Big data-driven early alert systems as means of enhancing university student retention and success

Abstract

This article argues for the establishment of big data early alert systems that inform data-driven student support mechanisms in universities. It proposes a guiding framework for integrated big data to enhance student success premised on a comprehensive understanding of students as people in the world who arrive at universities with various complex life problems that may disrupt their learning opportunities. It argues that various data components should be linked together to foster coherence and seamlessness in understanding student socio-economic and academic needs to develop responsive learning-enhancement intervention programmes. This is based on action research conducted through projects launched at the University of Witwatersrand in 2015 and at the University of Zululand in 2018. The systems were launched, and data was collected using the proposed student performance tracking system. This article explores conceptual and theoretical underpinnings of establishing big data-based student support systems in South African universities. A big student data model is proposed for wider use in South African universities.

Predicting student retention in higher education institutions (HEIs)

“…This can link biographic categories of data directly to the cumulative collection of student performance information. As a result, the institution would be able to carry out adequate analysis, which would help it better understand its students and the interventions they require to support them as they pursue their academic goals (Cele, 2021).…”

Avrupa Konularının Öğretiminde Sosyal Bilgiler Dersinin Önemi

“…Öğrencilerin bu davranışlarının zamanında analiz edilmesi bu sorunların çözümünde etkili olacaktır. Eğitimde büyük verinin temel bir bileşeni olan öğrenme analitiği, öğrenme etkinliklerinin gerçek zamanlı analizini yapma fırsatı sunar ve dolayısıyla öğrenmeyi olumsuz etkileyebilecek durumların erken fark edilmesini sağlama potansiyeline sahiptir (Cele, 2021;Wang vd., 2018). Risk durumundaki öğrencilerin tespit edilmesi öğretenlere uygun müdahaleyi sağlama noktasında yol gösterecektir (Huda vd., 2016;Prinsloo vd., 2015).…”

Technology-mediated advising for student success: Exploring selfmediated academic support for undergraduate students using AutoScholar Advisor System

“…Increasingly, it has been shown that the integration of technology with innovation in the educational process involves the student taking on a central role in learning (Jokhan et al, 2022). Research in South Africa (Cele, 2021) highlights the relevance of data-driven student support mechanisms at universities. Data-driven student academic support using automated advising mediation can elicit active engagement of the student.…”