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Though a number of technology-based approaches have been applied to higher education in a bid to boost student retention, student progression and cost saving, gaps exist. This work introduces Learning Analytics (LA) as antidote. LA ensures that the humongous learner-related data in higher institutions are harnessed for improved learning and the environment it takes place. The LA systems elicit hidden pattern in data and is trained using same observational data for purposes of classifying, recognizing, and predicting learners' behaviours. This way, decision making in higher institutions is based on reliable and factual information leading to improved higher educational services. The model proposed indicates that LA systems can be trained using machine learning algorithms as integral part of the application of data analytics in the learning environment.
About the author
The author is a PhD student in Computer Science with special interest in intelligent software engineering, data mining, optimization and artificial intelligence. He holds M.Sc. (Computer Science) and MBA degrees and passionate about applying computational theories to solve socio-economic problems.
