Harness the power of your campus data

 

SEAtS Optional Student Success Scoring Analytics uses machine learning algorithms and statistical modeling techniques to quickly and accurately predict and identify at-risk students and to help improve outcomes and attainment for all students. Student attendance data provides current insights. Predictive Analytics uses historical and current data to anticipate student interventions even sooner.

STUDENT SUCCESS
SCORING ANALYTICS

Evaluate each student using a single score calculated from hundreds of touch-points, both real-time and historical.

IDENTIFY AT-RISK
STUDENTS

Initiate engagement as early as the third week and make critical corrections for better student outcomes.

MACHINE LEARNING
AND STATISTCAL MODELLING

Statistical analysis on expected goals, pre-defined objectives and preferred outcomes allows faculty staff to engage with students so everyone is on the same page.

Product Features

 
 
 

AI ENGAGEMENT SCORE

Physical data like class attendance and library visits are interwoven with digital data from timetables, socio-demographic profiles and historic attainment to understand how a student learns. The result is a personalised view of engagement, retention and achievement for every student.

seats software learning analytics screengrabs

AI DATA VISUALISATION

Historic retention and attainment outcomes are used to train our machine learning models to identify patterns applicable to the student population as a whole. Statistical modelling generates comparative charts for future outcomes in attendance, grades and overall performance.

AI student success

AI REPOSITORY

SEAtS Predictive Analytics relies on data. That is why we have built our platform to integrate with all campus systems to immediately generate insights. We also have the option to manually import structured or unstructured data in CSV form to accommodate all data environments.

seats software learning analytics screengrabs
University of Hull logo

We are deploying SEAtS’ powerful predictive analytics
and data science software to enhance student retention
and academic attainment.

—Professor Alan Speight
PRO-VICE CHANCELLOR, UNIVERSITY OF HULL

Better Student Retention and Outcomes

SEAtS have developed a process driven approach to Retention and Engagement based on the simple principle that you cannot manage what you cannot measure. The answer to the Retention Conundrum lies in the data locked away in systems all over you campus, on paper attendance registers, in administrative and academic, pastoral and welfare notes and emails.

SEAtS offer our customers a single unified secure shared platform that bring all this data together. SEAtS processes it and identify causes for concern in patterns of engagement and attendance. It then prompts academics and administrators to take critical early interventions.

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