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This paper aims to examine the impact of using visual grammar on learning participle adjectives by EFL (English as a Foreign Language) learners.

The study follows an experimental design in which two groups participated in the study. The experimental group used visual grammar tools in learning participle adjectives. The control group was taught the participle adjectives in a traditional way. A pre–post test was designed and presented to the participants in the two groups.

The results showed that the experimental group made statistically significant improvements in their performance in using participle adjectives due to the use of visual grammar tools.

The current study is only limited to the effect of visual images on a particular grammatical issue, that is participle adjectives. Besides, the study does not include the gender variable; there may be variation in the results depending on the variable of gender.

The present study can provide language instructors with some guidelines on how to incorporate visual grammar applications in teaching grammar aspects. Learners can also be encouraged to have a better understanding of English grammar, using the different connotations of visual images.

Using visual images in teaching grammar will increase the learners' ability to think beyond their classroom environment. They can use this experience whenever they face visual images in different societal activities.

This paper is one of the initial attempts to investigate the effect of using visual grammar on learning participle adjectives.

“Can artificial intelligence produce architectural plan schemes?” discussion is the starting point of this study. The aim of this paper is to question whether this will be a new method in architectural design by producing plans with artificial intelligence interfaces working with human–computer interaction and to create a discussion environment.

The main research topic is the evaluation of architectural design decisions with the text-to-image generation AI algorithms method based on shape grammar rules. First, a sample space consisting of Palladio plans or plan diagrams was created. Plan diagram production experiments were made with different interfaces (Midjourney, Dall-e2, Stable Diffusion, Craiyon, Nightcafe), and alternative plan diagrams were recorded as outputs. The discussion of the outputs has been made over architectural design and space.

In the conceptual design phase of the architectural discipline and in the production of architectural plan scheme, AI algorithms are trending. This interaction imposes a new responsibility on architects. AI can create paradigm shifts in architectural processes with its tools with high data processing potential. On the other hand, in this study, it is emphasized that architecture is not just an act of producing visuals, but a functional act of producing visuals.

The technology is effective in producing architectural plans and directing them to artificial intelligence algorithms. With this study, multi-alternative architectural plan productions were tried with text-to-image bots with fast results. In this direction, a new method proposal has been developed for the conceptual design phase in architecture.

This study provides a configurational examination of how policy designs influence the innovation performance of the emergency industry in China.

This study employs the Data Envelopment Analysis Malmquist index (DEA-Malmquist) to quantify the innovation performance of the emergency industry and then codes the innovation policies to calculate the syntactic components based on institutional grammar tools (IGTs). The configurations of syntactic components were determined by applying the fuzzy-set qualitative comparative analysis (fsQCA).

The results indicate that rules- and norms-oriented policy designs would improve the innovation performance of China's emergency industry. In the developed provinces, the “Deontic” and “aIm” combinations in the policy are useful for improving performance. In the developing provinces, the ambiguity of the “aIm” and “Context” conditions in the policy is leading to low performance. Additionally, a lack of strategy-oriented policy design would also result in poor performance.

Most previous studies used substitute variables to understand policy impacts. This study contributes to identifying the impacts of the syntactic components of policy designs on the innovation performance of the emergency industry. The findings can assist policymakers in developing more effective policies to stimulate innovation development in the emergency industry.

This study examines the test's predictive validity of English language performance and compares test constructs to identify the most effective predictors of English language performance.

Data were collected and analysed from test scores of students enrolled in the foundation year (N = 84) and level 2 (N = 127) in the faculty of English at a Saudi university using correlation and regression tests.

The findings revealed that the General Aptitude Test (GAT) is effective in predicting English performance for students in level 2 and that the error detection task is the most effective predictor of performance in English reading.

The study provides support for the validity of the GAT as a university admission requirement for English language courses in the Arabic-speaking world.

This study examines the GAT's power using a fine-grained approach by deriving scores from its breakdown constructs to predict the performance of English skills at the university level.

This paper aims to describe several artificial intelligence (AI)-based applications that librarians can use to serve and design virtual library instruction, so it will be more effective and efficient.

The approach involves a comprehensive review of AI-based applications that bring benefits to librarian to enhance the virtual instructional services (AI). This study explores the existing papers to reveal the potential use of AI for research consultation, designing the instructional services and conducting evaluation of the program.

There are some AI-based applications that are available for free that will help instructional librarian jobs. Librarians use the AI to increase effectiveness of the services. The AI-based applications that can be used to support instructional services on research inquiries include virtual assistance, knowledge mapping and note making, and to support designing virtual instruction, librarians can use design apps, image generators, voice generator, grammar checker and paraphrasing.

There are many studies on AI at the library; however, it’s still rare a paper studied AI-based application that potentially will bring benefit for virtual instructional services. This paper will give overview of AI application that will help instructional librarian on transactions with users and help librarians to create innovative instructional media.

Using a newly compiled corpus module consisting of utterances from Asian learners during L2 English interviews, this study examined how Asian EFL learners' L1s (Chinese, Indonesian, Japanese, Korean, Taiwanese and Thai), their L2 proficiency levels (A2, B1 low, B1 upper and B2+) and speech task types (picture descriptions, roleplays and QA-based conversations) affected four aspects of vocabulary usage (number of tokens, standardized type/token ratio, mean word length and mean sentence length).

Four aspects concern speech fluency, lexical richness, lexical complexity and structural complexity, respectively.

Subsequent corpus-based quantitative data analyses revealed that (1) learner/native speaker differences existed during the conversation and roleplay tasks in terms of the number of tokens, type/token ratio and sentence length; (2) an L1 group effect existed in all three task types in terms of the number of tokens and sentence length; (3) an L2 proficiency effect existed in all three task types in terms of the number of tokens, type-token ratio and sentence length; and (4) the usage of high-frequency vocabulary was influenced more strongly by the task type and it was classified into four types: Type A vocabulary for grammar control, Type B vocabulary for speech maintenance, Type C vocabulary for negotiation and persuasion and Type D vocabulary for novice learners.

These findings provide clues for better understanding L2 English vocabulary usage among Asian learners during speech.

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.

In the era of artificial intelligence (AI), algorithms have gained unprecedented importance. Scientific studies have shown that algorithms are frequently mentioned in papers, making mention frequency a classical indicator of their popularity and influence. However, contemporary methods for evaluating influence tend to focus solely on individual algorithms, disregarding the collective impact resulting from the interconnectedness of these algorithms, which can provide a new way to reveal their roles and importance within algorithm clusters. This paper aims to build the co-occurrence network of algorithms in the natural language processing field based on the full-text content of academic papers and analyze the academic influence of algorithms in the group based on the features of the network.

We use deep learning models to extract algorithm entities from articles and construct the whole, cumulative and annual co-occurrence networks. We first analyze the characteristics of algorithm networks and then use various centrality metrics to obtain the score and ranking of group influence for each algorithm in the whole domain and each year. Finally, we analyze the influence evolution of different representative algorithms.

The results indicate that algorithm networks also have the characteristics of complex networks, with tight connections between nodes developing over approximately four decades. For different algorithms, algorithms that are classic, high-performing and appear at the junctions of different eras can possess high popularity, control, central position and balanced influence in the network. As an algorithm gradually diminishes its sway within the group, it typically loses its core position first, followed by a dwindling association with other algorithms.

To the best of the authors’ knowledge, this paper is the first large-scale analysis of algorithm networks. The extensive temporal coverage, spanning over four decades of academic publications, ensures the depth and integrity of the network. Our results serve as a cornerstone for constructing multifaceted networks interlinking algorithms, scholars and tasks, facilitating future exploration of their scientific roles and semantic relations.

This research shows how social media has affected learning at present during the COVID-19 pandemic and how it has become the largest and most convenient area of communication. In the current scenario, it seems that social networking sites not only had a profound impact on our social structure and intra-social interaction, but also affected education in general and learning English language in particular. It has been proven that these various social media platforms have created a realm of digital environment in today's new-age learning. Social media platforms are social networking sites through which people interact and communicate with each other easily and conveniently. Undoubtedly and unquestionably, social networking has been proven to be a global phenomenon that has caused a vast paradigm shift in the world of Learning and education during the current pandemic. Therefore, the present study aims to reach the extent of the impact of the various social media platforms on learning English language during the COVID-19 pandemic from the students' point of view.

The study was conducted at the undergraduate level for English language learners. The sample comprised 166 undergraduate students at Najran University. A survey questionnaire was administered to find out the impact of various social media platforms and social networking sites on learning English language in the academic year of 2020 due to the COVID-19 pandemic and to highlight possible suggestions for improving future virtual language learning.

The findings of the study contributed to the area of online learning of English language during the COVID-19 pandemic. Final results confirmed that the utilization of social media has been significantly perceived to have positively impacted learning English language in terms of writing style, reading skills, listening and lexical variation, communication skills and grammar usage.

The findings of the study can serve as fundamental indicators to implement prompt pedagogical reformations, for which a number of pedagogical implications can be proposed. Another equally important pedagogical implication is to design and provide professional development and training sessions to both students and educators on the ultimate utilization of social media as instructional technologies in the context of English language teaching and learning.

This research provides insights in developing policies to assist with the integration and utilization of social media platforms as instructional technologies in the context of English language teaching and learning and how institutions can respond to the advent of advancing technology, especially during and after the COVID-19 era. A model to improve online English language learning process is recommended as a guideline for all educators offering online learning.

Advanced big data analysis and machine learning methods are concurrently used to unleash the value of the data generated by government hotline and help devise intelligent applications including automated process management, standard construction and more accurate dispatched orders to build high-quality government service platforms as more widely data-driven methods are in the process.

In this study, based on the influence of the record specifications of texts related to work orders generated by the government hotline, machine learning tools are implemented and compared to optimize classify dispatching tasks by performing exploratory studies on the hotline work order text, including linguistics analysis of text feature processing, new word discovery, text clustering and text classification.

The complexity of the content of the work order is reduced by applying more standardized writing specifications based on combining text grammar numerical features. So, order dispatch success prediction accuracy rate reaches 89.6 per cent after running the LSTM model.

The proposed method can help improve the current dispatching processes run by the government hotline, better guide staff to standardize the writing format of work orders, improve the accuracy of order dispatching and provide innovative support to the current mechanism.