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Although MOOCs have become a pervasive online learning model, the problem of high dropout rates still persists. Gathering the reasons for the high dropout rate can help to improve the platform design and management of the MOOCs.

A total of 74 studies was extracted from the Web of Science and Scopus. Following the PRISMA (Preferred Reporting Items for systematic Reviews and Meta-Analyses) guidelines, the open-source program CiteSpace is employed to review and induce the studies on the antecedents of MOOC dropout.

The antecedents of the MOOC dropout rate are the psychological, social, personal, course-related, and time factors, and the unexpected hidden cost. Motivation and interaction, which have a decisive impact on the dropout rate of MOOCs, interact with each other. Interaction helps to strengthen the motivation, and appropriate course design enhances the degree of interaction.

From the perspective of a learner, the more knowledge and skills the learners acquire, the more likely they will complete the course. Possessing adequate foundational knowledge is one way to arrest the dropout rate. On the part of the MOOC platform, better course design eases the dropout rate. Further, the course duration and hidden cost in MOOCs contribute to the dropout rate.

This study investigated the trends of repetition and dropout rates in Myanmar's lower secondary education before and after the introduction of the “Continuous Assessment and Progression System (CAPS)” and probed the dependence of these tendencies on high-, middle- and low- socioeconomic status (SES). The obtained results were then examined to extract effective policy implications for the achievement of universal secondary education as specified in the Sustainable Development Goals.

Before and after the CAPS introduction at four government secondary schools, grade repetition and dropout rate trends were examined with respect to differences in students' SES. The analysis utilised a sample of 7,272 students from target secondary schools in urban Yangon Region, Myanmar.

It was found that since the introduction of CAPS, the grade repetition rates had fallen significantly in all SES groups, so was effective regardless of students' SES. The results also demonstrated the influence of unequal CAPS on dropout rates: in the middle-SES group, significant falls to nearly zero post-CAPS implementation. The high-SES group was at ceiling pre- and post-CAPS, so was unaffected. However, in the low-SES group, high dropout rates persisted, indicating that the poor socioeconomic backgrounds of these students significantly reduced the benefits of CAPS.

Rather than using cross-sectional data such as education statistics, this study used longitudinal data based on academic enrollment registries that included information on individual enrollment statuses, which allowed for the relationships between grade repetition, school dropout, education policies and socioeconomic circumstances to be elucidated.

Although China has instituted compulsory education through Grade 9, it is still unclear whether students are, in fact, staying in school. In this paper, the authors use a multi-year (2003–2011) longitudinal survey data set on rural households in 102–130 villages across 30 provinces in China to examine the extent to which students still drop out of school prior to finishing compulsory education.

To examine the correlates of dropping out, the study uses ordinary least squares and multivariate probit models.

Dropout rate from junior high school was still high (14%) in 2011, even though it fell across the study period. There was heterogeneity in the measured dropout rate. There was great variation among different regions, and especially among different villages. In all, 10% of the sample villages showed extremely high rates during the study period and actually rose over time. Household characteristics associated with poverty and the opportunity cost of staying in school were significantly and negatively correlated with the completion of nine years of schooling.

The findings of this study suggest that China needs to take additional steps to overcome the barriers keeping children from completing nine years of schooling if they hope to either achieve their goal of having all children complete nine years of school or extend compulsory schooling to the end of twelfth grade.

The authors seek to measure the prevalence of both compulsory education rates of dropouts and rates of completion in China. The study examines the correlates of dropping out at the lower secondary schooling level as a way of understanding what types of students (from what types of villages) are not complying with national schooling regulations. To overcome the methodological shortcomings of previous research on dropout in China, the study uses a nationally representative, longitudinal data set based on household surveys collected between 2003 and 2011.

The main purpose of this study is to identify the socioeconomic determinants of dropout from primary schools and to give policy suggestions to address the issue.

A total of 600 dropout and enrolled respondents were selected from 60 government primary schools of district Chiniot. School heads and parents of dropout children were taken as samples. The results were obtained by employing the Probit regression model.

Numbers of family members, age of the family head, exchange marriage and poverty status have positive relationship with dropout from primary schools. The findings revealed a higher rate of dropout among girls, which is a major cause of concern.

Education is regarded as a basic human right and a valuable human capital. It is included in Millennium Development Goals to achieve universal primary education and in Sustainable Development Goals as quality education. Underdeveloped countries are facing the problems of high dropout and lack of quality education, especially in Pakistan. These problems need to be addressed to keep pace with developed nations and to meet development goals.

It is recommended that government should create employment opportunities, family planning programs, legislature measures on exchange marriage and child labor. The involvement in co-curricular activities in learning and usage of audio-visual aids in the teaching process can improve the enrollment in the primary schools.

This paper aims to address the issue of why students want to drop out from a course and suggests appropriate strategies to enhance student retention.

A sample of 260 hospitality management students were surveyed based on both Tinto's model of student–institution integration and a theory of planned behavior on student departure. The research applies data mining and decision tree using the classification and regression trees (CART) method as an analytic tool to identify a group, discover relationships between groups and predict future events for segmentation.

The results regarding the demographics indicate that the most critical factors of dropout included residency status, financial situation, quality of class and occupation.

This is a limited US sample, based on student perceptions only and not lecturer or institution perceptions.

The paper provides empirical evidence of student perspective along with institutional and learning environment factors. It includes data from students who are currently enrolled (which previous literature has not covered) by testing student–institution integration and planned behavior on student departure.

The state of Tennessee is part of the United States that houses a special set of school districts known as the Black Belt. Named for the black fertile land, utilized for the agricultural industry for hundreds of years in the south, these school districts have the lowest levels of achievement among the one hundred and thirty six school districts in Tennessee. The purpose of this study is to identify just how extensive these achievement discrepancies are between Black Belt school students and non-Black Belt school students by answering the following research question: are Black Belt school students disproportionately scoring lower on college admittance exams (ACT) than students in non-Black Belt school districts? The data for this study was gathered from the Tennessee Report Card for Education over a period of ten years. Pooled time series cross-sectional regression analysis was the datatesting device employed in the study. The findings suggest that Black Belt students are disproportionately scoring lower on college admittance exams compared to non-Black Belt students. Policymakers need to use caution when generalizing this study because it only represents those Black Belt school districts in Tennessee.

Even if MOOCs (massive open online courses) are becoming a trend in distance learning, they suffer from a very high rate of learners’ dropout, and as a result, on average, only 10 per cent of enrolled learners manage to obtain their certificates of achievement. This paper aims to give tutors a clearer vision for an effective and personalized intervention as a solution to “retain” each type of learner at risk of dropping out.

This paper presents a methodology to provide predictions on learners’ behaviors. This work, which uses a Stanford data set, was divided into several phases, namely, a data extraction, an exploratory study and then a multivariate analysis to reduce dimensionality and to extract the most relevant features. The second step was the comparison between five machine learning algorithms. Finally, the authors used the principle of association rules to extract similarities between the behaviors of learners who dropped out from the MOOC.

The results of this work have given that deep learning ensures the best predictions in terms of accuracy, which is an average of 95.8 per cent, and is comparable to other measures such as precision, AUC, Recall and F1 score.

Many research studies have tried to tackle the MOOC dropout problem by proposing different dropout predictive models. In the same context, comes the present proposal with which the authors have tried to predict not only learners at a risk of dropping out of the MOOCs but also those who will succeed or fail.

The purpose of this paper is to examine resource allocation under the centrally sponsored scheme Sarva Shiksha Abhiyan (SSA) and its impact on development of elementary education in India. First, the author describes the current educational disparity across states in terms of state funding. Second, the author shows that interstate disparities in education resources have more to do with capacity of states to finance elementary education. For this, the author examines funding mechanism under SSA, focusing on principles of adequacy and absorptive rates. Third, the author analyzes the impact of additional funding on the progress of elementary education across states. Fourth, the author demonstrates how funding under SSA reinforces rather than reduces interstate disparity in school funding. Finally, the author concludes with certain policy implications for reforming federal transfers in Right to Education (RTE)-SSA, which can easily be extended to Rashtria Madhya Shiksha Abhiyan (RMSA) to be more responsive to educational inadequacy, effort and capacity across states.

The author uses box plots for illustrating interstate disparity across various indicators on financing and growth of elementary education. Box plots are good at portraying extreme values and illustrate differences between distributions. Because the thrust of the paper is examining difference in distribution across and within states, box plots appropriately portray the distribution of both. Further, coefficient of variation is estimated in education funding and its impact variables.

Interstate disparity in additional to the funding of SSA through discretionary transfers is examined by looking at two principles of inter-governmental transfers, viz., adequacy and absorptive rates. In a way, it appears that the educationally backward states getting the highest shares and also as per the requirement of the child population, but not necessarily so in terms of their relative proportions of enrolment, schools and teachers. Yet another revelation is that actual absorptive rates are much less than apparent absorptive rates. Unambiguously, additional resources coming from the Center for Development of Education can have a positive influence only after states have achieved a certain threshold level of absorptive capacities. As evidenced, fiscal disability is not compensated by transfers via SSA, as matching shares are uniform across states.

One significant limitations of the study is its use of administrative data. Often, administrative data from developing countries especially on social sector like education report inflated figures. The study uses primarily such but published secondary data sources.

Finally, the author suggests certain policy implications for reforming federal role in the current RTE-SSA, which can easily be extended to RMSA, a CSS in secondary education, to be more responsive to state effort and capacity.

Though SSA attempts to address regional imbalance, the accumulated initial advantage of better-off states with uniform norms under SSA funding widens the interstate disparity rather than reduce it. It is, hence, mandated to look at building capacities and enable states for a level-playing field.

It adds value to existing studies in two ways: rarely studies examine SSA expenditures and its impact on development and financing of elementary education, and examine a question on horizontal equalization mechanism whether additional allocation under SSA induce or reduce interstate disparity.

Examines the determinants of illegal drug arrests based on the 1991 data for 48 states in the USA. Major findings indicate that illegal drug arrests have a positive relation with welfare (AFDC) recipients, high school dropout rates, field police force and other types of crime, and a negative relation with the unemployment rate. Policy implications of this study are that to reduce illegal drug crime, the government and parents need to emphasize family values and work ethics, provide quality education and proper counselling, increase field police size or make police more productive, and reduce other types of crime.

Machine Learning algorithms fed with data sets which include information such as attendance data, test scores and other student information can provide tutors with powerful tools for decision‐making. Until now, much of the research has been limited to the relation between single variables and student performance. Combining multiple variables as possible predictors of dropout has generally been overlooked. The aim of this work is to present a high level architecture and a case study for a prototype machine learning tool which can automatically recognize dropout‐prone students in university level distance learning classes. Tracking student progress is a time‐consuming job which can be handled automatically by such a tool. While the tutors will still have an essential role in monitoring and evaluating student progress, the tool can compile the data required for reasonable and efficient monitoring. What is more, the application of the tool is not restricted to predicting drop‐out prone students: it can be also used for the prediction of students’ marks, for the prediction of how many students will submit a written assignment, etc. It can also help tutors explore data and build models for prediction, forecasting and classification. Finally, the underlying architecture is independent of the data set and as such it can be used to develop other similar tools