Analysis of UK/EU Law on Data Mining in Higher Education Institutions

2013 ◽  
Author(s):  
Andrés Guadamuz ◽  
Diane Cabell
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Higher Education Institutions ◽  
Eu Law

scholarly journals Aportaciones desde la minería de datos al proceso de captación de matrícula en Instituciones de Educación Superior particulares.

2016 ◽  
Vol 20 (3) ◽  
pp. 1 ◽  
Author(s):  
Rafael Isaac Estrada-Danell ◽  
Roman Alberto Zamarripa-Franco ◽  
Pilar Giselle Zúñiga-Garay ◽  
Isaías Martínez-Trejo
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Predictive Model ◽  
Higher Education Institutions ◽  
Model Management ◽  
Current Status ◽  
Educacion Superior ◽  
Correlational Method ◽  
Enrollment Process

 This article aims to analyze how data mining (DM) optimizes the enrollment process, with the intention of designing a predictive model to manage private enrollment for higher education institutions of Mexico. It analyzes the current status of the higher education institutions in relation to its enrollment process and the application of the DM. With a correlational method, a dataset (DS) was used to model an entropy decision tree with the help of Rapid Miner software. The results show that it is possible to build and test a predictive model management of private enrollment for higher education institutions of Mexico as the ZAM&EST model proposed by the authors.

scholarly journals The uses of educational data mining in academic performance analysis at higher education institutions (case study at UNJANI)

2021 ◽  
Vol 11 (1) ◽  
pp. 26-35
Author(s):  
Yulison Herry Chrisnanto ◽  
◽  
Gunawan Abdullah ◽  
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Academic Performance ◽  
Student Performance ◽  
Higher Education Institutions ◽  
Educational Data Mining ◽  
Strategic Decision ◽  
Data Set ◽  
Study Results ◽  
Mining Model

Education is an important thing in a person's life, because by having adequate education, one's life will be better. Education can be obtained formally through formal institutions that constructively provide a person's abilities academically. This study aims to determine student performance in terms of academic and non-academic domains at a certain time during their education using techniques in data mining (DM) which are directed towards academic data analysis. Academic performance is delivered through the Educational Data Mining (EDM) integrated data mining model, in which the techniques used include classification (ID3, SVM), clustering (k-Means, k-Medoids), association rules (Apriori) and anomaly detection (DBSCAN). The data set used is academic data in the form of study results over a certain period of time. The results of EDM can be used for analysis related to academic performance which can be used for strategic decision making in aca-demic management at higher education institutions. The results of this study indicate that the use of several techniques in data mining together can maximize the ability to analyze academic performance with the same data source and produce different analysis patterns.

Role of Big Data in Education

2022 ◽  
pp. 22-37
Author(s):  
Simin Ghavifekr ◽  
Seng Yue Wong
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Decision Making ◽  
Big Data ◽  
Information Management ◽  
Higher Education Institutions ◽  
Big Data Analytics ◽  
Time Performance ◽  
Teaching Support

Big data has the variety of characteristics, such as real-time performance, timeliness short, and data mining analysis of large value generated. Application of big data in education can be reviewed in various aspects such as 1) providing students with appropriate teaching, 2) providing teaching support to teachers, and 3) providing information management for the administrations. This chapter can serve as a guide for the management of higher education institutions to recognize possible challenges in big data analytics and better prepare for them in future decision making.

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