Transcription of Data Mining Applications in Higher Education - SPSS
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Jing Luan, PhDChief Planning and Research Officer, Cabrillo CollegeFounder, Knowledge Discovery LaboratoriesExecutive reportData Mining Applicationsin Higher EducationTa b l e of contentsIntroduction ..2 data Mining overview ..2 data Mining models and algorithms ..2 Frequently used algorithms ..3 data Mining in Higher Education ..3 Supervised and unsupervised modeling ..3 data Mining Applications in Higher Education ..4 Case study one: Creating meaningful learning outcome typologies ..4 Case study two: Academic planning and interventions transfer prediction ..5 Case study three: Predicting alumni pledges ..6 Conclusion ..7 About SPSS Inc..7 SPSSisa registered trademark and the other SPSS products named are trademarks of SPSS Inc. All other names are trademarks of their respective owners. 2004 SPSS Inc. DMHEWP-10042 data Mining Applications in Higher EducationIntroductionOne of the biggest challenges that Higher Education faces today is predicting the paths of students and alumni.
2 Data Mining Applications in Higher Education Introduction One of the biggest challenges that higher education faces today is predicting the …
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Predictive Modeling Using Logistic Regression, Boosted Regression (Boosting): An introductory, Boosted Regression (Boosting): An introductory tutorial, MODELING, Getting Started with SAS Enterprise Miner, Getting Started ® with SAS. Enterprise Miner, OF QUANTITATIVE TECHNIQUES IN, OF QUANTITATIVE TECHNIQUES IN MANAGERIAL DECISIONS, Artificial Neural Networks, To Insurance Customer, To Insurance Customer Churn Management, INFOSYS SUPPLY CHAIN EARLY WARNING SOLUTION