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The mobility of international students is a crucial tool for the European Union's goal of creating a unified European Higher Education Area. Despite the initial assumption that all European universities and students can benefit equally from cross-university study experiences, certain European regions have become disproportionately favored over time. This has resulted in specific geographical patterns, challenging the principles of equality and openness in the EU's higher education policy. To better understand these spatial effects and enhance the EU's mobility policy effectiveness, this research analyzes the network properties of Erasmus+, comparing it with traditional degree-seeking activities. Utilizing a modularity measure with data from the EU and UNESCO, the study reveals significant sub-regional variations in the Erasmus+ geographical network, posing challenges for policy implementation and limiting mobility alternatives.

Despite the growing recognition of the impact of affective factors on second/foreign language (L2) learning, there remains a paucity of knowledge regarding academic burnout in L2 learning. Moreover, the intricate interplay between L2 burnout, maladaptive emotion regulation strategies, and negative L2 emotions remains inadequately explored. Given the increasing acknowledgment of network analysis as an advanced and appropriate method for unraveling the complex relationships among psychological constructs in applied linguistics, the current study aimed to investigate the network structure of burnout, maladaptive emotion regulation strategies, and negative emotions among 841 Chinese undergraduates who were learning English as a foreign language (EFL). The results of the network analysis revealed that shame, emotional exhaustion, and avoidance emerged as the most central nodes within negative emotions, burnout, and maladaptive emotion regulation strategies, respectively; shame, emotional exhaustion, and avoidance were also the most powerful bridging nodes linking the aforementioned three constructs. Notably, the robust bridging association between emotional exhaustion and anxiety was observed. Overall, Chinese EFL students may experience high levels of burnout and negative emotions and apply counter-productive regulation strategies in English learning, but these reactions are intertwined rather than independent of each other. Students who are overwhelmed by anxiety and shame are more prone to burnout symptoms, while those dominated by anger are more likely to vent it out. Theoretical and pedagogical implications are discussed.

Learning a second language (L2) is dependent upon numerous external and internal factors, among which motivation plays a relevant role. In fact, motivation has been recognized as crucial in the L2 learning process (Ushioda, 2012). Such has been its importance that interest in L2 motivation has led to the development of theories such as the L2 motivational construct, and the L2 motivational self system (Dörnyei, 2005, 2009). Nevertheless, despite the academic focus on L2 learning motivation (Dörnyei & Ushioda, 2013), the impact of additional L2 lessons on students already engaged in formal L2 instruction at an official educational level (e.g., Higher Education) remaines vastly underexplored. Thus, this study aims to bridge this gap by analyzing the differences between 118 undergraduate EFL students who attended extra L2 lessons and those who did not. Considering the complex nature of the motivational construct, a Bayesian network analysis was used, categorizing motivations into two modules based on attendance of additional L2 lessons. This allowed us to observe the different factors of motivation as a whole construct, and not individually. The findings revealed that students who attended extra lessons are internally motivated toward self-improvement, whereas those who do not attend extra L2 lessons are influenced by external pressures and career aspirations.

The increase in borderless digital-based business competition shows how language education is impacted by neoliberalism in this global era. We explore how linguistic technopreneurship (LT) roles is increasingly constructed as a form of linguistic entrepreneurship to exploit language-related resources to enhance one's socioeconomic value strategically. This research aims to critically examine the influence of LT toward business success digitalization for Small Medium Enterprises in West Java and it's implication for Language Education. The research also focusing on the creation of novelty, namely linguistic technopreneurship (LT), which is a refinement of entrepreneurial linguistics (EL). LT is expected to be able to explain how linguistic entrepreneurship can be indexed from two different aspects, namely how to package language education and digital business success. We then discuss under what conditions the notion of linguistic technopreneurship can be applied to digital platform-based business settings and what kind of contradictions this gives rise to. The method used is quantitative, and it involves carrying out SEM analysis. A non-probability sampling technique was used to obtain a minimum of 250 Micro, Small and Medium Enterprises and Industry owners who run their businesses through digital platforms in West Java province, which is the province with the most significant number of Micro, Small and Medium Enterprises and Industries in Indonesia. The research results show that LT significantly influences the success of business success and impact the language education practice. It can be concluded that language education is an added value for a person and influences socioeconomic success.

ChatGPT has become a prominent tool for fostering personalized and interactive learning with the advancements in AI technology. This study analyzes 212 academic research articles indexed in the Scopus database as of July 2023. It maps the trajectory of educational studies on ChatGPT, identifying primary themes, influential authors, and contributing institutions. By employing bibliometric indicators and network analysis, the study explores collaboration patterns, citation trends, and the evolution of research interests. The findings show the exponential growth of interest in leveraging ChatGPT for educational purposes and provide insights into the specific educational domains and contexts that have garnered the most attention. Furthermore, the study reveals the collaborative dynamics and intellectual foundations shaping the field by examining co-authorship and citation networks. This bibliometric analysis contributes to a comprehensive understanding of the current state of ChatGPT research in education, offering researchers and practitioners valuable insights into evolving trends and potential future directions for this innovative aspect of AI and learning.

Teachers turn to many sources for support and professional learning, including social media-based communities that have shown promise to help teachers access resources and facilitate productive exchanges. Although such online communities show promise, questions about their quality for providing a suitable learning environment remain insufficiently answered. In this study, we examine how teachers' engagement on Twitter (now known as "X") may adhere to characteristics of high-quality professional development (PD) activities. In that, we employ advanced conversational analysis techniques that extend the primarily descriptive methods used in prior research. Specifically, we collected data from three Twitter communities related to Advanced Placement Biology (N = 2,040 tweets, N = 93 teachers). Qualitative two-cycle content analyses derived both tweet content and sentiment. Using epistemic network analyses, we examined the collaborative structures to examine how participation patterns can identify characteristics of high-quality online PD. Results indicate that some teachers use Twitter with a content focus and coherent to their individual contexts and prior knowledge. Notably, differences in collaboration and participation patterns by teachers' overall activity level hint at the existence of an online community of practice. More active teachers communicated more about how their individual contexts relate to instruction, whereas less active teachers exhibited more targeted engagement, for instance, related to sharing teaching resources and organizing learning opportunities. Overall, this study illustrates how Twitter may provide a meaningful learning environment to teachers so that it can serve as a relevant avenue for their professional learning.

The theoretical framework of communities of practice (CoP) is often used for framing research into online communities. However, there is an absence of measures and empirical work that evaluates knowledge-sharing within such communities. This represents a substantial gap in our understanding of informal learning for diverse people and in the case of communities that support participation in science, a potential loss of capacity for an enterprise that serves a critical function for society. Our objective is to operationalize "practice" within a designed online, scientific community and evaluate these behaviors as representative of seven theorized high-level groups. For this case study, content and social network analysis were applied to forums (n = 1858), activity posts (n = 1300), and direct messages (n = 667). Content analysis showed that community members most often used practices that were coded as social and not domain-specific. Differences existed in the ways that forums, messages, and activity posts were used as well as between education and outreach members and members of the public and scientists. Social network analysis revealed two domain-specific practices were central to the knowledge-sharing discourse. The seven theorized high-level groups were reduced to three. We provide a new empirically-based framework for use in identifying practices within the digital spaces as well as recommendations for designing online science communities that emphasize knowledge creation.

The emergence of Generative Artificial Intelligence (GAI) has caused significant disruption to the traditional educational teaching ecosystem. GAI possesses remarkable capabilities in generating human-like text and boasts an extensive knowledge repository, thereby paving the way for potential collaboration with humans. However, current research on collaborating with GAI within the educational context remains insufficient and the methods are relatively limited. This study aims to utilize methods such as Lag Sequential Analysis (LSA) and Epistemic Network Analysis (ENA) to unveil the "black box" of the human-machine collaborative process. In this research, 22 students engaged in collaborative tasks with GAI to refine instructional design schemes within an authentic classroom setting. The results show that the participants significantly improved the quality of instructional design. Leveraging the improvement demonstrated in students' instructional design performance, we categorized them into high- and low-performance groups. Through the analysis of learning behavior, it was observed that the high-performance group adhered to a structured GAI content application framework: "generate [right arrow] monitor [right arrow] apply [right arrow] evaluate." Moreover, they adeptly employed communication strategies emphasizing exercising cognitive agency and actively cultivating a collaborative environment. The conclusions drawn from this research may serve as a reference for a series of practical applications in human-machine collaboration and provide directions for subsequent studies.

At many research-intensive universities in North America, there is a disproportionate loss of minoritized undergraduate students from Science, Technology, Engineering, and Mathematics (STEM) majors. Efforts to confront this diversity, equity, and inclusion (DEI) challenge, such as faculty adoption of evidenced-based instructional approaches that promote student success, have been slow. Instructional and pedagogical change efforts at the academic department level have been demonstrated to be effective at enacting reform. One potential strategy is to embed change agent individuals within STEM departments that can drive change efforts. This study seeks to assess whether tenure-track, teaching-focused faculty housed in STEM departments are perceived as influential on the instructional and pedagogical domains of their colleagues. To answer this, individuals across five STEM departments at large, research-intensive campuses identified faculty who were influential upon six domains of their instruction and pedagogy. Social network analysis of individuals in these departments revealed heterogeneity across the instructional domains. Some, like the teaching strategies network, are highly connected and involve the majority of the department; while others, like the DEI influence network, comprise a significantly smaller population of faculty. Importantly, we demonstrate that tenure-track, teaching-focused faculty are influential across all domains of instruction, but are disproportionately so in the sparsely populated DEI influence networks.

Competency-oriented student activities are an important means of enabling teachers to move from teaching fundamental knowledge to developing students' subject competencies. To examine mathematics teacher novice--expert differences in organizing competency-oriented activities, this study collected data from three consecutive lessons taught by an expert and a novice teacher respectively. Epistemic network analysis (ENA) was used to identify the co-occurrence and structure of students' activities in each lesson. Results of the coding-and-counting method show statistically significant differences in the types of students' activities related to mathematics competency across the lessons taught by the expert teacher, but not in those taught by the novice teacher. By recognizing the temporal relationships between different activities, the ENA of the consecutive lessons reveals that the expert teacher facilitated better mathematical content and lesson connectedness by establishing connections between competency-oriented activities following the sequence of "understanding-applying-transferring and innovating" (Wang et al., 2022). In contrast, the novice teacher organized more mathematics activities on "understanding" and "applying," without building connections via "transferring and innovating" across three lessons. The results of the ENA are also supported by qualitative analysis. Finally, the implications, limitations, and possibilities for future research are discussed.

Though higher education scholars have long known that undergraduate student engagement is associated with student persistence, they have yet to fully unravel its character and how it evolves over time. We argue that this is in part due to the individualistic, static ways engagement is measured even though scholars recognize it as a fundamentally relational concept. In our theoretical exploration, we draw on existing thinking about engagement in higher education and fit it to a social network paradigm that is well suited to conceptualizing and measuring relational, multidimensional, and dynamic phenomena such as student engagement. We conclude with suggestions for research and practice to further explore the longitudinal character of student engagement and how network analysis may be employed to explore the roles of engagement in persistence.

Egocentric networks represent a popular research design for network research. However, to what extent and under what conditions egocentric network centrality can serve as reasonable substitutes for their sociocentric counterparts are important questions to study. The answers to these questions are uncertain simply because of the large variety of networks. Hence, this paper aims to provide exploratory answers to these questions by analyzing both empirical and simulated data. Through analyses of various empirical networks (including some classic albeit small ones), this paper shows that egocentric betweenness approximates sociocentric betweenness quite well (the correlation is high across almost all the networks being examined) while egocentric closeness approximates sociocentric closeness only reasonably well (the correlation is a bit lower on average with a larger variance across networks). Simulations also confirm this finding. Analyses further show that egocentric approximations of betweenness and closeness seem to work well in different types of networks (as featured by network size, density, centralization, reciprocity, transitivity, and geodistance). Lastly, the paper briefly presents three ideas to help improve egocentric approximations of centrality measures.

In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that occurred a long time ago--a phenomenon known as "memory decay". Previous studies either predefined a short-run and long-run memory or fixed a parametric exponential memory decay using a predefined half-life period. In real-life relational event networks, however, it is generally unknown how the influence of past events fades as time goes by. For this reason, it is not recommendable to fix memory decay in an ad-hoc manner, but instead we should learn the shape of memory decay from the observed data. In this paper, a novel semi-parametric approach based on Bayesian Model Averaging is proposed for learning the shape of the memory decay without requiring any parametric assumptions. The method is applied to relational event history data among socio-political actors in India and a comparison with other relational event models based on predefined memory decays is provided.

Background: Learner profiles detected from digital trace data are typically triangulated with survey data to explain those profiles based on learners' internal conditions (e.g., motivation). However, survey data are often analysed with limited consideration of the interconnected nature of learners' internal conditions. Objectives: Aiming to enable a thorough understanding of trace-based learner profiles, this paper presents and evaluates a comprehensive approach to analysis of learners' self-reports, which extends conventional statistical methods with psychological networks analysis. Methods: The study context is a massive open online course (MOOC) aimed at promoting physical activity (PA) for health. Learners' (N = 497) perceptions related to PA, as well as their self-efficacy and intentions to increase the level of PA were collected before and after the MOOC, while their interactions with the course were logged as digital traces. Learner profiles derived from trace data were further examined and interpreted through a combined use of conventional statistical methods and psychological networks analysis. Results and Conclusions: The inclusion of psychological networks in the analysis of learners' self-reports collected before the start of the MOOC offers better understanding of trace-based learner profiles, compared to the conventional statistical analysis only. Likewise, the combined use of conventional statistical methods and psychological networks in the analysis of learners' self-reports before and after the MOOC provided more comprehensive insights about changes in the constructs measured in each learner profile. Major Takeaways: The combined use of conventional statistical methods and psychological networks presented in this paper sets a path for a comprehensive analysis of survey data. The insights it offers complement the information about learner profiles derived from trace data, thus allowing for a more thorough understanding of learners' course engagement than any individual method or data source would allow.

Metacognition awareness is a fundamental skill for the 21st century. Accurately measuring metacognitive awareness would be highly relevant regardless of age, background, or cognitive abilities. The current study aimed to evaluate the psychometric properties of the 19-item Metacognitive Awareness Inventory-Arabic version (MAI-A) in the general population of Saudi Arabia. The current study employed Exploratory Factor Analysis (EFA), Confirmatory Factor Analysis (CFA), Cronbach's alpha and McDonald's omega (reliability), and average variance extracted and composite reliability (validity to evaluate the psychometric properties of MAI-A among a sample of the Arabian population. Measurement invariance across male and female samples had been conducted. Finally, the Exploratory Graph Analysis (EGA) was used to estimate the dimensional structure of the MAI. In the first step, quantitative face validity was presented to remove the one on the items because of poor indexes. So, the evaluated version was 18 items MAI. Also, the first-order and second-order CFA confirmed the 2-factor model. So, the 18-item MAI presented suitable internal consistency. Second-order average variance extracted validity showed suitable validity of the MAI-A. According to [delta]CFI and [delta]RMSEA, there was no gender invariance between males and females in the MAI-A structure. Finally, the EGA estimated a 3-dimensional structure of the MAI, which was different from the factor structure in the CFA. The MAI-A is a practical and cost-effective tool for evaluating metacognitive awareness in Arab populations. However, future studies should be conducted due to differences between traditional methods (CFA)I and novel methods (EGA) in extracting factors.

The interest in Data Envelopment Analysis (DEA) has grown since its first put forward in 1978. In response to the overwhelming interest, systematic literature reviews, as well as bibliometric studies, have been performed in describing the state-of-the-art and offering quantitative outlines with regard to the high-impact papers on global applications of DEA and the higher education system (DEA-HE). This study examines 75 systematic literature review (SLR) studies published between 2018 and 2022 and 508 bibliometric studies published between 1992and 2022. Four performance-focused areas are identified through SLR analysis: institutional performance, departmental performance, performance of study program, and performance of other higher education (HE) activities. This study highlights issues, methods, and resolutions in selected SLR literature. Bibliometric analysis revealed an increasing trend in DEA-HE since 2003, with the highest number of publications in 2021. Tommaso Agasisti was the most productive author, and Jill Johnes was the most influential. The journal Scientometric had the most publications in the area. This study lays the groundwork for future research. Future reviewers may find the common practises, constraints, and underlying assumptions presented in this study useful for the selection and analysis of relevant studies.

Increased levels of internationalization have led to individuals working in multicultural organizations, a trend that is likely to continue for the foreseeable future. To navigate these environments successfully, more emphasis is being placed on the importance of higher education in preparing and arming the future workforce with the international competencies required to be successful in contemporary organizations. The aim of this research is to shed much needed light on how the learning design of management courses influence how and with whom 263 students learn within two culturally diverse post-graduate management courses. We found that Course B (specific cross-cultural design) significantly and with large effect size increased intercultural interaction over time relative to Course A (generic learning design), whereby qualitative findings confirm substantial differences in lived experiences between the two courses. This highlights that educators need to carefully design intercultural interactions rather than hoping that these will develop naturally over time.

The rapid growth of technology and computer science, which has led to a surge in demand for skilled professionals in this field. The skill set required for computer science jobs has evolved rapidly, creating challenges for those already in the workforce who need to adapt their skills quickly to meet industry demands. To stay ahead of the curve, it is essential to understand the hottest skills needed in the field. The article introduces a new method for analyzing job advertisements using social network analysis to identify the most critical skills required by employers in the market. In this research, to form the communication network of skills, first 5763 skills were collected from the LinkedIn social network, then the relationship between skills was collected and searched in 7777 computer science job advertisements, and finally, the balanced communication network of skills was formed. The study analyzes the formed communication network of skills in the computer science job market and identifies four distinct communities of skills: Generalists, Infrastructure and Security, Software Development, and Embedded Systems. The findings reveal that employers value both hard and soft skills, such as programming languages and teamwork. Communication skills were found to be the most important skill in the labor market. Additionally, certain skills were highlighted based on their centrality indices, including communication, English, SQL, Git, and business skills, among others. The study provides valuable insights into the current state of the computer science job market and can help guide individuals and organizations in making informed decisions about skills acquisition and hiring practices.

Since social media is increasingly pervasive in modern society, this bibliometric study aims to investigate its educational applications. Using the Scopus database, the bibliometric method analyses publications published between 2010 and 2022. The research indicates that student participation and ease of access are the two main benefits of using social media in the classroom. However, it also spreads misinformation and poses privacy and security risks. Articles that discussed how social media could be used in the classroom were found and organised using a bibliometric analysis based on their subject matter, year of publication, and authors. The research shows that between 2001 and 2020, there was a rise in the number of papers discussing the use of social media in the classroom. In addition, the top five countries in terms of annual publication output include the United States, the United Kingdom, Australia/India, and Canada. To further explore the connections between relevant articles, a co-citation network analysis was performed. Therefore, there must be strict rules and policies for using social media in education to address privacy and security concerns and the spread of false information.

This paper presents a cartography of the digital literacy academic field. Such cartography is comprised of two sections: a categorization of the field through literature review and analysis, and an exploration of its main issues through thematic and network analysis. On the one hand, five conceptual categories of digital literacies are found: functional, sociocultural, critical, transformative, and sociomaterial. On the other, main issues are described with 21 recurring themes of digital literacy and a few networks depicting its most salient matters of concern, concluding with an interpretation of these in the composition of 8 encompassing issue spaces: digital literacies conceptions and practices, digital literacy in education, access and digital divide, digital texts and literacy, websites and social networks, digital technologies at the workplace and healthcare, digital technologies users and uses, and information issues. Finally, a few paragraphs are dedicated to the limitations of categorizing and issue mapping.

Rapid advancements and emergent technologies add an additional layer of complexity to preparing computer science and information technology higher education students for entering the post pandemic job market. Knowing and predicting employers' technical skill needs is essential for shaping curriculum development to address the emergent skill gap. Examining online advertisements to determine the skills sought by employers of new hires for these emerging areas and ensuring that program course content addresses these skills can be a daunting task. In this paper, the authors describe the development of a data-driven analytics framework that can be used for evaluating emerging skill clusters in online job adverts and the application of the framework to a mobile computing course at the authors' institution.

Learning from multiple documents is an essential ability in today's society. This experimental study used concept network analysis to consider how reading prompts and post-reading generative learning tasks can alter students' documents integration performance. Undergraduates (N = 119) read three documents about Alzheimer's disease with one of two reading prompts (integrative prompts vs. detailed prompts) and then after reading completed a generative learning task (concept mapping vs. summary writing). Three days later they completed a delayed writing task and an inference verification test. Participants' written texts were converted to concept networks to evaluate conceptual level integration, including the "quantity" of integration (measured by the proportion of integrative links), the "semantic quality" of integration (measured by the similarity of integrative links), and the "structural quality" of integration (measured by comparing network graph centrality). Results showed that the integrative prompts relative to the detailed prompts enhanced the quantity of integration but not the semantic and structural quality. Further, concept mapping relative to summary writing significantly improved the structural quality of integration. In summary, this study describes a new concept network approach for measuring different aspects of integration to advance theory and research in multiple document comprehension.

Background: Existing research has demonstrated that student characteristics, such as race/ethnicity, sex, and personal beliefs about engineering knowledge, shape students' experiences in ill-structured problem-solving, such as engineering design, where ideas must be communicated, negotiated, and selected in complex social processes. Purpose: The purpose of this research was to examine the how student characteristics, such as race/ethnicity, sex, and epistemological beliefs, are associated with patterns of idea contributions and ideas enactments in collaborative project teams. Method: In this article, I use the multilayer exponential random graph model (ERGM) for examining multiple complex social relationships simultaneously. Drawing on survey data from a study of engineering student teamwork, this research examines the relationship perceptions of idea contributions (layer 1) and idea enactments (layer 2) in collaborative project teams. Results: Results indicated no sex differences in the perceptions of idea contributions and enactments in student design teams. However, underrepresented minority students and Asian America/Pacific Islander students were reported as less frequently having their ideas enacted. Further, epistemological beliefs similarity effects were a significant predictor on the idea contribution layer, and epistemological beliefs sociality effects were significant on the idea enactments layer. Conclusion: Achieving equity in teamwork pedagogies requires understanding the dynamic social processes that shapes patterns of participation in student teams. This research demonstrates the power of social networks methodologies for modeling teamwork processes, pointing specifically to the ways that student characteristics are associated with perceptions shape idea contributions and enactments in student teams.

Since social networks analysis in education offers valuable insights into social structures and social dynamics that shapes individuals behaviors and information storage and transmission, it has become a hot topic in educational science studies. The aim of this study is to examine the educational sciences studies conducted at higher education level in which social network analysis is used. The studies were analyzed based on journals, years, author countries, number of citations, models, theories, and concepts, research methods and target audience. Content analysis method was used in the study. The reliability of inter-coder agreement was calculated as 0.88. The findings were categorized under certain themes according to the research questions. According to the results, Internet and Higher Education (n=6) and Computers and Education (n=5) were the journals with the most publications, while 2019 was the year that the most studies (n=12) were conducted. The studies were mostly conducted by authors in the USA. "Seeing the learning community: An exploration of the development of a resource for monitoring online student networking" was the most cited article. When the underlying models, theories and concepts in the studies were analyzed, six themes emerged: social paradigm, learning environments/tools, learning approaches/methods, feedback/assessment, informal approaches to teaching and individual characteristics. The most frequently used method was quantitative research, and the target group was undergraduate students. The target group size was mostly between 30-60, and convenience sampling was primarily employed for the target group selection. According to the findings and results of the study, suggestions for the use of social network analysis in the field of educational sciences were presented.

Indonesian workers in Taiwan have been taking synchronous online courses to enhance their education for several years. Still, the most crucial part of a synchronous course, the interaction quality, in the past, in general, was highly disturbed by the workers' busy daily work and had room to improve. Therefore, improving online interaction quality is a critical issue in workers' online courses. This study assigns an asynchronous learning strategy to supplement synchronous learning to enhance student interaction. Ten Indonesian workers as part of the Translation V course students of Indonesia Open University in Taiwan were involved. Action research was conducted to explore the effects of the asynchronous enhanced synchronous learning strategy on the students' interactions and perceptions. All the interaction threads were analyzed and described by social networking analysis, content analysis, and students' perceptions from their reflections. The results reveal that the students' interaction increased in the synchronous learning with informal assignment instructions directed by the tutor in the action research cycle II as an action to increase interaction in cycle I, which tended to be independent learning instructions. The study suggests implementing blended learning in synchronous online tutoring programs and providing asynchronous learning strategies to enhance online students' interaction.

A student's academic performance is conditioned on factors within the pupil or outside, in the environment. The Western body of knowledge emphasizes the positive role of "Self" and related variables such as self-esteem, self-efficacy, self-confidence, and others on the academic performance of students. An Indian counterpart to the concept of self is "Aham". The present study aimed at qualitatively understanding the role of Ahamkara in the academic performance of school students. For this purpose, a semi-structured interview technique was utilised for data collection. A total of 11 interviews were recorded and transcribed. Thematic Network analysis framework forwarded by Attride-Sterling (2001) was utilized for data analysis. Based on the analysis 4 global themes emerged. They are: Formation and maintenance of a sense of self through Identification; Active engagement of Self in the learning process leads to growth; Ignorance of Self, a path to failure; and Failure an opportunity to maintain the optimal sense of self.

This article provides a bibliometric overview of publications on eLearning trends in STE(A)M teaching and learning to give readers a better understanding of the current state of research in the field. The main objective of this study is to provide bibliometric data on publications on online teaching and learning trends for science, technology, engineering, and mathematics education (STEM) teaching and learning purposes printed in journals included in the Scopus database in the years 2011-2023. For the bibliometric analysis, STEM learning, STEM teaching, online education, bibliometric review keywords were used, and 136 documents from the Scopus database were chosen. The collected data of the publications scanned and published in the parameters of the study were subjected to a bibliometric analysis based on seven categories: number of articles and citations per year, most influential countries, most prolific author, most prominent affiliations, funding institutions, publication source, and subject areas. Network diagrams and bibliometric analyses were created using the Scopus database analysis. Most of the articles were published between 2016 and 2022. The United States of America, the United Kingdom, and China were among the top-three most productive countries, and the United States of America produced the most publications. The number of citations to publications indexed in the Scopus database is growing steadily and reached its peak in 2022 (178 citations). The most prolific author on this subject is Minichiello, A., with four publications. In addition, Stanford University and Utah State University have maximum publishing partners. By funding 16 publications for online STEM teaching and learning, the National Science Foundation has shown leadership. The topic areas of the publications' distribution were looked at. The articles' respective fields of study were social sciences and computer science. This study offers a vision for future research as well as a worldwide view of online learning for STEM teaching and learning.

Gaming is becoming a popular method of engaging students in learning processes across all levels of the educational community. The effective integration of gaming activities into course curricula has the potential to enhance student learning, motivation, and knowledge acquisition in a range of disciplines. However, gamification of education is not without its opponents, with many educators concerned about the negative impacts of game use on effective learning. This study enhances our understanding of contemporary practices related to the areas, usage and characteristics of gamification in education. It is of particular relevance to educational institutions with a focus on developing innovative teaching methods and curricula that utilize gamification techniques in a multi-disciplinary, cross-national context across all stages of formal learning. Through the use of bibliometric analysis techniques, our study of the citation relations of 3,617 publications identified ten prominent themes dominated by gamification: mobile gaming, physical education, health and medicine, business, learning performance, programming and computing, English language, teacher adoption, primary & secondary education, and mathematics. Clear evidence of increased student motivation to learn and improved course results were evident in the examined literature. This study will benefit serious game designers, educators, and educational institutions to develop more inclusive and engaging pedagogies that exploit the ubiquitous availability of gaming technologies for inclusion in more traditional course delivery methods.

The purpose of this study is to reveal the status of scientific publications on learning analytics from the past to the present in terms of bibliometric indicators. A total of 659 publications on the subject between the years 2011-2021 were found in the search using keywords after various screening processes. Publications were revealed through descriptive and bibliometric analyses. In the study, the distribution of publications by years and citation numbers, the most published journals on the subject, the most frequently cited publications, the most prolific countries, institutions and authors were examined. In addition, the cooperation between the countries, authors and institutions that publish on the subject was mentioned and a network structure was created for the relations between the keywords. It has been determined that research in this field has progressed and the number of publications and citations has increased over the years. As a result of the bibliometric analysis, it was concluded that the most influential countries in the field of learning analytics are the USA, Australia and Spain. The University of Edinburgh and Open University UK ranked first in terms of the number of citations and Monash University as the most prolific institutions in terms of the number of publications. According to the keyword co-occurrence analysis, educational data mining, MOOCS, learning analytics, blended learning, social network analysis keywords stand out in the field of learning analytics.

The three components that form the basis of the educational process are the teacher, the learner, and the environment. These three components are affected by the developing and changing technology as a result of globalization considerably. Teaching and learning techniques should be updated and connected with these developments; new tools are therefore needed to make the necessary updates. Determination and application of the new tools include many decisions. Decision-makers can make more effective decisions using Multi-Criteria Decision-Making Techniques (MCDM), a complex decision-making tool that includes both quantitative and qualitative factors at present time. This study aimed to determine which MCDM methods are used in studies conducted in higher education, which is one of the most important development level indicators of countries, and to present a systematic literature review of MCDM method applications. The study was conducted in three stages: first, known electronics were searched until the end of 2021 using keywords; then, all studies were listed in a systematic taxonomy, and in the last stage, Thematic Network Analysis was used to evaluate the development of MCDM studies in the higher education area. It is determined that the Analytical Hierarchy Process (AHP) method is the most widely used method in higher education in MCDM applications. It was observed that the most common use of MCDM applications in higher education is e-learning as well. This study aims to be a guide for all researchers and practitioners who will study in both higher education and the MCDM areas.

This review examines the potential of Project-Based Learning (PjBL) to enhance students' life skills in the field of biology, and analyzes the challenges that may hinder its implementation. Using a Systematic Literature Network Analysis (SLNA), which combines Bibliometrics Analysis (BA) and Systematic Literature Review (SLR), this study analyzed articles from SCOPUS that met specific criteria. The findings reveal that PjBL is associated with e-learning and blended learning, and that the majority of articles on PjBL were published in 2020, particularly in the Journal of Microbiology & Biology Education. Additionally, this SLNA highlights the contributions of these articles to the developing of various life skills in biology learning. By identifying key trends and insights from existing literature, this study sheds light on the potential of PjBL to enhance students' biology education and their ability to apply it in their daily lives.

Social network analysis (SNA) is a research method that, when applied to improvement science, can help leaders understand the strength of relationships within their organization. The COVID-19 pandemic has had a lasting impact on organizational norms, and it has interrupted relationship building efforts. This paper documents a case study of the Kentucky Department of Education (KDE), which deployed SNA techniques to strategically identify areas of growth within its network and design intentional, targeted solutions to improve the network health. As organizations emerge from the pandemic environment and begin to plan continuous improvement efforts, they would be well served to examine the impact of the pandemic on their level of connectedness. The broader impact and generalizability of the case study as well as considerations for replication are also discussed.

In South Korea, curriculum is revised, made public and implemented under a system known as a nation led curriculum. The South Korean national curriculum was completely revised 10 times between 1946 and 2015. At present, a complete revision is underway to replace the current 2015 national curriculum which is called the 2022 revised national curriculum. This study aims to analyze stakeholders' responses to the YouTube public hearing on social studies curriculum according to the 2022 revised national curriculum in South Korea in order to understand the context and causes of the issues that emerged. Text mining, semantic network analysis and word cloud techniques were employed to identify issues. As a result, three issues were identified in the social studies curriculum: the balanced development of general elective subjects in high school; the separation of social sciences and geography and division of textbooks in middle school social studies and the separate listing of four subjects, specifically geography, social sciences, history and morals. The issues revealed in this study provide beneficial implications for future social studies curriculum development, revision as well as the development of future research.

Social networking sites have become affinity spaces for teachers. Many teachers use them with different intentions and motivations, including learning. On social media platforms there are active teachers who have developed a certain leadership and recognition from many teachers. In some areas, like marketing or fashion, people with influence are called influencers. This paper investigates who they are, how their network is configured and how they perceive themselves. The questions that directed our research were: Who are the predominant Spanish teacher leaders on Twitter? What is the network structure that characterizes them? What perceptions do these teacher leaders have about their role and its impact on their professional development as teachers and others? This study has two distinct but interrelated phases. We investigated the structure and relationships among 54 Spanish teacher leaders. Using a social network analysis (SNA) approach, through the analysis of the social behavior of these teachers on the social network Twitter, we first identify educational profiles who have a high degree of centrality in the network. These are teachers who are recognized as opinion leaders by a significant proportion of their fellows. In addition to the degree of centrality that tells us how relevant a user is in a specific digital community, we identified teachers who play a key role in the circulation of information in the network studied. In some way, these teachers share common characteristics with activists in other fields. Of the 54 teachers, we selected 20 who were then interviewed. The findings demonstrate that they don't consider themselves micro-celebrities or influencers. We found a lack of identification not only with the term, but also with the image of an influencer which was understood as banal, superficial, commercial, and far from what they do in social networks. These teachers develop their identity as new digital artisans who foster a culture of collaboration and create affinity spaces that allow informal learning. Their motivation is intrinsic, through recognition and prestige among other teachers, which leads them to build a kind of constructivist leadership.

Education has been considered a cornerstone for human and economic development. Although there is a national educational strategy in most countries, various implementations are at the state level. This paper studies academic efficiency at the primary and secondary levels and the human development dimensions -- long and healthy life, being knowledgeable, and enjoying a decent standard of life -- at the state level. For this purpose, a network data envelopment analysis (NDEA) with two stages was proposed. The first stage studies the educational process efficiency, while the second evaluates its impact in the form of the human development index. The study found significant differences between the evaluated states in the education stage, where the lowest efficiencies are mainly in the southwest of Mexico. The results also indicate that better education quality leads to greater regional, economic, and social development at the state level. This study contributes to the NDEA applications on the understanding of the impact that education has in improving the development of the regions holistically.

This bibliometric study examined the development of research on the learning and teaching of second language (L2) listening from 1948 to 2020 (73 years). Specifically, the study involved: (1) a search and analysis of all the noun phrases to identify important research topics in the abstracts of the published journal articles on L2 listening over the 73 years (divided into three periods) using self-made Python scripts and (2) three co-citation analyses of the references in these articles regarding highly cited authors, publications, and journals, respectively, via the VOSviewer program. The keyword/phrase analysis produced results that helped uncover and delineate the research trends in L2 listening across the three time periods. The co-citation analyses identified the most highly cited authors, publications, and journals as well as the interrelations among the most highly cited items in each of the three categories illustrated with network maps. The results of the analyses and their implications are discussed.

It is generally accepted that Canadian universities are less stratified than their southern neighbours, a hypothesis popularized in the mid-2000s and verified by subsequent comparative empirical research. Through this piece, we revisit the Canadian "flatness" hypothesis, embracing a more sociological definition of status hierarchies and using social media followers as a focal proxy for status. Despite our theoretically based skepticism, adoption of an alternative status proxy, and use of more recent data, our analyses validate the flatness hypothesis. We theorize the implications of these findings, and our novel approach, for the study of organizational stratification in higher education.

STEM education emphasizes improving student learning by linking abstract knowledge with real-world problems and engaging students in authentic projects to solve real-world problems. Accordingly, project-based learning has been widely promoted in STEM programs and has shown a promising impact on student learning. However, solving real-world problems in STEM projects involves complex processes. It remains unclear how students engage in complex problem-solving processes in STEM projects and how their processes may differ among students. This study was conducted with secondary school students who engaged in a design-based STEM project in small groups. The findings show that questioning and responding appeared most frequently and connected with other elements in group discourse, while argumentation and justification appeared least frequently. The findings reveal distinctive discourse patterns that differ among high-, medium- and low-performance groups, based on which the implications of the findings were discussed.

This study employed dynamic network analysis to investigate the role of central relational links within the motivational system of 59 English for Academic Purposes (EAP) learners. The findings suggest that these links are best understood as central relational nodes, which act as hubs that give structure and stability to the network and enable the flow of information between clusters of factors within the second language (L2) motivation system. Results also indicate that while the centrality values of these nodes are dynamic, fluctuating from week to week, these nodes are relatively stable in their roles. Furthermore, although the motivational trajectory of individual learners may differ significantly and various motivational factors and their connections appear, disappear, and reappear in the system, the stability of central relational nodes facilitates an emergence of recurring patterns. This suggests that a shared socio-cultural and educational context may reduce the unpredictability of L2 motivation.

Research shows that a cohesive classroom community, or the relationships built between students within a classroom setting, leads to a long list of positive student outcomes in higher education. This research seeks to better understand how to build cohesion in a classroom community, a goal that has become even more urgent given the student isolation caused by the COVID-19 pandemic. It uses the tool of social network analysis, a tool particularly well suited for studying networks of relationships, to examine how two common collaborative learning techniques--small group discussions and team-based projects--affect the structure and strength of the classroom community networks in two public affairs undergraduate courses. The results show that both collaborative learning techniques created a network of denser, more inclusive relationships between students. Teamwork, in particular, had a large impact on the formation of relationships between students. Further, the collaborative teaching strategies were effective in improving student learning outcomes. Students received higher grades and reported higher satisfaction with the course if they were more embedded in the classroom community network. The results of this research reveal the importance of focusing on relationship-building instructional techniques for student success in higher education. [Note: The volume number (34), publication year (2022), and page range (78-89) shown on the PDF is incorrect. The correct volume number is 35, the correct publication year is 2023, and the correct page range is 79-91.]

Online discussion research has mainly been conducted using case methods. This article proposes a method for comparative analysis based on network metrics such as information entropy and global network efficiency as more holistic measures characterizing social learning group dynamics. We applied social learning optimization analysis of networks (SLOAN) to a data set consisting of Coursera courses from a range of disciplines. We examined the relationship of discussion forum uses and measures of network efficiency, characterized by the information flow through the network. Discussion forums vary greatly in size and in use. Courses with a greater prevalence of subject-related versus procedural talk differed significantly in seeking but not disseminating behaviors in massive open online course discussion forums. Subject-related talk was related to higher network efficiency and had higher seeking and disseminating scores overall. We discuss the value of SLOAN for social learning and argue for the experimental study of online discussion optimization using a discussion post recommendation system for maximizing social learning.

The COVID-19 pandemic induced a digital transformation of education and inspired both instructors and learners to adopt and leverage technology for learning. This led to online learning becoming an important component of the new normal, with home-based virtual learning an essential aspect for learners on various levels. This, in turn, has caused learners of varying levels to interact more frequently with virtual resources to supplement their learning. Even though virtual learning environments provide basic resources to help monitor the learners' online behaviour, there is room for more insights to be derived concerning individual learner performance. In this study, we propose a framework for visualising learners' online behaviour and use the data obtained to predict whether the learners would clear a course. We explored a variety of binary classifiers from which we achieved an overall accuracy of 80%-85%, thereby indicating the effectiveness of our approach and that learners' online behaviour had a significant effect on their academic performance. Further analysis showed that common patterns of behaviour among learners and/or anomalies in online behaviour could cause incorrect interpretations of a learner's performance, which gave us a better understanding of how our approach could be modified in the future.

Although education is acknowledged as an important tool to address climate change, only some countries refer to education within their Nationally Determined Contributions (NDCs). Using a mixed-methods approach, we explore the question of how countries frame education within their climate pledges. First, we examine NDCs with a discourse network analysis (DNA) to identify country framings of education and climate change and potential discourse coalitions among countries. Education is most often referred to as an instrument to adapt to climate change and to a lesser extent as an instrument to mitigate climate change. Second, we use a regression analysis to complement the DNA results. We find that countries with a higher exposure to climate change impacts are more likely to mention education, which explains the prevalence of the adaptation framing. Moreover, we find that countries with a higher GDP per capita are less likely to include education within their NDCs. These results confirm the traditional divide between Annex 1 and non-Annex 1 countries also with regard to education in NDCs.

As technology-enhanced children's learning has gained wide attention, programmable robots have been gradually introduced in early childhood education. Hence, it would be valuable to understand how young children perceive robot programming learning. Draw-a-picture technique is an ideal method to elicit ideas, thoughts, and feelings for children with limited literacy, and epistemic network analysis (ENA) is a novel analytical method to analyze children's conceptions through the visualized network model. Therefore, this study employed a draw-a-picture technique and ENA to explore 189 5-6-year-old young children's conceptions of robot programming learning and probe whether their conceptions differ by gender and learning achievements. Results revealed that most children believed that with robot programming kits, they could engage in programming activities with peers in any location and held positive emotions and attitudes. In addition, young children's conceptions of robot programming learning differ notably by gender and learning achievements. Based on the current findings, several suggestions were proposed, which could set a reference for future robot programming teaching in early childhood education.

The aim of this paper is to foreground network analysis as a statistical lens through which higher education institutions can articulate their own process of striving for teaching excellence, and how it is constituted in their own contexts. The paper offers an approach to analysis that extends the frontiers of methodologies in 'measurement' of teaching excellence; one that responds to the shortcomings of the current methodologies, critiqued for being reductive, performative, alienating, and promoting closure and convergence in how they assess teaching excellence. We review epistemological and methodological shifts in conceptualising teaching excellence and measurement that are required to work with our methodology, as well as provide statistical details, for anyone who wishes to reproduce our profiled examples. We thus build in the paper a link between the theory of (teaching) excellence and practice (of measurement) and champion a theory-based approach to the methodology of educational metrics.

Oral language abilities have been reported to be related to reading development. This relationship seems to be determined by orthographic systems and reading development. Children with developmental language disorder (DLD) show reading difficulties, although most studies focus on reading comprehension. The present study has two main objectives: to examine the reading performance of a group of Spanish-speaking children with DLD compared to a chronologically age-matched group of typically developing children (TD); and to study the relationship between oral language abilities and reading performance using network analysis. They were administered five standardized tests to obtain measures about oral language abilities and reading performance. Results showed that children with DLD were less accurate and slower in reading than TD children. Meanwhile, the network analyses suggest strong and stable connections between reading and oral production in the DLD group. This finding confirms the importance of language abilities for reading acquisition.

One expected outcome of physics instruction is for students to be capable of relating physical concepts to multiple mathematical representations. In quantum mechanics (QM), students are asked to work across multiple symbolic notations, including some they have not previously encountered. To investigate student understanding of the relationships between expressions used in these various notations, a survey was developed and distributed to students at six different institutions. All of the courses studied were structured as "spins-first," in which the course begins with spin-1/2 systems and Dirac notation before transitioning to include continuous systems and wave function notation. Network analysis techniques such as community detection methods were used to investigate conceptual connections between commonly used expressions in upper-division QM courses. Our findings suggest that, for spins-first students, Dirac bras and kets share a stronger identity with vectorlike concepts than are associated with quantum state or wave function concepts. This work represents a novel way of using well-developed network analysis techniques and suggests such techniques could be used for other purposes as well.

Physics graduate studies are substantial efforts on the part of individual students, departments, and institutions of higher education. Understanding the factors that lead to student success and attrition is crucial for improving these programs. One factor that has recently started to be investigated is the broadly defined students' experiences related to support structures. The Aspects of Student Experience Scale (ASES), a Likert-style survey, was developed by researchers to do just that. In this study, we leverage the network approach for Likert-style surveys (NALS) methodology to provide a unique interpretation of responses to the ASES instrument for well-defined demographic groups. We confirm the validity of our findings by studying the stability of the NALS themes and investigating how they are expressed within demographic-based networks. We find that for all four themes in the original ASES study, certain thematic trends capturing students' experiences vary across the demographic-based networks in meaningful ways. We also reveal that for some demographic groups, there is an interesting interplay between, and mixing of, the original themes. Finally, our study showcases how NALS can be applied to other Likert-style datasets.