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Early Prediction of Student Dropout in Higher Education using Machine Learning Models Or GorenLiron Coheir RubinsteinTelAviv University, IsraelTelAviv University, IsraelTelAviv University, Israelorgoren@gmail.com
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01
Collect data on student demographics, academic history, attendance, behavior, and any other relevant information.
02
Utilize predictive modeling software or tools to analyze the data and identify patterns or trends that may predict future academic performance.
03
Create a scoring system or algorithm to assign a likelihood of success or failure for each student based on the data analysis.
04
Regularly review and update the early prediction model as new data becomes available or student circumstances change.

Who needs early prediction of student?

01
Teachers who want to identify at-risk students and provide early interventions to help them succeed.
02
School administrators who need to allocate resources effectively and improve overall academic outcomes.
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Policy makers who are interested in implementing data-driven strategies to improve student achievement at a larger scale.
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Early prediction of student refers to the assessment and identification of a student's potential academic performance or eligibility for specific programs before the end of the academic term.
Typically, educational institutions, school counselors, or designated administrators are required to file early predictions for their students.
To fill out an early prediction, one must gather the necessary student information, evaluate their academic performance, and complete the designated prediction form with the required details and assessments.
The purpose of early prediction is to identify students who may benefit from additional support or to prepare them for future academic opportunities.
Information that must be reported typically includes the student's current grades, attendance records, behavior evaluations, and any relevant teacher comments.
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