Enhancing University Student Support Through Big Data Analytics
Explore the significance of big data in enhancing student support mechanisms in universities, focusing on identifying patterns, predicting success or failure, and designing interventions for student success. Learn about the challenges universities face and the role of data in improving teaching, curriculum, and student-centeredness.
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Presentation Transcript
The Beauty of Knowing your Students Nhlanhla Cele Executive Director:Institutional Planning University of Zululand
Background Big data early-alert systems that inform data-driven student support mechanisms in universities
Background Universities like fixing students but they hardly fix themselves and their systems. Big data helps us fix the experience, the environment, teaching, those who teach, social factors, curriculum, technology student centredness responsiveness
Background TWO STREAMS OF LEARNING CHALLENGES Cognitive academic deficiencies and non-cognitive challenges
Background Conservative Notion of a University under Question The pursuit of reason centred on autonomy and the ability to reflect upon a world of determinations from which it is liberated as a pure point of consciousness University is a sovereign entity above science, societal needs and political imperatives of any given social order Pre-occupied with scientific curiosity as the only means of knowledge generation
Why big data interventions? First integrated big data analytics can be used to identify patterns and trends that can predict student success or failure. Secondly it can be used to identify prevailing challenges that prevent successful learning. Thirdly to design and institutionalise intervention mechanisms and academic opportunities for student success.
Data Categories Biographical Data Enrolment Data Academic Performance Student Experience Personal Data Matric scores Academic Scores Learning Analytics (classes, tutorials, library) Academic Scores Learning Analytics (classes, tutorials, library) Participation - Schooling data Family Background Personal Experience Schooling Experience
Interventions Student Individual Interviews Quantitative data Bespoke attention Mentoring and tutoring
