Adaptive Multi-Class Learning for Predicting Student Anxiety Level
Keywords:
Versatile Dynamic Learning, Multi-Class Classification, Student Uneasiness Prediction, Educational Information Mining, Machine Learning in Education, Mental Wellbeing Assessment, Real-Time ExaminationAbstract
This examination presents a multi-class versatile dynamic learning structure to foresee understudy nervousness, meaning to upgrade early mediation and backing components in instructive conditions. Conventional uneasiness expectation models frequently miss the mark because of restricted named information and static educational experiences. Our methodology use versatile dynamic figuring out how to iteratively select the most enlightening pieces of information for naming, working on model precision and heartiness. By consolidating multi-class characterization, the model separates between different degrees of nervousness, giving a nuanced comprehension of understudy psychological well-being. Trial results exhibit the viability of the proposed strategy, showing critical enhancements in forecast exactness over standard models. This study highlights the capability of versatile dynamic learning in instructive information mining, offering a versatile answer for continuous uneasiness expectation and adding to additional responsive and steady schooling systems.
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