Methodology for detecting the feasibility of using data mining in an organization

Rangel, C.; Pacheco, Fannia; Aguilar, José; Cerrada, Mariela; Altamiranda, Junior

doi:10.1109/clei.2013.6670649

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…In this sub-section, we detail the design of the ACCI40-2 (The self-supervising autonomic cycle). This autonomic cycle was designed following MIDANO's methodology (Aguilar et al, 2017a;Pacheco et al, 2014;Rangel et al, 2013). MIDANO is a methodology used for developing data analytics tasks and consists of three phases (see Fig.…”

Section: Design Of the Autonomic Cycle Of Self-supervisingmentioning

confidence: 99%

Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context

Sánchez¹,

Expósito²,

Aguilar³

2020

Computers in Industry

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Section: Design Of the Autonomic Cycle Of Self-supervisingmentioning

confidence: 99%

Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context

Sánchez¹,

Expósito²,

Aguilar³

2020

Computers in Industry

Self Cite

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“…Normally, at this stage the data warehouse that contains both, historical and current data, is conceptually designed. For that, we use techniques and strategies from the "data sciences" domain [5], [6]. Data warehouses are based on multidimensional data models, in order to take into account the main data to analyse the objective situations [7].…”

Section: Step 2 Data Model Of the Bi Projectmentioning

confidence: 99%

“…For the implementation of the classification model and the construction of the motivational pattern, we have developed a Java application. For the development of the LA tasks, we are used a combination of the methodologies proposed in [5], [6] called MIDANO.…”

Section: The Knowledge Extracted Using Our Autonomic Cyclementioning

confidence: 99%

Learning Analytics focused on student behavior. Case study: dropout in distance learning institutions

Aguilar¹,

Riofrio

Encalada

2017

CLEIej

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Abstract: Normally, Learning Analytics (LA) can be focused on the analysis of the learning process or the student behavior. In this paper is analyzed the use of LA in the context of distance learning universities, particularly focuses on the students’ behavior. We propose to use a new concept, called "Autonomic Cycle of Learning Analysis Tasks", which defines a set of tasks of LA, whose common objective is to achieve an improvement in the process under study. In this paper, we develop the "Autonomic Cycle of LA Tasks" to analyze the dropout in distance learning institutions. We use a business intelligence methodology in order to develop the "Autonomic Cycle of LA Tasks" for the analysis of the dropout in distance learning. The Autonomic Cycle identifies factors that influence the decision of a student to abandon their studies, predicts the potentially susceptible students to abandon their university studies, and define a motivational pattern for these students. Spanish Abstract: Normalmente, La Análitica del Aprendizaje puede enfocarse en el análisis del proceso de aprendizaje, o en el análisis del comportamiento del estudiante. En este artículo se analiza el uso de LA en el contexto de las universidades a distancia, centrándonos particularmente en el comportamiento de los estudiantes. Para ello, proponemos utilizar un nuevo concepto, llamado "Ciclo Autonómico de Tareas de Análitica del Aprendizaje", que define un conjunto de tareas de LA, cuyo objetivo común es lograr una mejora en el proceso bajo estudio. En este artículo se desarrolla el "Ciclo Autonómico de Tareas LA" para analizar la deserción estudiantil en las instituciones de educación a distancia. Para ello, utilizamos una metodología de inteligencia de negocios con el fin de desarrollar dicho ciclo para el análisis de la deserción en el aprendizaje a distancia. El Ciclo Autonómico identifica factores que influyen en la decisión del estudiante de abandonar sus estudios universitarios, predice los estudiantes potencialmente susceptibles a desertar, y define un patrón de motivación para estos estudiantes.

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Methodological framework for data processing based on the Data Science paradigm

Pacheco¹,

Rangel²,

Aguilar³

et al. 2014

2014 XL Latin American Computing Conference (CLEI)

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Methodology for detecting the feasibility of using data mining in an organization

Cited by 3 publications

References 7 publications

Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context

Implementing self-* autonomic properties in self-coordinated manufacturing processes for the Industry 4.0 context

Learning Analytics focused on student behavior. Case study: dropout in distance learning institutions

Methodological framework for data processing based on the Data Science paradigm

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