Data Mining in Higher Education : University Student Dropout Case Study
نویسندگان
چکیده
منابع مشابه
Predicting Student Dropout in Higher Education
Each year, roughly 30% of first-year students at US baccalaureate institutions do not return for their second year and over $9 billion is spent educating these students. Yet, little quantitative research has analyzed the causes and possible remedies for student attrition. Here, we describe initial efforts to model student dropout using the largest known dataset on higher education attrition, wh...
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Data mining combines machine learning, statistics and visualization techniques to discover and extract knowledge. One of the biggest challenges that higher education faces is to improve student retention (National Audition Office, 2007). Student retention has become an indication of academic performance and enrolment management. Our project uses data mining and natural language processing tech...
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Higher Education targets to develop complex theoretical, abstract and analytical reasoning capabilities in the alumni. This objective can be accomplished addressing four major steps: Theoretical foundation, practice, communication and assessment (Petry, 2002). Theoretical background and practical exercise comprise the basic knowledge building process at initial stages. Assessment guides the alu...
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ژورنال
عنوان ژورنال: International Journal of Data Mining & Knowledge Management Process
سال: 2015
ISSN: 2231-007X,2230-9608
DOI: 10.5121/ijdkp.2015.5102