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School leadership research literature has a large number of widely used constructs. Could fewer constructs bring more clarity? This study evaluates construct content validity, defined as the extent to which a measure's items reflect a theoretical content domain, in school leadership literature. To do so, we reviewed 29 articles that used Teaching and Learning International Survey (TALIS) as data sources to study school leadership, and developed a construct co-occurrence network in which nodes represented constructs in the 29 reviewed articles and ties connected a pair of constructs that used the same TALIS survey items. Among the 82 constructs, 43 constructs had overlapping measures with one another in the TALIS literature on school leadership. Results of network analysis suggest three problematic issues with constructs of TALIS literature on school leadership: (1) same measures for different constructs, (2) different measures for the same constructs, and (3) missing alignment between theoretical and operational definitions. To strive for construct clarity, we provide four recommendations for future research: (1) efforts to prevent construct proliferation, (2) alignment of theoretical and operational definitions, (3) rigorous evaluation of construct validity, and (4) following a fundamental principle of parsimony.

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.

Dialectical thinking is a way of discussing and analyzing things from different viewpoints to reach a solution. It is often taught in language courses by conducting argumentation activities. However, without providing effective strategies or tools, learners generally encounter difficulties in structuring their viewpoints during the argumentation process. To solve this problem, this study proposed dialectical constructivist scaffolding-based argumentation mapping (DCS-AM), which integrates a structured, four-stage process to support students' dialectical thinking and oral and dialogical argumentation complexity. The argumentation map refers to a visualized tool that enables learners to structure their viewpoints for making arguments. A quasi-experiment was conducted in an English as a Foreign Language course. A total of 26 students were in the DCS-AM group, while 22 students were in the conventional constructivist scaffolding-based argumentation mapping (CS-AM) group, which adopted a more conventional format, emphasizing direct discussion and teacher-led knowledge transmission. The experimental results found that students in the DCS-AM group exhibited significantly better dialectical thinking than those in the CS-AM group. Also, an epistemic network analysis (ENA) of oral and dialogical argumentation revealed that students in the DCS-AM group frequently developed more complex argumentation than those in the CS-AM group in terms of the structural component and discourse activity, including the process of students' dialectical thinking that was found in both groups. This finding shows that technology-supported dialectical constructivist scaffolding can help students improve their dialectical thinking and argumentation skills.

This study looked at titles of research papers on educational technology that were published between 1927 and 2023 using computational text analysis. To map research trends, metrics for technology terminology use, network complexity, and knowledge updating rates were used. The findings showed that, despite some fluctuations, titles have become more technologically diverse and interconnected over time, indicating a greater emphasis on technology and interdisciplinarity. Escalating title complexity was visualized using network analysis. Citation patterns revealed that science/engineering and educational technology both update knowledge at comparable rates. This computational analysis shows how the fields of education and technology have been evolving together over time, giving historical context to understand current trends. The study shows how to use data science techniques to map the dynamics of research within a practical domain that connects technology and practice.

Artificial Intelligence (AI) education in primary schools has received a great deal of attention globally, and it is thus important to investigate primary school students' perceptions and understanding of AI learning. Therefore, in this study, 673 drawings of conceptions of AI learning by third to sixth grade students were collected. Firstly, a drawing analysis approach was used to code the drawings into five categories of 25 elements. Then, the epistemic network analysis approach was used to visualize the structure of the relationships between the elements within the conceptions of AI learning. The study found that (1) primary school students generally held positive attitudes toward AI learning and understood AI learning as programming or robot programming learning; (2) higher grade students showed more constructive learning concepts; and (3) girls had significantly more positive emotions and attitudes toward AI learning than boys, but had less participation in manipulating robotic human activities than boys. The over-reliance on programming and robotics in elementary AI education, as well as gender differences in elementary AI learning need more attention from educators.

Although traditional analytical techniques can characterize learners' internal cognitive abilities laterally and indirectly, it is difficult to present learners' learning development characteristics and changes comprehensively and dynamically. Epistemic network analysis, on the other hand, is an emerging educational research method that integrates qualitative and quantitative analyses and is able to visually and dynamically characterize the developmental trajectory of students' interactive dialogues. Therefore, this study used epistemic network analysis to dynamically track students' interactive dialogues. It was found that (1) there was a difference in the use of students' interactive dialogues between the intervention and control groups, with students in the intervention group using more exploratory dialogues, while students in the control group used more cumulative dialogues. (2) Observed from the time perspective, the development of interactive dialogues of the students in the control group was not significant, and the interactive dialogues of the students in the intervention group developed faster in the early stage and slower in the later stage. (3) The conversation patterns of the students in the excellent group focused on exploratory conversations and were mainly based on reasoning and reflection, and most of the students in the excellent group came from the intervention group. And vice versa for the control group.

Due to the pandemic, many students have been forced to study remotely. This study aims to investigate the impact of online collaboration scripts on learning outcomes in virtual reality (VR) co-creation learning activities during distance learning. The collaboration scripts were designed to foster students' remote teamwork. The participants consisted of 106 Taiwanese sixth-grade elementary students, divided equally into an experimental group and a comparison group. The experiment lasted for seven weeks, with approximately 40 minutes per week dedicated to VR co-creation activities. The experimental group followed online collaboration scripts, while the comparison group engaged in distance learning without scripts. Additionally, the researchers collected students' learning logs and analyzed behavioral sequences to enhance understanding of their learning behaviors. Epistemic network analysis was employed to analyze group interactions, focusing on Orientation, Planning, Executing, Monitoring, and Evaluation codes. Results revealed significant improvement in the experimental group's learning achievement and communication skills using online collaboration scripts in VR co-creation learning. The experimental group exhibited a stronger correlation in Planning--Executing, following the script stages, while the comparison group relied more on peer assistance. Overall, the online collaboration scripts could scaffold learning and support effective remote VR learning activities for content co-creation for elementary students.

Eye-tracking procedures generate copious process data that could be valuable in establishing the response processes component of modern validity theory. However, there is a lack of tools for assessing and visualizing response processes using process data such as eye-tracking fixation sequences, especially those suitable for young children. This study, which explored student responses to a spatial reasoning task, employed eye tracking and social network analysis to model, examine, and visualize students' visual transition patterns while solving spatial problems to begin to elucidate these processes. Fifty students in Grades 2-8 completed a spatial reasoning task as eye movements were recorded. Areas of interest (AoIs) were defined within the task for each spatial reasoning question. Transition networks between AoIs were constructed and analyzed using selected network measures. Results revealed shared transition sequences across students as well as strategic differences between high and low performers. High performers demonstrated more integrated transitions between AoIs, while low performers considered information more in isolation. Additionally, age and the interaction of age and performance did not significantly impact these measures. The study demonstrates a novel modeling approach for investigating visual processing and provides initial evidence that high-performing students more deeply engage with visual information in solving these types of questions.

This study aimed to develop and evaluate the psychometric properties of the Educational Leadership Scale for Faculty Members (ELSF) in Arabic. The scale was rigorously tested using both traditional methods--exploratory factor analysis (EFA) and confirmatory factor analysis (CFA)--as well as advanced network techniques, including exploratory graph analysis (EGA) and centrality measures. The study sample consisted of 350 faculty members (56% male, 44% female) from various universities, with a mean age of 42.3 years (SD = 7.8). Participants represented a range of academic disciplines, and all were involved in educational leadership roles within their respective institutions. EFA revealed a five-factor structure that explained 68.4% of the total variance. CFA further confirmed the model's adequacy, with fit indices showing a good model fit (CFI = 0.95, TLI = 0.94, RMSEA = 0.06). The ELSF, comprising 22 items across these five dimensions, demonstrated strong internal consistency, with a Cronbach's alpha coefficient of 0.89. Additionally, EGA supported the factor structure identified by traditional methods, reinforcing the stability and consistency of the scale. Centrality measures highlighted Item 18 as the most influential, with betweenness, closeness and strength centrality scores of 0.45, 0.78 and 0.62, respectively, within the 'Research-Ethical Leadership' subscale. These findings affirm the ELSF's credibility as a robust tool for assessing educational leadership among faculty members. The scale's strong psychometric properties suggest it is well-suited for application in diverse academic settings, including potential use across Arabic-speaking countries such as Saudi Arabia, Qatar, the United Arab Emirates, Egypt, Oman and Kuwait.

This study aims to characterise inter-organisational collaboration and support networks between schools in a privatised, marketised, and competitive school system. It is located within and contributes to a body of literature on school networks, which has primarily focused on studying the architecture and conditions for the sustainability of the arrangements where collaboration and support between schools are meant to take place. However, there has been less focus on examining the partnerships themselves, particularly those formed organically by schools. This is an exploratory study that takes a novel "convergent" mixed-method egonet Social Network Analysis approach. Primary data were produced from interviews with sixteen primary school headteachers from a local area in Santiago, Chile. Participants were encouraged to name and reflect on other schools with which their schools have an inter-organisational relationship. Insights show that schools draw on both mandated and organically formed inter-organisational relationships to collaborate and support each other. These ties tend to be built with schools alike and geographically close. Some of the main drivers to turn to others are ensuring the sustainability of schools, generating coordinated responses to policy mandates, sharing practice and knowledge, and ensuring students' schooling trajectories. This study, situated within the broader context of school networking literature, proposes that schools exist within multiple networks, with various connections serving different purposes, some of which are rarely acknowledged. It suggests that while formal school networks and governance structure are important, they are insufficient to represent the inter-organisational relationships in which schools are involved.

Background: This study examined the effect of computer-supported collaborative argumentation (CSCA) on secondary school students' understanding of socio-scientific issues (SSI). Engaging students in collaborative argumentation is known to help with deepening their understanding of SSI. Methods: In this study, a mixed-method design is used to investigate 84 students' collaborative argumentation processes and outcomes. The statistical analysis, epistemic network analysis and qualitative uptake analysis results showed that CSCA was effective in supporting secondary school students' evidence-based argumentation skills on SSI. Findings and Conclusion: Several cases were presented to show how students engaged in CSCA to explore meaningful learning opportunities and how CSCA helped students' learning on SSI. Implications: The findings provided insights for future innovative teaching and learning SSI in authentic classroom settings.

In this paper, we examined the relationship between social networking sites (SNSs) and the coping strategies international students adopt amidst cultural shocks in India, particularly in the vibrant locale of Chandigarh. The study scrutinizes pivotal factors that influence international students' SNS use and creates and validates an instrument that measures this relationship. Grounded in the uses and gratifications theory (UGT), this research introduces an instrument -- the Social Capital Assessment Scale and Inventory (SCASI), which was developed via exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The research concludes that SNSs play a pivotal role in the globalization of information, encompassing education, knowledge dissemination, interactivity, and cultural exchange in geographically diverse countries such as India.

Socially shared regulation in learning (SSRL) contributes to successful collaborative learning (CL). Empirical research into SSRL has received considerable attention recently, with increasingly available multimodal data, advanced learning analytics (LA), and artificial intelligence (AI) providing promising research avenues. Yet, integrating these with traditional datasets remains a challenge in SSRL research due to the misalignment between theoretical constructs, methodological assumptions, and data structure. To address this challenge and expand our understanding of the nature of SSRL, the present research adopted a human-AI collaboration approach in a three-layer analysis to examine group interactions in response to cognitive and emotional regulation triggering events. Two-level theoretical lenses -- macro-level (regulatory aspects) and micro-level (deliberative interactions) -- were used to analyze 2,125 utterances from video-recorded tasks of ten groups of three Finnish secondary students (N=30). Results showed two types of deliberation patterns for SSRL, namely 1) the Plan and Implementation Approach (PIA) associated with adaptive patterns, and 2) the Trials and Failure Approach (TFA) associated with maladaptive patterns. Our findings revealed that groups often fail to recognize, or are ill-equipped to respond to, emerging regulatory needs. These findings advance SSRL theories and research methodologies by utilizing AI-enhanced LA to offer new insights into group dynamics and regulatory strategies.

This study investigated the dynamics of complex interactions within inquiry-based (IB) discussions by visualizing patterns using social network analysis. Researchers explored network measures when learners participated in inquiry-based discussions with Practical Inquiry Model (PIM) and non-PIM questions while playing the weekly moderator's role. The findings revealed that at the group level, purposefully designed IB discussions can create fairly cohesive, evenly distributed, and proportionally consistent networks. Discussions using both PIM and non-PIM questions resulted in a moderate level of interaction, as learners followed the specified requirements for the number of responses. While discussions with non-PIM questions appeared more interactive, discussions with PIM questions actually resulted in greater interaction, as evidenced by students exceeding the average number of required responses per student. The findings revealed that despite similar discussion prompts and discussion requirements in both PIM and non-PIM, the flow of information can vary based on how closely learners are connected. At the individual level, the results showed that learners' levels of participation, influence, and network positions fluctuated and shifted in each discussion. In addition, the results did not reveal any impact of the moderator's role on learner participation and interaction in discussions with both PIM and non-PIM questions. This study's findings can help researchers and practitioners design a well-distributed network to enhance learner interaction in inquiry-based discussions with the balance of PIM and non-PIM questions.

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.

The application of network meta-analysis is becoming increasingly widespread, and for a successful implementation, it requires that the direct comparison result and the indirect comparison result should be consistent. Because of this, a proper detection of inconsistency is often a key issue in network meta-analysis as whether the results can be reliably used as a clinical guidance. Among the existing methods for detecting inconsistency, two commonly used models are the design-by-treatment interaction model and the side-splitting models. While the original side-splitting model was initially estimated using a Bayesian approach, in this context, we employ the frequentist approach. In this paper, we review these two types of models comprehensively as well as explore their relationship by treating the data structure of network meta-analysis as missing data and parameterizing the potential complete data for each model. Through both analytical and numerical studies, we verify that the side-splitting models are specific instances of the design-by-treatment interaction model, incorporating additional assumptions or under certain data structure. Moreover, the design-by-treatment interaction model exhibits robust performance across different data structures on inconsistency detection compared to the side-splitting models. Finally, as a practical guidance for inconsistency detection, we recommend utilizing the design-by-treatment interaction model when there is a lack of information about the potential location of inconsistency. By contrast, the side-splitting models can serve as a supplementary method especially when the number of studies in each design is small, enabling a comprehensive assessment of inconsistency from both global and local perspectives.

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.

Educational researchers have pointed to socioemotional dimensions of learning as important in gaining a more nuanced description of student engagement and learning. However, to date, research focused on the analysis of emotions has been narrow in its focus, centering on affect and sentiment analysis in isolation while neglecting how emotions potentially interact with social elements and course objectives in learning environments. In this paper, we present a case study analysis of seven asynchronous online discussions delivered as part of a blended-learning bachelor level course; we demonstrate the utility of a novel analysis workflow and visualization method which we refer to as SSEEN (social sentiment embedded epistemic networks) to uncover insights into the connection between social networks, course content, and detected student sentiment. The findings show that negative sentiment was most often associated with course content, but contrary to insights from prior research, negative sentiment served as a marker of engagement as students connected content to their own personal experiences. By simultaneously considering the social network alongside the sentiment-colored edges, we note that negative sentiment is not consistent within the discourse of particular students or between pairs indicating that sentiment did not appear to be an indicator of peer conflict or breaches in social contracts. The findings demonstrate how the proposed approach (SSEEN) can support educational researchers to gain a more nuanced understanding of the social, emotional, and epistemic dimensions of learning in asynchronous online discussions.

Networked and digital technologies are increasingly being used for learning in formal and informal contexts, and participant engagement occurs primarily via online discussions. In this paper, we describe an ongoing research project that focuses on examining and understanding student engagement in collaborative online discussions within a formal educational context. We use multiple methodologies including social network analysis and discourse analysis to answer our main research questions, which relate to examining students' behavioral and cognitive engagement in online discussion at the individual and group level. To support our discourse analyses, we also designed and implemented a machine learning model that uses the Interactive-Constructive-Active-Passive framework (Chi, 2009) to rapidly categorize student discourse. Data analyses indicate that social network analyses can provide immediate visual clues about individual and group behavioral engagement, and degree centrality data can clearly highlight the direction and valence of engagement. Similarly, discourse analyses can provide useful categorization of student discourse at the individual and group level. We conclude by illustrating how both types of analyses together allow for a nuanced insight into the types of roles that individual students may assume, as well as the ways in which individual and collaborative activity might unfold. Our methodology and results support the need to assume a broader perspective on learning by focusing on process and the integration of multiple methods.

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.

Network meta-analysis (NMA) is an extension of pairwise meta-analysis (PMA) which combines evidence from trials on multiple treatments in connected networks. NMA delivers internally consistent estimates of relative treatment efficacy, needed for rational decision making. Over its first 20 years NMA's use has grown exponentially, with applications in both health technology assessment (HTA), primarily re-imbursement decisions and clinical guideline development, and clinical research publications. This has been a period of transition in meta-analysis, first from its roots in educational and social psychology, where large heterogeneous datasets could be explored to find effect modifiers, to smaller pairwise meta-analyses in clinical medicine on average with less than six studies. This has been followed by narrowly-focused estimation of the effects of specific treatments at specific doses in specific populations in sparse networks, where direct comparisons are unavailable or informed by only one or two studies. NMA is a powerful and well-established technique but, in spite of the exponential increase in applications, doubts about the reliability and validity of NMA persist. Here we outline the continuing controversies, and review some recent developments. We suggest that heterogeneity should be minimized, as it poses a threat to the reliability of NMA which has not been fully appreciated, perhaps because it has not been seen as a problem in PMA. More research is needed on the extent of heterogeneity and inconsistency in datasets used for decision making, on formal methods for making recommendations based on NMA, and on the further development of multi-level network meta-regression.

When studies use different scales to measure continuous outcomes, standardised mean differences (SMD) are required to meta-analyse the data. However, outcomes are often reported as endpoint or change from baseline scores. Combining corresponding SMDs can be problematic and available guidance advises against this practice. We aimed to examine the impact of combining the two types of SMD in meta-analyses of depression severity. We used individual participant data on pharmacological interventions (89 studies, 27,409 participants) and internet-delivered cognitive behavioural therapy (iCBT; 61 studies, 13,687 participants) for depression to compare endpoint and change from baseline SMDs at the study level. Next, we performed pairwise (PWMA) and network meta-analyses (NMA) using endpoint SMDs, change from baseline SMDs, or a mixture of the two. Study-specific SMDs calculated from endpoint and change from baseline data were largely similar, although for iCBT interventions 25% of the studies at 3 months were associated with important differences between study-specific SMDs (median 0.01, IQR -0.10, 0.13) especially in smaller trials with baseline imbalances. However, when pooled, the differences between endpoint and change SMDs were negligible. Pooling only the more favourable of the two SMDs did not materially affect meta-analyses, resulting in differences of pooled SMDs up to 0.05 and 0.13 in the pharmacological and iCBT datasets, respectively. Our findings have implications for meta-analyses in depression, where we showed that the choice between endpoint and change scores for estimating SMDs had immaterial impact on summary meta-analytic estimates. Future studies should replicate and extend our analyses to fields other than depression.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.