Search

Various areas of educational research are interested in the transitions between different states--or events--in sequential data, with the goal of understanding the significance of these transitions; one notable example is affect dynamics, which aims to identify important transitions between affective states. Unfortunately, several works have uncovered issues with the metrics and procedures commonly used to analyze these transitions. As such, our goal in this work is to address these issues by outlining an alternative procedure that is based on the use of marginal models. We begin by looking at the specific mechanisms responsible for a recently discovered statistical bias with several metrics used in sequential data analysis. After giving a theoretical explanation for the issue, we show that the marginal model procedure appears to adjust for this bias. Next, a related problem is that the common practice of removing transitions to repeated states has been shown to have unintended side-effects--to account for this issue, we develop a method for extending the marginal model procedure to this specific type of analysis. Finally, in a recent study evaluating the problem of multiple comparisons and sequential data analysis, the Benjamini-Hochberg (BH) procedure, a commonly used approach to control for false discoveries, did not perform as expected. By applying a technique from the biostatistics and epidemiology literature, we show that the performance of the BH procedure, when used with the marginal model method, can be brought back to its expected level. In all of our analyses, we evaluate the proposed method by both running simulations and using actual student data. The results indicate that the marginal model procedure seemingly compensates for the problems observed with other transition metrics, thus resulting in more accurate estimates of the importance of transitions between states.

Background/purpose: School-based management (SBM) has gained international attention for the promotion of student learning and educational development. Senegal is one of the initiatives of French speaking African countries that adopted the move towards promoting SBM at the school level, but lacks substantial evidence of its effectiveness in enhancing educational quality. In addition, there has yet to be a clear consensus on the heterogeneity of SBM on learning achievement. This study aimed to examine the influences of SBM characteristics on learning achievement and investigate the relationship between them and students' gender status in Senegal primary education. Materials/methods: The study adapted the framework on what matters most in marginalized autonomy and school accountability for better education results. It linked autonomy and accountability, requiring parents and the community to participate in and trust the school. The study applied a multilevel regression model using the cross-sectional dataset of Program d'Analyse des Systemes Educatifsde la Confemen (PASEC) 2019 learning achievements survey. Results: The study revealed that parental and community participation has a positive and statistically significant association with reading scores regardless of student gender. On the other hand, a positive correlation was found regarding information sharing with parents, and a negative correlation concerning school autonomy, but with no significant relationship to reading scores. Conclusion: Participation from parents and the community can improve student learning and mitigate learning gaps in gender status. The findings of this study are expected to provide insight into narrowing the gap in student learning, especially for neighboring countries facing similar issues to Senegal.

Background/purpose: Awareness of the mathematical skills and knowledge children possess in their early years is widely accepted. This includes various common positive aspects, not only for educators but also for researchers and policymakers. This study presents a systematic review conducted to meticulously identify empirical studies published in the Scopus-Index Journal database about the mathematical skills children aged 3 to 8 years old have mastered. Materials/methods: This review followed the PRISMA guidelines and the research database comprised of Scopus-indexed journals. The technique followed used "keywords" and Boolean operators. The screening processes included reviewing abstracts, scanning complete texts of published articles, and rejecting those not meeting preset inclusion criteria. Moreover, systematic reviews, meta-analyses, and papers not written in English were also excluded. Of the 801 studies initially identified, a total of 15 empirical studies were included in the systematic review. Results: Children master various math skills from a very young age, mainly in "numbers and operations", but face difficulties in skills related to "algebra" as well as "geometry and measurement". Additionally, several preschoolers' characteristics help to explain the acquisition of these skills, with "age" being the primary factor. Researchers use various research instruments and mainly conduct individual semi-structured interviews. Children's geometry skills and knowledge appear to have been studied to a small extent. The areas of "measurement" and "data analysis and probability" were found to be under active investigation. Conclusion: It is worth noting that not only does a noticeable research gap exist for the math domains of "measurement," "geometry," and "data analysis and probability". Factors that seem to affect young children's math skills, such as "gender", "parents' educational level", and "attendance to a preparatory preschool" need further investigation. The implications of the current study's results extend beyond academia, providing valuable insights that educators and policymakers can leverage to enhance the quality of mathematics education during the early years period.

This study employed a critical inquiry paradigm to explore the significance of equity and social justice in teaching qualitative methodologies and methods to graduate students. Graduate students of multiple minoritized identities and a faculty member conducted a two-year inquiry into the research apprenticeship experience, including the stages of student-led conceptualization, data collection, analysis, international conference presentation, and publication. The study investigated the experiences of the faculty and students as well as the responsibilities of the institution against the backdrop of historical and contemporary pandemics. Consequently, we problematized hidden curriculum and unconscious assumptions to suggest research course design sequencing improvements. The findings emphasize the impact of these experiences on qualitative research teaching and learning practices and institutional responsibility to graduate student research apprenticeships. The Graduate Research Apprenticeship Nested Design Pathway is presented as a tool that institutions worldwide can adapt and implement to serve the complex needs of their students.

Programmatic advertising has come to dominate the landscape of digital media planning. To prepare ad majors for their future careers in the industry, it is essential to teach students programmatic buying and provide them with hands-on experience. In this article, the authors present their approach of integrating teaching programmatic buying into a media planning course. A key feature of their educational package is a custom digital media buying tool developed by the authors. They hope this article will benefit other college educators in this field and spark further discussions on best teaching practices of programmatic buying and digital media planning.

Obtaining reliable and timely estimates of migration flows is critical for advancing the migration theory and guiding policy decisions, but it remains a challenge. Digital data provide granular information on time and space, but do not draw from representative samples of the population, leading to biased estimates. We propose a method for combining digital data and official statistics by using the official statistics to model the spatial and temporal dependence structure of the biases of digital data. We use simulations to demonstrate the validity of the model, then empirically illustrate our approach by combining geo-located Twitter data with data from the American Community Survey (ACS) to estimate state-level out-migration probabilities in the United States. We show that our model, which combines unbiased and biased data, produces predictions that are more accurate than predictions based solely on unbiased data. Our approach demonstrates how digital data can be used to complement, rather than replace, official statistics.

Mixed-mode surveys are popular as they can save costs and maintain (or improve) response rates relative to single-mode surveys. Nevertheless, it is not yet clear how design decisions like survey mode or questionnaire length impact measurement quality. In this study, we compare measurement quality in an experiment of three distinct survey designs implemented in the German sample of the European Values Study: a single-mode face-to-face design, a mixed-mode mail/web design, and a shorter (matrix) questionnaire in the mixed-mode design. We compare measurement quality in different ways, including differences in distributions across several data quality indicators as well as equivalence testing over 140 items in 25 attitudinal scales. We find similar data quality across the survey designs, although the mixed-mode survey shows more item nonresponse compared to the single-mode survey. Using equivalence testing we find that most scales achieve metric equivalence and, to a lesser extent, scalar equivalence across the designs.

Not knowing the population size is a common problem in data-limited contexts. Drawing on work in Sierra Leone, this short take outlines a four-step solution to this problem: (1) estimate the population size using expert interviews; (2) verify estimates using interviews with participants sampled; (3) triangulate using secondary data; and (4) reconfirm using focus group discussions.

The purpose of this study is to derive a correlation between the technological thinking disposition and the computational thinking ability of gifted students in Korea. The correlation between each element was analyzed by looking at the sub-elements of computational thinking according to the components of technological thinking disposition. The experiment was conducted from September 2019 to February 2021 with 217 students of I Gifted School in Incheon, South Korea. The collected data were analyzed with Pearson's correlation coefficient using the statistical program R using Google COLAB. A summary of the study results is as follows. First, regarding the correlation between technological thinking ability, among the 6 components of technological thinking disposition, technological creativity and expression disposition and technological manipulation disposition show the highest correlation at 0.851. This shows that students who have an excellent ability to implement algorithms with new ideas or express them in various other attempts when implementing programs for gifted students also tend to enjoy program coding or have a tendency to like coding. Second, concerning the correlation between the technological thinking disposition and the sub-factors of computational thinking, some elements showed negative correlations and some had almost no correlation index. Students with high technological curiosity, however, tended to show a 0.287 in the parallelism factor compared to other factors. This showed a generally high trend. It can be said that students who want to know the functions, uses, forms, and characteristics of functions while implementing programs tend to have a better ability to divide large tasks into smaller tasks and process them simultaneously compared with other sub-elements of computational thinking. Third, regarding the correlation between computational thinking skills, the correlation between data analysis and pattern recognition was the highest at 0.637. This indicates that students who have an excellent ability to analyze a given coding problem also can find rules in data, showing that students at gifted schools in Korea tend to enjoy problem-solving.

We present data from the Schools and Staffing Survey and the National Teacher and Principal Survey to document the prevalence of instructional coaching programs (ICPs) and consider how ICPs are distributed by school level, urbanicity, new teachers in a school, student enrollment, school poverty levels, student achievement levels, and state. We show that ICPs are most common in elementary schools, schools located in cities, schools with larger proportions of new teachers, larger schools, schools enrolling larger fractions of economically disadvantaged students, and schools with lower student achievement levels. Additionally, more affluent and higher achieving schools experienced the sharpest increase in ICPs over time.

This investigation examines the efficacy of multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) over fixed-effects models when performing intersectional studies. The research questions are as follows: (1) What are typical strata representation rates and outcomes on physics research-based assessments? (2) To what extent do MAIHDA models create more accurate predicted strata outcomes than fixed-effects models? and (3) To what extent do MAIHDA models allow the modeling of smaller strata sample sizes? We simulated 3,000 data sets based on real-world data from 5,955 students on the LASSO platform. We found that MAIHDA created more accurate and precise predictions than fixed-effects models. We also found that using MAIHDA could allow researchers to disaggregate their data further, creating smaller group sample sizes while maintaining more accurate findings than fixed-effects models. We recommend using MAIHDA over fixed-effects models for intersectional investigations.

Balancing speed and quality during crises pose challenges for ensuring the value and utility of data in social science research. The COVID-19 pandemic in particular underscores the need for high-quality data and rapid dissemination. Given the importance of behavioural measures and compliance with measures to contain the pandemic, social science research has played a key role in policymaking during this global crisis. This study addresses two key research questions: How FAIR (findable, accessible, interoperable and reusable) are social science data on the COVID-19 pandemic? Which study features are related to the level of FAIRness scores of datasets? We assess the FAIRness of n = 1131 articles, retrieved through a keyword search in the Web of Science database, employing both automated and manual coding methods. Our study inclusion criteria encompass empirical studies on the COVID-19 pandemic published between 2019 and 2023 with a social science focus and explicit reference to the underlying dataset(s). Our analysis of n = 45 datasets reveals substantial differences in FAIRness for different types of research on the COVID-19 pandemic. The overall FAIRness of data is acceptable, although particularly Reusability scores fall short, in both the manual and the automatic assessment. Further, articles explicitly linked to the Social Science concept in the OpenAlex database exhibit a higher mean overall FAIRness value. Based on these results, we derive recommendations for balancing ethical obligations and the potential tradeoff between speed and data (sharing) quality in social-scientific crisis research.

We contribute to research on social inequalities in educational attainment by examining the role of institutional contexts for students' study abroad (SA) intent and participation. To do so, we extend the individual-level rational choice model predicting SA intent and participation depending on students' socioeconomic status (SES) into a multi-level model emphasizing the importance of context effects. We test our model based on unique micro-level student data, which we supplement with context data. Examining 18,510 students nested in 69 universities, we provide the first in-depth multi-level analyses of SA intent and participation of students from Japan. In line with findings from many Western countries, our results show that higher-SES students are more likely to (intend to) study abroad. Regarding the role of institutional contexts, we find that programs designed to improve SA opportunity structures hardly affect students' SA intent but significantly positively affect SA participation above and beyond other university-level and student-level characteristics. Importantly, both lower- and higher-SES students benefit from favorable SA opportunity structures. These findings suggest that Japan's recent push toward internationalization of higher education has created relevant SA opportunities for students from different social backgrounds. Still, higher-SES students are currently overrepresented among those studying abroad because they are more likely to select into universities offering favorable SA opportunity structures. Our analysis calls for more research combining individual-level with contextual-level theories and data to better understand the conditions shaping SES-specific SA intent and participation.

Adaptive science dispositions (epistemology, enjoyment, interest, and self-efficacy) have been shown to be related to schools' science climate and science literacy. School stratification may promote positive linkages among science dispositions, science literacy, and science climate. To examine the association between school stratification and such linkages, we undertook a comparative study of early secondary schools in Ireland (less stratified -- modest tracking, modest grade retention) and Flanders (Dutch-speaking Belgian region; more stratified -- multiple tracks, extensive grade retention). Using Program for International Student Assessment (PISA 2015) data and multilevel modeling methods, we included 5419 (Ireland) and 5675 (Flanders) students nested within 157 and 171 schools, respectively. Student- and school-level variables with potential associations with our outcome measures (science dispositions for the first research question (RQ1); science literacy for RQ2) were included in baseline models. Subsequent models estimated "over-and-above" associations of science climate with science dispositions (RQ1) and of both science dispositions and climate with science literacy (RQ2). Science dispositions, as outcomes, were associated with disciplinary climate and teaching support in science classroom, particularly in Ireland. All four science dispositions (as independent variables) were associated with science literacy (both separately and in concert) for both Irish and Flemish cases. Epistemology was most strongly associated with science literacy. A less stratified school system may grant teachers more opportunities to craft positive science learning environments. A more stratified school system may amplify existing divergences in science (dispositions and literacy).

The pervasiveness of racism in the U.S. and its negative relations with key development outcomes has led researchers to uncover mediators, of which this article argues efficacy should be considered. Self-efficacy, one's belief in their capability to accomplish a task or goal, can be measured in multiple domains of functioning and contexts to predict behaviour. The current study examines possible specificity in the relation between discrimination (general discrimination and school discrimination) and efficacy (academic efficacy and efficacy to combat discrimination), whether resilience moderates these associations, and if these processes differ by gender. A total of 879 Black youth (47% female; mean age = 12, SD = 0.58) were included in the analyses. Findings suggest that school discrimination experiences are differentially associated with domains of efficacy. Resilience was associated with increased efficacy, but no significant moderation effects were found.

We present an interactive spreadsheet that supports teaching essential concepts in classification using the logistic regression (LoR) model for binary classification. The interactive spreadsheet demonstrates the capabilities of LoR by integrating computation with visualization. Students will reinforce concepts like probabilities, maximum likelihood estimation (MLE), and the use of likelihoods to optimize parameters for the LoR. We then discuss using LoR for classifications while adjusting its decision boundary (DB), demonstrating how to convert assigned likelihoods into classification using the DB; impact classification outcome by varying DBs; designate predictions as true positive, true negative, false positive, or false negative; and determine the classification accuracy. We use a variety of performance measures, including sensitivity, specificity, precision, negative predictive value, F[subscript 1] and F[subscript 2] scores, the receiver operating characteristics curve, and lift/decile charts. These measures are dynamically adjusted when the DB changes. We also reiterate the usage of these measures in the context of crossvalidation and imbalanced data sets. We provide a case study that implements LoR and an option for teaching the details behind MLE. We discuss the pedagogical aspects of this spreadsheet based on a survey of the 2022 student cohort in the Master of Management Analytics Program at the Rotman School of Management.

Recently, the rise of generative AI tools such as "ChatGPT" have prompted deep and wide considerations about teaching and learning, student success, research and development, and the use of data for informed institutional decision making. In this volume, authors discuss specific concepts, considerations for use, and some specific tools and applications of advanced data analytics and artificial intelligence that are gaining prominence in higher education. Although these tools and our perceptions about the tools will continue to evolve, we believe that aspects of advanced data analytics and artificial intelligence will remain in our midst in the future. We believe that members of our community will become better educators and higher education managers as we engage deeply with these tools, stay vigilant about the risks and limitations, and establish policies that ensure student and institution success.

Background: Like other professional training programs, social work pedagogy has long recognized the value of experiential learning for professional development. Despite social work's rich experiential learning literature involving field education, direct practice courses, and program evaluation, there is a dearth of literature examining how to make learning in the policy classroom experiential, particularly for large class sizes. Purpose: We asked, "How might electoral candidate debates provide experiential learning opportunities for large classes?" Approach: The authors organized municipal and federal election candidate debates attended in-person and online by over 300 undergraduate students in a social work policy class at a Canadian university. Integrating our experiences as instructors/organizers and a teaching assistant, within a social constructivist framework, we used Kolb's experiential learning theory, and critiques thereof, to analyze reflective assignments from 73 students. Results and Conclusions: Candidate debates, when facilitated appropriately, can encourage students in large courses to work through the stages of experiential learning and consider related concepts and possible links among social justice course content and social policy, social work practice, and political engagement. Implications: The paper contributes to a broader understanding of the opportunities and constraints associated with employing experiential learning in the large social work classroom and beyond.

Despite the growing interest in using multimodal data to analyse students' actions in Computers-Supported Collaborative Learning (CSCL) settings, studying teacher's orchestration load in such settings remains overlooked. The notion of classroom orchestration, and orchestration load, offer a lens to study the implications of increasingly complex technology-supported learning environments on teacher performance. A combination of multimodal data may aid in understanding teachers' orchestration actions and, as a result, gain insights regarding the orchestration load teachers perceive in scripted CSCL situations. Studying teacher orchestration load in CSCL helps understand the workload teachers experience while facilitating student collaboration and assists in informing design decisions for teacher supporting tools. In this paper, we collect and analyse data from different modalities (i.e. electrodermal activity, observation notes, log data, dashboard screen recordings and responses to self-reported questionnaires) to study teachers' orchestration load in scripted CSCL. A tool called PyramidApp was used to deploy CSCL activities and a teacher-facing dashboard was used to facilitate teachers in managing collaboration in real time. The findings of the study show the potential of multimodal data analysis in investigating and estimating the orchestration load experienced by teachers in scripted CSCL activities. Study findings further demonstrate factors emerging from multimodal data such as task type, activity duration, and number of students influenced teachers' orchestration load.

Background: Parents are often involved in their child's homework with the goal of improving their child's academic achievement. However, mixed findings were observed for the role of parental involvement in homework in shaping students' learning outcomes. Aims: The present study examined whether and how the effect of parental involvement in homework on students' performance in science and math varies across sociocultural contexts by considering the degrees of societal collectivism-individualism. Methods: The present study used a large-scale dataset, consisting of participants from 43 countries/regions, to test whether societal collectivism-individualism would moderate the relationship between three types of parental involvement (asking, helping and checking) in homework and elementary school students' academic performance in math and science. Results: The multi-level analyses revealed diverse effects of different parental involvement behaviours for homework. Helping and checking behaviours were generally negatively associated with elementary school students' academic achievement, but asking behaviours were positively associated with their academic achievement. Importantly, the positive effect of asking behaviours was stronger, while the negative effect of helping behaviours was weaker in collectivistic societies than in individualistic societies. Conclusions: The present study highlights the importance of considering the multifaceted nature of parental homework involvement and the sociocultural contexts that vary in collectivism-individualism when evaluating the role of parents in elementary school students' learning in math and science.

Medical simulations allow trainees to work within teams to develop their self-regulated learning (SRL) and socially-shared regulated learning (SSRL) skills (Bransen et al., 2022). Both skillsets help to better prepare medical trainees for the multifaceted challenges inherent in clinical practice. SRL skills are imperative in empowering learners to optimize their performance and become autonomous guiders of their own learning (Jarvela & Hadwin, 2013), while SSRL skills are needed to ensure that teams can work collectively to regulate their behaviors and to regulate their own learning to make decisions (Hadwin & Oshige, 2011). Questions remain about not only how medical trainees' behaviors, regulation strategies, and physiological responses vary while they participate in a high-fidelity medical simulation, but how additional data channels to measure human response can provide indicators of teams' regulation strategies. Using a mixed-methods convergence design incorporating multimodal data (Azevedo & Gaševic, 2019), including behavioral, SRL and SSRL codes, and electrodermal activity, researchers studied twenty-nine (N = 29) 1st to 3rd year medical residents as they engaged in high-fidelity simulation scenarios. Results suggest that the mean-level of psychophysiological activation increase as simulations progress, in conjunction with an increase in team-regulated learning strategies to manage the effective provision of patient care from initial contact through to the delivery of critical procedures. These results provide valuable insights into the advancement of a team regulation-based framework within a high-fidelity medical simulation environment, leveraging multimodal data to reach an understanding of medical trainees' adoption of team-based approaches to team-regulation during simulation scenarios.

The empirical study investigates what log files and process mining can contribute to promoting successful learning. We want to show how monitoring and evaluation of learning processes can be implemented in the educational life by analyzing log files and navigation behavior. Thus, we questioned to what extent log file analyses and process mining can predict learning outcomes. This work aims to provide support for learners and instructors regarding efficient learning with computer-based learning environments (CBLEs). We evaluated log file and questionnaire data from students (N = 58) who used a CBLE for two weeks. Results show a significant learning increase after studying with the CBLE with a very high effect size (p < 0.001, g = 1.71). A cluster analysis revealed two groups with significantly different learning outcomes accompanied by different navigation patterns. The time spent on learning-relevant pages and the interactivity with a CBLE are meaningful indicators for Recall and Transfer performance. Our results show that navigation behaviors indicate both beneficial and detrimental learning processes. Moreover, we could demonstrate that navigation behaviors impact the learning outcome. We present an easy-to-use approach for learners as well as instructors to promote successful learning by tracking the duration spent in a CBLE and the interactivity.

Industrial drug discovery teams encompass scientists from multiple specialties and require participants to communicate effectively across disciplinary boundaries. In this paper, we present an undergraduate or graduate classroom simulation of this environment. Over a series of five workshops, student teams of mixed scientific backgrounds perform five iterations of the chemistry cycle of small molecule drug discovery. Students analyze physicochemical, structural, and (fictional) assay data and use these to design new compounds for testing. Simulated assay results are returned to students who use the information in the design of subsequent compounds. After workshop 5, each team selects a single lead compound, supported by a potential synthetic route, a portfolio of assay data, and logical scientific decision-making. Our exercise provides students with opportunities for hands-on student-responsive data handing, team-building, and technical knowledge acquisition--all within an industrially relevant scientific scenario.

The main goal of this study was to evaluate the impact of an animation and visualization of data structures (AVDS) tool on both perceptions and objective test performance. The study involved a rigorous experiment that assessed the usability, acceptability, and effectiveness of the AVDS tool in solving exercises. A total of 78 participants responded to questionnaires and were exposed to the AVDS tool, after which they completed a performance test, half (39) with the AVDS tool (the experimental group) and half (39) without the tool (the control group). Findings showed that the usability of AVDS was good; the experimental group even perceived AVDS usability as excellent. The results show that perceived usefulness, perceived ease of use, and attitudes toward usage jointly led to positive intention to use the AVDS tool. Furthermore, perceived ease of use was a key factor influencing participants' intention to use AVDS. In addition, the AVDS system improved test results and provided flexibility in use, enhancing learning experience and performance.

Data-based decision-making is a well-established field of research in education. In particular, the potential of data use for addressing heterogeneous learning needs is emphasized. With data collected during the learning process of students, teachers gain insight into the performance, strengths, and weaknesses of their students and are potentially able to adjust their teaching accordingly. Digital media are becoming increasingly important for the use of learning data. Students can use digital learning platforms to work on exercises and receive direct feedback, while teachers gain data on the students' learning processes. Although both data-based decision-making and the use of digital media in schools are already widely studied, there is little evidence on the combination of the two issues. This systematic review aims to answer to what extent the connection between data-based decision-making and the use of digital learning platforms has already been researched in terms of using digital learning data for further instructional design. The analysis of n = 11 studies revealed that the use of data from digital learning platforms for instructional design has so far been researched exploratively. Nevertheless, we gained initial insights into which digital learning platforms teachers use, which data they can obtain from them, and how they further use these data.

The significance of ethics in data science research has attracted considerable attention in recent years. While there is widespread agreement on the importance of teaching ethics within computing contexts, there is no clear method for its implementation and assessment. Studies focusing on methods for integrating ethics into data science courses reveal that students tend to neglect ethical concerns in their data analysis. Based on the data we collected from questionnaires distributed to undergraduate science and engineering students, this paper expands the discussion beyond human concerns and ethics in data science education. As we will show, students tend to neglect the context when attempting to solve data science questions. We argue that gaps in understanding the context relating to the data result in gaps in the analysis as well as in the interpretation of the data. Thus, we propose anthropological thinking as a pedagogy to overcome the context neglect. Placing the spotlight on the "context" promotes a holistic understanding of the phenomenon being analyzed, as it includes important considerations that do not necessarily fit the more commonly used term "human concerns."

Third Mission (TM) drives universities to a purpose beyond their traditional role, especially in local socioeconomic and innovation ecosystems. To investigate the issue, we consider an exploratory and comparative study of two different governance systems of public universities (Southeast Brazil-region 1 and Texas-US-region 2). The comparison took into account data from international universities' rankings performance to address TM and the structure of financial sources to address financial decentralization. The available evidence seems to suggest a positive association between the level of financial decentralization and the capability of the system of HE to deliver TM outputs. Universities under decentralized governance structures, which permit more autonomy to manage their resources, presented better results in most selected TM indicators. These findings reinforce the general belief that the governance arrangements that allow exploring synergies within local business ecosystems are more appropriate for ensuring organizational changes in the direction of entrepreneurial action.

This article argues that special and inclusive education policies in Nepal have been influenced by neoliberal policy reforms. The study employs discourse analysis as a theoretical perspective to analyse the effects of market-based schooling practices on students with disabilities in Nepal. The findings of this study are informed to some extent by the outcomes of the doctoral research project (Thapaliya, 2018). Data was collected through policies and documents. A selection of key education policies and documents between 1990 to 2020 were examined and analysed. One core theme and five sub-themes were identified from the data analysis. Disability as a resource management issue was a main theme. (i) Managing resources; (ii) resource allocation criteria; (iii) professional experts deciding resource funds; (iv) competing for limited resources; and (v) competition and school choice were sub-themes. The available evidence signals that the marketisation model of education does not assist students with disabilities adequately. The findings of this study reveal that the current policy and practice signal changes in government structure rather than working to fulfil these commitments in the everyday practice of students with disabilities. The limitations of the study and recommendations of this research are also discussed.

Increasing numbers of students require internet access to pursue their undergraduate degrees, yet broadband access remains inequitable across student populations. Furthermore, surveys that currently show differences in access by student demographics or location typically do so at high levels of aggregation, thereby obscuring important variation between subpopulations within larger groups. Through the dual lenses of quantitative intersectionality and critical race spatial analysis alongside a QuantCrit approach, we use Bayesian multilevel regression and Census microdata to model variation in broadband access among undergraduate populations at deeper interactions of identity. We find substantive heterogeneity in student broadband access by gender, race, and place, including between typically aggregated subpopulations. Our findings speak to inequities in students' geographies of opportunity and suggest a range of policy prescriptions at both the institutional and federal level.

The last decade witnessed a spectacular rise in the volume of available textual data. With this new abundance came the question of how to analyze it. In the social sciences, scholars mostly resorted to two well-established approaches, human annotation on sampled data on the one hand (either performed by the researcher, or outsourced to microworkers), and quantitative methods on the other. Each approach has its own merits -- a potentially very fine-grained analysis for the former, a very scalable one for the latter -- but the combination of these two properties has not yielded highly accurate results so far. Leveraging recent advances in sequential transfer learning, we demonstrate via an experiment that an expert can train a precise, efficient automatic classifier in a very limited amount of time. We also show that, under certain conditions, expert-trained models produce better annotations than humans themselves. We demonstrate these points using a classic research question in the sociology of journalism, the rise of a "horse race" coverage of politics. We conclude that recent advances in transfer learning help us augment ourselves when analyzing unstructured data.

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.

The risk of multitasking is high in online surveys. However, knowledge on the effects of multitasking on answer quality is sparse and based on suboptimal approaches. Research reports inconclusive results concerning the consequences of multitasking on task performance. However, studies suggest that especially sequential-multitasking activities are expected to be critical. Therefore, this study focusses on sequential-on-device-multitasking activities (SODM) and its consequences for data quality. Based on probability-based data, this study aims to reveal the prevalence of SODM based on the javascript function OnBlur, to reflect the its determinants and to examine the consequences for data quality. Results show that SODM was detected for 25% of all respondents and that respondent attributes and the device used to answer the survey are related to SODM. Moreover, it becomes apparent that SODM is significantly correlated to data quality measures. Therefore, I propose SODM behavior as a new instrument for researching suboptimal response behavior.

This paper outlines the journey of one Scottish primary school in taking nurturing approaches forward over a 5-year period, with the aims of improving staff understanding of their role and feeling skilled in using nurturing approaches and improving pupil's health and well-being and confidence. Using an action research methodology and the local authority How Nurturing is our school framework (Fife Council Educational Psychology Service, 2022), an audit of current practice was undertaken, and priorities identified at universal, additional and intensive levels were put in place. Evaluation data were gathered in a variety of formats from pupils, staff, and parents. Analysis of the evaluation data indicates that pupils had improved understanding of their emotions and how to manage these, staff were experiencing improved relationships and ethos across the school and parents were happy with the support their children were being offered via the nurturing approaches. There was visible improvement in the physical environments, staff thrived on the personal coaching offered and more nurturing practice could be seen and heard throughout the school. It is concluded that the project has made a sustained impact on the health and well-being of pupils and is now embedded in the school culture.

Parent rating scales are used at increasing rates across disciplines to track child development and determine diagnoses/needs. This study explored relationships among parental stress and the validity of parents' ratings of their child's behaviors. Participants include children who were referred for assessment of behavioral, social-emotional, and development difficulties at a major metropolitan hospital in the United States. Parents completed rating scales regarding their own stress (PSI-4-SF) and child behaviors (BASC-3). Present findings indicate caution is warranted when interpreting parent report data of their child's behavior in the context of significant parenting stress. Relying exclusively on parent-report information to assess young children in clinical setting appears contraindicated. Family/situational factors beyond the individual child are important to assess.

This research considers Pell grant restoration for incarcerated people for the field of higher education in prison. Using the original data, we outline the limits of Pell funding in the prison context by surfacing persistent funding challenges that the Pell grant alone cannot address and may exacerbate. By providing the necessary investments to support higher education in prison, Pell restoration could be an effective lever for advancing racial and economic justice. Using a lens of racial and socioeconomic justice, we identify gaps in prison program costs and argue that the long-term effects of Pell reinstatement will depend on whether the expansion is accompanied by investments in a range of institutional infrastructures and resources. Concluding are implications and recommendations to adequately and responsibly support the growth and infrastructure of higher education in prison programs.

Background: Data literacy (DL) is vital for teachers, as it enables them to build on data and improve teaching and learning. Therefore, developing DL among pre-service teachers is critical. Objectives: The purpose of this study is threefold: to evaluate whether a feedback visualisation of peer assessment-based teaching approach (FVPA-based teaching approach) can (1) promote pre-service teachers' DL; (2) enhance their learning motivation; and (3) improve their cognitive load. Methods: The research was conducted based on a pre-test-post-test control group quasi-experimental design. With 20 participants in the experimental group and 21 in the control group, a total of 41 pre-service teachers were included in the study. The pre-service teachers in the experimental group adopted the FVPA-based teaching approach, and those in the control group adopted the traditional peer assessment-based learning approach. Results and Conclusions: The experimental group participants outperformed the control group participants in DL, learning motivation, and cognitive load. FVPA was conducive to helping pre-service teachers critically interpret data, understand their teaching and learning issues, and improve self-reflection. The findings indicate a reciprocal relationship between learning motivation and DL; improving the learning motivation of pre-service teachers could promote their DL. Implications: This study contributes to current knowledge by providing empirical evidence on the benefits of an FVPA-based teaching approach in improving pre-service teachers' DL, motivation, and cognitive load. The study findings, limitations, and prospects for future research are discussed.

Background: Peer assessment has played an important role in large-scale online learning, as it helps promote the effectiveness of learners' online learning. However, with the emergence of numerical grades and textual feedback generated by peers, it is necessary to detect the reliability of the large amount of peer assessment data, and then develop an effective automated grading model to analyse the data and predict learners' learning results. Objectives: The present study aimed to propose an automated grading model with reliability detection. Methods: A total of 109,327 instances of peer assessment from a large-scale teacher online learning program were tested in the experiments. The reliability detection approach included three steps: recurrent convolutional neural networks (RCNN) was used to detect grade consistency, bidirectional encoder representations from transformers (BERT) was used to detect text originality, and long short-term memory (LSTM) was used to detect grade-text consistency. Furthermore, the automated grading was designed with the BERT-RCNN model. Results and Conclusions: The effectiveness of the automated grading model with reliability detection was shown. For reliability detection, RCNN performed best in detecting grade consistency with an accuracy rate of 0.889, BERT performed best in detecting text originality with an improvement of 4.47% compared to the benchmark model, and LSTM performed best with an accuracy rate of 0.883. Moreover, the automated grading model with reliability detection achieved good performance, with an accuracy rate of 0.89. Compared to the absence of reliability detection, it increased by 12.1%. Implications: The results strongly suggest that the automated grading model with reliability detection for large-scale peer assessment is effective, with the following implications: (1) The introduction of reliability detection is necessary to help filter out low reliability data in peer assessment, thus promoting effective automated grading results. (2) This solution could assist assessors in adjusting the exclusion threshold of peer assessment reliability, providing a controllable automated grading tool to reducing manual workload with high quality. (3) This solution could shift educational institutions from labour-intensive grading procedures to a more efficient educational assessment pattern, allowing for more investment in supporting instructors and learners to improve the quality of peer feedback.

Teachers form expectations that can influence their students' performance, and there are a variety of ways these expectations can be communicated. In the current study, we tested a novel method for communicating expectations via examples of student work--examples that contain basic, entry-level work and communicate low, but manageable expectations or examples that contain complex, advanced-level work and communicate high and challenging expectations. Across three semesters, 91 college students in a data management course completed a class assignment that involved exploratory coding activities. Prior to the assignment, students were randomly assigned to view basic or advanced examples of student work. Students assigned to the advanced-examples condition reported higher perceptions of task difficulty and frustration, but they also exhibited higher levels of performance in terms of the complexity of their own work. Results suggest that setting challenging expectations can create a desirable difficulty that ultimately benefits students' performance in an authentic learning environment.

As computational methods are widely used in science disciplines, integrating computational thinking (CT) into classroom materials can create authentic science learning experiences for students. In this study, we classroom-tested a CT-integrated geoscience curriculum module designed for secondary students. The module consisted of three inquiry investigations that were age-appropriately translated from the computational practices of volcanologists who study tephra hazards and risks. We developed a domain-specific, block-code-based computational modeling environment where students carried out 1 of the 3 science practices in each inquiry investigation: experimentation, data visualization and interpretation, and modeling. We examined (1) student learning outcomes using pre- and post-tests and (2) student reflections on the computationally supported science practices surveyed at the end of each inquiry investigation during the module. Results indicate that students made statistically significant gains in science content as well as in computationally supported experimentation, data visualization and interpretation, and modeling practices. Over 80% of students identified different ways in which block coding supported their practices during inquiry investigations. Based on these findings, we discuss implications for the future development of computationally supported inquiry investigations in science.

Extensive research into wilderness therapy has not explored who benefits the most and who does not thrive in these programs. The present study examined demographic, clinical, and familial characteristics that distinguished adolescents who improve most in wilderness therapy programs from those who deteriorate. Using data collected by the National Association of Therapeutic Schools and Programs Practice Research Network, the study sample consisted of 5639 adolescents attending wilderness therapy programs which collected and contributed data to the Practice Research Network between 2017 and 2022. Measures included the Youth Outcome Questionnaire-Self Report and the McMaster Family Assessment Device, as well as demographic, familial, and clinical data collected by program staff. Using binary and univariate logistic regression, the study found individual and familial factors that predicted membership in the top 10% of adolescent participants in terms of mental health improvement from pre-to-post wilderness therapy and those factors which predicted membership in the bottom 10% in terms of poorer mental health from pre-to-post program. Considering the intensity, length, and financial resources associated with wilderness therapy program participation, these findings have important implications for wilderness therapy program staff professional development, communication of expectations to adolescents' parents/caregivers, and program admission decisions.

Breaking through the company's own green knowledge barriers through school-enterprise cooperation has become an important means for companies to expand market competitive advantages and environmental governance. This article uses China's non-financial listed company data from 2010 to 2021 to study the causal effect of school-enterprise cooperation on corporate green innovation. The results passed a series of robustness tests and confirmed that school enterprise cooperation can improve the level of green innovation of enterprises. This paper also uses the instrumental variable method to mitigate the causal relationship between the two. The results show that school-enterprise cooperation can still significantly improve corporate green innovation. The results of the mechanism test confirm that school-enterprise cooperation will promote corporate green innovation by reducing corporate green innovation risks and easing corporate financing constraints. At the same time, the heterogeneity analysis results show that school-enterprise cooperation has a better effect on improving green innovation for enterprises in heavily polluting industries, enterprises in years with low economic policy uncertainty, and enterprises with a high degree of government-enterprise connection. The findings of this article help to understand the intrinsic mechanism between school-enterprise cooperation and corporate green innovation, and confirm that school-enterprise cooperation can improve the climate and environmental innovation system, which has important practical significance for the construction of the industry-university-research system.

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.

English language learning students in China often feel challenged to learn English due to lack of motivation and confidence, pronunciation and grammar difference, lack of practice and people to communicate with etc., which affects students mental health. Adopting Big data and AI will help in overcoming these limitations as it provides personalized guidance to the students in all aspects. The paper has established an automatic early warning system to monitor the students' psychological state at any time period. The data is collected from 650 respondents from four different public universities in China. The data analysis has been done with the help of powerful SPSS software and the methodology which we used for determining sample size is, Random sampling. The study involves a qualitative assessment to identify participants' characteristics and categorize them to appropriate clusters. The findings of the research showed that the most obvious differences in mental health between students who used automatic warning and those who did not use automatic warning were: depression, anxiety, hostility, terror, and psychosis. The proportion of students who use early warning was less than those who did not use early warning. Research contributes to policymakers to emphasize the importance of incorporating mental health support and resources into educational policies. The novelty of the study seeks to provide a deeper understanding of how AI and big data can optimize mental health education for English students. With the support of AI and Big data there is a constant monitoring and improvement effect on English education students' mental health.