Explanatory Variables of Dropout in Colombian Public Education: Evolution Limited to Coronavirus Disease

<p style="text-align: justify;">Student dropout, defined as the temporary or definitive suspension of the exercise of the right to education, is attributable to multiple variables classified into individual, academic, institutional, and socioeconomic determinants which may be exacerbated in the context of the Coronavirus disease (COVID-19) pandemic. Consequently, this work aims to synthesize, from the available evidence, the behaviour and influence of the explanatory variables of school dropout in infant school, primary school and, high school in Colombia for the period 2014-2019 compared to the period 2020-2021 under the COVID-19 pandemic conditions. The research methodology consisted of a systematic review of 125 indexed articles for 2014-2019 and 32 reports related to dropout in Colombian Basic education for the 2020-2021 period. The systematic review of the 157 articles revealed that dropout was studied and explained in both time periods, mainly from the academic determinant whose most cited explanatory variables were: ‘teachers’, ‘curriculum’ and ‘methodologies used’. Moreover, it could be perceived that in the period 2014-2019, the socioeconomic variable was the second dropout determinant, considering ‘family income” as the most important indicator, while in 2020-2021 the “infrastructure” and the ‘political environment’ remained as the most dominant. Lastly, in 2020-2021, the variable ‘teachers’ was highly cited showing that their practice made students maintain their interest despite the physical distance.</p>

Evasão escolar na Educação de Jovens e Adultos: um estudo na rede estadual de ensino de Pernambuco

A evasão escolar constitui um dos maiores problemas para as redes de ensino, em diferentes níveis e modalidades. O tratamento da problemática da evasão, no contexto da Educação de Jovens e Adultos, exige uma abordagem particularizada, tendo em vista que a própria modalidade existe como resultado desse fenômeno. Neste sentido, buscamos neste trabalho investigar os aspectos que influenciam a ocorrência da evasão escolar na EJA, no âmbito da Rede Estadual de Ensino de Pernambuco. O enfoque do estudo esteve voltado para compreender a questão a partir dos olhares de estudantes, professores e gestores escolares. Os participantes do estudo indicaram que a evasão escolar dos estudantes da EJA ocorre, principalmente, em decorrência de aspectos externos às escolas. Os dados também apontaram que a permanência pode ser impactada por ações didáticas, que promovam o fortalecimento e a visibilidade da EJA na comunidade escolar.Palavras-chave: Educação de Jovens e Adultos; Evasão escolar; Direito à educação; Permanência na escola.Student dropout in the Youth and Adult Education: a study in the public school system of PernambucoABSTRACTStudent dropout is one of the biggest problems for teaching networks at different levels and modalities. The treatment of the problem of student dropout in the context of Youth and Adult Education (YAE) requires a particularized approach because the modality itself exists as a result of this phenomenon. In this sense, we seek to investigate the aspects that influence the occurrence of student dropout in the YAE within the public school system of the state of Pernambuco. The focus of the study was to understand the issue from the perspectives of students, teachers and school managers. The study participants indicated that student dropout of YAE students occurs due to external aspects of schools. The data indicated that permanence can be impacted by didactic actions that promote the strengthening and visibility of YAE in school community.Keywords: Youth and Adult Education; Student dropout; Right to education; Stay in School.Deserción escolar en la Educación de Jóvenes y Adultos: un estudio en la red pública de enseñanza de PernambucoRESUMENLa deserción escolar es uno de los mayores problemas en las redes de enseñanza en diferentes niveles y modalidades. El tratamiento del problema de la deserción escolar en el contexto de la Educación de Jóvenes yAdultos (EJA) requiere un enfoque particularizado porque la modalidad en sí existe como resultado de este fenómeno. En este sentido, buscamos investigar los aspectos que influyen en la ocurrencia de la deserción escolar en la EJA en la red pública de enseñanza en el estado de Pernambuco. El enfoque del estudio fue comprender el tema desde las perspectivas de los estudiantes, profesores y gestores escolares. Los participantes del estudio indicaron que la deserción escolar de los estudiantes de la EJA se produce debido a aspectos externos de las escuelas. Los datos indicaron que la permanencia puede ser afectado por acciones didácticas que promuevan el fortalecimiento y la visibilidad de la EJA en la comunidad escolar. Palabras clave: Educación de Jóvenes y Adultos; Deserción escolar; Derecho a la educación; Permanencia en la escuela.

A Machine Learning Approach to Detect Student Dropout at University

In universities, student dropout is a major concern that reflects the university's quality. Some characteristics cause students to drop out of university. A high dropout rate of students affects the university's reputation and the student's careers in the future. Therefore, there's a requirement for student dropout analysis to enhance academic plan and management to scale back student's drop out from the university also on enhancing the standard of the upper education system. The machine learning technique provides powerful methods for the analysis and therefore the prediction of the dropout. This study uses a dataset from a university representative to develop a model for predicting student dropout. In this work, machine- learning models were used to detect dropout rates. Machine learning is being more widely used in the field of knowledge mining diagnostics. Following an examination of certain studies, we observed that dropout detection may be done using several methods. We've even used five dropout detection models. These models are Decision tree, Naïve bayes, Random Forest Classifier, SVM and KNN. We used machine-learning technology to analyze the data, and we discovered that the Random Forest classifier is highly promising for predicting dropout rates, with a training accuracy of 94% and a testing accuracy of 86%.

Predicting Dropout in Higher Education: a Systematic Review

In this article, we present a systematic literature review, carried out from February to March 2020, on the application of a machine learning technique to predict student dropout in higher education institutions. Besides describing the protocol followed during our research, which includes the research questions, searched databases and query strings, along with criteria for inclusion and exclusion of articles, we also present our main results, in terms of the attributes used by current research on this theme, along with adopted approaches, specific algorithms, and evalution metrics. The Decision Tree technique is the most used for the construction of models, and accuracy and recall and precision being the most used metric for evaluating models.

Predicting Student Dropout in Self-Paced MOOC Course Using Random Forest Model

A significant problem in Massive Open Online Courses (MOOCs) is the high rate of student dropout in these courses. An effective student dropout prediction model of MOOC courses can identify the factors responsible and provide insight on how to initiate interventions to increase student success in a MOOC. Different features and various approaches are available for the prediction of student dropout in MOOC courses. In this paper, the data derived from a self-paced math course, College Algebra and Problem Solving, offered on the MOOC platform Open edX partnering with Arizona State University (ASU) from 2016 to 2020 is considered. This paper presents a model to predict the dropout of students from a MOOC course given a set of features engineered from student daily learning progress. The Random Forest Model technique in Machine Learning (ML) is used in the prediction and is evaluated using validation metrics including accuracy, precision, recall, F1-score, Area Under the Curve (AUC), and Receiver Operating Characteristic (ROC) curve. The model developed can predict the dropout or continuation of students on any given day in the MOOC course with an accuracy of 87.5%, AUC of 94.5%, precision of 88%, recall of 87.5%, and F1-score of 87.5%, respectively. The contributing features and interactions were explained using Shapely values for the prediction of the model.

An Investigation into Contract Cheating in Tertiary Education, and How to Combat the Problem in a United Arab Emirates Context

This review article investigated the pervasive problem that contract cheating presents in higher education in the United Arab Emirates (UAE), and aimed to discover whether a solution could be found to combat the growing use of essay mills among students in the region. This literature review aimed to answer the following research questions; why do students use essay mills? Is current university student academic support adequate to facilitate branch campus learning at a level equivalent to a home campus? What methods can international branch campuses in the UAE employ to discourage the use of essay mills? What type and levels of services can universities provide in the future that better support students and stop them from becoming potential essay mill users? The point of the research was to find a means by which students can be dissuaded from using contract cheating sites by becoming so well-supported, and so aware of the threat that contract cheating poses to the value of their degree, that essay mills become a much less attractive option. The study began with a look into the possible circumstances that lead a student to cheat, which includes exploring the fraud triangle theory, the peer behaviour theory, the planned behaviour theory, and the subsequent methodology used. The study found a variety of reasons students cheat, citing laziness, a lack of ability or adequate depth of academic vocabulary in a second language learner, peer pressure, that it is so easy and convenient to use such sites, and the fact that there is a promise of plagiarism free work with a click of a mouse and simple financial transaction (Clarke & Lancaster, 2013). Furthermore, online contract cheating presents a significant challenge for higher education institutes to keep a check on standards and quality assurance. Many teachers are either unaware of or simply afraid to follow up on instances of contract cheating for fear of recriminations in the form of poor feedback or possible student dropout rates.