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In recent years, the use of mobile devices for academic persistence has grown to be an indispensable element of students’ learning, highlighting the broad acceptance and adaptability of mobile technology in learning environments. The current study examines how college students in rural areas use mobile devices and how self-efficacious they are when seeking online information. Additionally, the study investigated the connection between mobile devices usage (MDU), mobile devices self-efficacy (MDSE) and online information seeking behavior (OISB) on the basis of demographic differences.

A quantitative research design was used by deploying a five-point Likert scale for measurement, Statistical Package for Social Sciences (SPSS) v.26 was used for data analysis. A variety of statistical methodologies, including t-tests, ANOVA and correlation coefficients, were conducted to inspect and assess MDU, MDSE and OISB across gender and age groups. Data from 331 students at the public sector college in a rural region was gathered using a questionnaire. A total of 315 legitimate replies were received.

The study's conclusions showed that the respondents used their mobile devices for educational purposes less frequently. Nonetheless, the respondents' degrees of MDSE and OISB appear to be high. Furthermore, a strong link was demonstrated among the MDU, MDSE and OISB. On the contrary, there was a negative correlation link between MDU and both MDSE & OISB, while a positive correlation between MDSE and OISB was found. The results also showed substantial variance in all research components based on age and gender, indicating that male and younger respondents performed more efficiently than female and adult respondents.

These results indicate that information literacy guidelines and a variety of educational initiatives should be put together by the government, educational policymakers, librarians and educators, with a focus on how to use mobile devices for learning and information seeking. This will make it possible for students to more efficiently find the information using their portable devices.

This study aims to describe the m-learning experience of school students and teachers during the COVID-19 pandemic and explores the factors influencing the continuance intention of m-learning.

Semistructured interviews of 24 students and 09 teachers of schools in national capital territory (NCT) Delhi, India were conducted over 03 months and transcribed verbatim. A hermeneutic phenomenological design was used to interpret the text and bring out the “lived experiences” of m-learning.

The following 15 themes or factors influencing continuance intention emerged through the hermeneutic circle: (1) actual usage, (2) attitude, (3) context, (4) extrinsic motivation, (5) facilitating conditions, (6) intrinsic motivation, (7) perceived compatibility, (8) perceived content quality, (9) perceived mobile app quality, (10) perceived teaching quality, (11) perceived usefulness, (12) satisfaction, (13) self-efficacy, (14) self-management of learning and (15) social influence.

The study offers insightful recommendations for school administrators, mobile device developers and app designers. In addition, suggestions for effectively using m-learning during disasters such as COVID-19 have been provided. Several future research directions, including a nuanced understanding of m-assessment and online discussions, are suggested to enhance the literature on m-learning continuance.

The study enriches the literature on m-learning continuance. A qualitative approach has been used to identify relevant factors influencing m-learning continuance intention among secondary and higher secondary level (Grades 9 to 12) school students and teachers in India. In addition, a conceptual framework of the relationships among the factors has been proposed. Further, an analysis of the lived experiences of m-learning during the COVID-19 pandemic indicated several issues and challenges in using m-learning during disasters.

This paper is the third part of a research effort that aims to identify how various crises affected basic business processes using Institut Européen d'Administration des Affaires (INSEAD’s) industrial excellence framework (IEF) in large organizations.

This research compares three data sets from the same four companies against quality management benchmarks, with a span of more than two decades: (1) data collected in 2005 operating under a financially thriving corporate environment; (2) data collected in 2015, operating under a strong financial crisis and (3) data collected in 2022, operating under the serious COVID-19 crisis.

The authors identify the characteristics that helped them navigate successfully through almost two crises in a row. These companies successfully navigated crises by applying assessment and continuous improvement processes, forging close relationships with their suppliers and training their people systematically.

The findings can be adopted by practitioners who wish to navigate their organizations during serious crises. Researchers and academics may also find this study interesting for managing quality during a pandemic.

There are not many studies that investigate how the pandemic influenced the quality management practices in organizations in Greece in sequential crises. This paper brings insights into the enduring advantages of adopting a quality framework.

This study examines the factors influencing educational aspirations among adolescents in secondary and high schools across Kazakhstan, Kyrgyzstan (officially Kyrgyz Republic), and Tajikistan, with a focus on their aspirations for higher and postgraduate education. Through an analysis of a sample comprising 1360 students from grades 7 to 11, the research explores the likelihood of these students pursuing tertiary education.

The ordered logit regression models were used to analyze the impact of variables related to family background, including parents’ education, family support and SES, and school grade. Additionally, the Exploratory Factor Analysis was utilized to distill a smaller set of underlying factors or constructs from the measured variables.

The findings indicate that the mother's education level is significantly more influential compared to the father's education in shaping adolescents' aspirations for higher and postgraduate education in the Central Asian context. Additionally, hypotheses concerning the influence of family socioeconomic status (SES), family support, and grade-level differences on adolescents' higher educational aspirations have only been partially confirmed.

To the best of the authors' knowledge, no prior research work has indicated a significant impact of a mother's education on the educational aspirations of her offspring in the Central Asian region. This trend is consistent with the concept of educational hypogamy, wherein mothers possess higher educational levels than fathers, a phenomenon that is gaining prominence in contemporary family dynamics.

Given the scientific and practical difficulties inherent in measuring and comparing socioeconomic deprivation (SED), and the further complexity added in cross national measurements, the main aim of this paper was to check the validity of SED measures used in PISA 2018 dataset. The SED measure used in PISA 2018 was the PISA index of economic, social and cultural status abbreviated as the ESCS index. This assessment was important as PISA analysis is based on variables derived from this instrument and the ESCS index and these reports influence and reflect international and comparative education policies and practice.

This study critically evaluates the socioeconomic status measures in the PISA 2018 dataset, focusing on their convergent validity and cross-national comparability. Using responses from over 600,000 students in 73 countries, it examines the validity of SES indicators and their comparability across countries. The study employs principal component analysis to construct local SES measures and compares them with the existing Economic, Social, and Cultural Status (ESCS) index. It explores the relationship between these SES measures and academic achievement in reading, science, and mathematics, aiming to understand their predictive validity in diverse educational settings. Statistical analyses were conducted using the IEA’s IDB Analyser and SPSS, ensuring robustness and generalisability across the diverse participant countries.

Our research findings challenge the assumed superiority of local measures over broader constructs like the Economic, Social, and Cultural Status (ESCS). It suggests that standardised measures like ESCS may provide more reliable predictions of academic achievement across various educational contexts, underscoring the complex relationship between SES measures and academic performance.

Our novel analysis shows that local and cross-national SED measures are poorly correlated. Our findings raise questions about the measures' validity while acknowledging the methodological challenges. We provide empirical evidence to support ongoing debates on the topic.

This research explored the intersection of cognitive processes, emotions and their impacts on digital game-based vocabulary learning (DGVL) among university students. Recognizing the scant research in this area, especially with integrating innovative technologies, this study aims to understand the influence of these elements using advanced monitoring tools.

This inquiry was carried out as an observational study involving 44 university students segmented into three English language proficiency levels: high, intermediate and low based on their English course scores. The methodological tools included a portable eye tracker to observe visual behaviors and deep learning technology to identify and analyze the participants’ emotional responses and engagement with the DGVL during the learning process.

The results showed that distinct fixation sequences and variations in visual attention during DGVL were correlated with different levels of competency, suggesting a direct correlation between visual engagement and language competence. In addition, emotional transitions, predominantly from engagement (“flow”) to challenge (“frustration”), were common among participants, reflecting the emotional dynamics of learning. Furthermore, all participants consistently focused on the English vocabulary definitions, indicative of their targeted approach to understanding and test preparation. These findings highlighted the intricate dynamics between emotions and cognitive processes in learning environments.

Contribution of this study shows the interplay of cognitive engagement and emotional experiences in the context of DGVL. It underscored the complex nature of these factors and their collective influence on learners’ visual and emotional engagement, offering valuable implications for educational strategies and technological applications in language learning.

The purpose of this paper is to secure health data collection and transmission (SHDCT). In this system, a native network consists of portable smart devices that interact with multiple gateways. It entails IoMT devices and wearables connecting to exchange sensitive data with a sensor node which performs the aggeration process and then communicates the data using a Fog server. If the aggregator sensor loses the connection from the Fog server, it will be unable to submit data directly to the Fog server. The node transmits encrypted information with a neighboring sensor and sends it to the Fog server integrated with federated learning, which encrypts data to the existing data. The fog server performs the operations on the measured data, and the values are stored in the local storage area and later it is updated to the cloud server.

SHDCT uses an Internet-of-things (IoT)-based monitoring network, making it possible for smart devices to connect and interact with each other. The main purpose of the monitoring network has been in the collection of biological data and additional information from mobile devices to the patients. The monitoring network is composed of three different types of smart devices that is at the heart of the IoT.

It has been addressed in this work how to design an architecture for safe data aggregation in heterogeneous IoT-federated learning-enabled wireless sensor networks (WSNs), which makes use of basic encoding and data aggregation methods to achieve this. The authors suggest that the small gateway node (SGN) captures all of the sensed data from the SD and uses a simple, lightweight encoding scheme and cryptographic techniques to convey the data to the gateway node (GWN). The GWN gets all of the medical data from SGN and ensures that the data is accurate and up to date. If the data obtained is trustworthy, then the medical data should be aggregated and sent to the Fog server for further processing. The Java programming language simulates and analyzes the proposed SHDCT model for deployment and message initiation. When comparing the SHDCT scheme to the SPPDA and electrohydrodynamic atomisation (EHDA) schemes, the results show that the SHDCT method performs significantly better. When compared with the SPPDA and EHDA schemes, the suggested SHDCT plan necessitates a lower communication cost. In comparison to EHDA and SPPDA, SHDCT achieves 4.72% and 13.59% less, respectively. When compared to other transmission techniques, SHDCT has a higher transmission ratio. When compared with EHDA and SPPDA, SHDCT achieves 8.47% and 24.41% higher transmission ratios, respectively. When compared with other ways it uses less electricity. When compared with EHDA and SPPDA, SHDCT achieves 5.85% and 18.86% greater residual energy, respectively.

In the health care sector, a series of interconnected medical devices collect data using IoT networks in the health care domain. Preventive, predictive, personalized and participatory care is becoming increasingly popular in the health care sector. Safe data collection and transfer to a centralized server is a challenging scenario. This study presents a mechanism for SHDCT. The mechanism consists of Smart healthcare IoT devices working on federated learning that link up with one another to exchange health data. Health data is sensitive and needs to be exchanged securely and efficiently. In the mechanism, the sensing devices send data to a SGN. This SGN uses a lightweight encoding scheme and performs cryptography techniques to communicate the data with the GWN. The GWN gets all the health data from the SGN and makes it possible to confirm that the data is validated. If the received data is reliable, then aggregate the medical data and transmit it to the Fog server for further process. The performance parameters are compared with the other systems in terms of communication costs, transmission ratio and energy use.

Visually impaired people usually struggle with doing daily tasks due to a lack of visual cues. For image captioning assistive applications, most applications require an Internet connection for the image captioning generation function to work properly. In this study, we developed MyUEVision, an application that assists visually impaired people by generating image captions that can work with and without the Internet. This work also involves reviewing some image captioning models for this application.

The author has selected and experimented with three image captioning models for online models and two image captioning models for offline models. The user experience (UX) design was designed based on the problems faced by visually impaired users when using mobile applications. The application is developed for the Android platform, and the offline model is integrated into the application for the image captioning generation function to work offline.

After conducting experiments for selecting online and offline models, ExpansionNet V2 is chosen for the online model and VGG16 + long short-term memory (LSTM) is chosen for the offline model. The application is then developed and assessed, and the results show that the application can generate image captions with or without the Internet, providing the best result when having an Internet connection, and the image is captured in good lighting with a few objects.

MyUEVision stands out for its both online and offline functionality. This approach ensures the image captioning generator works with or without the Internet, setting it apart as a unique solution to address the needs of visually impaired individuals.

The purpose of this study is to evaluate the perceived usefulness and satisfaction levels of undergraduate students with respect to university library portal services, specifically within selected universities in South-West Nigeria.

A descriptive survey research design was used to investigate the perceived usefulness and satisfaction levels among undergraduate students regarding university library portal services. Data were collected through surveys administered to 658 undergraduate respondents across multiple universities in South-West Nigeria.

The findings indicate that undergraduate students’ top reasons for using the portal were for tasks such as locating research materials, accessing databases, requesting bibliographies and checking the library catalog. Library computers and school information and communication technology center computers were identified as the most preferred access point, followed by computer labs and campus Wi-Fi networks. Undergraduates rated the portals as highly useful, particularly for saving time locating resources, checking material status and patronizing the library. High satisfaction was reported for services such as attending workshops, events and receiving research support through the portals workshops. Moderate satisfaction levels existed for accessing policies/hours, the institutional repository and renewals of books online. A strong positive correlation (r = 0.785, p < 0.05) was found between perceived usefulness and satisfaction.

This study contributes to the understanding of undergraduate students’ perceptions regarding the utility and satisfaction of university library portal services in the context of South-West Nigeria. It underscores the importance of aligning portal services with students’ academic and research needs to enhance overall satisfaction levels. In addition, it provides insights into areas where improvements can be made to further enhance the effectiveness and satisfaction derived from these services.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.