Predictive Analytics in Student Performance

Academics should understand the distinction between GenAI & PredAI because it fundamentally affects research methodology, ethical considerations, and practical applications in their fields. Generative AI raises questions about authorship, originality, and academic integrity, while Predictive AI involves concerns about bias, fairness, and decision-making transparency. Predictive AI is very useful in teaching because it can analyze individual student learning patterns, performance data, and engagement metrics to identify knowledge gaps and predict which concepts each student will struggle with most. This enables academics to proactively customize lesson sequences even in a large class, adjust pacing, and provide targeted interventions before students fall behind, creating truly personalized learning pathways. #artificialintelligence #GenerativeAI #PredictiveAI #AIfluency

💡 What if your LMS data could reveal how your students learn—not just what they get wrong? In this study published in the Journal of Educational Psychology, Bernacki et al. (2025) used multimodal learning analytics to decode students’ “digital traces”—the clicks, downloads, and submissions that quietly capture how they self-regulate learning. By aligning digital behaviors with students’ think-aloud reflections, the team found patterns that not only validated these traces as indicators of self-regulated learning (SRL) but also predicted performance across semesters. This research points toward a future where real-time learning data can flag struggling students before they fail—and guide instructors to target the why behind the struggle. 🔗 https://lnkd.in/eFaQttau #LearningAnalytics #EducationResearch #EdTech #StudentSuccess #HigherEd

I am amazed by how intentional University of South Florida is when it comes to predicting student success. Check out their 8 different predictive models! "Predictive Analytics Research for Student Success is a group of university researchers who develop the analytical models that OAA uses to identify students with potential need for support, which include: FIRST-TIME-IN-COLLEGE (FTIC) MODELS • First-Year Retention: Indicates student has a low probability of returning in their second year. • First Semester GPA Differential: Indicates student’s GPA is far lower than expected. • Finish in Four: Indicates student has a low probability of graduating within four years. • Finish in Six: Indicates student has a low probability of graduating within six years. TRANSFER MODELS • Transfer First Year Persistence: Indicates student has a low probability of returning in their second year. • First Semester GPA Differential: Indicates student’s GPA is far lower than expected. • Finish in Two: Indicates student has a low probability of graduating within two years. • Finish in Three: Indicates student has a low probability of graduating within three years"

Are your student success teams still waiting until a student is on academic probation to intervene? 👀 If so, you're probably not seeing improved retention rates. ⤵ You have to be proactive. Tune into the quieter signals students send out. ⤵ Faculty, coaches, and advisors are often swamped with back-to-back meetings, large student caseloads, and inefficient processes. So, lean into technology to make your lives' easier AND your work more impactful. Use data and predictive analytics to see a more complete picture of a student's journey. Teach your teams how to drill down and disaggregate data so it's specific and actionable. 📊 ⤵ Questions we should be asking instead of waiting for a mid-semester academic flag: 1️⃣ How is a student's course engagement, compared to their peers in that course section? 2️⃣ Are they as actively engaged in extracurricular activities as they were last semester? 3️⃣ What about their visits to the library and tutoring center? 4️⃣ What are their attendance patterns like? 5️⃣ How's the student's progress toward degree completion? Are they accumulating credits that aren't aligned with their major or degree path? #proactivesupport #studentsuccess #predictiveanalytics P.S. Want to learn more about how Civitas Learning works with institutions to create more targeted, effective, and equitable support strategies? Shoot me a DM and let's set up a chat! 💌

This new Stanford study might change how we think about AI in education. Everyone’s talking about AI that writes lessons. But what about AI that understands students? The study, from Stanford University and Carnegie Learning, found that just 2–5 hours of student interaction with an edtech tool can predict end-of-year test performance with surprising accuracy. In some cases, it matched the predictive power of full-year data or even a formal pre-test. AI’s real value in education might not be content generation (e.g. lesson planning or rubric generation). It might be early prediction—the ability to identify struggling students before any test is given. That’s the bet we’re making at Flint. We’re not just helping teachers generate materials. We’re helping them understand where students are, how they’re progressing, and what to do next. All in real time via an army of AI teaching assistants. The next generation of AI edtech tools will focus on what students need—and when. Full study (and overview) linked in the comments 👇 #ai #edtech #aiedtech #flint