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This paper aims to provide a parsimonious but rigorous model to assist decision-makers to determine critical factors which can lead to higher graduation rates amongst higher education institution (HEI) participants. It predicts the odds of dropout amongst university students, using HEI data from a developing country. This is used as a basis for a Student Retention Predictive (SRP) Model to inform HEI administrators about predicted risks of attrition amongst cohorts.

A classification tool, the Logistic Regression Model, is fitted to the data set for a particular HEI in a developing country. The model is used to predict significant factors for student dropout and to create a base model for predicted risks by various student demographic variables.

To reduce dropout and to ensure higher graduation rates, the model suggests that variables such as age group, faculty, academic standing and cumulative GPA are significant. These indicative results can drive intervention strategies to improve student retention in HEIs and lessen the gap between graduates and non-graduates, with the goal of reducing socio-economic inequalities in society.

This research employs risk bands (low, medium and high) to classify students at risk of attrition or drop out. This provides invaluable insights to HEI administrators in the development of intervention strategies to reduce dropout and increase graduation rates to impact the wider public policy issue of socio-economic inequities.

User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.

Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.

UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.

The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.

One of the currently debated changes in scientific practice is the implementation of data sharing requirements for peer-reviewed publication to increase transparency and intersubjective verifiability of results. However, it seems that data sharing is a not fully adopted behavior among researchers. The theory of planned behavior was repeatedly applied to explain drivers of data sharing from the perspective of data donors (researchers). However, data sharing can be viewed from another perspective as well: survey participants. The research questions (RQs) for this study were as follows: 1 Does data sharing increase participant's nonresponse? 2 Does data sharing influence participant's response behavior? The purpose of this paper is to address these issues.

To answer the RQs, a mixed methods approach was applied, consisting of a qualitative prestudy and a quantitative survey including an experimental component. The latter was a two-group setup with an intervention group (A) and a control group (B). A list-based recruiting of members of the Medical Faculty of the University of Freiburg was applied for 15 days. For exploratory data analysis of dropouts and nonresponse, we used Fisher's exact tests and binary logistic regressions.

In sum, we recorded 197 cases for Group A and 198 cases for Group B. We found no systematic group differences regarding response bias or dropout. Furthermore, we gained insights into the experiences our sample made with data sharing: half of our sample already requested data of other researchers or shared data on request of other researchers. Data repositories, however, were used less frequently: 28% of our respondents used data from repositories and 19% stored data in a repository.

To the authors’ knowledge, their study is the first study that includes researchers as survey subjects investigating the effect of data sharing on their response patterns.

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.

Research questions: Is there a systemic relationship between different methods of screening candidates for predictive maintenance? How do the goals of a predictive project influence the choice of a dropout method? How do the company’s characteristics implementing the predictive project influence the selection of the dropout method?

The authors described and compiled a taxonomy of currently known methods of screening candidate aircraft components for predictive maintenance for maintenance, repairs and overhaul organizations; identified the boundaries of each way; analyzed the advantages and disadvantages of existing methods; and formulated directions for further development of methods of screening for maintenance, repairs and overhaul organizations.

The authors identified the relationship between various screening methods by developing the approach proposed by Tiddens WW and supplementing it with economic methods. The authors built them into a single hierarchical structure and linked them with the parameters of the predictive project. The principal advantage of the proposed taxonomy is a clear relationship between the structure of the screening methods and the goals of the predictive project and the characteristics of the company that implements the project.

The authors of the article proposed groups of screening methods for predictive maintenance based on economic indicators to improve the effectiveness and efficiency of the screening process.

This study aims to determine how school-based management (SBM) capacity developed through international cooperation functioned to overcome challenges during the coronavirus disease 2019 (COVID-19) pandemic in Nepal.

The research structure was designed based on the success case method. The researchers conducted a questionnaire survey of head teachers to understand schools’ responses during the COVID-19 pandemic period, identified likely success-case schools, held workshops at the likely success-case schools and conducted in-depth interviews with head teachers and school management committee (SMC) members to understand how SBM functioned and contributed to the success cases.

Storytelling from the success-case schools provided reliable evidence that the localised approaches of SBM are effective for planning and implementing suitable responses at school. The reviews of the head teachers showed that both head teachers and teachers had strong leadership and understood the importance of collaboration with teachers, SMC members, Parent Teacher Association (PTA), guardians and students. Although the research could not show clear evidence of a causal relationship between their achievement and Japan’s project input, the success-case schools clearly benefited from the head teachers’ appropriate execution of SBM with their strong leadership as well as the collaborative efforts of the stakeholders.

The research tries to clarify the influence of the effects of SBM capacity development projects by analysing the changes of head teachers and teachers through the storytelling aspect of the success case method with in-depth consideration of actual school responses during the emergency period of the COVID-19 pandemic.

This paper aims to develop indicators of happiness in learning of the Thai open university (TOU)'s undergraduate students.

Sampling for the study was comprised of two groups. Group I comprised eight lecturers who are experts in their disciplines and six students who were purposively sampled. The focus group was used to validate the appropriateness of the indicators. In Group II, 332 students were engaged in a multistage sampling process. The responses were analyzed using descriptive statistics, coefficient correlation, exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

The indicators of happiness in learning of undergraduate students of TOU were classified in six categories. These included satisfaction with learning environment (five indicators), learning anxiety (five indicators), satisfaction with learning (five indicators), enthusiasm to learn (six indicators), self-satisfaction (six indicators) and readiness to learn (seven indicators). The six categories explained happiness in learning of undergraduate students of TOU at the 65% and fit empirical data.

The TOU can use the indicators for the assessment of happiness in learning of its students as well as guidelines for the improvement of its student learning environments.

There have been very few studies on indicators of happiness in learning of TOU students. Most were done at the basic education level. This study disclosed the six factors affecting happiness in learning of TOU students; therefore, it should inspire and draw attention of many in the field of higher education distance learning.

Policy makers, professional associations and scholars continue to advocate for the integration of enhanced clinical experiences for future teachers’ preparation. These recommendations reflect the growing recognition that few events in preservice teachers’ education are more significant than their experiences in the classrooms of veteran peers. Aware of the fact that the field of teacher education needs examples of effective clinical experiences, the authors examined the “critical, project-based” (CPB) model, employing Photovoice activities in a dropout prevention course in a secondary education partner school at the beginning of the COVID-19 pandemic. This paper aims to discuss the aforementioned objective.

Aware that the field of teacher education needs examples of effective clinical experiences, the authors examined the CPB model, employing Photovoice activities in a dropout prevention course in a secondary education partner school at the beginning of the COVID-19 pandemic. In this article they detail a practitioner research examination that explores the experiences of 12 preservice middle/high school teachers, reporting on these individuals’ considerations of general pedagogies, writing instruction strategies and teaching personas.

Results suggest that preservice teachers might best identify pedagogical practices that are consistent with their nascent teaching identities via experiences that occur in school-university partnerships in which future teachers are positioned as pedagogues.

This manuscript explores the use of the “CPB” clinical experience model, identifying the impacts of this approach for preparing future teachers.

The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).

Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance.

The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%).

This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

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.