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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.

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.

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.

This study investigates the acceptance of mobile phone technology in Tanzanian small- and medium-sized enterprises (SMEs) using the Technology Acceptance Model (TAM) with a special focus on service quality.

The conceptual framework was designed by extending the TAM with an additional construct, service quality, before testing a model in a survey of 155 respondents and analysing using Smart PLS 4.

Service quality was found to be among the significant factors in the acceptance of mobile phone technology among SME employees.

This implies that the higher the quality of service offered, the more employees accept and use mobile phone technology in their duties and improve the productivity of SMEs.

The aspects of quality of mobile phone technology usage such as call dropouts, network quality, speed, etc., must be improved significantly.

The Mobile Network Operators and Regulators must understand that employees are offered the most accurate and reliable mobile phone services for its usefulness to be realised.

The originality is a modified version of a TAM that accommodates service quality that has been tested in the Tanzanian context.

Employees of Pakistani public sector organizations feel thwarted toward their goal attainment because of strict adherence to rules and regulations and tall hierarchies existing in this region. Therefore, keeping in view the harmful effects of perceived organizational obstruction, the aim of the current study was to investigate the perceived organizational obstruction as an attribution that triggers job neglect through perceived organizational frustration. Harvey’s expanded attribution-emotion model of workplace aggression and an attributional perspective on workplace aggression provide the theoretical justification. Moreover, the moderating role of self-control was proposed to mitigate the indirect effect of organizational obstruction on job neglect through perceived organizational frustration.

A three-wave data collection was done by using a close-ended questionnaire distributed to a total of 600 administrative employees of public sector organizations operating in Rawalpindi/Islamabad (Pakistan). However, matching three times and discarding the incomplete questionnaires led to a sample of 375 on which the analysis was done.

Perceived organizational obstruction positively predicted job neglect. Likewise, organizational frustration mediated the aforementioned link. Moreover, the higher level of self-control weakens this underlying process by suppressing job neglect behavior.

The current study added to the limited literature on public sector organizations that has taken perceived organizational obstruction as a predictor variable. Moreover, this study explains how this phenomenon translates into non-hostile behavior that is difficult to identify and punish in public sector organizations. Moreover, the trait of self-control is added to the literature of non-hostile behaviors that dampen the impulsivity to indulge in job neglect.

Introduction: The Udaan Scheme was implemented in response to enduring conflict, economic downturn, and employment scarcity. Under the Rangarajan Committee, the scheme aimed to address unemployment in a selected region through skill development programmes. Based on practical experience, Udaan aimed to build a competitive workforce for India and the global economy.

Purpose: The purpose of the study is to evaluate the success ratio of the Udaan Scheme in addressing the employment challenges faced by the youth.

Need of the study: The chapter highlights the potential of the scheme to be a part of a resilient industry for job employability in politically disturbed areas.

Methodology: The compiled data were analysed using a spreadsheet collected from online sources, providing information on the number of registrations for the skill development programme between March 2012 and May 2018, that is, the programme’s implementation in the pre-UT era, mainly sourced from the Udaan Impact Assessment Report and the Review of the Udaan Scheme in Jammu and Kashmir (J&K).

Findings: The programme, which provided professional training and increased the job-securing capacity of youth, has had a dismal success rate despite the government’s investment of Rs 246 crore. The initiative has employed less than 10,000 individuals, or at most 10% of the target population, falling short of its claimed goals.

Significance of the study in the global market: The scheme addresses unemployment and career development for educated youth, enhancing India’s economic growth and global competitiveness. By providing skill development and exposure to the corporate sector, it empowers youth and attracts international business opportunities. It aligns with global efforts to bridge the skills gap and showcases India’s commitment to human capital development in a conflict-driven state.

This study aims to construct a sentiment series generation method for danmu comments based on deep learning, and explore the features of sentiment series after clustering.

This study consisted of two main parts: danmu comment sentiment series generation and clustering. In the first part, the authors proposed a sentiment classification model based on BERT fine-tuning to quantify danmu comment sentiment polarity. To smooth the sentiment series, they used methods, such as comprehensive weights. In the second part, the shaped-based distance (SBD)-K-shape method was used to cluster the actual collected data.

The filtered sentiment series or curves of the microfilms on the Bilibili website could be divided into four major categories. There is an apparently stable time interval for the first three types of sentiment curves, while the fourth type of sentiment curve shows a clear trend of fluctuation in general. In addition, it was found that “disputed points” or “highlights” are likely to appear at the beginning and the climax of films, resulting in significant changes in the sentiment curves. The clustering results show a significant difference in user participation, with the second type prevailing over others.

Their sentiment classification model based on BERT fine-tuning outperformed the traditional sentiment lexicon method, which provides a reference for using deep learning as well as transfer learning for danmu comment sentiment analysis. The BERT fine-tuning–SBD-K-shape algorithm can weaken the effect of non-regular noise and temporal phase shift of danmu text.

All over the world, different types of disabilities affect people and their quality of life. And schools, families, and federal and state agencies are obligated to play very important roles in advancing special education values for students with physical and other health impairments. To maintain and advance these values, the needs of students must be met to the greatest extent possible. Advancing values comes with recognizing the strengths, preferences, interests, related services, community experiences, development of employment, other postschool adult living objectives, and the acquisition of daily living skills. The question is, are these values consistently met, especially for students with physical and other health impairments? This chapter answers this question by discussing how these values can be met and advanced for students with physical and other health impairments.

This paper aims to reflect on the outcomes of a community-based video-conferencing intervention for depression, predating the COVID-19 pandemic. The study investigates the potential implications of its findings for enhancing adherence to digital mental health interventions. The primary objective is to present considerations for researchers aimed at minimising the intention-behaviour gap frequently encountered in digital mental health interventions.

A randomised control feasibility trial design was used to implement a telehealth model adapted from an established face-to-face community-based intervention for individuals clinically diagnosed with depression. In total, 60 participants were initially recruited in association with a local mental health charity offering traditional talking-based therapies with only eight opting to continue through all phases of the project. Modifications aligning with technological advancements were introduced.

However, the study faced challenges, with low uptake observed after an initial surge in recruitment interest. The behaviour-intention gap highlighted technology as a barrier to service accessibility, exacerbated by participant age. Furthermore, the clinical diagnosis of depression, characterised by low mood and reduced interest in activities, emerged as a potential influencing factor.

The limitations of the research include its pre-pandemic execution, during a nascent stage of technological mental health interventions when participants were less familiar with online developments.

Despite these limitations, this study's reflections offer valuable insights for researchers aiming to design and implement telehealth services. Addressing the intention-behaviour gap necessitates a nuanced understanding of participant demographics, diagnosis and technological familiarity.

The study's relevance extends to post-pandemic society, urging researchers to reassess assumptions about technology availability to ensure engagement. This paper contributes to the mental health research landscape by raising awareness of critical considerations in the design and implementation of digital mental health interventions.

Reflections from a pre-pandemic intervention in line with the developments of a post-pandemic society will allow for research to consider that because the technology is available does not necessarily result in engagement.