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Studies have so far focused on learning in organizations, factors affecting learning, learning effectiveness and so on but the concept of learning in a hybrid work arrangement is yet unexplored. The purpose of this study is to measure the perception of faculty members in higher education institutions towards learning in a hybrid work arrangement and also to measure the differences of perception towards hybrid work arrangement based on employees’ gender and organization type.

The data was collected from a sample of 390 faculty members composing of Assistant Professors, Associate Professors and Professors, purposely chosen from two of the premier higher education institutions (one private and one public) located in Punjab, India. A self-structured questionnaire was administered to the faculty members who are working on a regular basis and have minimum of two years of work experience with the chosen university. For analysing the collected data exploratory factor analysis and other descriptive statistics have been applied.

The findings of the survey show that in terms of gender differences, it is the female employees who are more satisfied with different aspects of hybrid/remote work arrangement as compared to male employees. In regard to organizational differences in the perception towards learning in a hybrid work arrangement it is found that public university employees have a more positive attitude so far as individual factors are concerned, but in terms of organizational factors, it is the private university that is scoring better than the public university.

The study is limited to only two higher education institutions, and its findings to be applicable in all higher education institutions, further studies may be required on a larger canvas. Future studies may be undertaken using advanced statistical tools like structural equation modelling to explore various variables associated with learning in a hybrid work arrangement.

Applicability of hybrid work arrangement is very high in higher education institutions and to the best of the authors’ knowledge, this is the first study which adds to the literature on perception of employees towards organizational learning in a hybrid work arrangement.

This pilot study aims to examine the prevalence of test anxiety and its interplay with attitudes, confidence, efficacy, academic performance and socio-demographic factors within the domain of science, technology, engineering and mathematics (STEM) courses.

The authors employed a quantitative, cross-sectional design with 549 sixth-grade students from public lower secondary schools in Prishtina, Kosovo, using the Student Attitudes Toward STEM Survey (S-STEM) for middle/high schools and the test anxiety questionnaire.

Over 70% of Kosovo's sixth-grade students reported moderate to severe test anxiety. The age of students was found to be inversely related to academic performance in STEM. The father's employment was associated with favorable STEM attitudes, confidence, efficacy and academic performance. Having a personal study environment was connected with favorable STEM attitudes, confidence and efficacy in STEM, whereas access to technology was associated with positive academic performance. Test anxiety, academic performance and personal study space predicted students' attitudes, confidence and efficacy in STEM and 21st-century learning.

Educational institutions should prioritize student well-being. By addressing test anxiety, these institutions can create supportive learning environments that improve attitudes, confidence and efficacy in STEM fields. These efforts are crucial for STEM career development and student success in the 21st-century workforce.

The current study findings contribute to a deeper understanding of the factors influencing STEM student engagement and performance, highlighting the importance of addressing test anxiety for positive learning outcomes while emphasizing the need to consider socio-economic and contextual factors in education.

This paper aims first to describe the most prevalent teachers’ and students’ behaviors in synchronous online classes in emergency remote teaching; second, to discern behavior profiles and third, to investigate what features explain the observed behaviors.

An adapted COPUS observation protocol was employed to observe 292 online classes from 146 higher education teachers.

The most prevalent behaviors were: Presenting for teachers and Receiving for students, followed by Teachers Guiding and Students Talking to Class. Furthermore, cluster analysis showed two groups: Traditional and Interactive. The variables that better explained belonging to the Interactive lecture group were disciplinary area – social sciences and humanities –and teaching in technical institutions.

In a context where higher education institutions intend to project the lessons learned into post-pandemic learning experiences, this study provides observational evidence to realize the full potential expected from online and blended teaching and learning.

Despite the prevalence of synchronous online lectures during COVID-19, there is a paucity of observational studies on the actual behaviors that occurred in this context. Most research has been based on surveys and interviews. This study addresses this gap.

The purpose of this study is to reflect on and analyse the sensory experiences related to the transition to remote work during the COVID-19 pandemic. The research seeks to understand how these experiences have influenced the integration of work practices into home and family life and the subsequent adaptations and embodied learning that arise in response.

The authors' research approach incorporates autoethnographic methods to explore the sensory, affective and emotional experiences of transitioning to remote work. The authors draw on principles of embodied learning, as influenced by Gilles Deleuze, and utilise a range of ethnographic tools including note-taking, audio memos, photography, shared conversations and written reflections to gather their data.

The study illuminates the ways bodies learn to accommodate the new organisational contexts that arise when the spaces, affects and forces of home and work intersect. It demonstrates how the integration of work into the private domain resulted in new affective and material arrangements, involving novel sensory experiences and substantial embodied learning.

This study provides a distinct, sensory-oriented perspective on the challenges and transformations of remote work practices amid the pandemic. By focussing on the affective resonances and embodied learning that emerge in this context, it contributes to the emerging discourse around post-lockdown work practices and remote work in general.

In response to the COVID-19 pandemic, many employees had to switch to remote work. While some adjusted successfully to this transition, others have struggled. Leveraging information systems (IS) to adjust to major exogenous shocks is called digital resilience. The purpose of this paper is to understand what we can learn about employees' digital resilience from externally enforced transitions to remote work.

As digital resilience is challenging to measure, this study uses an embedded mixed methods approach. The authors conducted a qualitative analysis of 40 employees' statements on their remote work experience during the first six months of the pandemic and complemented these findings with scale-based digital resilience scores.

The authors find that employees' digital resilience largely depends on the amount of technical equipment and support they receive from their organizations as well as their ability and willingness to learn how to adequately use and communicate through information and communication technologies. Being self-disciplined and self-responsible positively affects digital resilience, while social isolation threatens it. Organizations can foster digital resilience building by encouraging digital networking, building a digital culture and netiquette, and treating digital resilience as a sociotechnical phenomenon.

This is one of the first empirical studies of digital resilience on a human level. It sheds light on the missing link between IS-enabled resilience and transitions to remote work. Specifically, it provides original insights into its development and manifestation in a remote work context during the COVID-19 pandemic. For researchers, it provides novel guidance on choosing appropriate measurement instruments to capture digital resilience.

Racially and ethnically minoritized (REM) women continue to be underrepresented in science, technology, engineering and mathematics (STEM) programs and careers. Peer mentoring is one strategy that can support their participation. This study explores the experiences of Black women peer mentors in an online peer mentoring program at two historically Black institutions.

A qualitative case study approach was utilized to explore the impact of an online peer mentoring program on peer mentors' STEM self-efficacy, sense of community, STEM identity and intent to persist in STEM.

Analysis identified five themes relating to peer mentors' experiences in the program: (1) an “I can do this” approach: confidence and self-efficacy; (2) utility of like others; (3) “beacons of light”: intersecting and malleable identities; (4) skills development and (5) motivation and reciprocity. Further, challenges of the online relationship were shared.

The study contributes to the body of knowledge by demonstrating the utility of an online peer mentoring model among women mentors enrolled in STEM programs at two historically Black institutions. The findings support those who are historically marginalized in participating in and remaining in STEM.

This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms.

This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning.

Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students’ lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables.

Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

This study aims to improve the Unified Theory of Acceptance and Use of Technology (UTAUT) model by examining technological anxiety and other influential factors on international students' adoption of mobile learning (m-learning) during COVID-19 emergency remote teaching (ERT).

This study utilized the modified UTAUT framework to test hypotheses through a cross-sectional survey method. Participants were university students studying Chinese as a foreign language who were selected using a convenience sampling approach. An online questionnaire was then administered. The data collected from the surveys were analyzed using the partial least squares method with SmartPLS 4 software.

The study examined 16 hypotheses and found support for six of them. The results confirmed that performance expectancy (PE) is a significant predictor of behavioral intention (BI), and anxiety influences both PE and effort expectancy. The negative effect of social influence on anxiety was found to be significant, while facilitating conditions had a negative impact on learners' self-efficacy. The model fit indices indicated a good overall fit for the model.

This study presents a valuable contribution to the literature on m-learning in emergency education by incorporating technological anxiety into the enhanced UTAUT model. Examining the relationships between the key factors of the model provides a better understanding of learners' intentions and can inspire researchers to establish new theoretical foundations to evaluate the roles of these factors in diverse educational settings.

The study found that performance expectations are linked to learners' intentions, and anxiety indirectly affects BIs to use mobile learning platforms. Thus, these platforms should be designed to meet learners' expectations with minimum effort and eliminate anxiety triggers to facilitate ease of use. Language curriculum developers and policymakers should incorporate mobile learning applications to support diverse language skills, address students' needs and encourage their use through professional development opportunities for instructors.

Social factors have been found to significantly influence anxiety levels among learners. Therefore, it is crucial for teachers and family members to play an essential role in mitigating anxiety's adverse effects. Discussing related issues can enhance the quality of mobile learning and stimulate social initiative by providers, ultimately improving the learning experience for all learners, regardless of their location or circumstances. This can also contribute to the growth and development of society.

This study contributes to the originality of m-learning development by proposing an enhanced UTAUT model that considers anxiety and emphasizes the critical role of foreign learners' BIs. It provides fundamental guidelines for designing and evaluating m-learning in ERT contexts.

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.

Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.

Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.

Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.

The digitalization of archival management has rapidly developed with the maturation of digital technology. With data's exponential growth, archival resources have transitioned from single modalities, such as text, images, audio and video, to integrated multimodal forms. This paper identifies key trends, gaps and areas of focus in the field. Furthermore, it proposes a theoretical organizational framework based on deep learning to address the challenges of managing archives in the era of big data.

Via a comprehensive systematic literature review, the authors investigate the field of multimodal archive resource organization and the application of deep learning techniques in archive organization. A systematic search and filtering process is conducted to identify relevant articles, which are then summarized, discussed and analyzed to provide a comprehensive understanding of existing literature.

The authors' findings reveal that most research on multimodal archive resources predominantly focuses on aspects related to storage, management and retrieval. Furthermore, the utilization of deep learning techniques in image archive retrieval is increasing, highlighting their potential for enhancing image archive organization practices; however, practical research and implementation remain scarce. The review also underscores gaps in the literature, emphasizing the need for more practical case studies and the application of theoretical concepts in real-world scenarios. In response to these insights, the authors' study proposes an innovative deep learning-based organizational framework. This proposed framework is designed to navigate the complexities inherent in managing multimodal archive resources, representing a significant stride toward more efficient and effective archival practices.

This study comprehensively reviews the existing literature on multimodal archive resources organization. Additionally, a theoretical organizational framework based on deep learning is proposed, offering a novel perspective and solution for further advancements in the field. These insights contribute theoretically and practically, providing valuable knowledge for researchers, practitioners and archivists involved in organizing multimodal archive resources.