Search

Introduction: Paper folding and punched hole tests are used to measure spatial abilities in humans. These abilities are relevant since they are associated with success in STEM (Science, Technology, Engineering, and Mathematics). This study addresses the challenge of teaching spatial reasoning skills using an educational videogame, the Paper Folding Reasoning Game. Methods: The Paper Folding Reasoning Game is an interactive game which presents activities intended to help users train and understand how to fold a paper to get a specific shape (Part I) and the consequence of punching a hole on a folded paper (Part II). This educational videogame can automatically generate paper-folding-and-punched-hole questions with varying degrees of difficulty depending on the number of folds and holes made, thus producing additional levels for training due to its embedded reasoning mechanisms (Part III). Results: This manuscript presents the results of analyzing the gameplay data gathered by the Paper Folding Reasoning Game in its three parts. For Parts I and II, the data provided by 225 anonymous unique players are analyzed. For Part III (Mastermode), the data obtained from 894 gameplays by 311 anonymous unique players are analyzed. In our analysis, we found out a significant difference in performance regarding the players who trained (i.e., played Parts I and II) before playing the Mastermode (Part III) vs. the group of players who did not train. We also found a significant difference in players' performance who used the visual help (i.e., re-watch the animated sequence of paper folds) vs. the group of players who did not use it, confirming the effectiveness of the Paper Folding Reasoning Game to train paper-folding-and-punched-hole reasoning skills. Statistically significant gender differences in performance were also found. [ABSTRACT FROM AUTHOR]

The apps for drawing are present in our children's life. Nevertheless, little is known about the impact of mobile technology on the freehand drawing educational activity. There are few works which are contextualised within short periods of time, with teachers who are not theirs and, in some cases, outside the children's classroom. In this paper, we are focussed on the use of technology on freehand drawing activity. Thus, we have compared the graphics produced by 4- and 5-year-old children with paper&crayon in comparison with those with tablet-finger. Children made the drawings during a planned free-drawing activity, in their ordinary classrooms, with their teachers and during five sessions. Assessment of drawings has evidenced tablet feasibility for making graphics. Nevertheless, with the passing of time, quality of graphics (tablet-finger vs paper&crayons), are nearly matched, demonstrating the low impact level technology has on this activity. In addition, if drawings are analysed specifically according to ages, results have shown that both groups have to develop adaptation strategies of visual perceptual skills and fine motor skills for the touch screen in order to obtain the same quality in the drawings made on both support types. [ABSTRACT FROM AUTHOR]

Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games' content, leading them to develop a critical stance towards the game data model and representations.

Artificial intelligence (AI) is now affecting all aspects of our social lives. Without always knowing it, we interact daily with intelligent systems. They serve us invisibly. At least that is the goal we assign to them: to make our lives better, task by task. Artificial intelligence has the potential to make biology education more engaging, personalized, and effective by providing students with interactive simulations, personalized learning experiences, and other tools that help them understand complex biological concepts. In this paper, we discuss the integration of AI into the virtual classroom, which significantly enhances student learning experiences in various ways. The study shows that an effective integration of technology into the virtual classroom requires a thoughtful approach that aligns with educational goals and the specific needs of students. In fact, interactive simulations can help make biology more engaging and memorable for students. Besides, personalized learning AI algorithms can help biology students receive a more tailored and effective learning experience, helping them to better understand the course material and develop a deeper appreciation for the natural world. In this work, we will discuss the use of AI to enhance interactive simulation-based cellular processes, with additional application in anatomy, physiology, and ecology teaching. Moreover, this paper discusses how AI could be used to analyze student data and propose personalized learning using adaptive assessments, content recommendations, and data sciences. This paper illustrates examples of AI algorithms that could be useful for teaching biology.

Objective: To comparatively evaluate performances of tablet-based versus paper-based Venous Insufficiency Epidemiologic and Economic Study-Quality of Life/Symptom (VEINES-QOL/Sym) questionnaire. Methods: We prospectively evaluated 78 consecutive patients who completed tablet-based and paper-based VEINES-QOL/Sym questionnaires and compared their scores, completion time, data entry time, and ease of use. We used Student's t-test and Wilcoxon test for quantitative variables, Bland–Altman test and kappa coefficient for agreement between questionnaires and patients, respectively. Spearman's correlation coefficient was used to assess correlations. Results: Most participants (83.3%) found it easier to use the tablet device. Less time was needed to complete the tablet-based (median, 4.75; IQR, 3–7 min) than the paper-based (median, 8.3; IQR, 6.3–11.3 min) questionnaire (p <.001). Better educated patients took less time to complete paper-based (p =.003) and tablet-based (p =.001) questionnaires and considered the latter easier to use (p =.010). Conclusions: The tablet-based VEINES-QOL/Sym proved to be an easy-to-use and time-saving tool. [ABSTRACT FROM AUTHOR]

This study traces the evolution of computer-assisted language learning (CALL) by investigating published research articles (RAs) in four major CALL journals: ReCALL, Computer Assisted Language Learning, Language Learning & Technology, and CALICO Journal. All 2,397 RAs published over four decades (1983-2019) were included in the pool of data, and the Google Scholar citation metric was adopted to assess the impact of the papers. By selecting the top 15% of widely cited papers from each individual year, we minimized the time bias between years, enabling a balanced narration of the history of CALL through a representative dataset of 426 high-impact RAs. To identify the evolution of research trends, the contexts, methodologies, theoretical underpinnings and research foci of all 426 RAs were investigated using NVivo 12 and AntConc. The analysis of the data yielded encouraging results such as the upward trend in the number of publications and the international reach of CALL in the last two decades, the physical or virtual presence of language learners with diverse language profiles, and the growing tendency to triangulate methodology for increased complexity. However, long-standing issues such as the heavy reliance on traditional research contexts, poor reporting practices of basic demographic information, the large number of atheoretical papers and the concentration on a limited number of research foci continue to pose challenges in CALL research. Based on the findings, the paper suggests solutions for the controversies and addresses key issues for future research in CALL. [ABSTRACT FROM AUTHOR]

This paper presents a unique advanced statistical approach based on Artificial Intelligence (AI) to examine factors affective on phonological awareness and print awareness of preschool children. Artificial Neural Network (ANN) models were created and correlations between the independent and dependent (outcome) variables were analyzed. The ANN models were trained using the data for phonological awareness and print awareness of children. According to the findings, the created ANN model had an excellent fit to the actual data (R[superscript 2] = 0.934 and 0.940). Furthermore, the ANN model results were tested with a traditional analysis technique, Pearson correlation analysis. The ANN models yielded similar results to the Pearson correlation analysis but with more detail as expected. The ANN models were run for user-generated synthetic datasets and the relationships between the dependent and independent variables were discussed using model results. Demographic variables, namely, children's age, mother's age, mother's education, and family income were found to be not effective on children's print and phonological awareness skills. On the other hand, home literacy environment-related variables were found to be very effective. In conclusion, this paper introduces a methodology for implementing ANN modeling in educational data. A novel and powerful approach is provided to assess and estimate essential components of early literacy skills. The study has important implications for advancing our understanding of potential benefits of employing an AI-based modeling techniques in the field of education. The utilization of machine learning methods in educational research, as presented in this paper, has the potential to fundamentally reshape our approaches in categorizing and analyzing educational data.

Peer-reviewed conferences, the main publication venues in CS, rely critically on matching highly qualified reviewers for each paper. Because of the growing scale of these conferences, the tight timelines on which they operate, and a recent surge in explicitly dishonest behavior, there is now no alternative to performing this matching in an automated way. This paper introduces Large Conference Matching (LCM) , a novel reviewer–paper matching approach that was recently deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021), and has since been adopted (wholly or partially) by other conferences including ICML 2022, AAAI 2022-2024, and IJCAI 2022-2024. LCM has three main elements: (1) collecting and processing input data to identify problematic matches and generate reviewer–paper scores; (2) formulating and solving an optimization problem to find good reviewer–paper matchings; and (3) a two-phase reviewing process that shifts reviewing resources away from papers likely to be rejected and towards papers closer to the decision boundary. This paper also describes an evaluation of these innovations based on an extensive post-hoc analysis on real data—including a comparison with the matching algorithm used in AAAI's previous (2020) iteration—and supplements this with additional numerical experimentation.2 [ABSTRACT FROM AUTHOR]

Scientometrics indicators are used to assess scientists, universities and research institutes for scientific policy-making. The aim of this research was to assess the status of top Iranian medical researchers using scientometric indicators. The study was carried out using scientometric methods. The statistical population included the top 500 Iranian researchers in the field of medicine who were ranked in the Iranian Scientometric Information Database (ISID) based on some scientometric indicators. The data were analyzed using SPSS 22 software and Pearson's correlation coefficient, stepwise regression analysis and Chi-square tests were applied for data analysis. Findings revealed that each researcher had an average h index of 24.04, g index of 40.15, and i10 index of 90.79. There was a positive significant relationship between the number of internationally-collaborated papers and the number of citations received (r = .606, p < .01). The stepwise multiple regression analysis showed that the three variables of paper number, citation counts and mean citation rate determined 72 % of the changes in the h-index. It can be concluded that researchers publishing more papers are more likely to be cited. However, their scientometric indexes are not always better than those of other scientists. [ABSTRACT FROM AUTHOR]

Simple Summary: Blood clots are formed when blood vessels are injured. They help stop bleeding and heal wounds, but can also cause serious problems if they block blood flow or break off and travel to other organs. This study investigates how blood clots differ in people with cancer compared to healthy or non-cancerous individuals. This study found that cancer patients have higher levels of histones in their blood, which make their clots stiffer and resistant to lysis. Further, clots formed in cancer patients have higher viscoelastic properties, and hence are harder to break down. These findings suggest that cancer patients have abnormal clotting properties that may increase their risk of developing thrombosis, and evaluating these properties can be helpful in detecting cancer. Background: Blood viscoelasticity and plasma protein levels can play an important role in the diagnosis and prognosis of cancer. However, the role of histones and DNA in modulating blood clot properties remains to be investigated. This study investigates the differences in blood viscoelasticity and plasma protein levels among cancer patients, individuals with other diseases, and healthy individuals. Methods: Blood samples were collected from 101 participants, including 45 cancer patients, 22 healthy individuals, and 34 individuals with other diseases. Rheological properties of clots formed in vitro by reconstituted elements of fibrinogen or plasma were analyzed with an Anton Paar Rheometer, USA. Plasma protein levels of D-dimer, TPA, EPCR, fibrinogen, and histone H3 were measured through ELISA. Blood clots were formed with or without DNA and histones (H3) by adding thrombin and calcium to plasma samples, and were evaluated for viscoelasticity, permeability, and degradation. Results: Cancer patients show higher blood viscoelasticity and plasma D-dimer levels compared to healthy individuals and individuals with other diseases. Our in vitro analysis showed that the addition of histone to the plasma results in a significant decrease in viscoelasticity and mean fiber thickness of the clot formed thereafter. In parallel studies, using plasma from patients, DNA and histones were detected in fibrin clots and were associated with less degradation by t-PA. Moreover, our results show that the presence of DNA and histones not only increases clots' permeability, but also makes them more prone to degradation. Conclusions: Plasma histones and DNA affect the structure of the clot formed and induce defective fibrinolysis. Moreover, the increased viscoelastic properties of plasma from cancer patients can be used as potential biomarkers in cancer prognosis. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to analyse data literacy in the new Digital Competence Framework for Citizens (DigComp 2.2). Mid-2022 the Joint Research Centre of the European Commission published a new version of the DigComp (EC, 2022). This new version focusses more on the datafication of society and emerging technologies, such as artificial intelligence. This paper analyses how DigComp 2.2 defines data literacy and how the framework looks at this from a societal lens. Design/methodology/approach: This study critically examines DigComp 2.2, using the data literacy competence model developed by the Knowledge Centre for Digital and Media Literacy Flanders-Belgium. The examples of knowledge, skills and attitudes focussing on data literacy (n = 84) are coded and mapped onto the data literacy competence model, which differentiates between using data and understanding data. Findings: Data literacy is well-covered in the framework, but there is a stronger emphasis on understanding data rather than using data, for example, collecting data is only coded once. Thematically, DigComp 2.2 primarily focusses on security and privacy (31 codes), with less attention given to the societal impact of data, such as environmental impact or data fairness. Originality/value: Given the datafication of society, data literacy has become increasingly important. DigComp is widely used across different disciplines and now integrates data literacy as a required competence for citizens. It is, thus, relevant to analyse its views on data literacy and emerging technologies, as it will have a strong impact on education in Europe.

Underlying thematic analysis are a few fundamental human cognitive processes, such as categorizing, prototyping and metaphorical mapping. By unpacking these basic processes of human cognition, this paper hopes to provide a cognitive basis for thematic analysis as a foundational method in data analysis for qualitative research. In particular, it hopes to address the gap between qualitative methodologists' assumption of thematic analysis as a subjective, creative and flexible process and editors/reviewers' expectation that thematic analysis shall be objective, reliable and rigorous. By consciously and purposefully applying these cognitive processes, thematic analysis can be subjective and yet disciplined, creative and yet rigorous, flexible and yet reliable. The ultimate goal of this paper is to demystify, delineate and further demarcate the thematic analysis process for young and novice qualitative researchers.

Managing and analysing large qualitative datasets pose a particular challenge for researchers seeking a consistent and rigorous approach to qualitative data analysis. This paper describes and demonstrates the development and adoption of a matrix tool to guide the qualitative data analysis of a large sample (N = 122) of interview data. The paper articulates the theoretical and conceptual underpinnings of the matrix analysis tool and how it was developed and applied in a longitudinal mixed methods out-of-home-care research study. Specific illustrations and examples of data integration and data analysis are provided to demonstrate the benefits and potentials of constructing matrix tools to guide research teams when working with large qualitative data sets alone or in combination with quantitative data sets.

The need for data-driven decision-making primarily motivates interest in analysing Big Data in higher education. Although there has been considerable research on the value of Big Data in higher education, its application to address critical issues within the sector is still limited. This systematic review, conducted in December 2021 and encompassing 75 papers, analysed the applications of Big Data and analytics in higher education. The focus was on their usage in supporting learning, teaching and administration as reported in papers indexed in SCOPUS, Web of Science and IEEE Xplore. The key findings from the review revealed that Big Data and analytics are predominantly used to support learning and, to a lesser extent, guide teaching and informing administrative decision-making processes. The review also identified a set of studies focused on supporting student well-being. Further, we extend the use of Big Data in higher education to include the well-being of students and staff. This paper contributes to the growing debate on the practical use of Big Data and analytics to provide valuable insights for solving systemic challenges facing high education in the twenty-first century.

Purpose: This paper defines and assesses student learning patterns under the influence of problem-based learning (PBL) and their classification into a reasonable minimum number of classes. Study utilizes PBL implemented in an undergraduate Statistics and Operations Research course for techno-management students at a private university in India. Design/methodology/approach: Study employs an in situ experiment using a conceptual model based on learning theory. The participant's end-of-semester GPA is Performance Indicator. Integrating PBL with classroom teaching is unique instructional approach to this study. An unsupervised and supervised data mining approach to analyse PBL impact establishes research conclusions. Findings: The administration of PBL results in improved learning patterns (above-average) for students with medium attendance. PBL, Gender, Math background, Board and discipline are contributing factors to students' performance in the decision tree. PBL benefits a student of any gender with lower attendance. Research limitations/implications: This study is limited to course students from one institute and does not consider external factors. Practical implications: Researchers can apply learning patterns obtained in this paper highlighting PBL impact to study effect of every innovative pedagogical study. Classification of students based on learning behaviours can help facilitators plan remedial actions. Originality/value: 1. Clustering is used to extract student learning patterns considering dynamics of student performances over time. Then decision tree is utilized to elicit a simple process of classifying students. 2. Data mining approach overcomes limitations of statistical techniques to provide knowledge impact in presence of demographic characteristics and student attendance.

In today's university era, reforming the English teaching model has become a major research topic for researchers. Based on this, this paper adopts a hierarchical and progressive model construction method to further explore the reform model of university English teaching in the context of educational ecology. First, this paper discusses the development of the layered progressive approach in various countries and the current status of the layered progressive approach. By allowing the use of the model of the layered progressive approach, the data related to the reform of university English teaching were analyzed and organized. The combination of internal optimization, data-theoretic learning algorithms, and data federation algorithms for the model of the hierarchical incremental approach are also investigated. The results of the study show that the layered progressive model of college English teaching reform has good results through specific practical applications in a strengthened educational ecosystem.

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 paper analyzes data from Native American students' attainment in a first-semester Spanish language course at an indigenous-serving institution before, during, and after the pandemic. The gap between Native American and non-Native American students increased during the first post-pandemic semester to the point that just one out of 11 Native Americans passed the course in the fall of 2021. After that, the gap between Native American and non-Native American students gradually narrowed until reaching the lowest failing grades of 23% in the spring 2023. In my teaching, Native American students benefit from a classic teaching style involving longer lecture time, monitored note-taking, consistent attendance requirements, in-person communication, and clearly communicated differentiation strategies for grading. This may align with the cycle of learning outlined by Benally (1994): Nitsáhákees (Thinking), Nahat'á (Planning), Iiná (Living) and Sihasin (Assuring).

Papers including articles that are produced because of the activities of LifeWatch ERIC, in the context of its second implementation period (2022 - 2026) and through the implementation of its new Strategic Working Plan, are published in this special collection. The articles include data papers, papers describing the development and functioning of analytical services and papers describing any other research outcome, produced either by LifeWatch ERIC or by any collaboration with any other ERIC, Research Infrastructure, global aggregator or other legal entity. [ABSTRACT FROM AUTHOR]

The article focuses on the retraction of a paper on cancer microbiome research, published in 2020 in the journal "Nature" that claims of identifying microbial signatures of different human cancers.

Objective: The purpose of this paper was to examine variables associated with food insecurity among community college caregivers during the COVID-19 pandemic. Methods: We used data from a multi-institutional survey of 15,051 caregivers enrolled at 130 community colleges in 42 states in fall 2020. We used a logistic regression to examine whether demographic, academic, caregiving-related, financial, or COVID-19-related variables were associated with caregivers' food insecurity. Results: Over half (52%) of community college caregivers experienced food insecurity. Transgender caregivers, first-generation caregivers, and caregivers who were divorced or single, had multiple disabilities, were previously in foster care, and had a family that experienced trouble making ends meet growing up had significantly (p < 0.05) higher probabilities of experiencing food insecurity. Community college caregivers who used childcare and those with at least one child up to 12 years old also had increased probabilities of experiencing food insecurity. Moreover, caregivers who felt childcare was not affordable and believed that they did not earn enough money to make employment worthwhile after paying for childcare expenses had higher probabilities of experiencing food insecurity, as did those who experienced housing insecurity and used Pell grants, student loans, and support from friends or family to pay for college. Losing a job, experiencing cuts to work hours or wages, employment as a frontline worker, and contracting COVID were associated with higher probabilities of food insecurity. Contributions: Community college caregivers experienced high rates of food insecurity during the pandemic and some caregivers were at greater risk of exacerbated probabilities of food insecurity. We advocate for targeted interventions, wraparound services, and increased advocacy for legislation to support student caregivers.

Situated in the education reform launched by the Egyptian Ministry of Education (MOE), which called for a socially-foreign shift away from memorization-based mathematics instruction, this paper explores the role of school-based teacher professional networks in implementing reform. Adopting the Goodson Change Model as a theoretical framework, we map out the power play between external agency (national society), internal agency (teacher professional networks) and the personal agency of individual teachers. Using an embedded case study model and a dataset consisting of the recordings of two full-day focus group discussions with teacher professional networks at two national schools, we contrast the two cases from the perspective of teacher group tensions and power dynamics in relation to the reform implementation. In one case, the teacher professional network (internal agency) was dominantly embracing the change, thus empowering the local teacher (personal agency) and enforcing its dogma on its stationed national society (external agency). In the other case, the teacher as a personal agent was solely attempting to break through both layers of internal and external agency. Our findings cast light on the role of teacher professional networks in curricular reform contexts, particularly within a collective cultural setup such as Egypt. Implications for the role of teacher professional networks in the diffusion of reform initiatives are discussed in light of the findings.

This document is an editorial celebrating the 50th anniversary of Seminars in Thrombosis and Hemostasis (STH), a medical journal that first published in 1974. The editorial discusses the various celebratory issues that have been published over the years, which contain historical content related to the fields of thrombosis and hemostasis. The document also mentions the republishing of highly cited and downloaded papers from STH, along with accompanying commentaries from experts in the respective fields. Additionally, the document provides a list of the 50 most highly downloaded papers from STH between 2014 and 2023. [Extracted from the article]

Sensor data fusion analysis is a crucial process that integrates data from multiple sensors to provide more accurate and comprehensive information. This analysis has various applications in fields such as industry, medicine, robotics, transportation, and information systems. The article highlights sixteen high-quality papers that explore different aspects of sensor data fusion, including facial detection, object detection, odometry systems, geospatial data evaluation, and fault detection. The papers demonstrate the potential of sensor data fusion in improving decision-making, efficiency, and productivity in various industries. Future research in this area will focus on miniaturization, multi-sensor systems, and addressing challenges related to data quality in mobile sensors. [Extracted from the article]

The Genetics Society of America is calling for submissions of papers on biobank-scale genomic data analyses. Biobanks, which are large collections of biological, medical, and genetic data on the same individuals, have revolutionized human genomics research. However, there is substantial heterogeneity between biobanks worldwide, creating both challenges and opportunities for data analysis. The series will focus on various topics, including population structures, selection bias, measurement errors, clinical observations, linkage with external data sources, meta-analysis, computational analysis methods, family-based analyses, ethical and privacy-related issues, and technological considerations. Manuscripts can be submitted until May 31, 2024, and the series will be an ongoing resource in this area. [Extracted from the article]

Quantile regression (QR) provides an alternative to linear regression (LR) that allows for the estimation of relationships across the distribution of an outcome. However, as highlighted in recent research on the motherhood penalty across the wage distribution, different procedures for conditional and unconditional quantile regression (CQR, UQR) often result in divergent findings that are not always well understood. In light of such discrepancies, this paper reviews how to implement and interpret a range of LR, CQR, and UQR models with fixed effects. It also discusses the use of Quantile Treatment Effect (QTE) models as an alternative to overcome some of the limitations of CQR and UQR models. We then review how to interpret results in the presence of fixed effects based on a replication of Budig and Hodges's work on the motherhood penalty using NLSY79 data.

When performing an aggregate data meta-analysis of a continuous outcome, researchers often come across primary studies that report the sample median of the outcome. However, standard meta-analytic methods typically cannot be directly applied in this setting. In recent years, there has been substantial development in statistical methods to incorporate primary studies reporting sample medians in meta-analysis, yet there are currently no comprehensive software tools implementing these methods. In this paper, we present the "metamedian" R package, a freely available and open-source software tool for meta-analyzing primary studies that report sample medians. We summarize the main features of the software and illustrate its application through real data examples involving risk factors for a severe course of COVID-19.

The severity of plastic pollution is increasing, and delaying sustainable development is not an option. China is a large consumer of plastic, so it is essential to encourage the use of eco-friendly shopping bags there. The paper focuses on the recognition and use of eco-friendly shopping bags among young people in Guangdong Province, China, and determines what factors can lead to a rise in the use of these bags. A questionnaire was utilized to gather data from 392 young people in Guangdong Province using a quantitative research approach. According to the data from the questionnaire, they generally are supportive of eco-friendly shopping bags. Ultimately, the paper's data emphasize the positive effects of materials (paper material), function (internal compartment), and design (green label). For the purpose of advocating sustainable development, this paper provides helpful guidance for the design of eco-friendly shopping bags. [ABSTRACT FROM AUTHOR]

Purpose: This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms. Design/methodology/approach: This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning. Findings: Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students' lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables. Originality/value: Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

This paper studies the problem of coordination and supervision of virtual teams and their capabilities. The goal is to develop a model suitable for managing virtual student teams specialized in the development of smart environments. The developed model is based on SAFe and DevOps, which when combined provide us with a framework for the evaluation of team capabilities in an academic environment. Additionally, DevOps principles can be more efficiently leveraged through an agile methodology to provide students with a better understanding of continuous value delivery. Through the application of the proposed model, virtual student teams gained practical experience in self-organization and virtual team management while being efficiently monitored and guided through the project lifecycle. Virtual student teams were likewise encouraged to be more agile, as this change in mindset is imperative in business, and as such must also be adopted in academic environments. By incorporating best practices of the corporate environments into the existing curriculum, we have proven that by adopting the proposed model these changes can be feasibly incorporated to the satisfaction of both the students and their future employers.

School inspections are a common feature of many education systems. These may be informed by quantitative background data about schools. It is recognised that there are pros and cons of using such quantitative information as part of the inspection process, though these have rarely been succinctly set out. This paper seeks to fill this gap by presenting arguments both for and against the use of quantitative data in informing school inspections. We argue that while quantitative data provide objective information about important outcomes, their usefulness is limited somewhat by a range of factors including missing data, small sample sizes, the creation of perverse incentives, and the fact that most readily available measures capture aspects other than school quality. We conclude by discussing how the Office for Standards in Education, Children's Services and Skills (Ofsted) -- the school inspectorate in England -- currently makes the trade-off between these pros and cons.

This paper examines how higher education researchers approach writing the rationale and justification for their work published in journal articles. A common way for establishing this justification is through claiming a gap, but the problem is that it is often hard to find a research gap, and if it is included, there is too often no explanation for why the gap is worthwhile in terms of its contribution to knowledge. What we do not know is how this task is approached across the field, what different approaches are taken, and what the implications might be for the quality of research and the advancement of knowledge. Therefore, we examined the gap statements from 124 articles from five top-ranked higher education journals. What we found is that the majority of articles do have a gap statement, but these are mostly implicit rather than explicit, and located somewhere in the introductory text. However, 20% of articles had no gap statement and 27% of all articles had no justification for the importance of the research. Based on the data and drawing on theory, we present a tool to assist with writing gap statements and comment on current practice in relation to knowledge contribution.

The study draws on the theory of 'migrant optimism' and anticipated discrimination to examine whether a gap in educational expectations and achievement exists between immigrant and native students in Qatar and explores whether the impact of educational expectations on educational achievement is contingent upon the migratory status of students (migrant or native students). It utilizes data on students aged 15-16 obtained from the 2018 Program for International Student Assessment (PISA). The results show that migrant students have higher educational expectations and achievement than Qatari students, while female students in all groups enjoy better performance and hold higher educational expectations. The results also show that the gender gap among Qatari students is bigger than the gender gap among migrant students. Educational expectations have a significant and positive impact on achievement among Qatari and migrant students alike. These results and their theoretical implications are further discussed in the paper.

Reported hate crimes in the United States have increased rapidly in recent years, alongside an increase in general racial animus. Scholars have shown that the larger sociopolitical environment can directly impact the campus climate and experiences of all students, particularly students of color. However, little is known about how reports of hate crime incidents relate to college enrollment levels of students of color. This lack of evidence has especially troubling implications for Black people, the most frequent targets of reported hate crimes. This paper helps to fill in that gap by exploring the association between the number of reports of hate crimes within states and Black students' college enrollment. We examine a comprehensive dataset of institutional enrollment and characteristics, reported hate crimes, and census data on state racial demographics from 2000 to 2017 using several techniques, including institution fixed effects. We find that a1 standard deviation increase in reports of state-level hate crimes predicts a 17 to 22 percent increase in Black first-time student enrollment at historically Black colleges and universities. As the number of reported hate crimes is almost assuredly an undercount of the actual number of incidents, we explore the implications of what these results mean.

A project involving data related to socioeconomic indicators was administered in an introductory statistics course. As part of this project, student groups investigated a data set from the United States Department of Agriculture Food and Nutrition Service website with information on recipients of Supplemental Nutrition Assistance Program (SNAP) benefits. Pre/post data from the Miville-Guzman Universality-Diversity Scale (M-GUDS) short form were collected to measure changes in diversity awareness. The survey administered also included demographic information and open-ended questions. Based on study results (n = 48), there was moderate evidence of a change in the "diversity of contact" subscale of the M-GUDS short form. Written feedback indicated that students demonstrated empathy for individuals with potentially different lived experiences. The focus of this paper is to describe the study results and provide information on the importance of incorporating elements of diversity into curriculum.

Computer-based learning environments (CBLEs) are powerful tools to support student learning. Increasingly of interest is the data that is recorded as learners interact with a CBLE. This "process data" yields opportunities for researchers to examine learners' engagement with a CBLE and explore whether specific interactions are associated with learner variables, with direct implications for improving learning outcomes and CBLE design. As CBLEs increase in number and complexity, researchers continue to seek more effective strategies to analyze process data. While a variety of strategies are in use, from visualizations to predictive modeling, none yet offer the capabilities to both uncover hidden, meaningful interactions and descriptively analyze those interactions rapidly across the complete data set. This paper details a method that addresses current challenges, and then applies the method to existing data from a prior study which investigated the effects of adding a visual scaffold to a chemistry-based CBLE. Using a biochemical coding approach through a cultural-historical activity theory (CHAT) framework, the method successfully identified 257 unique, meaningful patterns of interaction that were strategically grouped into nine categories of mediated actions. Though no differences in mediated actions were observed between learners in the experimental (visual scaffolds) and control conditions, three mediated actions were significantly and positively associated with higher learning outcomes in the visual scaffold condition. The results not only provide insight into why the addition of visual scaffolds led to higher learning outcomes among participants but have broader implications for filling a gap in the field of process data analytics for CBLEs in science education.

Business Analytics has gained the attention of academia and industry due to three main trends namely, availability of data, methodological developments, and explosion in computing power and storage capacity. Many higher education institutes show interest in developing an undergraduate business analytics program. This paper develops a framework including all the detailed steps needed to create and implement such a program for a business college in a state-funded public university. It first focuses on program justification and program development process. Then, it tackles program structure, learning outcomes, and resources. Finally, the lessons learnt are shared.

This paper examines performance differences among demographic groups on the ETS Major Field Test in Business. The study employs the Blinder-Oaxaca decomposition technique to analyze the test score differentials by gender and racial minority status. This technique decomposes the difference into two parts: an endowment effect (or explained portion) and a returns effect (or unexplained portion). The results demonstrate that the endowment effect fully explains the gap between white and racial minority students but virtually none of the gender gap. This large unexplained gap between male and female test performance suggests the need for further study of potential gender bias of the exam.

The last decade has seen a rise in big data approaches, including in the humanities, whereby large quantities of data are collected and analysed. In this paper, we discuss long-form audio recordings that result from individuals wearing a recording device for many hours. Linguists, psychologists and anthropologists can use them, for example, to study infants' or adults' linguistic behaviour. In the past, recorded individuals and communities have resided in high-income countries (HICs) almost exclusively. Recognising the need for better representation of all cultures and linguistic experiences, researchers have more recently started to collect long-form audio recordings in low- and middle-income countries (LMICs). We aim to help researchers to collect, analyse and use these recordings ethically. To do so, we identify four main ethical challenges linked to research that relies on long-form recordings in LMICs. We provide recommendations to overcome these challenges. These considerations should be useful to researchers employing other big data techniques collected via wearables.

Purpose: In light of a need for more critical education about datafication, this paper aims to develop a framework for critical datafication literacy that is grounded in theoretical and empirical research. The framework draws upon existing critical data literacies, an in-depth analysis of three well-established educational approaches - media literacy, the German "(politische) Bildung" and Freirean "critical pedagogy" - and empirical analyses of online educational resources about datafication. Design/methodology/approach: The study interconnects theoretical analyses with an empirical mixed methods investigation that includes expert interviews with creators of online educational resources about datafication and a qualitative survey with educators interested in teaching about data technologies. Findings: The research identified novel findings on the goals of resource creators and educators, such as a focus on empowering and emancipatory approaches, fostering systemic understanding of datafication and encouraging collective action. Such perspectives are rare in existing critical data literacy conceptualisations but show resemblance to traditional education scholarship. This highlights how much can be learnt from practitioners and from these more established educational approaches. Based on these findings, a framework for critical datafication literacy is suggested that aims for systemic understanding of datafication, encouraging critical thinking and enabling learners to make enlightened choices and take different forms of action. Originality/value: The study is unique in its interconnection of theoretical and empirical research, and it advances previous research by suggesting a grounded framework for critical datafication literacy.

Interaction is a much-claimed attribute of focus group research yet is often deficient in analysis when its essence can become lost. In this paper we aim to develop a flexible framework that can be operationalized and replicated when attempting to encourage and capture interaction. Working as outsider researchers with John Lewis & Partners (the UK's largest employee-owned business, with 80,000 employees known as 'Partners'), we conducted eight focus groups asking 18 questions about the company's giving activities and associated decision making. Using the transcriptions, we analyzed interaction through a taxonomy of questions, laughs, and pauses, identified as the features of both interaction, and sequential interaction. Employing a two-stage approach for encouraging, capturing, and evidencing interaction, we developed an exploratory framework. Through a transparent audit trail, we reduced the data to points of impact. We propose that these present a meaningful starting position for the theorizing iteration of the data.

Managers are increasingly being tasked with overseeing data-driven projects that incorporate prescriptive and predictive models. Furthermore, basic knowledge of the data analytics pipeline is a fundamental requirement in many modern organizations. Given the central importance of analytics in today's business environment, there is a growing demand for educational pedagogies that give students the opportunity to learn the fundamentals while also familiarizing them with how such tools are applied. However, a tension exists between the introduction of real-world problems that students can analyze and extract insight from and the need for prerequisite knowledge of mathematical concepts and programming languages such as Python/R. As a consequence, this paper describes an application-focused course that uses Microsoft Excel and mathematical programming to introduce MBA students with nontechnical backgrounds to tools from both prescriptive and predictive analytics. While students' gain proficiency in managing data and creating optimization and machine learning models, they are also exposed to broader business concepts. Teaching evaluations indicate that the course has helped students further develop their practical skills in Microsoft Excel, gain an appreciation of the real-world impact of data analytics, and has introduced them to a discipline they originally believed was best suited for more technically focused professionals.

In this article, we discuss the methodological implications of data and theory integration for Theory-Based Evaluation (TBE). TBE is a family of approaches to program evaluation that use program theories as instruments to answer questions about whether, how, and why a program works. Some of the groundwork about TBE has expressed the idea that a proper program theory should specify the intervening mechanisms underlying the program outcome. In the present article, we discuss in what way data and theory integration can help evaluators in constructing and refining mechanistic program theories. The paper argues that a mechanism is both a network of entities and activities and a network of counterfactual relations. Furthermore, we argue that although data integration typically provides information about different parts of a program, it is the integration of theory that provides the most important mechanistic insights.

Recurrence quantification analysis (RQA) is a time-series analysis method that uses autocorrelation properties of typing data to detect regularities within the writing process. The following paper first gives a detailed introduction to RQA and its application to time series data. We then apply RQA to keystroke logging data of first and foreign language writing to illustrate how outcome measures of RQA can be understood as skill-driven constraints on keyboard typing performance. Forty native German students performed two prompted writing assignments, one in German and one in English, a standardized copy task, and a standardized English placement test. We assumed more fluent and skilled writing to reveal more structured typing time series patterns. Accordingly, we expected writing in a well-mastered first language to coincide with higher values in relevant RQA measures as compared to writing in a foreign language. Results of mixed model ANOVAs confirmed our hypothesis. We further observed that RQA measures tend to be higher, thus indicating more structured data, whenever parameters of pause, burst, and revision analyses indicate more fluent writing. Multiple regression analyses revealed that, in addition to typing skills, language proficiency significantly predicts outcomes of RQA. Thus, the present data emphasize RQA being a valuable resource for studying time series data that yields meaningful information about the effort a writer must exert during text production.

Currently, dynamic structural equation modeling (DSEM) and residual DSEM (RDSEM) are commonly used in testing intensive longitudinal data (ILD). Researchers are interested in ILD mediation models, but their analyses are challenging. The present paper mathematically derived, empirically compared, and step-by-step demonstrated three types (i.e., 1-1-1, 2-1-1, and 2-2-1) of intensive longitudinal mediation (ILM) analyses based on DSEM and RDSEM models. Specifically, each ILM model was demonstrated with a simulated example and illustrated with the corresponding annotated Mplus codes. We compared two types of detrending methods in mediation analyses and showed that RDSEM was superior to DSEM because the latter included the timetj variable as a Level 1 predictor. Lastly, we extended ILM analyses based on DSEM and RDSEM to multilevel autoregressive mediation models, cross-classified DSEM, and intensive longitudinal moderated mediation models.