Predictive Modeling of Student Dropout Indicators in Educational Data Mining using Improved Decision Tree

Sivakumar, Subitha; Venkataraman, S.; Rajalakshmi, S.

doi:10.17485/ijst/2016/v9i4/87032

Cited by 56 publications

(51 citation statements)

References 6 publications

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“…Sivakumar et al [ 31 ] proposed to use Renyi entropy to replace the information entropy in the Gain heuristic. Moreover, the normalisation factor, V ( k ), was used to improve the Gain instability, thereby improving the performance of the decision tree.…”

Section: Related Workmentioning

confidence: 99%

Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree

Sun

Zhang

2019

Entropy

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Uncertainty evaluation based on statistical probabilistic information entropy is a commonly used mechanism for a heuristic method construction of decision tree learning. The entropy kernel potentially links its deviation and decision tree classification performance. This paper presents a decision tree learning algorithm based on constrained gain and depth induction optimization. Firstly, the calculation and analysis of single- and multi-value event uncertainty distributions of information entropy is followed by an enhanced property of single-value event entropy kernel and multi-value event entropy peaks as well as a reciprocal relationship between peak location and the number of possible events. Secondly, this study proposed an estimated method for information entropy whose entropy kernel is replaced with a peak-shift sine function to establish a decision tree learning (CGDT) algorithm on the basis of constraint gain. Finally, by combining branch convergence and fan-out indices under an inductive depth of a decision tree, we built a constraint gained and depth inductive improved decision tree (CGDIDT) learning algorithm. Results show the benefits of the CGDT and CGDIDT algorithms.

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Section: Related Workmentioning

confidence: 99%

Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree

Sun

Zhang

2019

Entropy

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“…Con el uso de distintos paquetes de software para ejecutar los algoritmos de análisis, encontraron un 90% de precisión en la predicción de la aprobación de las asignaturas. En un estudio más reciente, Sivakumar et al (2016), al igual que Pal (2012), propusieron un algoritmo de decisión para predecir la tasa de deserción de estudiantes universitarios en India. Para ello utilizaron una muestra de 240 estudiantes y consideraron 32 potenciales atributos que podrían influir en la decisión de desertar.…”

Section: Ramírezunclassified

Predicción de la Deserción Académica en una Universidad Pública Chilena a través de la Clasificación basada en Árboles de Decisión con Parámetros Optimizados

Ramírez

Grandón

2018

Form. Univ.

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El objetivo de este estudio es presentar una clasificación basada en árboles de decisión (CBAD) con parámetros optimizados para predecir la deserción de los estudiantes universitarios. El estudio analiza 5288 casos de estudiantes pertenecientes a una universidad pública chilena. Para la técnica CBAD se optimizaron los parámetros para mejorar la predicción usando el software RapidMiner. El resultado de la aplicación de esta técnica con parámetros optimizados logro una razón de precisión de un 87.27%. Se concluye que el uso de técnicas de CBAD con optimización de parámetros resulta en una mejor precisión en comparación a otras investigaciones con un número similar de datos.

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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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Predictive Modeling of Student Dropout Indicators in Educational Data Mining using Improved Decision Tree

Cited by 56 publications

References 6 publications

Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree

Attribute Selection Based on Constraint Gain and Depth Optimal for a Decision Tree

Predicción de la Deserción Académica en una Universidad Pública Chilena a través de la Clasificación basada en Árboles de Decisión con Parámetros Optimizados

Factors that Influence Undergraduate University Desertion According to Students Perspective

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