Student Success Data Models

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.

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"

LinkedIn Series: Because your ERP shouldn’t just track what happened—it should tell you what’s next. 🔹Post 3: AI + ERP = Student Success Superpowers? Let’s Talk Use Cases We’ve all heard the buzz about AI in higher education. But the real value happens when AI isn’t just layered on top—it’s integrated within the systems that power the student lifecycle. Enter: AI + ERP. This combo is transforming how institutions drive student success. Let’s break it down with real-world use cases: 🔍 Use Case 1: Predicting Student Attrition ✔️ Workday + Salesforce brought together predictive models and advising workflows ✅ Result: 7% increase in student retention through proactive, targeted interventions When your ERP can see risk coming—and your CRM can act on it—you move from reactive to responsive. 📊 Use Case 2: Dynamic Course Forecasting ✔️ Oracle Cloud used AI to detect patterns in course demand, enrollment, and historical trends ✅ Result: Smarter class scheduling and reduced overloads for students and faculty alike AI isn’t just for the registrar—it’s for student well-being and academic planning too. 💸 Use Case 3: Financial Aid Optimization ✔️ Anthology leveraged machine learning to assess which aid packages most influenced enrollment decisions ✅ Result: Increased yield and more efficient budgeting Helping the right students with the right aid, at the right time? That’s student-centric and fiscally sound. 💡 Key Insight: AI isn’t about fancy dashboards—it’s about better decisions at every level of the student journey. 🧠 What’s the secret to success? Extend your ERP with: • A real-time data layer (Snowflake, Azure Synapse) • A student-first CRM (Salesforce Education Cloud) • Digital adoption platforms for staff and students (Whatfix, WalkMe) You don’t need to rip and replace legacy systems. 👉 You need to unlock their potential with AI-powered extensions. Higher ed is evolving—and student expectations are rising. The institutions that win will be the ones that act on insights, not just report them. #StudentSuccess #AIUseCases #ERPwithAI #SmartCampus #HigherEdInnovation #DigitalTransformation #DataDrivenDecisions #EdTech #Workday #Salesforce #Oracle #Anthology #Snowflake #WalkMe #Whatfix

Deep Learning to Predict & Prevent University Dropouts: The Future of AI in Education  Student dropouts remain a critical challenge for universities worldwide. But what if we could predict failures before they happen and provide timely interventions? Deep Learning (DL) is revolutionizing higher education by identifying at-risk students early and enabling personalized learning experiences. Here’s how the future looks: Early Warning Systems – AI-driven models analyze academic performance, attendance, and engagement to detect students at risk of failing or dropping out. Personalized Learning Paths – Adaptive AI recommends customized coursework and study strategies tailored to each student's needs. Multimodal Data Integration – Combining academic records, behavioral signals, and even sentiment analysis from student interactions to get a 360-degree risk assessment. AI-Powered Chatbots & Mentors – Virtual assistants offer real-time academic and emotional support, keeping students engaged and motivated. Predictive Analytics for Universities – Institutions use AI-driven insights to optimize curriculum, faculty engagement, and student services, leading to higher retention rates. The Future? AI will not replace educators but will empower them with data-driven insights to provide proactive, targeted interventions. Universities that integrate deep learning with strong human-led strategies will redefine student success. What are your thoughts? Could AI be the key to reducing dropout rates and improving student outcomes? Let’s discuss! #AIinEducation #DeepLearning #StudentSuccess #HigherEd #PredictiveAnalytics #FutureOfEducation Glad to publish a paper titled "Enhancing Student Outcomes with LSTM-CNN and Data Analytics in Higher Education" during International Conference on Intelligent and Innovative Practices in Engineering & Management (IIPEM 2024) at Amity Global Institute,Singapore Shiv Nadar University With a focus on the use of Long Short-TermbMemory (LSTM) and Convolutional Neural Network (CNN) approaches to predict students' academic performance, the study highlights the possible advantages of implementing cutting-edge technology innovations like analytics and data mining in learning environments. Future research has exciting opportunities as the educational landscape changes, including the possibility of applying transfer learning models and the possibility of using lightweight models with extensive features for identifying students' learning results.