A Study on Learner Retention and Academic Performance using Student Data and Technologies Towards Building a Resilient Open Education System and Innovative Solution

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Date
2022-09
Authors
T Subramaniam, Thirumeni
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Commonwealth of Learning (COL)
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Abstract
PCF10 Sub-theme: Inspiring Innovations // This study on learner retention and academic performance is designed to utilize the rich learners’ database towards improving the education services offered by the Open University Malaysia. The objectives of this research are: (i) to identify possible trends in learner retention and academic performance; (ii) to identify possible factors that could influence learner retention and academic performance; and (iii) to develop an infographic system. The research requires mining of learners’ data and data analysis. During the first phase of data mining and data integration, a number of issues had to be resolved by involving different departments at the university. Data identification, data cleaning and coding was done to enable the achievement of the targeted objectives. The move to the use of ‘big data’ promotes transformation of institutional research projects at the university. Research methods employed also varies from previous survey method to graphical analysis, explorations, and improvement of data structures. Identification of students at risks in terms of retention and academic performance enables the university to make evidence-based decision making and provide targeted solutions. While, the transformation in institutional research offers numerous opportunities, there are also numerous challenges. Findings are presented using selected evidences with attention to the transformation in the research method and the benefits offered by such transition. The proposed transformation, and possible innovations of the mechanisms through which quality education services can be offered are discussed. Depicted findings along with the transformation that are in place could facilitate efficient evidence-based decision-making processes at the university. Use of machine learning and analytics software in research are being explored at present to develop auto-detection of the impact of an introduced solution, predictive modelling (retention, and academic performance) and real-time data visualization. Concurrent research efforts are also in place to promote innovations in: i) pedagogical processes, (ii) learning environment (process), (iii) education learning materials (product), and (iv) administration processes supporting the learners. // Paper ID 7980
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Learner Retention, Academic Performance, Research Transformation, Use of Technology, Research Methods
Country
Malaysia
Region
Asia
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