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 Analysis of Students' Misconducts in Higher Education using Decision Tree and ANN Algorithms

2020 ◽  
Vol 10 (6) ◽  
pp. 6510-6514
Author(s):  
A. H. Blasi ◽  
M. Alsuwaiket
Keyword(s):  
Higher Education ◽  
Data Mining ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Decision Tree ◽  
Higher Education Institutions ◽  
College Campuses ◽  
Classification Models ◽  
Data Mining Techniques ◽  
J48 Decision Tree

A major problem that the Higher Education Institutions (HEIs) face is the misconduct of students’ behavior. The objective of this study is to decrease these misconducts by identifying the factors which cause them on college campuses. CRISP-DM Methodology has been applied to manage the process of data mining and two data mining techniques: J48 Decision Tree (DT) and Artificial Neural Networks (ANNs) have been used to build classification models and to generate rules to classify and predict students' behavior and the location of misconduct in college campuses. They take into consideration seven factors: Student Major, Student Level, Gender, GPA Cumulative, Local Address, Ethnicity, and time of misconduct by month. Both techniques were evaluated and compared. The accuracy results were high for both classification models, whereas the J48 Decision Tree gave higher accuracy.

Docencia en contextos de educación superior: la contribución de “The scholarship of teaching” para el fortalecimiento de la enseñanza llevada a cabo por profesores universitarios

2015 ◽  
pp. 37
Author(s):  
Helena Montenegro Maggio
Keyword(s):  
Higher Education ◽  
Higher Education Institutions ◽  
University Teaching ◽  
Educación Superior ◽  
Field Of Study ◽  
Scholarship Of Teaching ◽  
Educacion Superior ◽  
New Challenges ◽  
Teaching In Higher Education

ResumenLa investigación de la docencia universitaria ha sido un campo ampliamente explorado en los países anglosajones pero escasamente abordado y debatido en nuestro país. El presente artículo tiene como propósito contribuir en el debate del fortalecimiento de la docencia universitaria chilena a través de la propuesta de “Scholarship of Teaching” desarrollada porBoyer (1990), lo cual implica nuevos desafíos para las instituciones de Educación Superiory los actores que forman parte de ella.Palabras clave: Docencia Universitaria - profesor universitario - scholarship of teaching- indagación reflexiva. Teaching in higher education contexts: the contribution of "the scholarship of teaching" to strengthen the teaching conducted by university professorsAbstractResearch on university teaching, an extensively explored field of study in Anglo-Saxons’countries, has been hardly examined and debated in Chile. By using Boyer’s “Scholarshipof Teaching”, the aim of this paper is to make a contribution on discussions on how to strengthen Chilean university teaching, which entails new challenges for higher education institutions as well as players that take part on it.Keywords: University teaching - university teacher - scholarship of teaching - practitionerinquiry.

Improved differentiation classification of variable precision artificial intelligence higher education management

2021 ◽  
pp. 1-10
Author(s):  
Chao Dong ◽  
Yan Guo
Keyword(s):  
Artificial Intelligence ◽  
Higher Education ◽  
Data Mining ◽  
Decision Tree ◽  
Classification Accuracy ◽  
Attribute Selection ◽  
Higher Education Management ◽  
Education Management ◽  
Decision Tree Classification

The wide application of artificial intelligence technology in various fields has accelerated the pace of people exploring the hidden information behind large amounts of data. People hope to use data mining methods to conduct effective research on higher education management, and decision tree classification algorithm as a data analysis method in data mining technology, high-precision classification accuracy, intuitive decision results, and high generalization ability make it become a more ideal method of higher education management. Aiming at the sensitivity of data processing and decision tree classification to noisy data, this paper proposes corresponding improvements, and proposes a variable precision rough set attribute selection standard based on scale function, which considers both the weighted approximation accuracy and attribute value of the attribute. The number improves the anti-interference ability of noise data, reduces the bias in attribute selection, and improves the classification accuracy. At the same time, the suppression factor threshold, support and confidence are introduced in the tree pre-pruning process, which simplifies the tree structure. The comparative experiments on standard data sets show that the improved algorithm proposed in this paper is better than other decision tree algorithms and can effectively realize the differentiated classification of higher education management.

scholarly journals Flexibility in Higher Education: Revisiting Expectations

Comunicar ◽  
2011 ◽  
Vol 19 (37) ◽  
pp. 15-25 ◽  
Author(s):  
Betty Collis ◽  
Jef Moonen
Keyword(s):  
Higher Education ◽  
Higher Education Institutions ◽  
Education Institution ◽  
Educación Superior ◽  
Major Conclusion ◽  
Pedagogical Change ◽  
Educacion Superior ◽  
Key Issues ◽  
Change In Higher Education ◽  
The Uk

We have studied the construct of flexibility in higher education for many years, as researchers and practitioners. In this context we define flexibility as offering the student choices in how, what, where, when and with whom he or she participates in learning-related activities while enrolled in a higher education institution. In a textbook we wrote on the topic in 2001 we identified options that could be available to students in higher education to increase the flexibility of their participation. We studied these from the perspective not only of the student but also in terms of their implications for instructors and for higher-education institutions and examined the key roles that pedagogical change and technology play in increasing flexibility. Now is it nearly a decade later. We will revisit key issues relating to flexibility in higher education, identify in broad terms the extent to which increased flexibility has become established, is still developing, or has developed in ways we did not anticipate directly a decade earlier. We will also review our scenarios for change in higher education related to flexibility and contrast these with a more-recent set from the UK. Our major conclusion is that flexibility is still as pertinent a theme for higher education in 2011 as it was in 2001. Llevamos bastantes años estudiando la construcción de la flexibilidad en la educación superior, tanto desde la óptica de la investigación como de la práctica. Entendemos por flexibilidad la opción de ofrecer a los estudiantes la posibilidad de elegir cómo, qué, dónde, cuándo y con quién participan en las actividades de aprendizaje mientras están en una institución de educación superior. En el libro que escribimos sobre esta temática en 2001 identificamos opciones posibles para los estudiantes de educación superior con la finalidad de incrementar la flexibilidad de su participación. Lo estudiamos no solo desde la perspectiva del estudiante sino también desde las implicaciones para los profesores y para las instituciones de educación superior, y examinamos el papel fundamental que desempeñan el cambio pedagógico y la tecnología en el aumento de la flexibilidad. Ahora, diez años después, revisamos los temas clave relacionados con la flexibilidad en la educación superior e identificamos, en términos generales, hasta qué punto se ha ido estableciendo el incremento de la flexibilidad, si todavía está evolucionando o si ha evolucionado de una forma que no pudimos prever hace diez años. Revisamos también nuestros escenarios para el cambio en la educación superior relacionados con la flexibilidad y los contrastamos con un estudio más reciente llevado a cabo en el Reino Unido. Nuestra conclusión principal es que la cuestión de la flexibilidad en la educación superior sigue siendo tan pertinente en 2010 como lo era en 2001.

Sign in / Sign up

Export Citation Format

Share Document