ABSTRACT

One effective methodology for distinguishing at-risk kids and moving forward teacher comes almost is the early interventions of machine learning in educator circumstances. The utilize of machine learning approaches to figure and recognize children who may require extra offer help to accomplish scholastically is examined in this one of a kind. ML models are able to see at a collection of characteristics relating to understudy execution, conduct, and monetary establishment by utilizing colossal datasets and progressed calculations. This licenses them to give instructors and chairmen with helpful and significant information. Compelling mediations that will stop academic dissatisfaction and dropout must be executed as some time recently long as at-risk kids are recognized. Standard strategies for keeping tabs on understudy execution frequently depend on human survey and cooperation examination, which has obstructions and may cause delays in disclosure. A more advanced technique is given by machine learning, which makes utilize of data-driven calculations to recognize plans and designs that will point to future academic challenges. Different factors, such as past execution data, interest records, measurement data, and behavioral signs, may be included into these models. The ampleness of machine learning in taking care of and analyzing gigantic volumes of data can be a major advantage when utilizing it for early intervention. Complex information may be taken care of by calculations like choice trees, self-assertive timberlands, and neural frameworks to reveal simple connections between assorted components and understudy comes almost. For case, ML models may recognize which understudies are likely to backslide based on designs in their engagement and execution data. With the utilize of this prescient capabilities, teaches may proactively step in and provide understudies centered offer assistance and resources a few time as of late they reach a key disillusionment point.

Keywords: Machine Learning, Early Intervention, At-Risk Students, Predictive Modeling, Educational Data, Decision Trees, Random Forests, Support Vector Machines, Neural Networks, Data Privacy

عنوان المؤتمر

international conference on sustainability in digital transformation era: driving innovation & growth

دولة المؤتمر

فلسطين

تاريخ المؤتمر

13 سبتمبر، 2024 - 14 سبتمبر، 2024

راعي المؤتمر

Taylor & Francis