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  • Enhancing prediction of student success: Automated machine . . .
    More and more, Machine Learning is used in the field of higher education management Specifically, there has been an increased interest in adopting Machine Learning to predict student performance and identify students at risk based on initial data gathered during their years of study, as surveyed in the work of Miguéis et al [6]
  • Student Performance Prediction Using Machine Learning . . .
    Another paper predicted student success at the beginning of an academic cycle based on academic records, achieving an accuracy of 85% The study in investigates machine learning (ML) approaches for predicting student performance in tertiary institutions Using 29 studies, six ML models were identified: decision tree, artificial neural networks
  • Predicting academic success in higher education: literature . . .
    Student success plays a vital role in educational institutions, as it is often used as a metric for the institution’s performance Early detection of students at risk, along with preventive measures, can drastically improve their success Lately, machine learning techniques have been extensively used for prediction purpose While there is a plethora of success stories in the literature
  • Predicting Students Performance Using Machine Learning . . .
    In recent years, many universities have been using researching machine learning in order to gain findings about students' academic progress, predict future behaviors, identify potential problems at an early stage or even improve inter-institutional collaboration and develop an agenda for the larger community of students and teachers Learning
  • Predicting academic success: machine learning analysis of . . .
    The machine learning models indicate that school effort is the most important factor, and student effort is the least important For instance, in Lasso, among the top 20 predictors, 10 variables related to school effort, 7 to parental effort, and 3 to student effort
  • Analyzing and Predicting Students’ Performance by Means of . . .
    Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from
  • Machine Learning Approach for Predicting Students’ Academic . . .
    Abstract—This research aims to develop machine learning models for students' academic performance and study strategy prediction which could be generalized to all courses in higher education Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) essential for students’ learning process





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