Katerina Tzafilkou

Background: Chorioamnionitis (CA) is a major pathological cause of preterm labor and is associated with both short- and long-term adverse outcomes in neonates, including early-onset sepsis (EOS) and late-onset sepsis (LOS). Neonatal sepsis remains a significant contributor to morbidity and mortality in neonatal intensive care units (NICUs). Aim: This study aimed to evaluate the association between maternal chorioamnionitis and the incidence of early-onset and late-onset neonatal sepsis in preterm neonates born at p = 0.014). Late-onset sepsis (LOS) developed in 66 neonates (34.9%), but no significant difference in incidence was observed between the two groups (p = 0.402). Parsimonious logistic regression analysis identified maternal chorioamnionitis as an independent predictor of EOS (Odds Ratio 2.07, 95% CI 1.85–5.08; p = 0.009). Conclusions: Intrauterine infection and inflammation caused by chorioamnionitis are linked to an increased risk of early-onset sepsis in neonates born before 32 weeks’ gestation. However, chorioamnionitis does not appear to significantly influence the incidence of late-onset sepsis, which appears to be more closely associated with postnatal factors.

Introduction: Assertive communication is a fundamental component of effective teaching, particularly in the context of the increasing shift toward online higher education. Despite its importance, there is a need for validated instruments specifically designed to assess assertive communication in online teaching environments. The present study aims to examine the psychometric properties and factorial structure of the Assertive Communication Scale in Online Teaching (ACSOT). Methods: The study involved 374 students from “Vasile Alecsandri” University of Bacău, Romania. An exploratory factor analysis (EFA) was conducted to investigate the factorial structure of the ACSOT. Correlational analyses were conducted to examine the relationships among the identified dimensions, and group differences were assessed by gender and age. Results: The exploratory factor analysis revealed a three-factor structure comprising expressiveness, communicativeness, and conflict management. Significant positive correlations were found among the three dimensions. Gender differences indicated that women demonstrated stronger conflict management skills than men. Age-related differences showed that older participants reported higher levels of assertive communication in terms of expressiveness and communicativeness compared to younger participants. Conclusions: The findings support the factorial validity of the ACSOT and suggest that the instrument is a reliable tool for assessing assertive communication in online teaching contexts. The identified gender and age differences underscore the importance of considering demographic variables when developing training programs designed to enhance assertive communication skills in higher education.

Educational robotics is known for its effects on learners' engagement and academic achievement. Few studies have examined the teacher's attitude towards the implementation of ER in teaching practice. The main goal of this study is to examine the teachers' attitudes, self‐efficacy and openness towards incorporating educational robotics into their teaching practices. The study also seeks to identify potential correlations between these constructs and to examine any differences based on individual and other characteristics. In order to accomplish this, 307 in‐service and pre‐service teachers completed three distinct scales measuring attitude, self‐efficacy and openness. In general, teachers conveyed a positive attitude and openness towards educational robotics, while demonstrating moderate levels of self‐efficacy. The results revealed strong associations among the measured constructs. Statistically significant variations emerged with respect to age and service status, wherein younger and pre‐service teachers exhibited higher self‐efficacy levels compared with older and in‐service teachers. Teachers with familiarity and knowledge of educational robotics exhibited more positive attitudes towards educational robotics, as well as higher levels of self‐efficacy and openness. Likewise, teachers who expressed interest in participating in future educational robotics courses displayed more positive attitudes. The outcomes underscore the necessity for specialised teacher training programmes oriented towards educational robotics, with the aim of fostering self‐efficacy and cultivating a more positive attitude towards the incorporation of educational robotics into teaching and learning pedagogies. The limitations and practical implications of the findings are discussed in the article.

YouTube is a popular social media platform in the contemporary digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments about food-related videos on YouTube, specifically within two pivotal food categories: plant-based and hedonic product. We labeled comments using sentiment lexicons such as TextBlob, VADER, and Google’s Sentiment Analysis (GSA) engine. Comment sentiment was classified using advanced Machine-Learning (ML) algorithms, namely Support Vector Machines (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and F1 score. The results from GSA showed a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics, such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

YouTube is a very popular social media platform in today’s digital landscape. The primary focus of this study is to explore the underlying sentiment in user comments on food-related videos on YouTube, specifically within two pivotal food product categories: Plant-Based and Hedonic. Our research involved labelling comments using sentiment lexicons such as TextBlob and VADER. Furthermore, the sentiments of the comments were classified using advanced Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), Multinomial Naive Bayes, Random Forest, Logistic Regression, and XGBoost. The evaluation of these models encompassed key macro average metrics, including accuracy, precision, recall, and f1-score. Results from VADER showcased a high accuracy level, with SVM achieving 93% accuracy in the plant-based dataset and 96% in the hedonic dataset. In addition to sentiment analysis, we delved into user interactions within the two datasets, measuring crucial metrics such as views, likes, comments, and engagement rate. The findings illuminate significantly higher levels of views, likes, and comments in the hedonic food dataset, but the plant-based dataset maintains a superior overall engagement rate.

Game-based learning (GBL) is considered the most engaging means of learning and brings several benefits as regards the learning outputs. Game-based learning is applied in all different educational stages from pre-school education to tertiary education, and workplace. This review study discusses and extends previous findings by organizing the application of GBL approaches in different educational stages and presenting the main learning outcomes. The review study collects and analyzes 104 scientific papers ranging from 2013-2021. The benefits and limitations of GBL are also discussed in the paper, while different types of games including augmented reality (AR) approaches are considered. According to the findings, in primary and secondary education, GBL demonstrates a positive impact on learning outcomes and engagement, enhancing students' understanding of complex concepts and fostering real-world application of learned knowledge. While GBL in tertiary education facilitates deeper understanding, critical thinking, and skill development, its implementation requires careful design and evaluation aligned with learning objectives. The study also explores various types of GBL games, including memory, simulation, interactive, quiz, puzzle, strategy, and reality-testing games, each tailored to different educational objectives and subjects. Furthermore, Augmented Reality (AR) in GBL shows promise across subjects but faces challenges like technical limitations, training needs, privacy concerns, potential distractions, and a lack of comprehensive research on its efficacy. Overall, while GBL demonstrates considerable potential in enhancing education, its successful implementation requires careful consideration of age-appropriateness, varied game types, and integration of emerging technologies like AR.

Aim/Purpose: This study aims to examine the influence of digital competences, technology acceptance, and individual factors (gender and educational level) on academic achievement in Physical Education and Sports Science (PESS). Background: Prior research has established a positive correlation between digital competences and performance, but the mediating role of technology acceptance remains unclear. Furthermore, there is no evidence in the literature about this relationship among students pursuing degrees in PESS. Methodology: A survey was administered to 344 students pursuing degrees in PESS. The Students’ Digital Competence Scale (SDiCoS) measured digital competences, while the Technology Acceptance Model (TAM) assessed technology acceptance. Academic performance was evaluated based on students’ GPAs. Contribution: This paper investigated the role of digital competence within the TAM framework and its influence on academic performance. We propose that digital competence variables positively impact students’ intention to use digital tools for learning. This aligns with TAM principles, where intention and attitude toward technology predict its actual use. Our findings further strengthen the understanding of TAM by confirming strong connections between perceived ease of use, perceived usefulness, and attitude toward technology. Additionally, the study suggests that digital competence and frequent device usage patterns might be more prevalent in postgraduate education. Findings: The investigation supports the link between digital competences and technology acceptance in PESS students. Specifically, TAM variables, particularly attitudes and intentions regarding technology use, significantly predicted these students’ academic performance. Interestingly, no direct association was found between SDiCoS digital competences and academic performance. Digital competence variables were positively associated with students’ intention to use digital tools for learning. Gender differences emerged, with females reporting higher academic performance and proficiency in Communicate, Collaborate, and Share (CCS) competences. Furthermore, postgraduate students reported digital competences, higher academic performance, stronger intentions to use technology, and more frequent utilization of laptops/tablets. Recommendations for Practitioners: Educators, administrators, and policymakers should consider targeted interventions and curriculum development to enhance academic performance in the fields of physical education and sports science. Specifically, strategies should focus on fostering digital competences in areas relevant to the field while addressing gender-specific needs. Recommendation for Researchers: Future research should further explore the nuanced relationship between digital competences, technology acceptance, and academic performance, with a focus on refining the predictive efficacy of TAM variables, and examining the role of individual factors, such as motivation and self-efficacy. Impact on Society: The findings have implications for improving academic outcomes in PESS, ultimately contributing to the development of a highly skilled and technology-literate workforce in this field. Future Research: Future research should examine the specific mechanisms through which digital competences and technology acceptance influence academic performance to develop effective interventions and strategies.

The popularity of social media is continuously growing, as it endeavors to bridge the gap in communication between individuals. YouTube, one of the most well-known social media platforms with millions of users, stands out due to its remarkable ability to facilitate communication through the exchange of video content. Despite its primary purpose being entertainment, YouTube also offers individuals the valuable opportunity to learn from its vast array of educational content. The primary objective of this study is to explore the sentiments of YouTube learners by analyzing their comments on educational YouTube videos. A total of 167,987 comments were extracted and processed from educational YouTube channels through the YouTube Data API and Google Sheets. Lexicon-based sentiment analysis was conducted using two different methods, VADER and TextBlob, with the aim of detecting the prevailing sentiment. The sentiment analysis results revealed that the dominant sentiment expressed in the comments was neutral, followed by positive sentiment, while negative sentiment was the least common. VADER and TextBlob algorithms produced comparable results. Nevertheless, TextBlob yielded higher scores in both positive and negative sentiments, whereas VADER detected a greater number of neutral statements. Furthermore, the Latent Dirichlet Allocation (LDA) topic clustering outcomes shed light on various video attributes that potentially influence viewers’ experiences. These attributes included animation, music, and the conveyed messages within the videos. These findings make a significant contribution to ongoing research efforts aimed at understanding the educational advantages of YouTube and discerning viewers’ preferences regarding video components and educational topics.

Learning mostly involves communication and interaction that leads to new information being processed, which eventually turns into knowledge. In the digital era, these actions pass through online technologies. Formal education uses LMSs that support these actions and, at the same time, produce massive amounts of data. In a distance learning model, the assignments have an important role besides assessing the learning outcome; they also help students become actively engaged with the course and regulate their learning behavior. In this work, we leverage data retrieved from students’ online interactions to improve our understanding of the learning process. Focusing on log data, we investigate the students’ activity that occur close to and during assignment submission due dates. Additionally, their activity in relation to their academic achievements is examined and the response time in the forum communication is computed both for students and their tutors. The main findings include that students tend to procrastinate in the submission of their assignments mostly at the beginning of the course. Furthermore, the last-minute submissions are usually made late at night, which probably indicates poor management or lack of available time. Regarding forum interactions, our findings highlight that tutors tend to respond faster than students in the corresponding posts.