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This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

This study aims to advance the understanding on individual variations in PhD candidates’ personal interest in their doctorate and supervisory and research community support, and several individual and structural attributes potentially having an impact on the profiles.

The authors explored the interrelationship between personal interest – social support profiles, and nationality, gender, research group and study status and the risk of dropping out. A total of 768 PhD candidates from a research-intensive university in Finland responded to a modified version of the cross-cultural doctoral experience survey. Latent profile analysis was used to explore the individual variations in PhD candidates’ interest and support from the supervisor and research community.

Three distinctive PhD interest-social support profiles were detected; the high interest–high support profile (74.4%, n = 570), the high interest–moderate support profile (18.2%, n = 140) and the moderate interest–moderate support profile (7.4%, n = 56). The profiles exhibited high to moderate levels of research, development and instrumental interest. Individuals in the high interest–moderate support and in the moderate interest–moderate support profiles were more prone to consider dropping out from their PhD than in the high interest–high support profile.

The results indicate that by cultivating PhD candidates’ interest and providing sufficient supervisory and the research community offers a means for preventing candidates from discontinuing their doctorate. Hence, building a supportive learning environment that cultivates a PhD candidate’s personal interest is likely to reduce high dropout rates among the candidates.

Automation of detecting cracked surfaces on buildings or in any industrially manufactured products is emerging nowadays. Detection of the cracked surface is a challenging task for inspectors. Image-based automatic inspection of cracks can be very effective when compared to human eye inspection. With the advancement in deep learning techniques, by utilizing these methods the authors can create automation of work in a particular sector of various industries.

In this study, an upgraded convolutional neural network-based crack detection method has been proposed. The dataset consists of 3,886 images which include cracked and non-cracked images. Further, these data have been split into training and validation data. To inspect the cracks more accurately, data augmentation was performed on the dataset, and regularization techniques have been utilized to reduce the overfitting problems. In this work, VGG19, Xception and Inception V3, along with Resnet50 V2 CNN architectures to train the data.

A comparison between the trained models has been performed and from the obtained results, Xception performs better than other algorithms with 99.54% test accuracy. The results show detecting cracked regions and firm non-cracked regions is very efficient by the Xception algorithm.

The proposed method can be way better back to an automatic inspection of cracks in buildings with different design patterns such as decorated historical monuments.

Adolescent pregnancy stands as a societal challenge, compelling young individuals to prematurely discontinue their education. Conversely, an expansion of high school education can potentially diminish rates of adolescent pregnancy, given that educational attainment stands as the foremost risk factor influencing sexual initiation, the use of contraceptive methods during initial sexual encounters and fertility. The aim of this paper is to analyze the impact of the implementation of the public educational policy introducing full-time schools (FTS) for high schools in the state of Ceará, Brazil, on early pregnancy rates.

Using the difference-in-differences method with multiple time periods, we measured the average effect of this staggered treatment on the treated municipalities.

The main result indicates a reduction of 0.849 percentage points in the teenage pregnancy rate. Concerning dynamic effects, the establishment of FTS in treated municipalities results in a 1.183–1.953 percentage point decrease in teenage pregnancy rates, depending on the timing of exposure. We explored heterogeneous effects within socioeconomically vulnerable municipalities, yet discerned no impact on this group. Rigorous tests confirm the robustness of the results.

This paper aims to contribute to: (1) the consolidation of research on the subject, given the absence of such research in Brazil to the best of our knowledge; (2) the advancement and analysis of evidence-based public policy and (3) the utilization of novel longitudinal data and methodology to evaluate adolescent pregnancy rates.

The purpose of this study is to identify the main perspectives and trends in educational data mining (EDM) in the e-learning environment from a managerial perspective.

This paper proposes a systematic literature review to identify the main perspectives and trends in EDM in the e-learning environment from a managerial perspective. The study domain of this review is restricted by the educational concepts of e-learning and management. The search for bibliographic material considered articles published in journals and papers published in conferences from 1994 to 2023, totaling 30 years of research in EDM.

From this review, it was observed that managers have been concerned about the effectiveness of the platform used by students as it contains the entire learning process and all the interactions performed, which enable the generation of information. From the data collected on these platforms, there are improvements and inferences that can be made about the actions of educators and human tutors (or automatic tutoring systems), curricular optimization or changes related to course content, proposal of evaluation criteria and also increase the understanding of different learning styles.

This review was conducted from the perspective of the manager, who is responsible for the direction of an institution of higher education, to assist the administration in creating strategies for the use of data mining to improve the learning process. To the best of the authors’ knowledge, this review is original because other contributions do not focus on the manager.

The steady dropout of senior women from the corporate ladder motivated us to gain insights into their opting out decisions. This exploratory study revealed internal aspiration as a contributory factor of opting out beyond the well-established push and pull factors.

Nineteen senior women executives who had opted out of successful careers due to their internal aspirations were interviewed and grounded theory was leveraged to derive the emergent themes.

The spirit of autonomy and strong personal value emerged as a common thread amongst the women and the basis for their opting out decision. These factors led these senior women to embark upon newer pastures, which included entrepreneurship, dedication to a cause, a passion or academics. The findings were mapped with the protean career concept.

Recommendations would help organizations reimagine and strengthen the existing interventions for the retention of women at the senior levels while simultaneously empowering the women to align their career with their aspirations.

The study uniquely identifies the protean career concept as the force behind the journey undertaken by these women executives. This is in contrast with the push and pull factors that have been vastly studied as reasons for the opting out decisions.

In early 2014, recent Stanford University graduate Tyler Shultz was in a quandary. He had been working at Theranos, a blood-diagnostic company founded by Elizabeth Holmes, a Stanford-dropout wunderkind, for almost a year. Shultz had learned enough about the company to realize that its practices and the efficacy of its much-touted finger-prick blood-testing technology were questionable and that the company was going to great lengths to hide this fact from the public and from regulators.

Theranos and Holmes were Silicon Valley darlings, enjoying positive press and lavish attention from potential investors and technology titans alike. Just as companies like PayPal had revolutionized the stagnant payments industry and Uber had upended the for-hire transportation sector, Theranos had been positioned as the latest technology firm to substantially disrupt yet another mature sector: the medical laboratory business. By the start of 2014, the company had raised more than $400 million in funding, and had an estimated market valuation of $9 billion.

Shultz's situation was exacerbated by the fact that his grandfather, the highly respected former US Secretary of State George Shultz, was on the Theranos board and was one of Elizabeth Holmes's biggest supporters.

But Tyler Shultz worried about the customers he was convinced were receiving highly unreliable and often inaccurate blood-test results. With so much at stake, Shultz wondered how he should proceed. Should he raise his concerns with the firm's investors? Blow the whistle externally? Report to industry regulators? Go away quietly?

This case and its subsequent four brief follow-up cases are based largely on interviews with Tyler Shultz, and outline the dilemma he faced and the various steps he would take both to extricate himself from his unsavory position and let the public know the full extent of the deception at Theranos.

Five optional handouts are available to instructors to further discussion after the case has been debriefed. The handouts serve as additional decision points for the students if your class time permits.

This study analyzed the impact on the persistence of Chilean university students who had received a government-guaranteed loan (CAE).

Using academic and administrative data from 2016 to 2019, provided by 11 Chilean universities, a discrete-time survival model was constructed. The model was based on data of 5,276 students in the 2016 cohort and included sociodemographic variables, academic background prior to entering university and academic performance once in university. As a robustness check of our results to observable confounding, the analysis was repeated using a control group constructed using propensity score matching (PSM).

The results reveal that students who receive a bank loan (CAE) were more likely to remain in undergraduate studies for at least the first two years of university, as opposed to their peers who did not receive financial aid. In addition, they show the importance of academic performance in retention.

The article advances in the identification of the impact of bank loans on permanence. Although previous research has evaluated the impact of the CAE, it has been conducted on small samples of students. These studies also lacked student records associated with their academic performance at the university. The present research overcomes both weaknesses, allowing us to estimate the impact of the CAE on a larger population of students that is representative of the system.

This study aims to analyze to what extent distance education is feasible and efficient with the limited technological infrastructure in Egypt. The study answers this question from the perspective of households' preparedness level regarding possessing information and communication technologies (ICTs). In addition, it assesses whether the pattern of students' ICT ownership is influenced by disability- and socioeconomic-based inequality in education and whether the pattern of ICT ownership exacerbates such biases.

A three-stage probit model with double sample selection (PMDSS) was applied to estimate the factors likely to influence ICT possession, considering the selection process for school enrollment and education continuation. The authors utilized nationally representative data from the Egypt Labor Market Panel Survey 2018.

About 40% of students aged 12–25 did not have ICTs. Most socioeconomically poor households, particularly those living in Upper Egypt, were the least likely to obtain ICTs and rely on distance education. In addition, female students, particularly those with disabilities, had the lowest chance of benefitting from distance learning.

The persistent structural deprivation of school enrollment and educational progression has led to the positive selection of well-off children in education, which is extended to ICT possession and internet use. Without addressing these structural biases, the study suggests that distance education will likely exacerbate educational inequalities.

The study analyzed the extent to which Egyptian families were prepared in 2018 regarding ICT possessions for distance education for their children, particularly those with disabilities. Furthermore, it investigated whether access to distance learning was influenced by disability- and socioeconomic-based inequalities in education.

This paper examines the evolution of massive open online courses (MOOCs) literature over the past 15 years and identifies its significant developments.

Utilizing main path analysis (MPA) on a dataset of 1,613 articles from the Web of Science (WoS) databases, the authors construct the main pathway in MOOC literature through a citation analysis. Pajek software is used to visualize the 34 influential articles identified in the field.

Three phases emerge in MOOC research: connectivism as a learning theory, facilitating education reform and breaking barriers to MOOCs adoption. Multiple-Global MPA highlights sub-themes including self-regulated learning (SRL), motivation, engagement, dropouts, student performance and the impact of COVID-19.

First, data limitations from the WoS core collection might not cover all research, but using reputable sources enhances data validity. Second, despite careful algorithm selection to enhance accuracy, there remains a limitation inherent in the nature of citations. Such biased citations may result in findings that do not fully align with scholars' perspectives.

The authors' findings contribute to the understanding of MOOCs literature development, enabling educators and researchers to grasp key trends and focus areas in the field. It can inform the design and implementation of MOOCs for more effective educational outcomes.

This study presents novel methodologies and important findings for advancing research and practice in MOOCs.