Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Tan, Mingjie; Shao, Peiji

doi:10.3991/ijet.v10i1.4189

Cited by 107 publications

(59 citation statements)

References 16 publications

(17 reference statements)

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“…En este mismo sentido, trabajos previos han desarrollado investigaciones similares, por ejemplo, en el análisis de la deserción estudiantil (Lykourentzou et al, 2009), utilizan Redes Neuronales para crear un indicador de severidad asociado al nivel de riesgo del alumno, por su parte Tan and Shao (2015) implementan algoritmos de aprendizaje automático en una muestra de sesenta mil estudiantes, encontrando el modelo de árbol de decisión como el de mejor efectividad. En otro enfoque, se utilizan técnicas de aprendizaje no supervisado para identificar patrones en temas educativos, es el caso de (Fontalvo-Herrera et al, 2018) donde relacionan las competencias profesionales del Instituto de Ingenieros Industriales (IES) con los planes de estudio universitarios, identificando perfiles de educación en Ingeniería Industrial utilizando clustering y PCA, también en la investigación de Ezen-Can et al, (2015) utilizan técnicas de clustering para analizar el contenido de los foros académicos y promover así soporte a los estudiantes.…”

Section: Otros Antecedentesunclassified

Análisis de la acreditación de calidad en programas de ingeniería industrial y los resultados en las pruebas nacionales estandarizadas, en Colombia

2020

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El presente artículo presenta un análisis relacional entre las acreditaciones universitarias sobre calidad en programas de ingeniería industrial y el resultado en las pruebas estandarizadas de la educación superior en Colombia, usando técnicas de aprendizaje automático. Primero, se desarrolló una revisión literaria concerniente a las acreditaciones sobre calidad, evaluación del desempeño organizacional y técnicas de aprendizaje automático. Segundo, se desarrolló un análisis de componentes principales para identificar factores de agrupación entre las universidades estudiadas. En tercer lugar, se evaluaron modelos de aprendizaje supervisado, para pronosticar la acreditación de una institución según los resultados en los exámenes estandarizados. Los resultados muestran que la componente de comunicación escrita es una variable crítica para determinar la clasificación de universidades como acreditadas. En conclusión, la evidencia empírica muestra la existencia de una relación compatible de contar con una acreditación sobre calidad y tener un buen desempeño en los resultados de las pruebas estandarizadas.

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Section: Otros Antecedentesunclassified

Análisis de la acreditación de calidad en programas de ingeniería industrial y los resultados en las pruebas nacionales estandarizadas, en Colombia

2020

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“…The sensitivities of detection for NN, k-NN, NB, and DT were 87%, 76.8%, 73.9%, and 79.7%, respectively. Tan et al [4], developed prediction models using decision tree (DT), bayesian networks (BNs), and artificial neural network (ANN). A huge data sample of students was used in the time of the model training and model testing.…”

Section: Literature Reviewmentioning

confidence: 99%

Registration Status Prediction of Students using Machine Learning in the Context of Private University of Bangladesh

Mia*,

Biswas,

Sattar

et al. 2019

IJITEE

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Bangladesh is a densely populated country where a large portion of citizens is living under poverty. In Bangladesh, a significant portion of higher education is accomplished at private universities. In this twenty-first century, these students of higher education are highly mobile and different from earlier generations. Thus, retaining existing students has become a great challenge for many private universities in Bangladesh. Early prediction of the total number of registered students in a semester can help in this regard. This can have a direct impact on a private university in terms of budget, marketing strategy, and sustainability. In this paper, we have predicted the number of registered students in a semester in the context of a private university by following several machine learning approaches. We have applied seven prominent classifiers, namely SVM, Naive Bayes, Logistic, JRip, J48, Multilayer Perceptron, and Random Forest on a data set of more than a thousand students of a private university in Bangladesh, where each record contains five attributes. First, all data are preprocessed. Then preprocessed data are separated into the training and testing set. Then, all these classifiers are trained and tested. Since a suitable classifier is required to solve the problem, the performances of all seven classifiers need to be thoroughly assessed. So, we have computed six performance metrics, i.e. accuracy, sensitivity, specificity, precision, false positive rate (FPR) and false negative rate (FNR) for each of the seven classifiers and compare them. We have found that SVM outperforms all other classifiers achieving 85.76% accuracy, whereas Random Forest achieved the lowest accuracy which is 79.65%.

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“…References Adjustment [12] Age [4], [5], [8], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31] Change of goal [12], [32] Choice to change to current major [24] Country or city of origin [13], [26], [33] Domicile [12], [16], [17], [18], [30], [34] Encouragement and support of parents [27] Engagement of student [5], [35], [36] Engagement of student [5], [35] Ethnicity [4], [13], [16], [17], [21], [22], [24], [25], [27], [28]...…”

Section: Factorsmentioning

confidence: 99%

Factors that Influence Undergraduate University Desertion According to Students Perspective

Albán¹,

Mauricio²

2018

IJET

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University desertion is defined as a withdrawal from the academic system that has a negative impact on the economic and social environment. Desertion is influenced by internal and external factors that affect students, institutions, and governments. A literature review shows empirical evidence concerning research conducted to solve the desertion problem. However, most of the studies we analyzed do not consider students' perceptions, which are a key factor in college dropout rates. For this reason, we propose some new factors that influence university desertion. These factors are supported by a study of 65 organizational and educational theories. To examine the influence of these factors, a case study was conducted with 3773 students at a public university in Ecuador. Logistic regression was used to determine the causal relationship between the identified factors and the students' university desertion. As a result, we established that when all the identified factors are present at the same time, the probability of a student deciding to drop out of college is 95% your paper to be published in the conference proceedings, you must use this document as both an instruction set and as a template into which you can type your own text. If your paper does not conform to the required format, you will be asked to fix it. Keyword-Educational data mining, logistic regression, university desertion, desertion factors I. INTRODUCTION University desertion is a problem that affects most higher education institutions around the world. This topic generates controversy among administrators, professors, and students [1]. Today, high dropout rates are considered a possible deficiency of the undergraduate education system [2]. This can be seen from the academic and administrative indicators presented by management personnel at universities and higher education control agencies. In 2016, the dropout rate in India was 15.9% [3], while in Belgium, it was 26.9%; in the UnitedStates, the desertion rate for first and second year students was 44.8% [4], and in Costa Rica, it reached up to 49%. According to the United Nations (UN), the 2016 university desertion rate was 40% in several Latin-American countries, such as Colombia and Ecuador, and was approximately 54% in Brazil. University desertion is influenced by a set of interacting factors that have a negative impact on students' decisions to drop out of college [5]. A literature review allows us to identify several studies related to factors that influence desertion. Nevertheless, these studies do not consider students' perspectives, which play a key role in university desertion. Consequently, the following study is based on organizational and educational theories and proposes 11 new factors that affect university desertion while taking students' perspectives into consideration. To examine the influence of these factors, an empirical study is performed with 3773 students. This article is organized into six sections. In section two, the literature review is presented. The methodology applied i...

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Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Cited by 107 publications

References 16 publications

Análisis de la acreditación de calidad en programas de ingeniería industrial y los resultados en las pruebas nacionales estandarizadas, en Colombia

Análisis de la acreditación de calidad en programas de ingeniería industrial y los resultados en las pruebas nacionales estandarizadas, en Colombia

Registration Status Prediction of Students using Machine Learning in the Context of Private University of Bangladesh

Factors that Influence Undergraduate University Desertion According to Students Perspective

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