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28 sie 2024 · Early prediction of student performance in online programming courses is essential for implementing timely interventions to enhance academic outcomes. This study aimed to predict academic success by comparing four machine learning models: Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Network (Multilayer Perceptron, MLP). We analyzed data from the Moodle Learning ...
1 wrz 2024 · Addressing this crucial gap, our study introduces an AI Student Success Predictor empowered by advanced machine learning algorithms, capable of automating grading processes, predicting student risks, and forecasting retention or dropout outcomes.
In this article we review the state of the art in predictive models of student success in MOOCs and present a categorization of MOOC research according to the predictors (features), prediction (outcomes), and underlying theoretical model.
25 maj 2022 · Student success analytics holistically integrates several conventional areas within education analytics for the purposes of positively impacting student experiences and outcomes. These include institutional analytics, learning analytics, and academic analytics.
10 lut 2020 · Degree level: predicting students’ success at the time of obtention of the degree. Year level: predicting students’ success by the end of the year. Course level: predicting students’ success in a specific course. Exam level: predicting students’ success in an exam for a specific course.
6 lip 2021 · To advance the field of student engagement, some conceptual models have sought to broaden the construct by characterizing the behavioral, psychological, social, and academic aspects of students’ schooling experience (e.g., Appleton et al., 2006).
10 paź 2019 · Drawing upon the literature within higher education, and psychology, we reposition student engagement as consisting of four distinct yet interrelated dimensions, namely behavioural engagement, affective engagement, cognitive engagement and social engagement (Bowden et al. 2017).
