AI-Powered Models That Predict and Support Student Success
From identifying college-ready students to flagging early risks, our machine learning models give educators the insights needed to act early, guide smarter, and improve outcomes across K–12 and higher education.
Why Use Machine Learning?
Our predictive models are built to:
Detect early signs of academic momentum
Assess alignment between coursework and a student’s intended path
Predict postsecondary readiness with precision
Identify transfer shock risks before students fall behind
Strengthen your enrollment predictions with richer data and deeper analysis
These insights help institutions intervene earlier, advise more strategically, to create a smoother student journey.
See how two Central Florida partnerships are using these models to improve outcomes across K–12 and higher education.
MCG Machine Learning Models
From readiness to retention, our predictive models help educators act earlier, plan smarter, and support student success at every step. Each model is designed to address a specific challenge in the student journey—offering actionable insights that drive better decisions in K–12 and higher education.
How it works
From raw data to predictive insight—our machine learning models follow a proven path to deliver value.
Step 1: Unified Data Foundation
We begin by securely integrating student, academic, and local datasets—everything from transcripts and course history to attendance, test scores, and census or permit data.
Step 3: Validation + Training
Your team reviews the outputs, offering local context to refine model accuracy and scenario simulations. This ensures the results align with your goals and environment.
Step 2: Multi-Layered Machine Learning
Using ensemble machine learning models, we run simulations that detect patterns, surface momentum signals, and project student outcomes across key milestones—before they happen.
Step 4: Predictive Dashbards + Flags
We deliver intuitive dashboards that are available on a continuous basis which include student-level analytics, program alignment insights, and institution-level planning views—ready to inform decisions across advising, enrollment, and support services.
Smarter Course Sequencing Starts with AI
Our machine learning models don’t just flag risks—they help students build better paths forward.
By analyzing past course patterns from your institutions transcripts our models identify:
Synergistic Pairs – course combinations that improve performance
Reinforcing Sequences – the right order to take courses for mastery
Toxic Combinations – pairings that often lead to lower GPAs or retakes
These AI driven insights power better course scheduling and advising that provides insights to today’s students based on actual data and results from your institution.
Flexible Inputs. Tailored Insights.
Each model adapts to your data environment. Whether you want to include course transcripts, school choice history, building permits, or performance metrics, our team helps you align the right inputs for more applicable insights.
