In recent years, automated written feedback (AWF) has gained popularity in language learning and teaching as a form of artificial intelligence (AI). The present study aimed at providing a comprehensive state-of-the-art review of AWF. Using Scopus as the main database, we identified 83 SSCI-indexed published articles on AWF (1993–2022). We investigated several main domains consisting of research contexts, AWF systems, feedback focus, ways of utilizing AWF, research design, foci of investigation, and results. Our results showed that although AWF was primarily studied in language and writing classes at the tertiary level, with a focus on English as the target language, the scope of AWF research has been steadily broadening to include diverse language environments and ecological settings. This heterogeneity was also demonstrated by the wide range of AWF systems employed (n = 31), ways of integrating AWF (n = 14), different types of AWF examined (n = 3), as well as varied research designs. In addition, three main foci of investigation were delineated: (1) the performance of AWF; (2) perceptions, uses, engagement with AWF, and influencing factors; and (3) the impact of AWF. We identified positive, negative, neutral, and mixed results in all three main foci of investigation. Overall, less positive results were found in validating AWF compared to results favoring the other two areas. Lastly, we grounded our findings within the argument-based validity framework and also examined the potential implications.

Over the past decades, the application of Rasch measurement in language assessment has gradually increased. In the present study, we reviewed and coded 215 papers using Rasch measurement published in 21 applied linguistics journals for multiple features. We found that seven Rasch models and 23 software packages were adopted in these papers, with many-facet Rasch measurement (n = 100) and Facets (n = 113) being the most frequently used Rasch model and software, respectively. Significant differences were detected between the number of papers that applied Rasch measurement to different language skills and components, with writing (n = 63) and grammar (n = 12) being the most and least frequently investigated, respectively. In addition, significant differences were found between the number of papers reporting person separation (n = 73, not reported: n = 142) and item separation (n = 59, not reported: n = 156) and those that did not. An alarming finding was how few papers reported unidimensionality check (n = 57 vs 158) and local independence (n = 19 vs 196). Finally, a multilayer network analysis revealed that research involving Rasch measurement has created two major discrete communities of practice (clusters), which can be characterized by features such as language skills, the Rasch models used, and the reporting of item reliability/separation vs person reliability/separation. Cluster 1 was accordingly labelled the production and performance cluster, whereas cluster 2 was labelled the perception and language elements cluster. Guidelines and recommendations for analyzing unidimensionality, local independence, data-to-model fit, and reliability in Rasch model analysis are proposed.

A recent review of the literature concluded that Rasch measurement is an influential approach in psychometric modeling. Despite the major contributions of Rasch measurement to the growth of scientific research across various fields, there is currently no research on the trends and evolution of Rasch measurement research. The present study used co-citation techniques and a multiple perspectives approach to investigate 5,365 publications on Rasch measurement between 01 January 1972 and 03 May 2019 and their 108,339 unique references downloaded from the Web of Science (WoS). Several methods of network development involving visualization and text-mining were used to analyze these data: author co-citation analysis (ACA), document co-citation analysis (DCA), journal author co-citation analysis (JCA), and keyword analysis. In addition, to investigate the inter-domain trends that link the Rasch measurement specialty to other specialties, we used a dual-map overlay to investigate specialty-to-specialty connections. Influential authors, publications, journals, and keywords were identified. Multiple research frontiers or sub-specialties were detected and the major ones were reviewed, including “visual function questionnaires”, “non-parametric item response theory”, “valid measures (validity)”, “latent class models”, and “many-facet Rasch model”. One of the outstanding patterns identified was the dominance and impact of publications written for general groups of practitioners and researchers. In personal communications, the authors of these publications stressed their mission as being “teachers” who aim to promote Rasch measurement as a conceptual model with real-world applications. Based on these findings, we propose that sociocultural and ethnographic factors have a huge capacity to influence fields of science and should be considered in future investigations of psychometrics and measurement. As the first scientometric review of the Rasch measurement specialty, this study will be of interest to researchers, graduate students, and professors seeking to identify research trends, topics, major publications, and influential scholars.

Eye tracking technology has become an increasingly popular methodology in language studies. Using data from 27 journals in language sciences indexed in the Social Science Citation Index and/or Scopus, we conducted an in-depth scientometric analysis of 341 research publications together with their 14,866 references between 1994 and 2018. We identified a number of countries, researchers, universities, and institutes with large numbers of publications in eye tracking research in language studies. We further discovered a mixed multitude of connected research trends that have shaped the nature and development of eye tracking research. Specifically, a document co-citation analysis revealed a number of major research clusters, their key topics, connections, and bursts (sudden citation surges). For example, the foci of clusters #0 through #5 were found to be perceptual learning, regressive eye movement(s), attributive adjective(s), stereotypical gender, discourse processing, and bilingual adult(s). The content of all the major clusters was closely examined and synthesized in the form of an in-depth review. Finally, we grounded the findings within a data-driven theory of scientific revolution and discussed how the observed patterns have contributed to the emergence of new trends. As the first scientometric investigation of eye tracking research in language studies, the present study offers several implications for future research that are discussed.

This study sought to examine research trends in computer-assisted language learning (CALL) using a retrospective scientometric approach. Scopus was used to search for relevant publications on the topic and generate a dataset consisting of 3,697 studies published in 11 journals between 1977 and 2020. A document co-citation analysis method was adopted to identify the main research clusters in the dataset. The impact of each publication on the field was measured by using the burst index and the betweenness centrality and the content of influential publications was closely analysed to determine the focus of each cluster and the key themes of the studies in focus. Overall, we identified seven major clusters. We further found that leveraging synchronous computer-mediated communication and negotiated interaction, multimedia, telecollaboration or e-mail exchanges, blogs, digital games, Wikis and podcasts to support language learning was probably beneficial for language learning. Varying degrees of support were found in various studies for each of these technologies. Stronger support was found for synchronous computer-mediated communication and negotiated interaction, multimedia, telecollaboration or e-mail exchanges and digital games and weaker support was found for blogs, Wikis, and podcasts. The limitations the supporting studies listed were also considered inconsequential. On the other hand, while there was strong support for blogs, Wikis and podcasts, some major drawbacks were observed. The findings of the study would be helpful for teachers and instructors who want to decide whether to use technology in the classroom for instructional purposes. Additionally, researchers and graduate students who need to identify a research topic for their thesis or dissertation may find the results of the study useful for them, too.