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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.

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.

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.