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 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.

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

Key takeaways on AI and higher Ed from the 2025 Gartner Hype Cycle: 1. AI as everyday infrastructure in the academic workplace. - Institutions are moving from pilots to production for student communication, research support, curriculum alignment, grant development and course build workflows. - Agentic administrative systems that can draft budgets, automate onboarding, and handle purchasing workflows. 2. Value-driven analytics is accelerating. -Higher ed is shifting from dashboards to actionable, predictive insights for retention risk forecasting, enrollment optimization, instructional quality insights and resource allocation modeling. 3. Digital credentials, adaptive learning, and emotion AI are converging and shifting away from a one-size-fits alls model. - Personalized academic pathways - Recognition of learning across providers - Improved employability What should institutions do now? 1. Develop an AI governance framework that is practical, flexible, and faculty-inclusive. 2. Modernize core academic and administrative workflows using agentic AI. 3. Invest in AI literacy for faculty, staff, and students. 4. Adopt tools that scale personalized learning, not just automate tasks. 5. Integrate curriculum mapping and digital credential pathways into academic planning. 6. Prioritize student experience projects that deliver near-term value and long-term differentiation. #HigherEd #AIinEducation #EdTech #GenerativeAI #HigherEducationInnovation #DigitalTransformation #AIEcosystem #StudentExperience #FutureOfLearning #EducationAnalytics #DigitalCredentials #AdaptiveLearning #AIGovernance #InstitutionalStrategy https://lnkd.in/g7edsRnn

Courage is in the Data. Few will argue that there is an element of leadership courage involved in higher education. It is often said that data provides the answers, but for higher education leaders, data more often provides the challenge. The true financial health of a college isn't found in a static spreadsheet; it is found in the courage to look at what those numbers are actually saying about the financial health and even viability of a college. While data can highlight shrinking enrollment or increasing tuition discount rates, it takes leadership courage to move beyond "monitoring" and toward the decisive action required to preserve the institutional mission. The Mirror of Data Leadership courage starts with a radical honesty about the measures that define institutional viability. It is easy to find comfort in "vanity metrics" or one-time budget surpluses or qualified enrollment increases, but courageous leaders use data as a mirror, not a shield. This means facing the "unpopular" numbers—like the true cost of under-enrolled programs or the long-term liability of deferred maintenance—and bringing those realities into the light for the entire campus community. Transparency is the highest form of courage in a sector that has historically preferred the sanctuary of silos. Decisive Action Over "Wait and See" One of the most dangerous phrases in higher education finance is "this too shall pass." Data-informed courage is the antidote to this inertia. It involves the willingness to reallocate resources away from legacy initiatives that no longer serve student success and toward new, high-impact growth areas. This kind of "wise courage" where data acts as the fuel for innovation rather than just a report on decline. Building a Culture of Trust Finally, the reality of leadership courage is that it must be shared. When leaders are transparent about financial data, they invite the faculty and staff into a partnership of stewardship. By grounding unpopular decisions in objective data leaders build the trust necessary to have difficult conversations with students, faculty, staff and communities. Ultimately, the data is just the framework; courage is the will to lead the institution through the difficult terrain it reveals, increasing the chances that the college remains accessible and relevant for the next generation of students.

At Unity Environmental University, we have undergone a significant transformation over the past decade. Initially facing challenges rooted in outdated structures and governance inefficiencies, we have evolved into a dynamic institution of over 10,000 learners. Our Enterprise Model now enables us to adapt and make decisions more quickly, showcasing our commitment to scale access in higher education. Our transformation is grounded in a strategic partnership with Salesforce, whose platform powers our Stratus system and anchors our design principle of “one student, one record, one experience.” This alignment enables us to dismantle silos, operationalize intelligence at scale, and establish an institutional framework that is ready for an AI-native future. So, as we navigate the evolving landscape shaped by advancements in Artificial Intelligence (AI), we now recognize the need to move beyond mere automation. Our focus has shifted towards deconstructing and rebuilding our systems to embrace intelligence, integration, and agility at every organizational level. This shift marks a new phase where structural clarity and adaptability take precedence over sheer scale. In my recent article, I delve into the next chapter of our journey and shed light on the essential steps organizations (regardless of industry) must take to thrive in an AI-driven era. It's not merely about digital transformation; it's about redefining organizations and ensuring their continued relevance in a rapidly changing environment. Key insights from the article include: 1. The critical risks posed by outdated organizational structures 2. Viewing fragmented data as a strategic liability, not just a technological challenge 3. Common misconceptions among leaders regarding AI integration 4. The imperative for both employees and executives to swiftly embrace transformative technologies This paradigm shift underscores the importance of adaptability and a holistic approach to organizational evolution in the face of AI advancements.

Is our pursuit of AI in Education truly serving the student? This critical opinion piece that includes Jeremy Roschelle of Digital Promise argues that for AIEd to be truly responsible and transformational, we must look #BeyondBenchmarks and integrate deep expertise from the Learning Sciences (LS). It's not enough for an ML model to optimize a technical score. We must center our efforts on human learning outcomes and processes. The key recommendations for responsible AIEd development: - Focus on Learning Outcomes: Stop prioritizing technical benchmarks and instead use evidence-based educational standards to evaluate actual student mastery and changes in learning processes. - Involve LS Experts: Bring in learning scientists to navigate the complexity of pedagogical research and ensure that science-based concepts (like cognitive load and metacognition) are accurately applied, not oversimplified. - Ground the Goal in the Learner: Adopt an iterative, data-driven learning engineering approach that centers on the student's context and utilizes diverse human expertise to interpret data, cautioning against relying solely on automated measures. We need stronger connections between generative AI teams and scientific teams to earn the trust of educators and build systems that genuinely improve learning. Read the full perspective in Communications of the ACM: https://lnkd.in/gnJzKjWx #AIinEducation #LearningSciences #ResponsibleAI #EdTech #Education #GenerativeAI