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The search for new techniques to teach English nowadays has been more than ever. These techniques have to be interesting and enjoyable in order to lower the anxiety levels of students when learning English (Bakhsh, 2016). That is why many scholars and teachers look forward to integrating technology into language teaching. Social media platforms (SMPs) are among these techniques since millions of people around the world utilize them for daily interaction. Yet, teaching English for specific purposes (ESPs) relies on learners’ needs and employs an eclectic approach in delivering its course content. For this reason, the current study reviewed articles that tackled the topic of teaching or learning ESP from SMPs so as to uncover their effect and the attitude or motivation of learners.

The researchers used the PRISMA flowchart model in order to identify, screen and include articles in the study.

The results revealed that SMPs are effective in teaching and learning ESP writing, speaking and vocabulary. Yet, the included studies showed that learners’ attitude toward SMPs is positive as they believe that they are motivating and interesting.

Some aspects of social media have turned out to be beneficial in the learning process and they need further investigation from ESP practitioners and scholars.

According to the study, it is crystal clear that the various social networks and platforms are beneficial and helpful for improving ESP productive skills.

This paper aims to explore the potential of metaverse in human resource (HR) learning as a new paradigm postpandemic scenario. This article highlights the use of the metaverse as a training tool for today’s organizations.

This paper offers the author’s opinion based on experience with the application of Metaverse into HR learning for the transformative learning experience.

Research and trends in metaverse hold strong potential to be implemented as a transformative learning experience across organizations. The article then presents the metaverse learning application with the TL concept.

This is a viewpoint article and opens threads of the plethora of future directions in the application of the metaverse as a learning and development tool through empirical research work.

While many works have reported adopting exploratory practice (EP) principles in language teaching research, only a few studies have explored the enactment of EP in an online extensive reading of students majoring in English education. Given the relative paucity of attention to the use of EP as the practitioner research in English language teaching (ELT), the present EP investigates how students understand online extensive reading practice mediated by online group discussion and extensive reading logs, where the first author served as the online extensive reading practice instructor.

The exploratory practice focuses on incorporating research into pedagogy and fastens the importance of the quality-of-life in the classroom. The data were collected through students reading logs and semi-structured interviews. The collected data were analyzed using the thematic analysis. In this case, there were six phases including familiarizing with the data, generating initial codes, searching for the themes, reviewing the themes, defining the theme and writing up.

The findings reveal that online group work driven by EP enables everybody to engage in learning activities. EP assists the students in perceiving their potential and gaining a better awareness of the need to devote themselves to the class. In the EP activities, they work together to build a peaceful situation to advance the quality of learning in EFL classrooms.

The present study’s limitation is the small sample. Apart from that, the research results cannot be generalized to other places.

This study suggests that EP is suitable to create a mutual understanding among the learners and teachers. To conclude, English language competency can be achieved in a pleasant atmosphere through EP.

The present study succeeded in adding new literature studies related to EPs by discussing online group discussions and their challenges during the learning process. These aspects were identified through reading logs and interviews with students. Thus, it focuses on the implementation and challenges of online group discussions.

While most language departments of the university offer service-learning (SL) subjects based on language teaching, such as “Teaching Chinese as a Second Language in Local Schools” and “Serving the Community through Teaching English,” this paper aims to argue that teaching students to teach language(s) is yet to be the best strategy to serve the service recipients.

SL is widely understood as an experiential learning pedagogy that integrates academic focus, reflection and community service and is shown to be impactful. In Hong Kong, the first university that has made SL a graduation requirement is the Hong Kong Polytechnic University (the University). Considering this, new SL courses have proliferated over the past decade. Adopting a narrative inquiry approach, this paper examines personal narratives from a new SL subject aiming to raise awareness of refugees in Hong Kong. The data includes students’ reflective journals, co-created personal narratives and podcasts and semi-structured interviews.

This paper finds that crafting and recording narratives of shared experiences deepens cultural understanding, cultivates empathy and facilitates language learning in a genuine setting.

Ultimately, this paper advocates a well-designed SL that combines language, content and technology as a powerful, transformational experience for both college students and service recipients.

This paper focuses on a brand new SL course, “Storytelling for Understanding: Refugee Children in Hong Kong,” offered in Semester 1, 2022–2023. The subject was developed by the two authors from a language division affiliated to the University. The deliverables were podcast recordings, co-authored and co-edited by the students and the children.

The purpose of this study is to review literature on the relationship between leadership and workplace learning, to critically analyze and discuss findings and to suggest future research paths based on the synthesis.

This study applied a refined literature review process leading to a selection of 40 articles, which originated from 14 internationally acclaimed journals.

When explaining leadership influence regarding individual and team learning, the concepts of role modeling behavior, relational support and negotiation of meaning is significant. If leaders provide support, show exemplary behavior and negotiate individual arrangements with employees, workplace learning development is positively affected.

Future studies should focus on empirical cases further illustrating how the leader–employee relationship is formed in practice, to further understand differences in leadership influence on employee workplace learning.

The gathered knowledge implicates that carefully designed leadership training programs and personalized work arrangements between leader and employees are beneficial for leader’s ability to influence employee workplace learning.

The reviewed studies were solely published in top management journals, which resulted in an original literature selection. This study also discusses implicit or articulated assumptions about the view of learning in the selected studies, offering additional understanding about the underlying learning views in leadership–workplace learning research.

Given the persistent receptive skills challenges faced by language learners and the numerous merits of using technologically-enhanced learning in the form of mobile-assisted language learning in language teaching, this study decides to incorporate a context-analysis-practice-exploration lesson-planning framework with mobile learning to improve the receptive skills of learners. This step is necessary on the recommendation by scholarly research that while implementing technology, teacher training, digital literacy and expertise, incorporating good teaching practices and lesson planning frameworks may assist in yielding positive results.

This study decides to utilize a mixed-method research design through an experimental approach to elicit information from 90 EFL students concerning the effectiveness of incorporating the context-analysis-practice-exploration framework in a mobile-leaning mode instructional delivery to improve their reading and listening skills.

After collecting and analyzing data, the study found that while the participants perceived enhanced-mobile learning to make lessons enjoyable, integrating this framework and m-learning improved their reading skills. This study used notable findings to make pedagogical conclusions on the objective of this study.

This study contributes through empirical evidence on the effectiveness of incorporating mobile learning and a responsive lesson planning framework towards improving English as a foreign language learners' learning outcome. Besides, this study is the first to probe and unpack the effectiveness of a responsive lesson-planning framework in language education.

Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.

This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.

Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.

At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.

This study aims to investigate the usability of a public university website for measuring its efficiency, users’ satisfaction or anxiety while searching for and retrieving information through different devices.

A task-based approach was adopted for the study. Twenty-eight participants were asked to complete 11 information-searching tasks on the website. The participants were divided into two groups. The tasks were carried out by members of each group, using desktop and mobile devices in a rotating fashion. Volunteers observed the participants' actions and recorded information regarding their productivity, time usage (using a timer), satisfaction or annoyance while performing each task. Finally, based on the use of the devices, a comparison was established between the participants' performance accuracy, efficiency and anxiety.

The study provides an overview of a task-based user experience carried out on the university website using a combination of qualitative and quantitative research methods. According to the results, participants' satisfaction levels were generally high, and their anxiety levels were low while completing the tasks on a mobile device. In comparison to the desktop, it took less time overall to complete all tasks. On the other hand, using a desktop computer (97.1%) resulted in better task completion success rates for participants than using a mobile device (85.7%).

No previous task-based evaluation study of this kind has been conducted to assess the usability of any university website in Bangladesh.

This study developed new measures of the routine and non-routine task contents of managerial, professional, technical, and clerical occupations from a workload perspective. Here, we present a comparative analysis of the workload structures of state and industrial sector employees.

Our method involves detailed descriptions of work processes and an element-wise time study. We collected and analysed data to obtain a workload structure that falls within three conceptual task categories: (i) non-routine analytic tasks, (ii) non-routine interactive tasks and (iii) routine cognitive tasks. A total of 2,312 state and industrial sector employees in Kazakhstan participated in the study. The data were collected using a proprietary web application that resembles a timesheet.

The study results are consistent with the general trend reported by previous studies: the higher the job level, the lower the occupation’s routine task content. In addition, the routine cognitive task contents of managerial, professional, technical, and clerical occupations in the industrial sector are higher than those in local governments. The work of women is also more routinary than that of men. Finally, vthe routine cognitive task contents of occupations in administrative units are higher than those of occupations in substantive units.

Our study sought to address the challenges of using the task-based approach associated with measuring tasks by introducing a new measurement framework. The main advantage of our task measures is a direct approach to assessing workloads consisting of routine tasks, which allows for an accurate estimation of potential staff reductions due to the automation of work processes.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.