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Objectives: To examine Indigenous Governance of Data processes in Australian clinical registries., Design, Setting, Participants: Audit (via desktop review and interviews) of registries in the Australian Register of Clinical Registries from 17 January 2022 to 30 April 2023., Main Outcome Measures: The number of clinical registries collecting ethnicity data, reporting Aboriginal and/or Torres Strait Islander representation on registry governance or steering committees, and reporting human research ethics committee approval., Results: A total of 107 clinical registries were reviewed. Of these registries, 65 (61%) collected ethnicity data; when these were grouped by geographical coverage, those most likely to collect ethnicity data were binational (24/40 [60%]), national (19/26 [73%]) or state based (19/26 [73%]). Of the registries that collected ethnicity data, 29 (45%) classified their ethnicity item as Aboriginal and/or Torres Strait Islander. Only eight clinical registries (7%) reported Aboriginal and/or Torres Strait Islander representation on their governance or steering committees. Human research ethics approval was reported in 94 registries (88%), with only 11 (12%) having Aboriginal human research ethics committee approval., Conclusion: Significant variability is evident in clinical registry recording of Indigenous governance of data, meaning that Aboriginal and Torres Strait Islander communities remain invisible in data which is used to inform policy, clinical models of care, health services and initiatives. Radical change is required to facilitate meaningful change in quality indicators for clinical registries nationally., (© 2024 The Author(s). Medical Journal of Australia published by John Wiley & Sons Australia, Ltd on behalf of AMPCo Pty Ltd.)

Collecting reference papers from the Internet is one of the most important activities for progressing research and writing papers about their results. Unfortunately, the current process using Google Scholar may not be efficient, since a lot of paper files cannot be accessed directly by the user. Even if they are accessible, their effectiveness needs to be checked manually. In this paper, we propose a reference paper collection system using web scraping to automate paper collections from websites. This system can collect or monitor data from the Internet, which is considered as the environment, using Selenium, a popular web scraping software, as the sensor; this examines the similarity against the search target by comparing the keywords using the Bert model. The Bert model is a deep learning model for natural language processing (NLP) that can understand context by analyzing the relationships between words in a sentence bidirectionally. The Python Flask is adopted at the web application server, where Angular is used for data presentations. For the evaluation, we measured the performance, investigated the accuracy, and asked members of our laboratory to use the proposed method and provide their feedback. Their results confirm the method's effectiveness. [ABSTRACT FROM AUTHOR]

mHealth applications (apps) are tools that can enhance research by efficiently collecting and storing large amounts of data. However, data collection alone does not lead to change. Innovation and practice change occur through utilisation of evidence. The volume of data collected raises questions regarding utilisation of data by nurses and midwives, and how data from mHealth apps can be used to improve person-centred practice. There is limited empirical evidence and a lack of direction from global health authorities to guide nurses and midwives in this area. To describe strategies for nurses and midwives that could enhance the effective use of data generated by mHealth apps to inform person-centred practice. The purpose of this paper is to stimulate reflection and generate actions for data utilisation when using mHealth apps in nursing research and practice. This discussion paper has been informed by current evidence, the integrated-Promoting Action on Research Implementation in Health Services (i-PARIHS) framework, and research experience as part of doctoral study. Before engaging in data collection using mHealth apps, nurses and midwives would benefit from considering the nature of the evidence collected, available technological infrastructure, and staff skill levels. When collecting data and interpreting results, use of a team approach supported by engaged leadership and external facilitation is invaluable. This provides support to operate apps, and more importantly use the data collected to inform person-centred practice. This paper addresses the limited available evidence to guide nurses and midwives when using mHealth apps to collect and use data to inform practice change. It highlights the need for appropriate technology, external facilitative support, engaged leadership, and a team approach to collect meaningful evidence using mHealth apps. Clinicians, leaders, and researchers can apply the strategies provided to enhance the use of mHealth apps and ensure translation of evidence into practice. [ABSTRACT FROM AUTHOR]

Objective: There are several electronic patient-reported outcomes (ePROs) vendors that are being used at institutions to automate data collection. However, there is little known about their success in collecting patient-reported outcomes (PROs) and it is unknown which patients are more likely to complete these surveys. In this study, we assessed rates of PRO completion, as well as determined factors that contributed to the completion of baseline and follow-up surveys., Methods: We queried our ePRO platform to assess rates of completion for baseline and follow-up surveys for patients from October 2019 to June 2022. All baseline surveys were administered before pediatric orthopaedic procedures, and follow-up surveys were sent at 3 months, 6 months, 1 year, and 2 years after surgery to patients with baseline data. Descriptive statistics were used to summarize the data. Univariate and multivariate analyses were performed to assess differences in patients who did and did not complete surveys., Results: This study included 1313 patients during the study period. Baseline surveys were completed by 66% of the cohort (n = 873 patients). There was a significant difference in race/ethnicity and language spoken in the patients who did and did not complete baseline surveys ( P < 0.01) with lower rates of completion in African American, Hispanic, and Spanish-speaking patients. At least one follow-up was obtained for 68% of patients with baseline surveys (n = 597 patients). There were significant differences in completion rates based on race/ethnicity ( P = 0.03) and language spoken ( P = 0.01). There were lower rates of baseline completion for patients with government insurance in our multivariate analysis (odds ratio: 0.6, P < 0.01)., Conclusion: Baseline and follow-up PRO data can be obtained from the majority of patients using automated ePRO platforms. However, additional focus is needed on collecting data from traditionally underrepresented patient groups to better understand outcomes in these patient populations., Level of Evidence: Level III-retrospective cohort study., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Artificial intelligence in education (AIEd) is a fast-growing field of research. In previous work, we described efforts to explore the possible futures of AIEd by identifying key variables and their future prospects. This paper re-examines our discussions on the governance of data and the role of students and teachers by considering the implications of (1) a recent case related to the General Data Protection Regulation (GDPR) and (2) the release of ChatGPT, a generative AI model capable to producing 'human-like' text. These events raise questions for the future of AIEd and the underlying function of assessment, and highlight the importance of active student participation in the integration of AI in education. [This article has been presented in the 5th International Open & Distance Learning Conference-IODL 2022.]

The National Assessment Program for Literacy and Numeracy (NAPLAN) is intended to assess all Australian students in Years 3, 5, 7 and 9 to ensure equity and inclusion. This paper explores the extent to which equity and inclusion are achieved for students with disability through NAPLAN reporting. Document analysis of publicly accessible NAPLAN reports, technical reports, handbooks and protocols was conducted to evaluate how this cohort is represented in NAPLAN reports. This paper demonstrates the paucity of NAPLAN reporting about students with disability. It highlights opportunities for NAPLAN data from this cohort to be reported and used to inform educational decisions at all levels (teachers, schools, systems and parents) for students with disability.

Gaining ethical approval for qualitative health research and implementing all the planned research processes in a proposed study are not straightforward endeavours. The situation becomes more complex when qualitative research is conducted in a cross-national healthcare and academic context. Also, it is even exhausting when the study is student-based, as student researchers may be considered novices and inexperienced researchers, especially for field-based research. Our aim in this reflective paper is to present, reflect, and discuss the experiences of a doctoral researcher in dealing with two independent institutional review boards in Canada and Ghana during an interdisciplinary Ph.D. project and the ethical dilemmas encountered while collecting data in Ghana. Based on the researcher's experiences, it became apparent that consent and its documentation can have cultural implications in different settings; hence, institutional review boards must exercise reflexivity in their protocol review practice. Also, sharing research data with participants and institutional leaders while maintaining participant confidentiality and privacy in institutional ethnographic research requires sensitivity to bi-lateral ethical values. With the experiences shared in this paper, we advocate for a dialogic ethical review process in qualitative research where researchers and research ethics boards engage in ongoing dialogue rather than the usual prescriptive format research ethics reviews often assume.

Background: Driven by the ongoing need to provide high-quality learning and teaching, universities recently have shown an increased interest in using learning analytics (LA) for improving learning design (LD). However, the evidence of such improvements is scarce, and the maturity of such research is unclear. Objectives: This study is aimed to evaluate the maturity of research discussing LA-driven LD improvements in higher education. Methods: The systematic review analyses 49 empirical papers, assesses their quality and suggests further research directions. The review elaborates on methodological (research questions, strategy and methods, LA-LD integration theoretical backgrounds) and substantial (LA-driven LD improvements, types of data used, LA software development) features of the papers. Results and Conclusions: The findings demonstrated the lack of theoretical alignment between LA and LD, with research tending towards user experience studies. The most frequently used research strategy was a case study; experiments were very rare. Researchers predominantly used parsing for collecting data and AI methods for analysing it; mostly used data types related to registering learners' engagement with learning activities as well as resources and tools provided in digital learning environments. Takeaways: The research area discussing LA-driven LD improvements still has a way to go before attaining the level of full maturity. Only a third of the papers reported actual LA-driven LD improvements; moreover, only three papers measured their effectiveness. The presented LA software was mostly at the beta or implementation stages and did not assess the impact of using this software.

This paper considers the ethical and practical issues of recruiting for, and administering a quantitative survey with marginalised populations. These issues were identified through a focus group discussion, which consolidated and expanded upon informal conversations held previously by five researchers about their experiences of conducting a face-to-face survey (using predominantly quantitative questions) with people who used amphetamine type substances in North East England, UK. Inductive and deductive thematic analysis of the focus group discussion led to the generation of three key themes: researcher positionality, emotions, and role dilemmas; study design; and ethics in practice. This paper therefore aims to extend literature which explores ethical and practical issues involved in studies with marginalised populations. It makes methodological suggestions for how work across a range of disciplines could make face-to-face survey research, and future studies with marginalised populations, more inclusive for both participants and researchers.

Due to the global COVID-19 pandemic, in-person data collection methods have been considerably hampered by requirements for social distancing and safety. Consequently, academic inquiry has shifted largely to virtual means, leading to the considerable growth of virtual qualitative research. Conducting virtual research in post-conflict contexts, such as Sri Lanka, with increased state surveillance, security concerns, and censorship presents researchers with additional tensions, particularly during a pandemic. Limited literature, however, has grappled with these unique situations. This paper addresses this gap by reflecting on the process of conducting virtual qualitative research through a case study of faculty members in peace education instruction at Sri Lankan universities. The study drew on semi-structured interviews (n = 32), documentary evidence, and memos created during the data collection and analysis stages. This paper discusses the challenges and complexities of conducting virtual research within the intersections of peace education, post-conflict legacies, ethical dimensions, and positionality dimensions, which are interwoven, adding several layers of considerations in this context. Furthermore, the paper chronicles the key tensions faced: surveillance and consent, residual embodiments, and the choices made in response to navigate them. This paper concludes with a discussion around these tensions and aims to expand the literary discourse beyond the technological aspects of conducting virtual research. The study highlights the need for future research into residual embodiments, ethical and micro-ethical issues, and practical challenges in virtual research in conflict-affected contexts, suggesting that institutions should provide researchers with training to address these complexities and support robust knowledge co-creation.

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.

Purpose: This study aims to describe and evaluate various visual and creative tools for supporting the in-depth biographical interview aimed at analyzing educational communities and their stakeholders. Design/methodology/approach: Researching educational spaces today requires new ways of understanding, analyzing and studying. The complex characteristics, functions and realities demand research that responds to educational singularities. It is a matter of deeply understanding the educational phenomenon's peculiarities. For these purposes, instruments and research paradigms are needed to extract data and reach information saturation regarding the data obtained from the proposed objects of study. With this in mind, the following paper suggests reflecting on data collection tools that can complement the interview and biographical-narrative research approach. The authors highlight the use of photo-elucidation, the biogram-based timeline, the organigram and the flight of the geese, all of which are instruments endowed with a visual character that allows a deeper understanding of the object studied. Findings: The main contribution of this paper is to unpack the uses and applications of four visual tools that support the interview technique. First, photo-elucidation is presented as a sensory strategy to stimulate the narrative during the dialogical exchange of the interview. Next, the timeline is described as a visual concretization of the traditional biogram widely used in educational research. Next, the authors unravel the uses of the organizational chart in educational research, which, due to its nature and utility, provides a glimpse of the organizational functioning of an institution and is particularly suitable for research in institutional frameworks. Finally, the tool known as the flight of the geese is presented. This tool is recommended for use in educational leadership and teamwork studies due to its simplicity and high representativeness of the hierarchy of roles and functions. Originality/value: Researching educational spaces today requires new ways of understanding, analyzing and studying. The complex characteristics, functions, and realities demand research that responds to educational singularities. It is a matter of deeply understanding the educational phenomenon's peculiarities. For these purposes, instruments and research paradigms are needed to extract data and reach information saturation regarding the data obtained from the proposed objects of study. With this in mind, the following paper invites us to reflect on data collection tools that can complement the interview and biographical-narrative research approach. The authors highlight the use of photo-elucidation, the biogram-based timeline, the organigram, and the flight of the geese, all of which are instruments endowed with a visual character that allows a deeper understanding of the object studied.

While the use of video recording as a method of data collection has helped researchers to resolve the challenge of capturing classroom interactions between teachers and students, it can be challenging for the researchers and teachers to make sense of the rich data collected. This paper describes an approach of analysing and visualising a language lesson with lesson microgenre and network graphs to provide an overview map of the lesson enactment. Studying the language lesson from a lesson microgenre perspective can provide both the co-text and context of the lesson when specific segments of the lesson are identified for interpretation and reflection by the teacher. The lesson map is visualised using network graphs to show the progression, connections, and patterns of the lesson microgenres. The paper describes the application of the approach in a study of the English lessons conducted by two teachers in a Singapore primary school and discuss the implications of the approach on teacher training.

While public consultation is a signature process of democratic policy formulation, many governments manoeuvre to refract citizen's opinions or conduct it perfunctorily. Using the case of a medium of instruction policy in Hong Kong, this article unveils the strategies that the state and citizens employ to put their opinion through to the final policy text, during a public consultation process. Recent literature has identified the mechanisms through which individual actors or organisations contribute to broad policy agenda-setting or policy programme development. However, yet to be investigated is how they -- sometimes with conflicting interests -- collectively negotiate a policy with the state via public consultations. This paper investigates this very phenomenon, building on previous work conducted in the public policy field, analysing 51 government-generated documents through both thematic content analysis and critical discourse analysis. The paper uncovers four strategies adopted by administrations ("non-commitment," "case closure," "disengagement for irrelevance," and "placation") to evade citizens' equity-oriented demands and stakeholders' three counter strategies ("mobilising" other stakeholders into a coalition, "reopening the case" pointing out a new problem, and "appealing" by affirming relevance). The state's discrete refusals and stakeholders' conjoint reengagement tactics draw our attention to the complexity and subtlety involved in negotiation via public consultations.

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 amount of grey literature and 'softer' intelligence from social media or websites is vast. Given the long lead-times of producing high-quality peer-reviewed health information, this is causing a demand for new ways to provide prompt input for secondary research. To our knowledge, this is the first review of automated data extraction methods or tools for health-related grey literature and soft data, with a focus on (semi)automating horizon scans, health technology assessments (HTA), evidence maps, or other literature reviews. We searched six databases to cover both health- and computer-science literature. After deduplication, 10% of the search results were screened by two reviewers, the remainder was single-screened up to an estimated 95% sensitivity; screening was stopped early after screening an additional 1000 results with no new includes. All full texts were retrieved, screened, and extracted by a single reviewer and 10% were checked in duplicate. We included 84 papers covering automation for health-related social media, internet fora, news, patents, government agencies and charities, or trial registers. From each paper, we extracted data about important functionalities for users of the tool or method; information about the level of support and reliability; and about practical challenges and research gaps. Poor availability of code, data, and usable tools leads to low transparency regarding performance and duplication of work. Financial implications, scalability, integration into downstream workflows, and meaningful evaluations should be carefully planned before starting to develop a tool, given the vast amounts of data and opportunities those tools offer to expedite research.

Purpose: The Thai video game domain has witnessed substantial growth in recent years. However, many games enjoyed by Thai players are in foreign languages, with only a handful of titles translated/localized into the Thai locale. Some Thai video game enthusiasts have taken on the role of unofficial translators/localizers, contributing to a localization domain that accommodates both official and unofficial translation/localization efforts. This general review paper aims to outline the author's experiences in collecting data within the domain of video game translation/localization in Thailand. Design/methodology/approach: Using a descriptive approach, this general review paper employs the netnography method. It sheds light on the complexities of video game translation/localization in Thailand and incorporates semi-structured interviews with a snowball sampling technique for the selection of participants and in-game data collection methods. Findings: The netnography method has proved instrumental in navigating the intricacies of this evolving landscape. Adopting the netnography method for data collection in this research contributes to establishing more robust connections with the research sites. "Inside" professionals and individuals play a significant role in data gathering by recommending additional sources of information for the research. Originality/value: While netnography is conventionally applied in the market and consumer research, this paper demonstrates its efficacy in unraveling the dynamics of video game translation/localization in Thailand.

LearnSphere is a web-based data infrastructure designed to transform scientific discovery and innovation in education. It supports learning researchers in addressing a broad range of issues including cognitive, social, and motivational factors in learning, educational content analysis, and educational technology innovation. LearnSphere integrates previously separate educational data and analytic resources developed by participating institutions. The web-based workflow authoring tool, Tigris, allows technical users to contribute sophisticated analytic methods, and learning researchers can adapt and apply those methods using graphical user interfaces, importantly, without additional programming. As part of our use-driven design of LearnSphere, we built a community through workshops and summer schools on educational data mining. Researchers interested in particular student levels or content domains can find student data from elementary through higher-education and across a wide variety of course content such as math, science, computing, and language learning. LearnSphere has facilitated many discoveries about learning, including the importance of active over passive learning activities and the positive association of quality discussion board posts with learning outcomes. LearnSphere also supports research reproducibility, replicability, traceability, and transparency as researchers can share their data and analytic methods along with links to research papers. We demonstrate the capabilities of LearnSphere through a series of case studies that illustrate how analytic components can be combined into research workflow combinations that can be developed and shared. We also show how open web-accessible analytics drive the creation of common formats to streamline repeated analytics and facilitate wider and more flexible dissemination of analytic tool kits.

Understanding students' learning of knowledge components (KCs) is an important educational data mining task and enables many educational applications. However, in the domain of computing education, where program exercises require students to practice many KCs simultaneously, it is a challenge to attribute their errors to specific KCs and, therefore, to model student knowledge of these KCs. In this paper, we define this task as the KC attribution problem. We first demonstrate a novel approach to addressing this task using deep neural networks and explore its performance in identifying expert-defined KCs (RQ1). Because the labeling process takes costly expert resources, we further evaluate the effectiveness of transfer learning for KC attribution, using more easily acquired labels, such as problem correctness (RQ2). Finally, because prior research indicates the incorporation of educational theory in deep learning models could potentially enhance model performance, we investigated how to incorporate learning curves in the model design and evaluated their performance (RQ3). Our results show that in a supervised learning scenario, we can use a deep learning model, code2vec, to attribute KCs with a relatively high performance (AUC > 75% in two of the three examined KCs). Further using transfer learning, we achieve reasonable performance on the task without any costly expert labeling. However, the incorporation of learning curves shows limited effectiveness in this task. Our research lays important groundwork for personalized feedback for students based on which KCs they applied correctly, as well as more interpretable and accurate student models.

There can be many reasons why students fail to answer correctly to summative tests in advanced computer science courses: often the cause is a lack of prerequisites or misconceptions about topics presented in previous courses. One of the ITiCSE 2020 working groups investigated the possibility of designing assessments suitable for differentiating between fragilities in prerequisites (in particular, knowledge and skills related to introductory programming courses) and advanced topics. This paper reports on an empirical evaluation of an instrument focusing on data structures, among those proposed by the ITiCSE working group. The evaluation aimed at understanding what fragile knowledge and skills the instrument is actually able to detect and to what extent it is able to differentiate them. Our results support that the instrument is able to distinguish between some specific fragilities (e.g., value vs. reference semantics), but not all of those claimed in the original report. In addition, our findings highlight the role of relevant skills at a level between prerequisite and advanced skills, such as program comprehension and reasoning about constraints. We also suggest ways to improve the questions in the instrument, both by improving the distractors of the multiple-choice questions, and by slightly changing the content or phrasing of the questions. We argue that these improvements will increase the effectiveness of the instrument in assessing prerequisites as a whole, but also to pinpoint specific fragilities.

While research into PhD programs and doctoral students' experiences has increased in recent years, fieldwork is overlooked as a source of learning and support. In education, the focus of this paper, fieldwork remains laced with notions of the anthropologist gathering data in a place that is not their own, which narrowly construct the role of the novice and their expectations around 'doing' research. To demonstrate the relevance of these issues and key ethical tensions that they underpin, we explored our recent PhD fieldwork experiences within classrooms in Australian and Zimbabwean schools. By analysing fieldnotes from our lived experiences, we identified similarities between conducting fieldwork 'out there' (in Zimbabwe) and 'at home' (in Australia). These similarities highlighted a multitude of roles and dynamics associated with the researcher presence, as well as the importance of balancing complex needs during fieldwork. Our analyses also revealed how daily in situ interactions with participants--and others--were crucial to the development of our identities and data gathering practices. We argue that ongoing efforts to demystify fieldwork experiences are critical for understanding that 'the field' is not simply 'out there' and offer practical suggestions for current and future doctoral students to consider.

Adaptive survey design refers to using targeted procedures to recruit different sampled cases. This technique strives to reduce bias and variance of survey estimates by trying to recruit a larger and more balanced set of respondents. However, it is not well understood how adaptive design can improve data and survey estimates beyond the well-established post-survey adjustment. This paper reports the results of an experiment that evaluated the additional effect of adaptive design to post-survey adjustments. The experiment was conducted in the Detroit Metro Area Communities Study in 2021. We evaluated the adaptive design in five outcomes: 1) response rates, 2) demographic composition of respondents, 3) bias and variance of key survey estimates, 4) changes in significant results of regression models, and 5) costs. The most significant benefit of the adaptive design was its ability to generate more efficient survey estimates with smaller variances and smaller design effects.

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.

This paper introduces a Patron Counting and Analysis (PCA) system that leverages Wi-Fi-connection data to monitor space utilization and analyze visitor patterns in academic libraries. The PCA system offers real-time crowding information to the public and a comprehensive visitor analysis dashboard for library administrators. The system's development was driven by the need for occupancy restrictions during the pandemic, ensuring a spacious environment for library visitors as well as balancing between efficient utilization and adhering to social distancing regulations. Traditional methods of patron behavior performance and library spatial analysis, such as manual head counting or card-swiping systems, often incur additional costs for labor, hardware installation, or software subscription. The PCA system, however, utilizes existing Wi-Fi-connection data, providing a cost-effective solution to represent patron demographics and spatial usage. Limitations may arise when patrons do not carry Wi-Fi-enabled devices or during periods of low Wi-Fi service functionality. Implemented in Node.js and integrated with Python Flask framework and related libraries, the PCA system was piloted at the King Library in Miami University, successfully demonstrating a high validity compared to manually collected data. It filters out noise and redundancy, visualizes the occupancy index meter in real time, and generates statistical reports by linking user IDs with demographic information. The PCA system's reliability was validated through manually head counting data collected at the King Library in Miami University, establishing it as a reliable tool for library space management and patron analysis.

Experimental designs in the social sciences have received increasing attention due to their power to produce causal inferences. Nevertheless, experimental research faces limitations, including limited external validity and unrealistic treatments. We propose combining qualitative fieldwork and experimental design iteratively--moving back-and-forth between elements of a research design--to overcome these limitations. To properly evaluate the strength of experiments researchers need information about the context, data, and previous knowledge used to design the treatment. To support our argument, we analyze 338 pre-analysis plans submitted to the Evidence in Governance and Politics repository in 2019 and the design of a study on public opinion support for punitive policing practices in Montevideo, Uruguay. The paper provides insights about using qualitative fieldwork to enhance the external validity, transparency and replicability of experimental research, and a practical guide for researchers who want to incorporate iteration to their research designs.

Family income questions in general purpose surveys are usually collected with either a single-question summary design or a multiple-question disaggregation design. It is unclear how estimates from the two approaches agree with each other. The current paper takes advantage of a large-scale survey that has collected family income with both methods. With data from 14,222 urban and rural families in the 2018 wave of the nationally representative China Family Panel Studies, we compare the two estimates, and further evaluate factors that might contribute to the discrepancy. We find that the two estimates are loosely matched in only a third of all families, and most of the matched families have a simple income structure. Although the mean of the multiple-question estimate is larger than that of the single-question estimate, the pattern is not monotonic. At lower percentiles up till the median, the single-question estimate is larger, whereas the multiple-question estimate is larger at higher percentiles. Larger family sizes and more income sources contribute to higher likelihood of inconsistent estimates from the two designs. Families with wage income as the main income source have the highest likelihood of giving consistent estimates compared with all other families. In contrast, families with agricultural income or property income as the main source tend to have very high probability of larger single-question estimates. Omission of certain income components and rounding can explain over half of the inconsistencies with higher multiple-question estimates and a quarter of the inconsistencies with higher single-question estimates.

Purpose: Mobile learning has seen tremendous growth over the years. Like any other software application, usability is one of the key concerns in its successful implementation. There is a lack of study that provides a comprehensive overview of usability testing of mobile learning applications. Motivated by this a mapping study is conducted. Design/methodology/approach: A systematic mapping study was conducted using 51 papers retrieved from the Scopus database published between 2005 and 2022 that reported on usability testing of mobile learning applications. Findings: The key findings suggest that research is expected to expand in the near future. User-based testing is the commonly used method, while data are collected mainly through questionnaires, observation and interviews. Testing is mainly conducted in a controlled environment. Originality/value: The study provides (1) an evidence-based discussion on usability testing of mobile learning applications, (2) an up-to-date map on state of the art on usability testing of mobile learning applications and (3) providing direction for further research to scientifically strengthen the field.

This paper describes, and lays out an argument for, the use of a procedure to help groups of reviewers to judge the quality of prior research reports. It argues why such a procedure is needed, and how other existing approaches are only relevant to some kinds of research, meaning that a review or synthesis cannot successfully combine quality judgements of different types of research. The proposed procedure is based on four main factors: the fit between the research question(s) for any study and its design(s); the size of the smallest group of cases used in the headline analyses; the amount and skewness of missing data; and the quality of the data collected. This simple procedure is now relatively widely used, and has been found to lead to widespread agreement between reviewers. It can fundamentally change the findings of a review of evidence, compared to the conclusions that would emerge from a more traditional review that did not include genuine quality rating of prior evidence. And powerfully, because it is not technical, it permits users to help judge research findings. This is important as there is a growing demand for evidence-led approaches in areas of social science such as education, wherein summaries of evidence must be as trustworthy as possible.

As Twitter's (or X's) influence permeates aspects of education, researchers must consider how to ethically and effectively leverage the unique types of data that this social media platform offers. This paper provides recommended methodological practice considerations for working with qualitative Twitter data toward the advancement of education research. To inform our methodological protocol, we draw from a larger study that investigated disability disclosure during graduate school on Twitter. We use examples from our study to highlight similar protocol considerations that future researchers might take when working with qualitative Twitter data, including use of the website's advanced search feature and use of multifaceted analysis approaches for capturing this data's often unique complexity. We further provide ethical considerations for conducting social media research in education. Finally, we discuss the utility of the practices described in this article for moving education research forward via qualitative Twitter data.

As a result of the growing commercial marketplace for teachers' digital data, a new organization that includes educational data brokers has evolved. Educational data brokerage is relatively intangible due to the ease of de-identified data being collected and sold via educational technology. There is an urgent need to expose how the brokerage of educational data relates to the commercial mediation of consent and privacy in educational settings. It is difficult due to a lack of consistent terminology about organizations that buy and sell data. This paper offers an extensive analysis of the social learning platform Edmodo and provides evidence that justifies the term 'educational data broker'. The results aim to provide new terminology to a largely obfuscated process in educational settings and bring to light a concrete example of brokerage activity focusing on teachers' online activity.

This study combines product- and process-oriented research methods and tools to observe whether and how the presence of pre-translated text affects translation quality and influences the translator's research patterns. It is part of the LeMaTTT project, a simulated longitudinal empirical study exploring the impact of MT on info-mining and thematic competences in legal translation. Data were elicited through a translation task completed by a cohort of 110 final-year MA trainees with training in legal translation and basic MT literacy, and a cohort of 54 first-year MA trainees with no to very limited experience in specialised translation and post-editing. This paper provides a first analysis of selected process-related data concerning the allocation of time and the use of external resources throughout the translation or post-editing processes of 40 participants, 20 from each cohort. Preliminary results highlight some correlations between the use of time and external resources and both (a) the development of thematic and info-mining competences and (b) the specific type of task, i.e. whether from-scratch translation or post-editing.

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.

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.

Purpose: It has been demonstrated that AI-powered, data-driven tools' usage is not universal, but deeply linked to socio-cultural contexts. The purpose of this paper is to display the need of adopting situated lenses, relating to specific personal and professional learning about data protection and privacy. Design/methodology/approach: The authors introduce the results of a case study based on a large educational intervention at a fully online university. The views of the participants from degrees representing different knowledge areas and contexts of technology adoption (work, education and leisure) were explored after engaging in the analysis of the terms and conditions of use about privacy and data usage. After consultation, 27 course instructors (CIs) integrated the activity and worked with 823 students (702 of whom were complete and correct for analytical purposes). Findings: The results of this study indicated that the intervention increased privacy-conscious online behaviour among most participants. Results were more contradictory when looking at the tools' daily usage, with overall positive considerations around the tools being mostly needed or "indispensable". Research limitations/implications: Though appliable only to the authors' case study and not generalisable, the authors' results show both the complexity of privacy views and the presence of forms of renunciation in the trade-off between data protection and the need of using a specific software into a personal and professional context. Practical implications: This study provides an example of teaching and learning activities that supports the development of data literacy, with a focus on data privacy. Therefore, beyond the research findings, any educator can build over the authors' proposal to produce materials and interventions aimed at developing awareness on data privacy issues. Social implications: Developing awareness, understanding and skills relating to data privacy is crucial to live in a society where digital technologies are used in any area of our personal and professional life. Well-informed citizens will be able to obscure, resist or claim for their rights whenever a violation of their privacy takes place. Also, they will be able to support (through adoption) better quality apps and platforms, instead of passively accepting what is evident or easy to use. Originality/value: The authors specifically spot how students and educators, as part of a specific learning and cultural ecosystem, need tailored opportunities to keep on reflecting on their degrees of freedom and their possibilities to act regarding evolving data systems and their alternatives.

Research involving young people is a challenging process that requires managing relationships with diverse individuals and groups, including the young participants and their various gatekeepers. While it is normally assumed that the researcher is in overall control of their research, by using a Foucauldian conception of 'power as effects' that operate in the form of relations and through discourse as the articulation of norms, this paper discusses how, in practice, the researcher can lose control over their research and so be forced into making substantial compromises concerning the nature and extent of the data they can collect. I do this by reflecting on my experience of conducting research involving UK secondary school students using online data collection methods during the COVID-19 pandemic. I identify several factors that generated power effects which influenced the conduct of the research, including: an ethics review that relied on a simplistic discourse concerning young participants' (in)competence; my own self-regulation of my conduct in respect of 'ethical' research; my 'positionality' in the field; and a researcher's general dependence on participants and gatekeepers to complete their research. I conclude by reflecting on how these factors may impact upon the conditions for viable social research involving young people.

Introductory undergraduate statistics courses widely focus on statistical concepts or software-based data analysis. Despite the fact that the analysis of real data has shown to enhance students' engagement, the step of data collection is often neglected. Once students know the challenges of data collection, they are more aware of potential imperfections, such as a lack of representativeness, during data analysis. In this paper, we present a course that closes the gap allowing Business and Economics students to conduct a full survey under realistic conditions including questionnaire design, sampling, and data analysis. It entangles theory and application by combining course-based research experiences with cooperative learning and a flipped classroom approach. Students do not only obtain competences in the field of statistics, they also gain experiences and self-confidence for future research projects because the lecturer acts as a mentor guiding the students throughout the project. Although statistics is usually an unpopular field for Business and Economics students, their motivation was high throughout the semester as they acted as researchers who analysed a specific research question. This is in agreement with student feedback, which highlights the promotion of research-related competences and self-efficacy within the course.

Qualitative research has been strongly affected by the COVID-19 pandemic, highlighting the possibilities that Voice over Internet Protocol (VoIP) technologies such as Skype, WhatsApp, and Zoom offer to qualitative scholars. Based on the experience of using such technologies to collect qualitative data for our PhD studies, we present how we dealt with the challenges of this interview mode. Precisely, we discuss problems related to rapport, technology, digital exclusion, and ethics frequently pointed out in the methodological literature on online interviews. Thereby we put forward strategies and techniques that helped us to 1) build a rapport, 2) manage technical difficulties, 3) reflect on risks of digital exclusion, and 4) comply with the ethical standards of our institution. In doing so, we draw on our qualitative data to support the arguments. The aim of this paper is, thus, to deepen the methodological debate on online interviews in social sciences.

Although some critics consider it time-consuming and demanding, proponents of the monolingual approach for field research (i.e., learning to speak a target field language as part of the research process) argue that it can provide a unique insight into its structures. However, this claim remains largely unsubstantiated in the available literature on field methods. The present paper sets out to achieve a twofold objective: First, it reviews prior observations about the monolingual method in documentary-linguistics publications, highlighting important gaps in research. Secondly, based on qualitative data from the author's fieldwork context in rural, indigenous China, it contributes to addressing one such gap by demonstrating how, when, and why basic to intermediate communicative competence can enhance the documentation, description, and analysis of a field language, in ways that complement and sometimes outperform other approaches such as bilingual and stimuli-based elicitation.

The global scale of COVID-19 has constrained academics from conducting much person-facing research. Reactively, trend is increasing for digital-based methodologies capturing already existing online data. Scholars often 'scrape' user-postings from internet forums using coding algorithms and text capture tools, before analysing data, drawing conclusions and publishing findings. The online social news aggregation and discussion website Reddit is a particularly rich source of data for researchers. The public nature of Reddit materials may suggest rationale for user-data to be replicated, analysed and archived; indefinitely and in multiple locations, for scholarly research. However, this position overlooks several key ethical considerations. This paper presents an overview and explanation of Reddit, followed by an exploration of studies that use Reddit-acquired data. Arising ethical issues are discussed, and solutions to salient dilemmas presented. This is to enhance awareness of potential problems and improve protections for those whose data is unknowingly used for research.

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.

Data extraction is a time-consuming and resource-intensive task in the systematic review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the review process, and enhancing the quality and reliability of extracted data. In this paper, we propose a method for using Bing AI and Microsoft Edge as a second reviewer to verify and enhance data items first extracted by a single human reviewer. We describe a worked example of the steps involved in instructing the Bing AI Chat tool to extract study characteristics as data items from a PDF document into a table so that they can be compared with data extracted manually. We show that this technique may provide an additional verification process for data extraction where there are limited resources available or for novice reviewers. However, it should not be seen as a replacement to already established and validated double independent data extraction methods without further evaluation and verification. Use of AI techniques for data extraction in systematic reviews should be transparently and accurately described in reports. Future research should focus on the accuracy, efficiency, completeness, and user experience of using Bing AI for data extraction compared with traditional methods using two or more reviewers independently.