RETRACTED CHAPTER: Differential Evolution Clustering and Data Mining for Determining Learning Routes in Moodle

Viloria, Amelec; Borrero, Tito Crissien; Villa, Jesús Vargas; Torres, Maritza; Guiliany, Jesús García; Mercado, Carlos Vargas; Llinás, Nataly Orellano; Zea, Karina Batista

doi:10.1007/978-981-32-9563-6_18

Cited by 5 publications

(4 citation statements)

References 18 publications

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“…The previous studies conducted [3][4][5][6][7][8][9][10] focus on determining the academic performance of undergraduates. The majority of these studies state that the observation process can be assisted using data mining [3-7, 9, 11-15], and clustering [3,5,11,12,14] techniques. Some studies state that the data used to evaluate performance are the scores realized from the Using Clustering Evolution Analysis", CommIT Journal 17 (2), 209-219, 2023. courses offered [3-9, 15, 16].…”

Section: Introductionmentioning

confidence: 99%

“…However, the result has not been associated with their success rate in pursuing higher education. Additionally, previous research has been conducted on data mining techniques in e-learning platforms, such as Moodle, WebCT, Claroline, and virtual learning systems [12]. The results of the student groupings are presented in tabular and visual representation forms, making those easier to read because Moodle does not provide special visualization tools.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis

Setiabudi,

Santoso

2023

CommIT (Communication and Information Technology) Journal

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Educational data mining is a technique to evaluate educational process of university students, especially in their early stages. Most preliminary studies focus on observing courses undertaken by students from one semester to the next to predict their success rate. However, besides studying, many students are also involved in non-academic activities, which tends to affect their grades. Therefore, the research aims to determine the effect of student activities on grades while taking into account their academic activities. The method used for clustering is K-Means. Data are collected by observing students’ activity patterns in lectures. The research is conducted in two study programs at Petra Christian University: Business Management and Architecture. The results show that the K-Means method gives good results. The clusters formed from the data show non-homogenous groups and produce insights from several groups. The results show a tendency for students’ performance to increase along with the number of activities and points earned. Most students have increased activities during busy times in the third, fourth, fifth, and sixth semesters. The peak is between the fifth and sixth semesters. Then, it starts to decrease in the seventh and eighth semesters. Therefore, students’ activities in the Business Management study program affect performance significantly. Meanwhile, in the Architecture study program, it has an insignificant effect on performance.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis

Setiabudi,

Santoso

2023

CommIT (Communication and Information Technology) Journal

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“…Estas exigencias provocadas por esta nueva sociedad del conocimiento ha tocado a los entornos educativos como a todos sus miembros en el mundo, específicamente el area de las tecnologías de información y comunicación como lo plantean Viloria, Crissien et al (2019), vinculando las TIC como impulsor fundamental de los procesos continuos de cambio, lo que además ha ocasionado la necesidad de formularse una nueva visión, sobre el rol del profesor, que según Pérez et al (2019), determinan como los retos a asumir, de igual manera las universidades en el siglo XXI.…”

Section: Introductionunclassified

Acciones creativas como factor clave del perfil emprendedor del estudiante universitario

PACHECO¹,

Núñez²,

Hernández³

2020

Espacios

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La investigación busca identificar las acciones creativas como factor clave del perfil emprendedor del estudiante. La misma fue de tipo descriptivo, no experimental, transeccional y de campo. La población fue de 54 docentes. Se utilizó un cuestionario con 28 items, cuya confiabilidad fue 0,91. Los datos se analizaron bajo estadística descriptiva estimándose las medidas de tendencia central, así como las medidas de dispersión. Los hallazgos consideran que los estudiantes desarrollan acciones creativas que fomentan y refuerzan su perfil emprendedor.

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“…The advantages of DE are ease of use, speediness, simple structure, and robustness. Therefore, during the recent decades, it has drawn more and more attention in many scientific and engineering fields, such as electrical power system [9,10], optima power flow [11,12], neural network training [13], data mining [14], geophysical inversion [15] and image processing [16,17], etc. Generally, DE uses mutation and recombination as search mechanisms to explore new components of potential solutions.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Differential Evolution Based on Successful Experience Information

Cheng

Wang

et al. 2020

IEEE Access

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As a powerful optimization algorithm for solving nonlinear, complex and tough global optimization problems, differential evolution (DE) has been widely applied in various science and engineering fields. In this paper, considering that the evolution direction of each individual is not fully exploited to guide the search process in most DE algorithms, a new DE variant ( named ADEwSE), which incorporates the successful experience of evolved individuals into classic "current-to-pbest/1" mutation strategy to reduce the randomness of search direction, is proposed. Moreover, crossover matrix sorting scheme based on real crossover rate, opposition learning of crossover rate and adaptive adjustment of top p% values are combined with the new mutation strategy to improve the global search ability. In addition, to improve the searching ability of ADEwSE further, an ADEwSE variant by introducing the linear reduction of population size is proposed. In order to verify and analyze the performance of ADEwSE, numerical experiments on a set of 29 test problems from CEC2017 benchmark for 30, 50 and 100 dimensions are executed. And the experimental results are compared with that of 21 state-of-art DE-based algorithms. Comparative analysis indicates that the ADEwSE and its improved version are competitive with these stateof-art DE variants in terms of solution quality obtained.

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RETRACTED CHAPTER: Differential Evolution Clustering and Data Mining for Determining Learning Routes in Moodle

Cited by 5 publications

References 18 publications

Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis

Effect of Students’ Activities on Academic Performance Using Clustering Evolution Analysis

Acciones creativas como factor clave del perfil emprendedor del estudiante universitario

Adaptive Differential Evolution Based on Successful Experience Information

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