Abstract:
These days, the instructional learning methods are not restricted to the traditional strategies,
which used to be ten years prior. The 21st-century students need to be outfitted with
information and abilities to establish fruitful and long-lasting learners. Innovation made it
conceivable to attempt new learning strategies. So, with the development of technology
and the necessary technology being more and more affordable and accessible to the general
public, learning online is gaining popularity. Our exploration objective was to examine and
investigate the Online Activity information to acquire significant knowledge of educators
and their educating designs and eventually devise a prediction model to anticipate the
outcome dependent on their action inside the learning time frame. Subsequently, Steps like
data selection, data generation, data structure, feature engineering, feature selection were
applied. Then, distinctive ML algorithms like CATBOOST, XGBOOST were applied for
the prediction. This paper outlines the methods used to predict student's final results by
various Machine Learning algorithms.
