Autumn Leaves - A System for Identifying Student Dropouts
How did the Autumn Leaves system come to be?
The Autumn Leaves system was created by Tipli Information Systems Inc. after more than 10 years of experience in managing learning management systems (LMS) in institutions of higher education.
We set ourselves the goal of trying to identify and prevent dropouts by students. We believe that it is possible to identify trends below the surface which can serve to indicate the beginning of a dropout process. We want to try to identify these processes as well as the relevant students. Often, a skilled lecturer will be able to identify these processes, but the educational institution itself can only recognize them upon receiving the final exam grades for courses, which could be up to two to three weeks into the second semester.
We want to make the prediction of student dropouts earlier, so that already during the semester, the educational institution will be able to identify students at risk of dropping out and provide them with the assistance they need in order to succeed in their studies, thus reducing dropout rates.
How do we do it?
Over the course of more than a year, we discussed and developed a method to identify a potential dropout even before the student realizes that they are at risk, while making sure that the system is relatively easy to implement.
For this purpose, we teamed up with the Nogamy company, which developed for us a model based on advanced analytics from the fields of AI and ML.
The system learns the behavior of the students in each and every institution. The system relies on more than 40 different parameters and knows how to identify those who are prone to dropping out at an early stage, while the institution still has the option of providing help and assistance during the semester itself.
A student who drops out or extends the degree, causes great damage to the academic institution.
Recruiting resources going down the drain, loss of future income that has been deleted, difficulty in long-term budget planning, and an occupied place that cannot be filled by another student.
It is impossible to prevent all instances of students dropping out, but identifying the beginning of the dropout process as early as possible can keep the student in the classroom with the help of an appropriate intervention plan.
The added value ?
Identifying potential dropouts, before the exam period
40 parameters
ML \ AI
10 years of experience
Want to hear more?
We will be happy to meet and present our system, and together we can lead real social change and prevent the “autumn leaves” from falling prematurely.
Email us at fall@tipli.info
Or leave your information in the contact form.
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