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The rapid development of education informatization has accumulated a large amount of data for learning analytics, and adopting educational data mining to find new patterns of data, develop new algorithms and models, and apply known predictive models to the teaching system to improve learning is the challenge and vision of the education field in the era of big data. Learning intervention, as a core concept of learning analytics, refers to the purposeful and planned adoption of direct or indirect strategies or behaviors based on tracking learning behaviors and integrating information about learners' characteristics to give learners personalized guidance and assistance in order to help learners break through the status quo of learning difficulties and improve their learning abilities, so as to achieve tailored teaching. In this study, data mining was conducted on the performance records of students on math problems in an online learning system, and a learning intervention suggestion prediction model was constructed on the basis of decision tree algorithm using Python, with a view to understanding the effectiveness, willingness, style, and other characteristics of the learners' online learning through the analysis results, providing personalized guidance to students, and enabling teachers to intervene with at-risk students and successfully complete the teaching goals. It was found that the most significant impact of the learning intervention advice provided to learners was the number of hints they sought during the learning process, and that learners who needed to be "intervened" or "monitored" could be categorized into two groups: independent inefficient and dependent inefficient according to the model. Therefore, teachers or adaptive learning systems should intervene in a timely and appropriate way for different types of learning crisis groups to solve the problems of poor learning performance, insufficient commitment to learning, poor learning habits, low participation in learning, low self-efficacy and other problems of learners in different learning scenarios.

This study explores the complexities of qualitative fieldwork and unpacks the fieldwork dynamics drawing on critical reflections based on the experiences of interviewing primary level teachers in exploring their identities. I argue that qualitative fieldwork is not a one-shot, linear activity but a negotiated and relational task requiring a flexible and context-specific plan. This study identifies challenges associated with the fieldwork relating to establishing and sustaining a good relationship and reciprocity between the researcher and the participants, unequal power dynamics between the researcher and the participants, participants' familiarity with and habitual response to the survey questionnaires, narrow understanding of confidentiality and anonymity as ethical considerations, unmatched gender between the researcher and the participants, and through and detailed capture of micro field jottings and writing the fieldnotes. I suggest initiating fieldwork with sufficient informal conversation and establishing a rapport to gain the participants' trust, positioning oneself as an insider to acquire valuable and meaningful data.

This review explores the integration and effects of the Internet of Things (IoT) in education, highlighting its importance in transforming traditional teaching and learning techniques. It examines the early uses and historical growth of IoT, its development, and the turning points in its adoption. It explores IoT platforms, tools, and technologies in education, including wearables, smart devices, augmented and virtual reality, gamification, and collaborative learning. It discusses the role of IoT in improving campus management, including intelligent campuses with IoT-enabled infrastructure, energy-saving technologies, and safety and security improvements. The study discusses data security and privacy issues in IoT installations and the ethical and legal implications of data collection in the classroom. The study also discusses upcoming trends and prospects for IoT usage in education, including AI and machine learning integration. Finally, the review provides insights for educators, decision-makers, and stakeholders, identifying research gaps and recommending areas for future IoT implementation.

Northeastern University's EdD faculty faced unique challenges during the pandemic and racial reckoning following George Floyd's murder. During this period, however, we found opportunities to adapt and improve our program. We prioritized compassion and connection. We made significant strides in curriculum development through design and implementation of three new concentrations. We focused all program elements on how social justice works in a variety of educational settings. We altered our approach to data collection and doctoral supervision. In so doing, we were able to maintain consistency for our students and develop a closer bond with our faculty colleagues.

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.]

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 describes the study of enforcement of prerequisites in the Computer Science program at a regional university in the Southwest. Prerequisites are a significant factor in programs of study in higher education. Allowing students to register in courses may assume that they have existing knowledge and skills. Some programs treat prerequisites as advisory, while others consider them mandatory. In the latter case, procedures usually exist to make exceptions in the form of registration overrides. The state of prerequisite enforcement at our university over the years, and some factors that may have influenced adherence to the prerequisite structure over the years, will be discussed in this paper.

This paper responds to the need to support complexities of literacy instruction by identifying and illustrating teaching strategies used by a practicing special education teacher rooted in a multi-theoretical approach to teaching literacy. We argue the importance of teaching from multiple theoretical standpoints and utilizing student-centered, asset-based approaches to pedagogy, assessment, and learning relating to literacy. We share our multi-theoretical approach to understanding and teaching literacy, defining literacy and its complexities. Then, we illustrate several teaching practices including using growth mindset, implementing asset-based data collection, utilizing feedback, and integrating inquiry-based learning that ultimately supports the cultivation of empowered literacy learners who deem learning as both interesting and valuable. Finally, we discuss tensions and challenges inherent to implementing a multi-theoretical approach. Throughout, we provide reflection points to empower teachers to rely on their agency, self-efficacy, and expertise and to feel capable in their knowledge and agency in an era where teachers are increasingly experiencing deprofessionalization through disempowering factors.

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.

Household expenditure data is at the core of efforts to measure living standards, inequality and financial protection against illness. Currently it is mainly derived from recall-based surveys that are time consuming and prone to measurement errors. Diaries are often used as an alternative approach, however this results in fatigue and low compliance. Smartphone- based diaries could overcome many of these challenges, but it is yet unknown how this approach could be used and its relationship with data from the household surveys. This study evaluated a smartphone-based diary application for collecting daily household expenditures and compared data with that of household surveys. Results show that smartphone-based diary applications could be used to optimize or complement the information from conventional survey-based indicators.

Ethnoracial identity refers to the racial and ethnic categories that people use to classify themselves and others. How it is measured in surveys has implications for understanding inequalities. Yet how people self-identify may not conform to the categories standardized survey questions use to measure ethnicity and race, leading to potential measurement error. In interviewer-administered surveys, answers to survey questions are achieved through interviewer-respondent interaction. An analysis of interviewer-respondent interaction can illuminate whether, when, how, and why respondents experience problems with questions. In this study, we examine how indicators of interviewer-respondent interactional problems vary across ethnoracial groups when respondents answer questions about ethnicity and race. Further, we explore how interviewers respond in the presence of these interactional problems. Data are provided by the 2013-2014 Voices Heard Survey, a computer-assisted telephone survey designed to measure perceptions of participating in medical research among an ethnoracially diverse sample of respondents.

The use of data to explain natural phenomena has been a core feature of science education, and science educators continue to call for an increased emphasis on teaching data practices. This mixed methods design-based research study adds to the growing body of research on data practices in science by explaining the learning trends of science teachers involved in a three-year collaborative professional development (PD) using computational thinking (CT) and self-regulated learning (SRL) as a means to support teachers in implementing data practices. The PD resulted in long-term high-quality teacher learning outcomes for all three elements of the PD (data practices, CT, and SRL) despite the upheaval of teaching platforms due to the COVID-19 pandemic. Since the teachers were involved in design-based research, their professional learning focused on collaboratively creating an electronic, interactive notebook with lessons for use in science classrooms across the United States. Creating a common product as an outcome of the PD may have served as motivation for teachers to learn about and implement more data practices, CT, and SRL, which suggests that design-based research is another valuable design for teacher PD.

There is a need for public health undergraduates to acquire skills in data collection, statistical programming, and infectious diseases modeling. Public health officials and accreditation bodies underline the importance of a cumulative, "real-world" experience as part of a student's education. The Watermelon Meow Meow (WMM) outbreak is a cumulative experience that teaches upper-level undergraduate/graduate students about infectious disease dynamics by asking students to: participate in a fictitious outbreak; collect and analyze outbreak data. Innovative to our approach is the use of DataCamp as a technology to support learning statistical programming and framing WMM under principles of Universal Design Learning (UDL). We evaluated 27/32 student responses using a mixed-methods approach. We found WMM: augmented traditional lecture-style instruction and increased student awareness of heterogeneous risks associated with infectious diseases. We identified three student typologies: students who learn best from: (i) integrating traditional lecture plus WMM; (ii) participating in WMM data collection but not coding; and (iii) from lecture and classroom-based learning from peers. WMM is an example of a more general approach - which we call Slate, Operate, Translate - that instructors can follow to combine technology and a hands-on experiment to satisfy both UDL principles and increasing demands of public health education in a mathematics/statistics class.

Despite recent methodological advances in saturation, guidelines for its estimation in more complex research designs--such as ethnographic studies--have been lacking. We present an accessible, step-by-step approach to empirical assessment of data saturation, tested on a moderately sized ethnographic study with 109 combined direct observations and interviews. The three-phase method includes a priori sample size prediction, provisional saturation estimation during data collection, and post hoc confirmation. Post hoc analysis indicated we reached key thresholds of data saturation before ending fieldwork. We achieved 80% saturation at our 69th data collection event (63% of the full sample) and 90% saturation at our 91st data collection event (83% of the full sample). The method is designed to account for on-the-ground realities confronted in contemporary ethnographic research, including time constraints during fieldwork, and can accommodate studies with medium to large scopes, broad foci, heterogeneous populations, and analysis of multiple qualitative data sources.

Social network research often depends on the willingness of respondents to provide personal information about themselves and alters. Survey design strategies that increase willingness to share this information are necessary for social network research to be feasible, especially when name generators are used for sampling because rosters are unavailable. We conducted an experiment in which one group of respondents (n = 94) received an online survey that included an example network map and the other group (n = 100) received one that did not. Results show the map did not increase or decrease provision of network contacts nor influence the types of ties reported. Furthermore, respondents were reluctant to provide names and contact information of alters. Our study demonstrates the difficulty of collecting network information without a previously defined roster and an attempt to improve data collection through strategic survey design.

Students with and at risk for emotional and behavioral disorders (EBD) struggle to acquire and develop writing skills. To support their students' unique needs, it is important for teachers to monitor student writing progress to make instructional decisions based on data. In this article we describe methods for progress monitoring focused on writing skills. We describe a feasible system for continuous progress monitoring. We also address setting and monitoring goals for students with and at risk for EBD.

Despite the elevated importance of Data Science in Statistics, there exists limited research investigating how students learn the computing concepts and skills necessary for carrying out data science tasks. Computer Science educators have investigated how students debug their own code and how students reason through foreign code. While these studies illuminate different aspects of students' programming behavior or conceptual understanding, a method has yet to be employed that can shed light on students' learning processes. This type of inquiry necessitates qualitative methods, which allow for a holistic description of the skills a student uses throughout the computing code they produce, the organization of these descriptions into themes, and a comparison of the emergent themes across students or across time. In this article we share how to conceptualize and carry out the qualitative coding process with students' computing code. Drawing on the Block Model to frame our analysis, we explore two types of research questions which could be posed about students' learning. Supplementary materials for this article are available online.

Validity refers to the extent to which a given study reflects the dominant values of a research community regarding what constitutes "good" research. Methodological texts on qualitative research provide a wide range of criteria and strategies to help qualitative researchers validate their studies. Given the importance of these strategies to establish a study as trustworthy and legitimate, the objective of this study was to understand the strategies commonly used by school psychology researchers in qualitative research. We therefore reviewed qualitative research articles published in seven school psychology journals between 2006 and 2021. We found 15 strategies authors used to enhance the validity of their research. We also found that a strategy could be enacted in many different ways by different researchers depending on the context. We conclude by recommending four ways in which qualitative researchers in school psychology can improve their validation practices.

Purpose: End-user participation is often encouraged to promote the uptake of Digital Livestock Technologies (DLTs). However, managing participation during DLT development can be challenging. We explore how participation decisions can impact end-users' engagement and attitudes towards the process, before suggesting strategies for improved management of the participation process. Methodology: We explored the experiences of end-users (e.g. farmers and farm assessors) and other stakeholders (e.g. developers, researchers, industry) involved in the development and testing of DLTs on UK farms, using semi-structured, in-depth interviews (N = 31). Findings: Participation can help develop technologies that better align with users' needs, promote learning, and encourage feelings of ownership. However, participation can be a double-edged sword. Inadequate levels of involvement, management of stakeholder relationships and expectations, and available support can negatively impact end-users' engagement and attitudes. Practical implications: Our study highlights the importance of understanding how management decisions during the participatory development of DLTs can influence the engagement and attitudes of end-users towards the process. Theoretical implications: The study contributes to the participation literature in agriculture and demonstrates the importance of using a critical lens to avoid making normative assumptions that participation necessarily promotes uptake in a linear, uncomplicated fashion. Originality/Value: Participation is seen as key for technology adoption. However, the potential downsides of participation have received less attention in relation to the engagement of end-users in the process.

Previous work has demonstrated similarities and differences between aerial and terrestrial image viewing. Aerial scene categorization, a pivotal visual processing task for gathering geoinformation, heavily depends on rotation-invariant information. Aerial image-centered research has revealed effects of low-level features on performance of various aerial image interpretation tasks. However, there are fewer studies of viewing behavior for aerial scene categorization and of higher-level factors that might influence that categorization. In this paper, experienced subjects' eye movements were recorded while they were asked to categorize aerial scenes. A typical viewing center bias was observed. Eye movement patterns varied among categories. We explored the relationship of nine image statistics to observers' eye movements. Results showed that if the images were less homogeneous, and/or if they contained fewer or no salient diagnostic objects, viewing behavior became more exploratory. Higher- and object-level image statistics were predictive at both the image and scene category levels. Scanpaths were generally organized and small differences in scanpath randomness could be roughly captured by critical object saliency. Participants tended to fixate on critical objects. Image statistics included in this study showed rotational invariance. The results supported our hypothesis that the availability of diagnostic objects strongly influences eye movements in this task. In addition, this study provides supporting evidence for Loschky et al.'s (Journal of Vision, 15(6), 11, 2015) speculation that aerial scenes are categorized on the basis of image parts and individual objects. The findings were discussed in relation to theories of scene perception and their implications for automation development.

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.

In this research, our objective is to characterize the problem-solving procedures of primary and lower secondary students when they solve problems in real class conditions. To do so, we rely first on the concept of heuristics. As this term is very polysemic, we exploit the definition proposed by Rott (2014) to develop a coding manual and thus analyze students' procedures. Then, we interpret the results of these analyses in a qualitative way by mobilizing the concept of semantic space (Poitrenaud, 1998). This detailed analysis of students' procedures is made possible by collecting audiovisual data as close as possible to the students' work using an action camera mounted on the students' heads. We thus succeed in highlighting three different investigation profiles that we have named explorer, butterfly, and prospector. Our first results tend to show a correlation with these profiles and the success in problem-solving, yet this would need more investigation.

Autism is heterogeneous at many levels, including clinical symptoms and etiology. A key strategy in studying heterogeneous conditions is having large enough sample sizes to stratify into smaller groups that are more homogeneous. SPARK and Simons Searchlight are large and growing research cohorts of individuals with autism in the United States and individuals with genetically defined neurodevelopmental conditions around the world, respectively. They both provide freely available phenotypic and genotypic data with the ability to re-contact participants through the research match program. Deep dives into each gene in Searchlight provide comprehensive natural history data to understand the differing clinical courses to inform proper clinical care, and work toward treatment for each condition. Moreover, pilots of genetically based newborn screening programs for neurogenetic disorders can provide opportunities for equitable and early diagnosis to try to improve outcomes with earlier interventions.

Introduction: Due to usability, feasibility, and acceptability concerns, observational treatment fidelity measures are often challenging to deploy in schools. Teacher self-report fidelity measures with specific design features might address some of these barriers. This case study outlines a community-engaged, iterative process to adapt the observational Treatment Integrity for Elementary Settings (TIES-O) to a teacher self-report version designed to assess the use of practices to support children's social-emotional competencies in elementary classrooms. Method: Cognitive walkthrough interviews were conducted with teachers to improve the usability of the teacher self-report measure, called the Treatment Integrity for Elementary Schools-Teacher Report (TIES-T). Qualitative content analysis was used to extract themes from the interviews and inform changes to the measure. Results: Increasing clarity and interactive elements in the measure training were the dominant themes, but suggestions for the measure format and jargon were also suggested. Conclusion: The suggested changes resulted in a brief measure, training, and feedback system designed to support the teacher's use of practices to support children's social-emotional competencies in elementary classrooms. Future research with the TIES-T will examine the score reliability and validity of the measure.

This article provides an original and timely contribution to current cutting-edge methodological debates by discussing the ongoing need to ensure communities in zones which are inaccessible through war, conflict or disease still have a voice and are not side-lined. As seen during COVID-19, traditional methods of gaining opinions from these communities, such as face-to-face interviews and focus groups, may be restricted and even impossible. Instead, remote methods using WhatsApp provide many additional benefits, providing qualitative and quantitative data (not always simultaneously provided by surveys or interviews), and allowing voice and text messages to be used. This article draws out the generic implications for the methodology using the substantive findings of a study conducted in the Sahel in 2019-20. Whilst also providing 'how to' discussions on this novel approach, the article critically reflects on the advantages and disadvantages of using WhatsApp as it relates to conducting social research in general.

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.

Searching for trials is a key task in systematic reviews and a focus of automation. Previous approaches required knowing examples of relevant trials in advance, and most methods are focused on published trial articles. To complement existing tools, we compared methods for finding relevant trial registrations given a International Prospective Register of Systematic Reviews (PROSPERO) entry and where no relevant trials have been screened for inclusion in advance. We compared SciBERT-based (extension of Bidirectional Encoder Representations from Transformers) PICO extraction, MetaMap, and term-based representations using an imperfect dataset mined from 3632 PROSPERO entries connected to a subset of 65,662 trial registrations and 65,834 trial articles known to be included in systematic reviews. Performance was measured by the median rank and recall by rank of trials that were eventually included in the published systematic reviews. When ranking trial registrations relative to PROSPERO entries, 296 trial registrations needed to be screened to identify half of the relevant trials, and the best performing approach used a basic term-based representation. When ranking trial articles relative to PROSPERO entries, 162 trial articles needed to be screened to identify half of the relevant trials, and the best-performing approach used a term-based representation. The results show that MetaMap and term-based representations outperformed approaches that included PICO extraction for this use case. The results suggest that when starting with a PROSPERO entry and where no trials have been screened for inclusion, automated methods can reduce workload, but additional processes are still needed to efficiently identify trial registrations or trial articles that meet the inclusion criteria of a systematic review.

The number of meta-analyses of aggregate data has dramatically increased due to the facility of obtaining data from publications and the development of free, easy-to-use, and specialised statistical software. Even when meta-analyses include the same studies, their results may vary owing to different methodological choices. Assessment of the replication of meta-analysis provides an example of the variation of effect "naturally" observed between multiple research projects. Reproducibility of results has mostly been reported using graphical descriptive representations. A quantitative analysis of such results would enable (1) breakdown of the total observed variability with quantification of the variability generated by the replication process and (2) identification of which variables account for this variability, such as methodological quality or the statistical analysis procedures used. These variables might explain systematic mean differences between results and dispersion of the results. To quantitatively characterise the reproducibility of meta-analysis results, a bivariate linear mixed-effects model was developed to simulate both mean results and their corresponding uncertainty. Results were assigned to several replication groups, those assessing the same studies, outcomes, treatment indication and comparisons classified in the same replication group. A nested random effect structure was used to break down the total variability within each replication group and between these groups to enable calculation of an intragroup correlation coefficient and quantification of reproducibility. Determinants of variability were investigated by modelling both mean and variance parameters using covariates. The proposed model was applied to the example of meta-analyses evaluating direct oral anticoagulants in the acute treatment of venous thromboembolism.

This article presents a novel digital method of capturing finger-based gestures on touchscreen devices for the purpose of exploring tracing gestures in educational research. Given that tracing has been found to support cognition, learning, and problem solving in educational settings, data related to the performance of these gestures are increasingly of interest to researchers. Most educational research methods exploring the use of hand gestures rely on in-person data collection, whether through direct observation or video recording of participants' behavior for later analysis. These methods, while effective for observing gross movements, may not provide researchers with detailed insights into how learners interact with learning materials. Using custom tools to record touchscreen engagement on tablet computing devices can address this limitation while also providing the means to visually represent touch-based interactions with these devices. Geometry Touch is an iPad app developed and tested by the primary author as a part of a pilot study. The research study, theoretically grounded in cognitive load theory (CLT), demonstrated that Geometry Touch could efficiently collect data on touchscreen interactions while also providing potential avenues to quantify touchscreen interactions through computational means. The purpose of this article is to report on the development and testing of this app while providing an explanation of how it was used as a method of data collection by leveraging touchscreen technology. The article concludes by discussing how this digital method of capturing movement can provide further insight into how finger-based gestures can influence learning and, as such, could increase the reach of gesture-based research.

Estimating prescription medicine use is challenging due to recall bias associated with surveys and coverage bias in administrative data. This study assesses how making operational improvements and combining both survey and administrative data sources can increase data quality on filled prescriptions. We use data from the Medicare Current Beneficiary Survey (MCBS) and administrative data from the Centers for Medicare and Medicaid Services (CMS). First, we investigate improvements from a prescription medicine lookup (PMLU) tool integrating a commercial medicine database into the MCBS. We then examine impacts of matching survey-reported medicines to Part D claims. We find that the PMLU improves accuracy and reduces measurement bias. Claims matching identifies additional medicines, especially for beneficiaries with more chronic conditions and medicines. This study shows that integrating a commercial database and supplementing with administrative data improves data quality and reduces sources of error.

Community-based participant-observation purposefully combines participant-observation and community-based participatory research. While participant-observation is the core method of ethnography and foundational to cultural anthropology, community-based participatory research initially emerged from health and related applied sciences to align researchers' and communities' agendas through focused collaboration. Participant-observation and community-based participatory research have different scholarly origins and norms but are united in centering communities' understandings on their terms. Combining the strengths of both, we provide a step-by-step explanation of community-based participant-observation, with examples from a study of water insecurity in colonias north of the U.S.-Mexico border. Using community-based participant-observation, researchers can facilitate the co-production of knowledge and community benefit by analyzing high-quality data that inform theory building and basic research. [The authors acknowledge the co-authorship of the Action for Water Equity Consortium.]

Although it has long been acknowledged that interviewers play a crucial role in the survey data collection process, there is little research concerning interviewer effects on how respondents perceive the interview. We investigate whether interviewer effects exist regarding how much respondents report having enjoyed the interview and whether these effects can be explained by interviewer characteristics. We use data from wave 9 of the German Family Panel pairfam, combined with data from an interviewer survey conducted prior to this wave. Applying multilevel models, we find large interviewer effects that can be explained only partly by interviewers' sociodemographic characteristics, attitudes, and behavior. Interviewers' gender, intrinsic work motivation, and their attitude toward respondents significantly affect how much respondents enjoy the interview.

This systematic literature review critically evaluates 14 empirical studies published over a 14 years span (2006-2019) to answer questions about the models and the effects of transformational school leadership on student academic achievement. The analysis of the related literature utilized vote counting and narrative synthesis to delineate the status quo of the current research field. It was found that the majority of these studies were conducted in Western and English- speaking countries and these studies utilizing different research methods and models reported mixed results. Recommendations for future research directions include use of an integrated leadership framework and complexity in the study of leadership in schools.

Purpose: This study aims to analyze the platforming scenario at a Brazilian university as well as the data security process for students and professors. Design/methodology/approach: This research brings an analysis through a qualitative approach of the platformization process in a Brazilian teaching institution. Findings: The results point to a lack of knowledge on the part of teachers regarding data security in the platforming scenario, as well as the lack of effectiveness of institutions in protecting student data. Originality/value: Within the Brazilian scenario, this research seeks to contribute to the discussion on platformization in view of the gaps and existing demands on this process in the country.

The majority of adolescents communicate via text-based messaging, particularly through WhatsApp, a widely used free communication application. Written content on WhatsApp has the methodological potential to provide rich qualitative interview data. This study compares data collected using text-based WhatsApp versus face-to-face interview techniques. Semi-structured interviews were conducted with a sample of early school leavers in South Africa, using WhatsApp (n = 9) and face-to-face (n = 27) followed by a focus group discussion with interviewers. Mann-Whitney U and chi-squared were used to assess associations. WhatsApp text-based interviews took significantly longer to complete but were comparable to face-to-face on the number of themes generated. Rapport, measured as the number of statements from the interviewer aimed at creating a sense of affinity, comfort, and distress reduction, differed between interview conditions. It may be methodologically appropriate for researchers to offer participants a choice of a preferred method of interviewing or consider pragmatic blended approaches of face-to-face and WhatsApp.

In today's digital world, higher education institutions collect and use more data than ever. However, institutional silos create barriers for stakeholders who need data for daily operations and strategy. This article presents a vision of a unified, collaborative future for data governance and actionable steps stakeholders can take.

Gamification of behavioral experiments has been applied successfully to research in a number of disciplines, including linguistics. We believe that these methods have been underutilized in applied linguistics, in particular second-language acquisition research. The incorporation of games and gaming elements (gamification) in behavioral experiments has been shown to mitigate many of the practical constraints characteristic of lab settings, such as limited recruitment or only achieving small-scale data. However, such constraints are no longer an issue with gamified and game-based experiments, and as a result, data collection can occur remotely with greater ease and on a much wider scale, yielding data that are ecologically valid and robust. These methods enable the collection of data that are comparable in quality to the data collected in more traditional settings while engaging far more diverse participants with different language backgrounds that are more representative of the greater population. We highlight three successful applications of using games and gamification with applied linguistic experiments to illustrate the effectiveness of such approaches in a greater effort to invite other applied linguists to do the same.

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.

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.

Down syndrome (DS) is a complex condition associated with multiple medical, developmental, and behavioral concerns. A prospective, longitudinal clinical database was integrated into a specialty Down Syndrome Program, with the goals of better understanding the incidence, course, and impact of co-occurring medical, neurodevelopmental, and mental health conditions in DS. We describe the process of developing the database, including a systematic approach to data collection and database infrastructure, and report on feasibility, challenges, and solutions of initial implementation. Between March 2018 and November 2021, data from 842 patients (ages 4.8 months to 26 years) was collected. Challenges included caregiver form completion as well as time and personnel required for successful implementation. With full integration into clinical visit flow, the database proved to be feasible. The database enables identification of patterns of development and health throughout the lifespan and it facilitates future data sharing and collaborative research to advance care.

This article explores potential uses, misuses, beneficiaries, and tensions of learning analytics in higher education. While those promoting and using learning analytics generally agree that ethical practice is imperative, and student privacy and rights are important, navigating the complex maze of ethical dilemmas can be challenging, particularly in situations involving competing principles. We tease out the 'clouds' behind the silver lining of learning analytics and propose a principles-based operational framework for ethical data governance to help educational developers navigate situations involving individual and institutional uses of learner data, where what might be legally and technologically permissible is not necessarily ethical.

This study evaluated the forecasting accuracy of trend estimation methods applied to time-series data from computer adaptive tests (CATs). Data were collected roughly once a month over the course of a school year. We evaluated the forecasting accuracy of two regression-based growth estimation methods (ordinary least squares and Theil-Sen). The precision, or accuracy, of predictions were heavily influenced by how far performance was forecasted into the future (1, 3, or 5 months) and the number of observations available to estimate growth (3, 4, or 5). When performance was forecasted further and growth estimates were based upon fewer observations, predictions were off by as much as two times the average conditional standard error of measurement for a grade level. No combinations of data collections schedule, forecasting length, or estimation method led to consistent bias in predictions. Educators should be cautious when using trend-line decision rules to predict future performance. Suggestions for alternate decision rules to explore with CATs are offered.

With recent advances in artificial intelligence (AI), machine learning (ML) has been identified as particularly useful for organizations seeking to create value from data. However, as ML is commonly associated with technical professions, such as computer science and engineering, incorporating training in the use of ML into non-technical educational programs, such as social sciences courses, is challenging. Here, we present an approach to address this challenge by using no-code AI in a course for university students with diverse educational backgrounds. This approach was tested in an empirical, case-based educational setting, in which students engaged in data collection and trained ML models using a no-code AI platform. In addition, a framework consisting of five principles of instruction (problem-centered learning, activation, demonstration, application, and integration) was applied. This paper contributes to the literature on IS education by providing information for instructors on how to incorporate no-code AI in their courses and insights into the benefits and challenges of using no-code AI tools to support the ML workflow in educational settings.

This teaching tip describes using Microsoft Power BI Desktop in a class to analyze unstructured data from an exit survey of prior students from a Master of Science in Management Information Systems program. Results from a short survey administered to these students showed that the students, using the no-code Power BI, were able to accomplish their text analytics tasks in a shorter period, and with less overall effort, as compared to using traditional code-rich text analytics methods. The process is described in detail as to how the students, new to unstructured data analysis, can uncover these results. This process can be replicated by other instructors teaching text analytics.

This article provides teachers with a toolkit for assessing students in the context of data-based individualization (DBI) in mathematics. Assessing students is a critical component of DBI because it provides teachers with information about what they may need to modify in their instructional programs. In this article, we provide teachers with step-by-step procedures for assessing students within the DBI process, including selecting an appropriate progress-monitoring tool, collecting baseline data, and analyzing graphed data to determine whether a student is making adequate progress. In addition, diagnostic assessment is discussed to help teachers target instructional priorities based on student needs. Providing a toolkit for assessment and supplementary resources should enable teachers to use DBI procedures more effectively and, ultimately, to boost students' mathematics achievement.

Purpose: Writing is an essential skill that secondary students need to establish for success in their further education, careers, and lives. However, most secondary students in the United States do not achieve the level of writing proficiency expected to ensure academic and future success. One approach that is emerging more in the research literature is the use of a metalinguistic approach to writing. Method: This tutorial begins with a discussion of current educational demands and performance trends in secondary-level academic writing and how secondary students and their educators are presently struggling to meet those demands. The tutorial continues further with a discussion of existing effective writing interventions, as well as current gaps in areas of writing intervention that have the potential to be filled if metalinguistic abilities are also targeted as part of comprehensive secondary-level writing instruction. The author then provides rationale for the use of a metalinguistic approach to secondary school writing including explanation of the metalinguistic processes of writing, how to target those processes while writing, and existing metalinguistic writing interventions. The tutorial concludes with step-by-step suggestions and methods for instructors/interventionists to implement a metalinguistic approach to academic writing with secondary students including (a) review of the critical aspects of students' academic writing expectations, (b) collection of baseline data of students' metalinguistic abilities, (c) facilitation of metalinguistic discussion while working on writing with students, and (d) differentiation of students' instructional level of support. Conclusion: This tutorial will provide instructors/interventionists with the background information, rationale, and a framework to implement a metalinguistic approach to writing that can be utilized when working with secondary students during any academic writing task.

Background: A paucity of qualitative research on sensitive topics that focuses on participants with intellectual disabilities leaves their views unexplored. This scoping review mainly aimed to provide an overview of qualitative data collection methods used in research involving participants with intellectual disabilities to explore death and dying. Method: A scoping review of primary research and methodological papers published between January 2008 and March 2022 was conducted. The PRISMA-ScR checklist was followed. Results: We identified 25 articles utilising four data collection methods: interviews, focus groups, the Nominal Group Technique, and participant observation. Data collection trends were identified, including accommodations for participants with intellectual disabilities, visual media used as a facilitator, and reporting of distress protocols. Most participants had mild to moderate intellectual disabilities. Conclusions: The included studies demonstrate a flexible approach that relies on the use of multiple methods. Future research must adequately report study characteristics to ensure transparency and reliability.

Purpose: This study aims to identify how literature has addressed sustainability reporting in universities over time and determine traceable patterns and trends. Design/methodology/approach: A comprehensive systematic review protocol of the Emerald Insight, Web of Science, Science Direct, Scopus, Springer Link and Wiley Online Library databases was performed for the studies published during the past decade. The following steps were involved in this study: search and collection of literature, study selection, quality assessment, data extraction and data analysis. A total of 40 articles met the inclusion criteria and the quality assessment. Findings: The results demonstrated that the most used tools to assist sustainability reporting in universities are the global reporting initiative framework and the graphical assessment of sustainability in universities. However, the adoption of the integrated reporting framework is encouraged. The level of reporting is not yet high, and social and educational indicators are being disclosed in the following areas: community, services and transfer; curriculum and teaching; operation; and research. Originality/value: This study provides a recent overview of sustainability reporting worldwide. By discussing the findings, the authors presented the practical implications of starting a sustainability report at the university and the associated difficulties and benefits.

Despite vital professional development support through the National Art Education Association's Data Visualization Working Group and other scholars' engagement with the topic in the literature, pedagogical literature that connects contemporary data visualization methods to art teaching practices is limited. While acknowledging that many of us have just begun to explore the potential of data visualization as a K-12 art curriculum, in this article we connect conceptualizations of data visualizations for art education practice to curriculum choices. We share our experiences and learning to offer foundational support for K-12 art educators interested in integrating data visualization into their art curriculum.

Contributions: This article explores the impact of environmental education interventions about e-waste recycling and management practices as well as about the energy usage of data centers (DCs) into the Information and Communication Technologies (ICTs) university curricula. Intended Outcomes: An education program was implemented aiming to raise awareness about energy conservation, attitudinal disposition, and e-waste management practices of ICT students in a Greek University. Methodology: The results were derived from the processing of two questionnaires (pretest and post-test data), which were distributed during two lectures on sustainability and green information technology at a two-week interval. Findings: The findings of the study prove that although the majority of the ICT students had a deficient level of knowledge of e-waste recycling and energy usage in DCs, they showed a significant increment of knowledge and awareness regarding them. Moreover, it was observed that the direct effect of the educational intervention on the intention of the students to engage in e-waste recycling was significant. The research also revealed that ICT students showed a positive perception and attitude toward e-waste recycling and recognized the positive financial effect of the proper e-waste recycling and management after the educational intervention.

The designs of many student-facing learning analytics (SFLA) dashboards are insufficiently informed by educational research and lack rigorous evaluation in authentic learning contexts, including during remote laboratory practical work. In this article, we present and evaluate an SFLA dashboard designed using the principles of formative assessment to provide feedback to students during remote lab activities. Feedback is based upon graphical visualizations of student actions performed during lab tasks and comparison to expected procedures using TaskCompare--our custom, asymmetric graph dissimilarity measure that distinguishes students who miss expected actions from those who perform additional actions, a capability missing in existing graph distance (symmetrical dissimilarity) measures. Using a total of N = 235 student graphs collected during authentic learning in two different engineering courses, we describe the validation of TaskCompare and evaluate the impact of the SFLA dashboard on task completion during remote lab activities. In addition, we use components of the Motivated Strategies for Learning Questionnaire as covariates for propensity score matching to account for potential bias in self-selection of use of the dashboard. We find that those students who used the SFLA dashboard achieved significantly better task completion rate (nearly double) than those who did not, with a significant difference in TaskCompare score between the two groups (Mann-Whitney U = 453.5, p<0.01 and Cliff's [delta] = 0.43, large effect size). This difference remains after accounting for self-selection. We also report that students' positive rating of the usefulness of the SFLA dashboard for completing lab work is significantly above a neutral response (S = 21.0 and p<0.01). These findings provide evidence that our SFLA dashboard is an effective means of providing formative assessment during remote laboratory activities.

Culture is a key determinant of children's development both in its own right and as a measure of generalizability of developmental phenomena. Studying the role of culture in development requires information about participants' demographic backgrounds. However, both reporting and treatment of demographic data are limited and inconsistent in child development research. A barrier to reporting demographic data in a consistent fashion is that no standardized tool currently exists to collect these data. Variation in cultural expectations, family structures, and life circumstances across communities make the creation of a unifying instrument challenging. Here, we present a framework to standardize demographic reporting for early child development (birth to 3 years of age), focusing on six core sociodemographic construct categories: biological information, gestational status, health status, community of descent, caregiving environment, and socioeconomic status. For each category, we discuss potential constructs and measurement items and provide guidance for their use and adaptation to diverse contexts. These items are stored in an open repository of context-adapted questionnaires that provide a consistent approach to obtaining and reporting demographic information so that these data can be archived and shared in a more standardized format.

Over one-third of preschoolers with disabilities are fully included in the classroom with their typically developing peers and another large percentage receive some instruction in the inclusive classroom. In order to support young children and increase the likelihood of the desired learning and behavior outcomes, teachers must use individual data for each child to guide decisions regarding instructional and behavioural interventions. Many early childhood special educators are busy and struggle to find an efficient way to collect data on a regular basis. One solution is the use of smartphone or tablet tools. This article provides early childhood special educators with recommendations for individual child data collection through the use of easy-to-use tools that can be customized to meet teacher and classroom needs. Specifically, we recommend the use of: (a) Google forms, (b) video and audio recordings, (c) speech-to-text technology, (d) counter apps, and (e) the smartphone stopwatch function.

This study focuses on Grounded Theory, which is one of the qualitative research designs. Glaser and Strauss developed the Grounded Theory; it has been revised by other scientists, resulting in three distinct Grounded Theory approaches: the systematic design (Corbin and Strauss approach), the classical design (Glaser approach), and the constructivist approach (Charmaz approach). This research aims to discover the key characteristics of grounded theory through a comprehensive examination of these three methods and to show the systematic design in depth. In Grounded Theory research, the systematic design is favoured above other designs, so it is essential to understand the application steps carefully. As a result, the systematic design, which is similar to other designs in terms of fundamental characteristics, exhibits coding stage variances. In this regard, it is intended that the study will alleviate ambiguity in Grounded Theory research, particularly during the coding phase.

This study provides a picture of the impact the novel Coronavirus (COVID-19) had on action research performed by graduate students at a small Midwest university. A qualitative case study was conducted to examine how the participants' abilities to implement their research, gather data, and analyze the results was impacted by COVID-19. Research participants, graduate students at the time of the study, were asked a series of questions regarding modifications made, the impact to the research that was done, the impact to their findings, and implications for future research. Based on the responses to these surveys, researchers determined four prominent themes: altered timelines, limited access to data and materials, quality of academic work, and long-term impacts. Overall, while most research participants were impacted by COVID-19, few were discouraged. Considering the research findings of this study, education can greatly be enhanced by a shift in mindset about the processes of teaching and lessons learned by the pandemic.

This study analyzed studies done in Turkey in the context of curriculum evaluation (CE) by asking, "How is it made? The study was carried out in two stages. In the first stage, the document analysis method used 215 theses written between 1991 and 2020 on CE were analyzed according to the "thesis review form." In the second stage, depth analysis was made through semi-structured interviews with the authors (students) and the field experts (supervisors of the authors) of the theses to make the results of the first stage more understandable. Interviews were conducted with 32 participants. A maximum sampling method was used to determine the participants. The data analysis calculated percentage and frequency values for the data obtained in the first stage. In the second stage, descriptive analysis and content analysis were carried out with the MAXQDA 2020 qualitative data analysis program. The majority of theses did not employ a CE model as a consequence of the research, and the CIPP model was the most popular CE model. Many of the theses were not justified in using the CE model. Model usage increased as time passed to the present day. Many theses used quantitative models but did not explicitly state the sampling technique. Teachers were mainly used in this research as a source for data gathering, and participant numbers ranged from 10 to 50. Additionally, most studies used questionnaires and interviews as the primary data-gathering tools. All of these findings suggest that CE studies have several flaws.

Language sample analysis (LSA) is valuable clinical tool and an important component of comprehensive language assessment. However, LSA is underutilized in real-world practice. SLPs have identified time constraints, insufficient training, and lack of confidence in LSA skills as barriers to regular LSA implementation. Communication science and disorders (CSD) programs have opportunities to provide LSA instruction to address these barriers and prepare students to reliably, feasibly, and confidently use LSA in clinical practice. This pilot study examined CSD students' perspectives on LSA instruction using the Language ENvironment Analysis System (LENA). Undergraduate students (n = 38) completed a series of two electronic surveys prior to and following LSA instruction using LENA. Changes in students' self-ratings of knowledge and skills in collecting and analyzing language samples, attitudes towards studying LSA and child language development, and critical thinking skills were assessed. Significantly higher student ratings were found for all items measuring language sampling knowledge and skills following digital LSA instruction compared to baseline. In addition, student ratings of enthusiasm for and confidence in studying LSA and child language development were also significantly higher. Students' self-ratings of critical thinking skills did not increase significantly following instruction in digital LSA. Implications for LSA teaching and learning are discussed.

COVID-19 and the public health policies emerging in response have laid bare a multiplicity of issues related to educational access and knowledge equity on a global scale. Among these, the quick shift to online and hybrid education models led teachers to adapt a plethora of digital platforms to deliver content and sponsor interactions). Such platforms range from institutionally sanctioned (and subscribed) Learning Management Systems (LMSs) to software provided by organizations beyond the institution and can pose a threat to student data and privacy. Data surveillance in educational contexts is not a new issue, nor is it only a strictly digital problem. However, the current milieu of constant and continuing public health crises has led to more frequent, uncritical, and hurried adoption of learning technologies. This article challenges professionals in higher education specifically to take a more critical look at the various EdTech platforms they are, have, and will adopt in the post-COVID-19 era, and the spectrum of surveillance such platforms enact. Through a review of common entities such as LMSs, Google Workspace for Education, and Zoom video conferencing software, this article demonstrates how these technologies place both teachers and students in a relationship to data and learning characterised by "epistemic inequality" or "unequal access to learning imposed by private commercial mechanisms''. By taking a closer look at the problematic surveillance functioning across EdTech, this article makes a case for Commons-based Peer Production communities as equitable, open educational alternatives that have resisted market-based neoliberalism and surveillance capitalism.

Analysis of programming process data has become popular in computing education research and educational data mining in the last decade. This type of data is quantitative, often of high temporal resolution, and it can be collected non-intrusively while the student is in a natural setting. Many levels of granularity can be obtained, such as submission, compilation, edit, and keystroke events, with keystroke-level logs being the most fine-grained of commonly used dataset types. However, the lack of open datasets, especially at the keystroke level, is notable. There are several reasons for this failing, with the most prominent being the challenges of deidentification that are peculiar to keystroke log data. In this paper, we present the public release of two fully deidentified keystroke datasets that are the first of their kind in terms of both event and metadata richness. We describe our collection technique and properties of the data along with deidentification techniques that, while not fully relieving researchers of significant effort, at least reduce and streamline manual work in hopes that researchers will release similar datasets in the future.

Previous research has extensively analysed teachers' and students' Facebook use for instructional engagement, writing, research dissemination and e-learning. However, Facebook as a data collection mechanism for research has scarcely been the subject of previous studies. The current study addressed these gaps by analysing students' awareness, willingness, and utilisation of Facebook for research data collection [RDC]. This study aimed to predict students' Facebook use for research data collection based on their awareness and willingness and to determine age and gender differences in such predictions. A sample of 11,562 students of tertiary institutions participated in an online survey. The researchers designed and validated the online survey. Exploratory Factor Analysis was used for dimensionality. Average Variance Extracted [AVE] was used for convergent validity, whilst the Fornel-Larcker criterion and Heterotrait-Monotrait ratio were used for discriminant validity. Composite reliability indices of 0.97, 0.94 and 0.90 proved the instrument's three sub-scales usable. One sample t-test and multigroup analysis were conducted using SPSS 27 and Smart PLS 3. The study found high awareness but low usage of Facebook for RDC among Nigerian university students. Younger and male students showed higher willingness and actual use of Facebook for RDC. Awareness directly impacted by awareness, and acted as Facebook usage for RDC, with stronger impacts for males and students aged 19 to 28. Willingness was positively impacted and acted as a negative mediator between awareness and usage for some age groups and genders. These findings provide important insights into using Facebook for RDC among students in Nigerian universities and highlight the need to consider the impact of demographic factors such as age and gender when promoting social media platforms for academic purposes.

Randomized A/B tests within online learning platforms represent an exciting direction in learning sciences. With minimal assumptions, they allow causal effect estimation without confounding bias and exact statistical inference even in small samples. However, often experimental samples and/or treatment effects are small, A/B tests are underpowered, and effect estimates are overly imprecise. Recent methodological advances have shown that power and statistical precision can be substantially boosted by coupling design-based causal estimation to machine-learning models of rich log data from historical users who were not in the experiment. Estimates using these techniques remain unbiased and inference remains exact without any additional assumptions. This paper reviews those methods and applies them to a new dataset including over 250 randomized A/B comparisons conducted within ASSISTments, an online learning platform. We compare results across experiments using four novel deep-learning models of auxiliary data and show that incorporating auxiliary data into causal estimates is roughly equivalent to increasing the sample size by 20% on average, or as much as 50-80% in some cases, relative to t-tests, and by about 10% on average, or as much as 30-50%, compared to cutting-edge machine learning unbiased estimates that use only data from the experiments. We show that the gains can be even larger for estimating subgroup effects, hold even when the remnant is unrepresentative of the A/B test sample, and extend to post-stratification population effects estimators.

The Expanded Food and Nutrition Education Program (EFNEP) uses a group 24-hour dietary recall (Gr24HDR) to measure changes in diet quality. Participant perceptions of the tool can guide implementation practices used by EFNEP Extension staff. Focus group (FG) sessions were conducted in five states and transcripts analyzed following a framework analysis approach. According to FG participants, a range of factors, condensed into six themes with potential interactions, influence Gr24HDR including implementation processes and community settings. Findings inform overarching considerations EFNEP staff may take when collecting Gr24HDR data in the field to improve the evaluation process for participants.

The Scholarship of Teaching and Learning (SoTL) faces an emerging challenge as it seeks to balance commitments to disciplinary inclusivity and scholarly quality. We undertake a scoping review of 64 articles across three leading SoTL journals to investigate how the literature balances these twin commitments by exploring what questions are being asked, what methods are being used, and how these may be impacting the inferences that are being made within that scholarship. We advocate for a more focused definition of SoTL that can help reinforce its legitimacy within institutional power structures of scholarship, and for partnerships across disciplinary boundaries to be a central pillar of SoTL that is both high quality and disciplinarily inclusive.

During a two-term observational study of my Secondary English Language Arts (ELA) class, I introduced "Surrealism" to the existing curriculum. Jot notes, personal interviews, and a self-study comprised my data strands. The COVID-19 pandemic struck shortly before my scheduled in-person interviews. This uncertainty disrupted my doctoral study plans, but offered a valuable opportunity for critical reflection. The fears and questions prompted by the pandemic were captured in the vulnerable "safe space" of our at-home Zoom interviews. This process thus prompted my contemplation about interviews as a continued method for combatting the stagnancy of educational spaces.

The study explored the social perceptions of young children's use of smart devices in South Korea using big data methodologies. Big data methodologies allowed to uncover underlying thoughts and feelings about young children's use of smart devices that had not been discovered in existing studies. The study extracted raw data from three different groups: the public, the journalist, and academia. Then, the study conducted keyword frequency, sentiment analysis, and CONCOR analysis with UCINET 6.0. The results of the study revealed that each group viewed young children's use of smart devices in a different way. The public was interested in effective use of smart devices while the journalist focused on educational aspects. The academia focused on parents' perception of smart devices from a developmental perspective. Regarding the results of sentiment analysis, they showed that each group had different opinion on young children's use of smart devices. The public had an ambivalent attitude toward young children's use of smart devices. While the journalist showed a positively inclined attitude, the academic had a negatively inclined attitude.

Social media can contribute to an inclusive society, but they are also asymmetrical and polarised communication spaces. This requires competent teachers to build critical digital citizenship. The aim of this article is twofold: to present web scraping and text analytics as tools that define teachers' digital competences, and to investigate which posts on Twitter and Instagram are most viral in relation to education, disability and inclusion. A total of 48,991 publications in Spanish and English were analysed, corresponding to the period from 13 October 2021 to 1 May 2022. The 100 most viral posts were selected, and correlations were identified between the sentiment, gender and influence associated with the content, its temporal and geographic space. The results show that economic and political influence groups are the most viral, relegating non-profit organisations or individuals with altruistic outreach to second place; only on international days is this trend reversed. Bots do not interfere to impose messages; it is artificial intelligence algorithms that overshadow vindictive and humanistic content. The most influential people are predominantly male, associated with institutional accounts in the political sphere. It is concluded that Big Data and Business Intelligence tools help teachers to analyse relevant educational and social issues, and to acquire a collective ethic in the face of new educational challenges.

Likert-type item is the most popular response format for collecting data in social, educational, and psychological studies through scales or questionnaires. However, there is no consensus on whether parametric or non-parametric tests should be preferred when analyzing Likert-type data. This study examined the statistical power of parametric and non-parametric tests when each Likert-type item was analyzed independently in survey studies. The main purpose of the study is to examine the statistical power of Wilcoxon-Mann-Whitney, Welch's t, and Student's t tests for Likert-type data, which are pairwise comparison tests. For this purpose, a Monte Carlo simulation study was conducted. The statistical significance of the selected tests was examined under the conditions of sample size, group size ratio, and effect size. The results showed that the Wilcoxon-Mann-Whitney test was superior to its counterparts, especially for small samples and unequal group sizes. However, the Student's t-test for Likert-type data had similar statistical power to the Wilcoxon-Mann-Whitney test under conditions of equal group sizes when the sample size was 200 or more. Consistent with the empirical results, practical recommendations were provided for researchers on what to consider when collecting and analyzing Likert-type data.

This essay describes the design, and subsequent redesign, of the research methods courses included in an Instructional and Performance Technology (IPT) EdD program at a regional comprehensive university in the southeast United States. The program under examination was developed based on the principles of the Carnegie Project for the Education Doctorate (CPED) and research and best practices aligned with the practice of performance improvement. The curriculum includes three research methods courses. The first introduces the students to the principles of action research as applied to the analysis of performance problems in organizational settings. The second addresses instrumentation and data collection processes used in quantitative, qualitative, and mixed methods research, and the third examines analyzing and reporting quantitative, qualitative, and mixed methods research. Collectively these courses provide students with the knowledge, skills, and abilities necessary to serve as scholarly practitioners, examining any type of problem of practice in any organizational setting.

This paper is a critical case study tracing the professional history of a self-professed open educator over more than two decades. It frames the narrative of an individual as a window on the broader arc of the field, from early open learning as a means of widening participation, through the rise of the participatory web at scale, to the current datafied and extractive infrastructure of higher education. It outlines how the field of online education has changed, as the web and the social and societal forces shaping use of the web have shifted. Through these lenses of change, the case study explores the dilemma facing open and participatory education at this juncture: that the current structure of the web threatens privacy, higher education governance structures, and the spirit of open, participatory sharing. The paper explores the problem of the web as one without direct solutions but does consider ways that educators might mitigate their open practice in more critical directions.

Childhood research is increasingly being conducted from different disciplines, and research methods for showing the child's world are also increasing and evolving. This article examines the challenges and opportunities of childhood research in an early childhood education and care (ECEC) environment from an ethnographic approach. The purpose of the article is to create a vision of what ECEC ethnography is, which can be used to make visible the culture of an under-school-age child, his or her experience, feelings, voice, and actions. A key challenge in ECEC ethnography is that the researcher is a representative of adult culture, emphasizing the need for reflexivity. We highlight five key themes for the researcher: the acquisition of material, the degree of researcher participation, taking on the role of a researcher, reaching the child's voice, and describing the results in a child-centered and ethically sustainable way. Respect for the child and earning the child's trust are essential. When successful, ECEC ethnography provides knowledge and understanding of childhood in a way that can contribute to supporting children's development and overall well-being.

The project presented in this paper aims to formulate a recommendation framework that consolidates the higher education students' particulars such as their academic background, current study and student activity records, their attended higher education institution's expectations of graduate attributes and self-assessment of their own generic competencies. The gap between the higher education students' generic competence development and their current statuses such as their academic performance and their student activity involvement was incorporated into the framework to come up with a recommendation for the student activities that lead to their generic competence development. For the formulation of the recommendation framework, the data mining tool Orange with some programming in Python and machine learning models was applied on 14,556 students' activity and academic records in the case higher education institution to find out three major types of patterns between the students' participation of the student activities and (1) their academic performance change, (2) their programmes of studies, and (3) their English results in the public examination. These findings are also discussed in this paper.

This article provides a conceptual framework for considering both the theoretical and methodological factors that underpin the successful validation of a competency framework. Drawing on educational assessment literature, this article argues that a valid competency framework relates to an interpretive judgement of the credibility of the claims made. To establish a credible approach to competency framework validation, there is a requirement to align the purposes of the competency framework, the claims developers make concerning the uses of the framework, and evidence collection methods to substantiate or challenge these claims. This article concludes with a template of questions for competency framework developers to consider in determining the range of potential claims to be made concerning their framework, and in understanding competency framework users and contexts.

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