across departments, not just pilot programs. This is how technology stops being enrichment and starts becoming an institutional strategy. The future of Higher Ed won’t be built on more platforms. It will be built on integrated pathways between learning and work. 💬 If you’re rethinking how work, data, and digital systems connect on your campus, I’d welcome the conversation. Link to article in the comments.

Recently, I conducted user testing for some exciting projects at Stanford, and decided to share some insights. This post feels especially personal because it’s not just about design—it’s about my journey as both a student and a designer. When I first came to Stanford as an international student, I struggled with navigating its complex academic systems. It was frustrating, and I remember wishing for tools that could make things simpler and more intuitive. Fast forward to today, and I have the incredible opportunity to work on improving those very systems—side by side with current students. Listening to their frustrations during user testing brings back so many of my own memories. It’s a full-circle moment, where my past experiences fuel my passion to make these tools better for everyone. Here are some interesting insights: • 𝗠𝗲𝗻𝘁𝗮𝗹 𝗠𝗼𝗱𝗲𝗹𝘀 𝗮𝗻𝗱 𝗠𝗶𝘀𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Users often approach academic tools with mental models shaped by other apps or systems they use. Identifying and aligning with these expectations can significantly reduce confusion and improve engagement. • 𝗦𝘁𝗿𝗲𝘀𝘀 𝗮𝘀 𝗮 𝗗𝗲𝘀𝗶𝗴𝗻 𝗙𝗮𝗰𝘁𝗼𝗿: Academic tools are often used in high-pressure moments (e.g., enrollment deadlines). Testing revealed that reducing friction in the interface during these times significantly improves the overall experience. • 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗘𝘅𝗽𝗲𝗰𝘁𝗮𝘁𝗶𝗼𝗻𝘀: Today’s students expect tools to adapt to their preferences, like saving search filters or suggesting classes based on their academic history. • 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗖𝗹𝗮𝗿𝗶𝘁𝘆: Students value clear, visual representations of information, such as progress bars for degree completion or graphs showing their weekly workload distribution. • 𝗜𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗶𝘁𝘆 𝗕𝗲𝘆𝗼𝗻𝗱 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆: Designing for inclusivity means accounting for diverse backgrounds, from non-traditional students to those who are the first in their family to attend college. • 𝗜𝘁𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗶𝘀 𝗚𝗼𝗹𝗱: Even after a design seems polished, user testing consistently uncovers areas for refinement, proving that the design process is never truly finished. User testing can be really challenging but truly rewarding in the end. I decided to share these moments to contribute to a community that’s all about learning and growing together. If you’ve got user testing stories or tips, I’d love to hear them—let’s inspire each other! #UXDesign #UIDesign #UserTesting #HumanCenteredDesign #DesignForEducation