Learning Analytics and Student Success: Predictive Models for Academic Performance

Abstract

Learning Analytics is a key strategy to enhance the quality of education by converting the data generated by students into valuable information for educational decision making. Schools and colleges are increasingly using digital learning places that present huge amounts of data on attendance, assessment performance, interactions with the learning management system, participation and engagement. These data give the opportunity to identify learning patterns and predict academic outcomes prior to significant learning problems. The purpose of the present study is to investigate the relationship between learning behaviors and success of learners and to clarify the role of predictive models in improving learners' success. The quantitative research design was adopted and primary data were obtained from the respondents (i.e. undergraduate and postgraduate students) using a structured questionnaire and with the aid of institutional academic records. Some statistical methods such as descriptive analysis, correlation, regression, and predictive modelling were used to see the effect of learning engagement on academic achievement, digital participation on academic achievement, learning habits on academic achievement, and assessment performance on academic achievement. The results suggest that frequent engagement in online learning activities, frequent completion of assessment, and timely feedback significantly enhance the prediction of student performance. Predictive models were identified as effective predictors of student risk and allowed for personalized learning interventions, approaches and academic support services to be provided to students at risk. The study also identifies issues related to data quality, data privacy, ethical issues, and readiness of institutions for the implementation of learning analytics. The study highlights the potential of learning analytics to inform evidence-based learning and the need to make learning analytics a part of institutional planning and student support. The results hold much promise for education stakeholders who wish to increase student retention, student success, and learning results based on data-informed decisions regarding educational technology.