An integrated clustering method for pedagogical performance

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Raed A. Said¹  Kassim S. Mwitondi² 

[1] Canadian University Dubai, United Arab Emirates;;Sheffield Hallam University, College of Business, Technology &

关键词: Association rules;    Big data;    CHEDS;    Data mining;    Data science;    Internship;   

DOI  :  

来源: DOAJ

【 摘 要 】

We present an interdisciplinary approach to data clustering, based on an algorithm originally developed for the Big Data Modelling of Sustainable Development Goals (BDMSDG). Its application context combines mechanics of machine learning techniques with underlying pedagogical domain knowledge–unifying the narratives of data scientists and educationists in searching for potentially useful information in historical data. From an initial structure masking, results from multiple samples of identified set of two to five clusters, reveal a consistent number of three clear clusters. We present and discuss the results from a technical and soft perspectives to stimulate interdisciplinarity and support ​decision making. We explain how the findings of this paper present not only continuity of on–going clustering optimisation, but also an intriguing starting point for interdisciplinary discussions aimed at enhancement of students performance.

【 授权许可】

Unknown