Common Words Research Articles

Visual word identification beyond common words: The role of font and letter case in brand names.

While abstractionist theories of visual word recognition propose that perceptual elements like font and letter case are filtered out during lexical access, instance-based theories allow for the possibility that these surface details influence this process. To disentangle these accounts, we focused on brand names embedded in logotypes. The consistent visual presentation of brand names may render them much more susceptible to perceptual factors than common words. In the present study, we compared original and modified brand logos, varying in font or letter case. In Experiment 1, participants decided whether the stimuli corresponded to existing brand names or not, regardless of graphical information. In Experiment 2, participants had to categorize existing brand names semantically - whether they corresponded to a brand in the transportation sector or not. Both experiments showed longer response times for the modified brand names, regardless of font or letter-case changes. These findings challenge the notion that only abstract units drive visual word recognition. Instead, they favor those models that assume that, under some circumstances, the traces in lexical memory may contain surface perceptual information.

Pediatric Cancer Communication on Twitter: Natural Language Processing and Qualitative Content Analysis.

During the COVID-19 pandemic, Twitter (recently rebranded as "X") was the most widely used social media platform with over 2 million cancer-related tweets. The increasing use of social media among patients and family members, providers, and organizations has allowed for novel methods of studying cancer communication. This study aimed to examine pediatric cancer-related tweets to capture the experiences of patients and survivors of cancer, their caregivers, medical providers, and other stakeholders. We assessed the public sentiment and content of tweets related to pediatric cancer over a time period representative of the COVID-19 pandemic. All English-language tweets related to pediatric cancer posted from December 11, 2019, to May 7, 2022, globally, were obtained using the Twitter application programming interface. Sentiment analyses were computed based on Bing, AFINN, and NRC lexicons. We conducted a supplemental nonlexicon-based sentiment analysis with ChatGPT (version 3.0) to validate our findings with a random subset of 150 tweets. We conducted a qualitative content analysis to manually code the content of a random subset of 800 tweets. A total of 161,135 unique tweets related to pediatric cancer were identified. Sentiment analyses showed that there were more positive words than negative words. Via the Bing lexicon, the most common positive words were support, love, amazing, heaven, and happy, and the most common negative words were grief, risk, hard, abuse, and miss. Via the NRC lexicon, most tweets were categorized under sentiment types of positive, trust, and joy. Overall positive sentiment was consistent across lexicons and confirmed with supplemental ChatGPT (version 3.0) analysis. Percent agreement between raters for qualitative coding was 91%, and the top 10 codes were awareness, personal experiences, research, caregiver experiences, patient experiences, policy and the law, treatment, end of life, pharmaceuticals and drugs, and survivorship. Qualitative content analysis showed that Twitter users commonly used the social media platform to promote public awareness of pediatric cancer and to share personal experiences with pediatric cancer from the perspective of patients or survivors and their caregivers. Twitter was frequently used for health knowledge dissemination of research findings and federal policies that support treatment and affordable medical care. Twitter may serve as an effective means for researchers to examine pediatric cancer communication and public sentiment around the globe. Despite the public mental health crisis during the COVID-19 pandemic, overall sentiments of pediatric cancer-related tweets were positive. Content of pediatric cancer tweets focused on health and treatment information, social support, and raising awareness of pediatric cancer.

COVID-19 Vaccine Information and Infertility Posts on X: Insights on a Misinformation Pandemic.

This study aimed to evaluate misinformation surrounding infertility and the COVID-19 vaccine on X (formerly known as Twitter) by analyzing the prevalence and content of this misinformation across a sample of posts on X. This study is a retrospective review of posts on X (formerly known as tweets) from the COVID-19-TweetIDs dataset from July 2021 and November 2021. Included posts were from crucial time points in the COVID-19 vaccine discourse and contained at least one word related to COVID-19 vaccination and fertility. Posts were analyzed and categorized based on factuality, common words, and hashtags. Descriptive statistics on total followers, account verification status, and engagement were obtained. Differences between posts on X classified as factual and misinformation were examined using analysis of variance or χ2 tests. Sentiment analysis determined if post content was generally positive, neutral, or negative. A total of 17,418 relevant posts on X were reviewed: 11,436 from timeframe 1 (July 2021) and 5982 from timeframe 2 (December 2021). Misinformation posts rose from 29.9% in July 2021 to 45.1% in November 2021. In both timeframes, accounts sharing factual information had more followers (p < 0.001), and verified users were more likely to share accurate posts (p ≤ 0.001). Factual and misinformation posts had similar engagement. Sentiment analysis identified that real posts were more positive and misinformation posts were more negative (p < 0.001). Misinformation about the COVID-19 vaccine and fertility is highly prevalent on X and threatens vaccine uptake in patients desiring future fertility. Accounts sharing factual information were likely to have more followers and be verified; therefore, verifying more physicians sharing accurate information is critical.

The Effects of Problematic Internet Use and Emotional Connotation on Internet Slang Processing: Evidence from a Lexical Decision Task.

Young people use slang for identifying themselves with a particular social group, gaining social recognition and respect from that group, and expressing their emotional state. One feature of Internet slang is its active use by youth in online communication, which, under certain conditions, may cause problematic Internet use (PIU). We conducted two studies in young Russian speakers (n1 = 115, n2 = 106). In study 1, participants were asked to rate a set of slang and common words using Self-Assessment Manikin. The study revealed that the most reliable predictor of higher emotional ratings was word familiarity. There were no significant effects of slang vs. common words or word frequency. In study 2, we used a dual lexical decision task to reveal the effects of word characteristics and propensity for PIU on reaction time (RT) for Internet slang words in pairs with semantically related vs. unrelated common words. Study 2 did not reveal any significant semantic priming effect. Word frequency was a significant predictor of lexical decision facilitation. Common, but not slang, word valence and dominance significantly affected RT in the opposite direction. Individuals with higher cognitive preoccupation with the Internet responded significantly faster, while those more likely to use online communication for mood regulation responded significantly slower to the stimuli. Apparently, on explicit and implicit levels, in-depth knowledge of Internet slang can be one the PIU markers. The results are discussed in line with Davis' approach to determining the general pathological Internet use.

Preliminary Data from the Small World of Singlish Words Project: Examining Responses to Common Singlish Words

Preliminary Data from the Small World of Singlish Words Project: Examining Responses to Common Singlish Words

From Daʿwah to Shahādah: A Move beyond Vatican II and the Common Word

The Second Vatican Council and the Common Word document constitute turning points in the history of Christian–Muslim Relations. Nostra Aetate and Lumen Gentium appealed to a shared Abrahamic heritage between Christianity and Islam, and the Common Word appealed to a God-based theology, as opposed to the long-standing Prophet-based theology. Authorities in both traditions did so in the search for a shared theological foundation. While the article recognizes the vitality of the two steps, it equally recognizes that there is still much that can be done to advance Christian–Muslim relations. In this context, this article aims to achieve three primary goals: first, to demonstrate the successes of the two initiatives; second, to critically engage with them by examining their limitations; and third, to suggest “practical theology” as a medium through which the aspirations of Vatican II and the Common Word can reach a greater audience. In doing so, it proposes the concept of shahādah “bearing witness”, as opposed to the Islamic concept of daʿwah “making invitation” and the Christian concepts of preaching and messianism.

Impaired cortical tracking of speech in children with developmental language disorder.

In developmental language disorder (DLD), learning to comprehend and express oneself with spoken language is impaired, but the reason for this remains unknown. Using millisecond scale magnetoencephalography (MEG) recordings combined with machine learning models, we investigated whether the possible neural basis of this disruption lies in poor cortical tracking of speech. The stimuli were common spoken words (e.g., 'dog', 'car', 'hammer') and sounds with corresponding meanings (e.g., dog bark, car engine, hammering). In both children with DLD (10 boys and 7 girls) and typically developing (TD) control children (14 boys and 3 girls), aged 10-15 years, the cortical activation to spoken words was best modeled as time-locked to the unfolding speech input at ∼100 ms latency between sound and cortical activation. Amplitude envelope (amplitude changes) and spectrogram (detailed time-varying spectral content) of the spoken words, but not other sounds, were very successfully decoded based on time-locked brain responses in bilateral temporal areas; based on the cortical responses, the models could tell at ∼75-85% accuracy, which of two sounds had been presented to the participant. However, the cortical representation of the amplitude envelope information was poorer in children with DLD compared to TD children at longer latencies (at ∼200-300 ms lag). We interpret this effect as reflecting poorer retention of acoustic-phonetic information in short-term memory. This impaired tracking could potentially affect the processing and learning of words as well as continuous speech. The present results offer an explanation for the problems in language comprehension and acquisition in DLD.Significance statement The neural basis of impaired speech processing in developmental language disorder (DLD) was probed with magnetoencephalography (MEG), natural spoken words and sounds, and state-of-the-art machine learning models. Cortical tracking of speech was normal at initial stages, but impaired at syllable-level latency, reflecting problems in maintaining cortical memory representations of the incoming speech across the word. This offers an explanation for the problems in DLD in identifying words and learning new ones.

Words with the root &lt;i&gt;жи(ть)&lt;/i&gt; in biological and medical lexicon

The article describes and analyzes words with the ancient root жи(ть), which have undergone terminologicalisation. Nowadays, the focus of the research is the issue of the terminological vocabulary influence on the development of language as a whole. In order to reveal the semantic potential of the words with the root жи(ть), we studied these words in biological and medical terminology. The subject of our study is focused on the semantics of these words, since biology studies different aspects of life of living beings, medicine is concerned with healing people, and the words we are interested in denote signs, actions and processes related to all animate things. The research material is based on dictionaries of biological (botanical, zoological) and medical terms, explanatory dictionaries of the Russian language, which reveal the semantic scope of the words of interest and provide interpretations of their meanings. The research was carried out using the descriptive method and the method of dictionary definitions. We considered the word-forming and semantic features of the words with the root жи(ть) serving as names of different scientific objects (plants and animals), phenomena, features, properties, methods, actions and processes using the material of biological and medical terminology. The article describes the interaction and interpenetration of common and terminological words and identifies two-component terminological word-combinations, in which words with the given root are used, to define objects at the level of species nomination. As a result of the study, we found more than one hundred medical and biological terms with the root жи(ть); determined terminological word-formation chains; highlighted the terminological homonymy and sub-spheres in biological and medical terminology (botanical, zoological, entomological and pharmaceutical), in forensic medicine, space medicine, etc. We have come to the conclusion that the words with the root жи(ть) are used to nominate plants, living organisms, processes in living organisms, biological formations, abilities of living organisms and diseases.

Artificial intelligence based on falling in older people: A bibliometric analysis.

This study aimed to analyze publications on artificial intelligence (AI) for falls in older people from a bibliometric perspective. The Web of Science database was searched for titles of English-language articles containing the words "artificial intelligence," "deep learning," "machine learning," "natural language processing,", "neural artificial network," "fall," "geriatric," "elderly," "aging," "older," and "old age." An R-based application (Biblioshiny for bibliometrics) and VOSviewer software were used for analysis. Thirty-seven English articles published between 2018 and 2024 were included. The year 2023 is the year with the most publications with 16 articles. The most productive research field was "Engineering Electrical Electronic" with seven articles. The most productive country was the United States, followed by China. The most common words were "injuries," "people," and "risk factors." Publications on AI and falls in the elderly are both few in number and the number of publications has increased in recent years. Future research should include relevant analyses in scientific databases, such as Scopus and PubMed.

Application of machine learning techniques in the diagnostic approach of PTSD using MRI neuroimaging data: A systematic review

BackgroundAt present, the diagnosis of post-traumatic stress disorder（PTSD） mainly relies on clinical symptoms and psychological scales, and finding objective indicators that are helpful for diagnosis has always been a challenge in clinical practice and academic research. Neuroimaging is a useful and powerful tool for discovering the biomarkers of PTSD,especially functional MRI (fMRI), structural MRI (sMRI) and Diffusion Weighted Imaging（DTI）are the most commonly used technologies, which can provide multiple perspectives on brain function, structure and its connectivity. Machine learning (ML) is an emerging and potentially powerful method, which has aroused people's interest because it is used together with neuroimaging data to define brain structural and functional abnormalities related to diseases, and identify phenotypes, such as helping physicians make early diagnosis. ObjectivesAccording to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) declaration, a systematic review was conducted to assess its accuracy in distinguishing between PTSD patients, TEHC(Trauma-Exposed Healthy Controls), and HC(healthy controls). MethodsWe searched PubMed, Embase, and Web of Science using common words for ML methods and PTSD until June 2023, with no language or time limits. This review includes 13 studies, with sensitivity, specificity, and accuracy taken from each publication or acquired directly from the authors. ResultsAll ML techniques have an diagnostic accuracy rate above 70%，and support vector machine（SVM） are the most commonly used techniques. This series of studies has revealed significant neurobiological differences in key brain regions among individuals with PTSD, TEHC, and HC. The connectivity patterns of regions such as the Insula and Amygdala hold particular significance in distinguishing these groups. TEHC exhibits more normal connectivity patterns compared to PTSD, providing valuable insights for the application of machine learning in PTSD diagnosis. ConclusionIn contrast to any currently available assessment and clinical diagnosis, ML techniques can be used as an effective and non-invasive support for early identification and detection of patients as well as for early screening of high-risk populations.

Sign Language Recognition by Image Processing

A prototype sign language interpreter that can converse in American Sign Language (ASL) is presented in this study. The visual stimulation of sign language aids in the development of speech and language. It promotes social contact, fosters the development of cognitive structures, and lessens harmful social behaviors. Early exposure to sign language supports language development in academic, social, and emotional domains. Building a user-hand gesture-only human-computer interface (HCI) is the foundation of the dependable paradigm. Using Python and OpenCV, hand gesture recognition is built using the theories of hand segmentation and the hand detection system. The training photos are labeled with different names based on what is in the database. A binary feature vector is created from each image. Convolutional neural networks (CNNs) and cameras are used to capture images from hand sign videos in real time, which are then used to carry out the feature extraction process. The algorithm generates output based on the projected maximum similarity between these features and the features of database photographs. With the aid of an application, this model provides adequate accuracy even in the absence of a constant or monochromatic background. The person's hand movements are captured on video with the camera; the video is subsequently processed and translated into text. The user hears the speech that has been transformed from the output. The suggested system recognizes letters, numbers, and certain common words in sign language. Raw photos and videos are transformed into corresponding text that can be read and comprehended by using image processing algorithms and neural networks to map the gesture to the right text in the training data. When paired with a sizable source database, this protocol will surely be highly beneficial for closing the communication gap between people who can talk and hear and those who cannot.

Impact of Green Features on Rental Value of Residential Properties: Evidence from South Africa

In recent years, scholars have called for an increase in the usage of green features in the built environment to address climate change issues. Governments across the developed world are implementing legislation to support this increased uptake. However, little is known about how the inclusion of green features influences the rental value of residential properties located in developing countries. Data on 389 residential properties were extracted and collected from a webpage. Text mining and machine learning models were used to evaluate the impact of green features on the rental value of residential properties. The results indicated that floor area, number of bathrooms, and availability of furniture are the top three attributes affecting the rental value of residential properties. The random forest model generated better predictions when compared with other modelling techniques. It was also observed that green features are not the most common words mentioned in rental adverts for residential properties. The results suggest that green features add limited value to residential properties in South Africa. This finding suggests that there is a need for stakeholders to create and implement policies targeted at incentivising the inclusion of green features in existing and new residential properties in South Africa.

Creating a more equitable future: development, implementation, and evaluation of a novel surgical disparities curriculum for medical students

PurposeThe AAMC has expanded its focus on healthcare disparities training during undergraduate medical education (UME). Currently, no standardized published curriculum dedicated to surgical disparity training exists. Therefore, our purpose was to develop, implement, and evaluate a novel Surgical Disparities Curriculum to equip medical students to recognize peri-operative disparities.MethodsThe Surgical Disparities Curriculum was developed based on the Surgical Disparity Framework created by the NIH-American College of Surgeons Summit on Health Disparity using Kern’s Model of Curriculum Development. The curriculum includes four components which are illustrated in Fig. 1: (1) a pre-session review of a journal article and multimedia adjunct highlighting one category within the framework, (2) a didactic session highlighting key elements of the state of surgical disparities and teaching the NIH-ACS framework, (3) a review of a clinical vignette and articles highlighting a framework factor, and (4) a discussion session applying all elements of the framework to disparities identified in pre-session materials, clinical vignette, and personal clinical experiences. The curriculum was administered at a single large academic institution to 223 third-year medical students. Curriculum evaluation was performed using Kirkpatrick’s Model of Training Evaluation and included post-participation surveys to evaluate participant reaction to the curriculum as well as pre- and post-course assessments to evaluate learning. Results were pooled and a Fisher’s exact test or independent samples t test was used for relevant analysis with p < 0.05 considered significant. Thematic analysis, using common word groupings, was also performed on post-survey comments.Fig. 1Outline of novel Surgical Disparities Curriculum for Medical Students. Pre-session preparation includes a survey and a guided reading with a relevant journal article and a multimedia adjunct. Next is a didactic session that introduces a Surgical Disparities Framework established by the ACS in 2016 and gives students a background in the language of discussing disparities. Next is a clinical vignette that highlights a framework factor and several disparities of interest. This is followed by a guided discussion on the pre-session materials, the didactic component, and the clinical vignette within the framework accompanied by a discussion on how to apply the framework and identify and prevent disparities. This was followed by a post-session surveyResultsParticipants stated they had prior dedicated training in recognizing health disparities integrated throughout the UME curriculum during pre-clinical (83%) and clinical (89%) experiences. Only 31% of students agreed surgical disparities had been discussed during their rotation prior to curriculum implementation. After curriculum participation, 90% of students endorsed an improved understanding of surgical disparities in healthcare and there was a 20% improvement in self-reported identification of peri-operative disparities from 68 to 88% (20% [CI 12–28%], p < 0.0001). Participation in the curriculum also resulted in knowledge acquisition with students scoring better (78% v 59% at baseline; p < 0.0001) on post-participation assessment.ConclusionThe Surgical Disparities Curriculum provides a novel solution for bridging the gap in training medical students to recognize disparities in the care of surgical patients. Future work will be done to assess long-term retention and behavioral changes because of our intervention. In addition, we will be working on the translatability of our curriculum to other institutions with the goal of training clerkship students across the country to better recognize surgical disparities.

Fuzzy Decision-Making and Resource Management Model of Performance Evaluation Indices

The Performance Evaluation Matrix (PEM) is an excellent decision-making tool for assessment and resource management. Satisfaction Index and Importance Index are two important evaluation indicators of construction and PEM. Managers can decide whether the service item needs to be improved based on the Satisfaction Index of the service item. When resources are limited, managers can determine the priority of improving the service item based on the Importance Index. In order to avoid the risk of misjudgment caused by sample errors and meet the needs of enterprises’ rapid decision-making, this study proposed a fuzzy test built on the confidence intervals of the above two key indicators to decide whether essential service items should be improved and determine the priority of improvement. Since the fuzzy test was relatively complex, this study further came up with fuzzy evaluation values and fuzzy evaluation critical values of service items following fuzzy testing rules. Besides, evaluation rules were established to facilitate industrial applications. This approach can be completed with any common word processing software, so it is relatively convenient in application and easy to manage. Finally, an application example was presented in this paper to explain the applicability of the proposed approach.

Application of sentiment and word frequency analysis of physician review sites to evaluate refractive surgery care

BackgroundOnline physician reviews increase transparency in health care, helping patients make informed decisions about their provider. Language processing techniques can quantify this data and allow providers to better understand patients' experiences, perspectives, and priorities. The objective of this study was to assess patient satisfaction and understand the aspects of care that are valued by patients seeking refractive care using sentiment and word frequency analysis. MethodsWritten reviews and Star ratings for members of the Refractive Surgery Alliance Society practicing in the United States were collected from Healthgrades, a popular physician rating website. Surgeons with at least one written review were included in the study. Reviews were scored from -1 (most negative) to +1 (most positive) using Valence Aware Dictionary sEntiment Reasoner (VADER). Reviews were stratified by demographic characteristics, namely gender, region, and years in practice. Word frequency analysis was applied to find the most common words and phrases. ResultsA total of 254 specialists and 3104 reviews were analyzed, with an average of 4.4/5 stars and mean 48 ratings each. Most physicians had positive reviews (96%, average VADER ​= ​0.69). Younger physicians (<20 years since residency) had significantly higher Stars rating than senior peers (>20 years) (P ​< ​0.001). A similar trend was observed in VADER score (0.71 vs 0.69), although not statistically significant (P ​= ​0.06). No statistical differences were observed between Stars rating and VADER score by gender (P ​= ​0.66, P ​= ​0.83) or by geographical region (P ​= ​0.74, P ​= ​0.07). "Staff" (n ​= ​1269), "professional" (n ​= ​631), "office" (n ​= ​523), "questions" (n ​= ​424), and "friendly" (n ​= ​386) were frequently used in reviews, along with phrases such as "the staff" (n ​= ​273) and "my questions" (n ​= ​174). "Surgery" (n ​= ​719), "staff" (n ​= ​576), "procedure" (n ​= ​251), "experience" (n ​= ​243), and "professional" (n ​= ​240) were the most common words in positive reviews, while "surgery" (n ​= ​147), "office" (n ​= ​86), "staff" (n ​= ​54), "time" (n ​= ​47), and "insurance" (n ​= ​28) were the most commonly used in negative reviews. ConclusionsBoth the average Stars and VADER sentiment score suggest a high satisfaction among refractive patients. Word frequency analysis revealed that patients value non-clinical aspects of care, including interactions with staff, insurance coverage, and wait-times, suggesting that improving non-clinical factors could enhance patient satisfaction with refractive surgery.

A machine-based corpus optimization method for extracting domain-oriented technical words: an example of COVID-19 corpus data

Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data.

Considerations Regarding the Paradigmatic and Syntagmatic Dimension of Occasionalisms

Vocabulary changes are an inherent part of the process of existence and development of a language system. In order to give a name to the phenomena of the surrounding reality, the human, as a creative being, not only makes use of the existing possibilities of the language system, but also resorts to possible means of creating words that are not yet used, or not used at all, and is sometimes put in the position of breaking the established lexical rules. Nonce words, being definite attributes of the speech, display a paradigmatic-syntagmatic combinatory capacity similar to that of common words, thus filling the gaps in the language system. Being, on the one hand, a means of naming through the process of its selection from the multitude of possible lexical variants, the nonce word falls within the realm of paradigmatics, and, on the other hand, being part of a context and interacting with the linguistic signs in its proximity, the nonce word also enters into relations of a syntagmatic order. The present paper proposes a paradigmatic and syntagmatic investigation, thus highlighting the various paradigmatic (hyponymy/hyperonymy, polysemy, homonymy, paronymy, synonymy and antonymy) and syntagmatic (logical-lexical, associative and syntactic relations) relationships in which nonce words can be embedded.

Investigating the relationship between math literacy and linguistic synchrony in online mathematical discussions through large‐scale data analytics

AbstractPrevious literature has associated math literacy with linguistic factors such as verbal ability and phonological skills. However, few studies have investigated linguistic synchrony, shown in mathematical discussions. This study modelled math literacy and examined the relationship of math literacy with linguistic synchrony between students and facilitators. We retrieved data from 20,776 online mathematical discussion threads at a secondary school level. First, we assessed students' math literacy based on their discussions and classified them into high‐ and low‐math literacy groups. Then, we conducted Cross‐Recurrence Quantification Analysis (CRQA) to calculate linguistic synchrony within each thread. The result implies that students with high math literacy are more likely to share common words (eg, mathematical terms) with facilitators. At the same time, they would paraphrase the facilitators' words rather than blindly mimic them as the exact sentences or phrases. On the other hand, students with low math literacy tend to use overlapping words with facilitators less frequently and are more likely to repeat the exact same phrases from the facilitators. The findings provide an empirical data analysis and insights into mathematical discussions and linguistic synchrony. In addition, this paper implies the directions to improve online mathematical discussions and foster math literacy. Practitioner notesWhat is already known about this topic Mathematical discussions are known to be an effective way to promote math literacy. Math literacy and linguistic skills have a strong link. Linguistic synchrony is related to better collaboration and common knowledge building. What this paper adds Reveals the relationship between math literacy and linguistic synchrony and deepens the understanding of digital communication in online learning environments. Provides empirical analysis of natural language data in group discussions using CRQA. Conceptualizes linguistic synchrony with three sub‐concepts: linguistic concurrence, predictability, and complexity. Implications for practice and/or policy Educators and practitioners could utilize the automatic formative assessment of math literacy based on the student's language use in mathematical discussions. Educational technology researchers and designers could include CRQA indices and recurrence plots in the dashboard design to provide information to support teachers and learners. Teachers would be able to provide real‐time interventions to promote effective mathematical communication and foster math literacy throughout mathematical discussions.

The Era of Digitalization in Education where do Universities 4.0 Go?

Abstract Digitalization has revolutionized one of the most significant industries in the modern world, which is education. It has proven to be a successful and renowned strategy throughout the entire spectrum of the education industry. It has dissolved the distinction between physical and virtual distance and integrated more scientific methods for instructors to teach pupils. In order to select the most relevant studies in the digitalization field, bibliometric analysis was used, with the principal source of scientific articles selected being the academic platform Scopus. The content of 3836 research articles has been taken into consideration since 2019 and 2023. The search documents related to the “digitalization” issue are in title, abstract, and keywords. In order to highlight the structure of the scientific field, we used a content analysis, inspecting the most common words and the relationships between words. Additionally, a network of co-occurrences was taken into account. The analysis was performed using the WOSviever program. In the next part of the research, the term “digitalization” was examined in more detail. The Scopus search engine uses filters, including the terms “digitalization,” “higher,” and “education,” but is limited to key words. The content of 104 research articles has been taken into consideration since 2019 and 2023. The research results showed that the fields of technology, education, and management provide the theoretical basis for research on the digitalization of higher education. The findings also demonstrate the topic’s interdisciplinary character (as seen by publications published in several fields) and its strong relationship to concepts like online learning, digital transformation, and sustainable development. Based on the literature analysis, it can be concluded that University 4.0 will go to the smart fully digital University (University 5.0), will take care of aspects of sustainable development, and will develop teachers’ soft skills, especially emotional intelligence.

Exploring Health Trends and Prevalent Ailments across Grade Levels: An In-Depth Analysis of Reported Complaints among Students

This qualitative study reconnoiters student health complaints at Saint Michael’s College Senior High School, employing a mixed-methods approach that integrates existing data from school nurses’ logbook records with new data obtained through surveys and interviews. Thematic analysis is utilized to identify common words and patterns within the qualitative data. The data collection process involves a letter of request distributed to students, assuring confidentiality, and the random selection of 30 participants for surveys and interviews. The results reveal prevalent health themes, including stress, sleep-related issues, mental health concerns, and technology-related complaints. Students’ personal experiences underscore the complexity of health challenges, including musculoskeletal complaints, allergies, and mental health issues. The diverse impacts on academic courses emphasize the importance of proactive support systems addressing the specific needs of students. Recognizing the transition from external to internal health issues necessitates a holistic strategy that prioritizes both mental and physical well-being. The study underscores the importance of adapting health programs to evolving classroom demands, addressing not only physical ailments but also prioritizing mental health aspects for comprehensive student well-being. This examination will underwrite valuable information for educators, health professionals, and policymakers to address the specific health needs of students and implement targeted interpolations for improved health outcomes within educational environments.