How COVID-19 Has Exacerbated Inequality in Higher Education in Brazil

Drawing on educational census data and a review of news articles and higher education policies in Brazil, this article examines the impact of COVID-19 on the access and retention of the low-income Brazilian population in higher education. Guided by the question, “What is the impact of COVID-19 on the most vulnerable population in Brazil in terms of access to, and retention in higher education?”, the paper is structured in two sections: the first offers a short historical overview of Brazilian higher education; the second examines the impact of the pandemic on student retention in higher education, looking at factors such as social isolation, job and income precarity, use of Information and Communication Technologies (ICT), internet access, and technological resources. I argue that distance education offered by private higher education institutions benefits the privileged students and that the effects of the pandemic are detrimental to the socially disadvantaged students since those who are in public universities do not always have access to technology, and those who study in private universities feel the impact of not being able to pay tuition fees, besides the loss of several jobs in different sectors. In conclusion, I recommend policy initiatives to improve access to higher education.

Impact of Relationship Marketing on Customer Retention in Higher Education

This research attempts to measure the impact of relationship marketing on customer retention in higher education. Exploratory cum descriptive and causal research design was used. Sources of data are students, parents, and faculties of different colleges. A convenient sampling method was used for sample selection. Structured questionnaires were used for collecting responses. Primary cross-section data were collected. PLS-SEM was used for testing the psychometric and econometric properties of the model. This research confirmed that relationship marketing, customer orientation, customer satisfaction, and customer retention are possible in the academic sector. Policymakers and academic experts should focus on relationship marketing factors and customer orientation to enhance customer satisfaction and customer retention.

Data Mining and Machine Learning Retention Models in Higher Education

This study presents a systematic review of the literature on the predicting student retention in higher education through machine learning algorithms based on measures such as dropout risk, attrition risk, and completion risk. A systematic review methodology was employed comprised of review protocol, requirements for study selection, and analysis of paper classification. The review aims to answer the following research questions: (1) what techniques are currently used to predict student retention rates, (2) which techniques have shown better performance under specific contexts?, (3) which factors influence the prediction of completion rates in higher education?, and (4) what are the challenges with predicting student retention? Increasing student retention in higher education is critical in order to increase graduation rates. Further, predicting student retention provides insight into opportunities for intentional student advising. The review provides a research perspective related to predicting student retention using machine learning through several key findings such as the identification of the factors utilized in past studies and methodologies used for prediction. These findings can be used to develop more comprehensive studies to further increase the prediction capability and; therefore, develop strategies to improve student retention.