Improving Student Outcomes Through Data-Driven Identification of At-Risk Learners in Educational Environments
DOI:
https://doi.org/10.53555/es.v12i1.2515Keywords:
Learning Analytics, Student Engagement, At-Risk Learners, Educational Data Mining, Student PerformanceAbstract
The growing use of online learning settings has produced a lot of educational data, which offers the prospect to enhance the performance of the students by using data-intensive strategies. The research aims to examine the issue of identifying at-risk learners by utilizing an extensive dataset comprising demographics, engagement, performance, and risk-related factors. The study employs various quantitative research design methods such as descriptive statistics, correlation analysis, and predictive modeling to test the hypotheses of the relationships between student behavior and academic outcomes. The results indicate that student engagement is a pivotal measure of success and the more the interaction, the better the performance and the lower the dropout rate. Variables of risk classification also show a high correlation with the ultimate academic achievement and confirm their place in identifying at-risk learners early. Predictive models, especially ensemble models, were very accurate in separating the favorable and adverse outcomes, and this point indicates the usefulness of learning analytics in educational settings. The paper emphasizes the significance of incorporating data-driven insights into the educational practice in order to allow early intervention and targeted support. The study can be used practically by teachers and institutions to improve retention and performance through finding significant predictors of student success. On the whole, the outcomes of the research add to the future of learning analytics, as they prove that it can transform the aspect of educational decision-making and enhance learning results.
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References
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