Search

The COVID-19 pandemic caused tremendous upheaval in schooling. In addition to devasting effects on students, these disruptions had consequences for researchers conducting studies on education programs and policies. Given the likelihood of future large-scale disruptions, it is important for researchers to plan resilient studies and think critically about adaptations when such turbulence arises. In this article, we utilize qualitative analysis of interviews with research study leaders to illuminate practical and methodological challenges, as well as promising practices that arose during the pandemic period. We find that researchers made pivots to address practical challenges and protect the feasibility of their studies. We also find that researchers took precautions, where possible, to understand and bolster internal validity. However, these pivots frequently surfaced additional threats to construct and external validity. We conclude with recommendations for future studies conducted in times of prolonged unplanned school closures or other large-scale disruptions.

Big Data offers opportunities and challenges in all aspects of human life. In relation to research ethics, Big Data represents a normative difference in degree rather than a difference in kind. Data are more messy, rapid, difficult to predict, and difficult to identify owners; but the principles of informed consent, confidentiality, and prevention of harm apply equally to digital data. Recognition that technologies are not inherently value neutral, and that data collection, aggregation, and their use in decision making can both create and intensify inequities and harms is central to applying these principles. Data justice extends concern with voice and authenticity into the digital domain. Universities act as gatekeepers to professional accreditation in fields including software engineering. The relation between academic freedom of enquiry, state and corporate interests in the Big Data age raises important questions about power and control in the academy, which have governance implications.

Chinese students studying abroad have been increasing rapidly in the past decades and become a significant financial contribution to receiving countries. Accordingly, understanding their enrollment choice is essential to facilitate college marketing and admission strategies. Though the decision process is believed to be different from domestic students, empirical analysis of Chinese students' enrollment choices is still lacking. This paper fills the void by examining the influential factors of Chinese students' enrollment choice with novel student-level data. We find that in addition to factors domestic students typically consider, such as financial aid and academic quality, Chinese students particularly emphasize college ranking, reputation, and location in their decision process. Furthermore, unlike domestic students who usually prefer colleges with proximity to home, Chinese students' location preference is linked to job prosperity. We also find that the impact of the factors varies for students from different regions of China, which can be attributable to uneven economic development within the country.

Institutions of higher education are increasingly highlighting community engagement activities to make the benefits of higher education more visible. The most transformational community engagement is linked to curriculum, so it is faculty who must incorporate community-engaged pedagogy. This content analysis of faculty narratives about community engagement reveals motivations for faculty to engage in this work. These findings connect to social capital theory and suggest a new direction for faculty development efforts to promote community engagement.

The use of commercial digital platforms in public schools like Google and Microsoft, which was exacerbated during the pandemic, requires analysis to encourage a safer and more appropriate educational use. The research objective behind this article was to analyse the concerns of school students in obligatory primary and secondary education in public schools within the Autonomous Region of Catalonia (Spain) together with the opinions of their families regarding the use of digital platforms offered by large technology companies (BigTech) in schools. This is a mixed design study, consisting of eight discussion groups with pupils (n = 56) and a questionnaire issued to 2,330 family members. The results show that both students and families are concerned about the lack of knowledge surrounding the data they generate when using these digital platforms, and their effect on democratic school governance and the reproduction of gender stereotypes. In conclusion, the study suggests it is necessary to create greater critical awareness among children, adolescents and families at all socio-economic levels, particularly in those who are most vulnerable.

The integration of artificial intelligence (AI) topics into K-12 school curricula is a relatively new but crucial challenge faced by education systems worldwide. Attempts to address this challenge are hindered by a serious lack of curriculum materials and tools to aid teachers in teaching AI. This article introduces the theoretical foundations and design principles for implementing co-design projects in AI education, empirically tested in 12 Finnish classrooms. The article describes a project where 4th- and 7th-graders (N = 213) explored the basics of AI by creating their own AI-driven applications. Additionally, a framework for distributed scaffolding is presented, aiming to foster children's agency, understanding, creativity, and ethical awareness in the age of AI.

Research has shown that critical data literacies development for educators is seldom a core component of most campus conversations about datafication, even as extractive, datafied systems become pervasive throughout the higher education sector. This article outlines findings from an international, qualitative, Comparative Case Study (CCS) of university professionals teaching online during the COVID-19 pandemic. It overviews beliefs and barriers shaping educators' responses to datafication and focuses specifically on their perceptions of faculty development opportunities related to digital classroom tools and to datafication more broadly. The article presents insights into how faculty understands higher education's contemporary datafied infrastructure and highlights participants' voices about faculty professional development and critical data literacies. Based on our findings, we recommend formal faculty development and broader professional learning conversations as a means of enhancing faculty awareness and agency within the higher education sector.

A major pre-award administrative challenge research universities face is turnaround time for generation of high-quality NIH Data Training Tables for NIH training grants (e.g., T32, K12, TL1, KL2, R25s) which are required for training grant submission proposals to the National Institutes of Health (NIH). Universities with dedicated training grant submission offices generally require data preparation following a structured timeline of several months in advance of the grant submission due date, while other universities with less or no dedicated support for training grant submissions use an ad hoc approach. In these cases, department or program administrators may collect the data manually, in Excel or REDCap, or similar manually maintained methods for those tables requested by the specific NIH grant announcement for the relevant participating graduate predoctoral and/or postdoctoral (including clinical) training programs across the university, depending on the training focus and the "participating faculty" provided by the proposed program director (PD/PI) for the grant. We describe an efficient "federated" method of data collection and construction for NIH Tables (2, 4, 5A/B, 6A/B & -8A part III/8C part III) for new and renewal applications by combining the use of REDCap and NIH xTRACT, leveraging the strengths of each.

Data transfer between isolated clusters is imperative for cybersecurity education, research, and testing. Such techniques facilitate hands-on cybersecurity learning in isolated clusters, allow cybersecurity students to practice with various hacking tools, and develop professional cybersecurity technical skills. Educators often use these remote learning environments for research as well. Researchers and students use these isolated environments to test sophisticated hardware, software, and procedures using full-fledged operating systems, networks, and applications. Virus and malware researchers may wish to release suspected malicious software in a controlled environment to observe their behavior better or gain the information needed to assist their reverse engineering processes. The isolation prevents harm to networked systems. However, there are times when the data is required to move in such quantities or speeds that it makes downloading onto an intermediate device untenable. This study proposes a novel turnstile model, a mechanism for one-way file transfer from one enterprise system to another without allowing data leakage. This system protects data integrity and security by connecting the isolated environment to the external network via a locked-down interconnection. Using medium-security isolated clusters, the researchers successfully developed a unidirectional file transfer system that acts as a one-way "turnstile" for secure file transfer between systems not connected to the internet or other external networks. The Turnstile system (source code available at github.com/monnin/turnstile) provides unidirectional file transfer between two computer systems. The solution enabled data to be transferred from a source system to a destination system without allowing any data to be transferred back in the opposite direction. The researchers found an automated process of transferring external files to isolated clusters optimized the transfer speed of external files to isolated clusters using Linux distributions and commands.

Contribution: This research explores the effectiveness of a proposed teaching strategy in blockchain education, finding that it enhances learning outcomes, cognitive well-being, and student engagement in tertiary education, ultimately resulting in a shallower learning curve for STEM knowledge. Background: In the context of Industry 4.0, blockchain technology has emerged as a key driver of transformation in data management and system automation across a range of industrial applications. Despite its significance, the intricate theories and concepts associated with blockchain often serve as a deterrent for novice learners, inhibiting their ability to appreciate the value of industrial blockchain. Consequently, there is a pressing need to develop interactive teaching content that alleviates the steep learning curve. Intended Outcomes: The teaching strategy for the gamification in blockchain education is proposed, which positively influence students' cognitive well-being in terms of knowledge retention, cognitive curiosity, and heightened enjoyment. Application Design: Based on the experimental learning theory, the gamification of blockchain education, namely "BlockTrainHK", is implemented in the experimental learning cycle. Therefore, the gamified learning in experimental learning (GEL) strategy is proposed to examine the effectiveness of concrete experience, reflective observation, abstract conceptualization and active experimentation by two case studies. Findings: The results of the two-year study on the gamified blockchain education are encouraging: test groups using the GEL strategy were better in the cognitive well-being, and students' cognitive well-being is positively proportional to the level of individual technical knowledge and skills.

Effective research data management (RDM) is essential to modern scientific investigations. As the volume and complexity of research data increase, researchers, research institutions, and countries are pressured to improve data management practices to ensure transparency, reproducibility, sharing, and reuse of their findings. Researchers and institutions in Kenya, like those in many other developing countries, have begun to adopt the practice. This review examines the early indicators of improved research data management practices in Kenya to identify leaders who would drive the culture of RDM and thus improve research output.

Broadband is not equally accessible among students despite its increasing importance to education. We investigate the relationship between broadband and housing policy by joining two measures of broadband access with Depression-era redlining maps that classified neighborhoods based in part on racist and classist beliefs. We find that despite internet service provider self-reports of similar technological availability, broadband access generally decreases in tandem with historic neighborhood classification, with further heterogeneity by race/ethnicity and income. Our findings demonstrate how past federally-developed housing policies connect to the digital divide and should be considered in educational policies that require broadband for success.

The paper argues that learning analytics as a research field can benefit from a theory-informed shared language to describe sensemaking of learning and teaching data. To make the case for such shared language, first, I critically review prominent sensemaking theories to then demonstrate how studies in learning analytics do not use coherent descriptions of sensemaking, eclectically combining the paradigms that have underlying differences. I then propose a conceptualization of sensemaking that overcomes the differences between these theories and explains how the concepts of "activity system," the "definition of the situation" and "affordances" can be used to capture individual differences in sensemaking. The paper concludes with a preliminary framework and examples demonstrating its utility in raising new theoretical questions, informing design principles and providing shared language for researchers in learning analytics.

Despite huge investments in the educational sector, many countries in Africa have not fared well in the education indices. This study examined the effect of teaching equipment and teachers' effort on learning outcomes in 744 Nigerian primary schools using Stochastic Frontier and Ordinary Least Square regression. The mean technical efficiency was 0.43 which showed most of the surveyed schools were technically inefficient. Also, 42.8% of public primary schools operate below 20% efficiency level as against 12.3% in private primary schools. Factors influencing efficiency include school ownership type, visits from the Local Government Education Authority Student-Teacher ratio, and school hygiene.

One way to achieve equitable design is to directly include users who will be impacted the most in the planning and facilitation of a project. Common financial, logistical, and/or temporal constraints reveal that direct inclusion of the people most impacted is not always possible. If this barrier arises, one promising alternative is the creation and use of personas. Using a vignette and case study qualitative methodological approach, three researchers at a large urban university in the Pacific Northwest detail personas and journey mapping as an equitable design practice during a LMS migration on a rapid development timeline. This paper details how personas were created using empirical data, how journey mapping impacted various teams, and how centering equity better prepared staff to support instructors throughout the migration while addressing the student learning impact.

Today's college students increasingly rely on technology in their daily activities, and a basic awareness of data privacy is becoming essential from both a personal and professional standpoint. This article describes the design of an interdisciplinary undergraduate course focused on data privacy law. It outlines the key content areas that encompass the foundational principles of this emerging field and includes descriptions of hands-on exercises that illustrate the technical and managerial elements of a comprehensive privacy compliance program. The course framework and sample assignments can serve as a model for other faculty seeking to teach data privacy law to undergraduate students.

Drawing on annual, nationally representative survey data from 2007-2022, I demonstrate that partisan gaps - the average differences in public opinion between Democrats and Republicans - have widened on many education issues. This pattern consistently exceeds what would be expected due to the changing demographic compositions of the parties alone. Widening partisan gaps are primarily attributable to sorting (alignment of one's party affiliation and one's issue positions) rather than polarization (increasing support for more extreme positions relative to more moderate positions). However, polarization is also increasing on some of the most divisive issues. Among those who are sorting, individuals are overwhelmingly switching their issue positions to align with their party affiliations rather than switching their party affiliations to align with their issue positions.

Dyslexia is the most prevalent disorder in the world that causes difficulties with reading, writing, and spelling. Pupils with dyslexia show trouble with their cognitive skills. Various interventions were already introduced for their treatment but dyslexia is still a trending disorder. The available interventions utilized for these pupils' learning open the research area for the current state-of-art of learning interventions for pupils with dyslexia. The results of this Systematic Literature Review show the trending interventions, sensory approaches utilized, and difficulties for pupils with dyslexia learning. Papers published over a period of 5 years were reviewed and their data was collected using a rigid systematic process. Based on the gathered data, several analyses were conducted. The search shows that nowadays, technological-based interventions are trending specifically apps and games, in parallel haptics technology is in its very initial stage. The most predominant sensory approaches were visual and auditory, followed by kinesthetic and tactile, mainly intervening with non-technological and technological interventions. There are still many open issues and research opportunities in the field of learning interventions for pupils with dyslexia, as most researchers utilized the visual and auditory approaches for the feedback and guidance of these pupils, while they lack to utilize the kinesthetic and tactile.

Individuals with intellectual and developmental disabilities (IDD) continue to experience disparities in health and well-being despite improved provisions of person-centered care. Patient-centered outcomes research (PCOR) translates evidence into practice for meaningful outcomes. This piece describes findings from an environmental scan and stakeholder outreach to identify and prioritize opportunities to enhance IDD PCOR data infrastructure. These opportunities include developing a standardized research definition; advancing data standards for service systems; improving capture of IDD at point of care; developing standardized outcome measures; and encouraging Medicaid data use for IDD research. Within this piece, we discuss the implications of addressing data gaps for enhanced research. While the identified activities provide a path towards advancing IDD PCOR data infrastructure, collaborative efforts between government, researchers, and others are paramount.

There is widespread concern about declining literacy skills in recent young Japanese. The present study investigated how higher-level reading and writing proficiencies are underpinned by basic literacy skills in Japanese adolescents. From a large database of the most popular literacy exams in Japan, we retrospectively analyzed word- and text-level data for middle and high school students who had taken the exams during the same period in the 2019 academic year using structural equation modeling. We extracted main data for 161 students as well as six independent datasets for validation. Our results validated the three-dimensional view of word-level literacy (reading accuracy, writing accuracy, and semantic comprehension) and demonstrated that writing and semantic skills underpinned text writing and reading, respectively. The semantic comprehension of words affected text writing indirectly via text reading; however, it could not replace the direct effect of word writing accuracy. These findings, which were robustly replicated with multiple independent datasets, provided new evidence of dimension-specific relationships between word- and text-level literacy skills and confirmed the unique contribution of word handwriting acquisition to text literacy proficiency. The replacement of handwriting by digital writing (e.g., typing) is a global trend. However, the dual-pathway model of literacy development identified in this study suggests there are advantages in sustaining early literacy education by handwriting for the growth of higher-level language skills in future generations.

It is widely recognized that we need to prepare students to think with data. This study investigates student interactions with digital data graphs and seeks to identify what might prompt them to shift toward using their graphs as thinking tools in the authentic activity of doing science. Drawing from video screencast data of three small groups engaged in sensor-based and computer simulation-based experiments in high school physics classes, exploratory qualitative methods are used to identify the student interactions with their graphs and what appeared to prompt shifts in those interactions. Analysis of the groups, one from a 9th grade class and two from 11th/12th grade combined classes, revealed that unexpected data patterns and graphical anomalies sometimes, but not always, preceded deeper engagement with the graphs. When shifts toward deeper engagement did occur, transcripts revealed that the students perceived the graphical patterns to be misaligned with the actions they had taken to produce those data. Misalignments between the physical, digital, and conceptual worlds of the investigations played an important role in these episodes, appearing to motivate students to revise either their experimental procedures or their conceptions of the phenomena being explored. If real-time graphs can help foster a sense in students that there should be alignments between their data production and data representations, it is suggested that pedagogy leverage this as a way to support deeper student engagement with graphs.

The COVID-19 pandemic has forced the rapid adoption of remote teaching modalities including "hyflex" where students attend some class sessions in person and some online. Managing the hyflex course requires faculty to quickly generate several reports and to update these reports rapidly when the authorities adjust the rules, students add/drop, or the number of course sessions is changed. The creation of these reports is tedious and error-prone, so they need to be automated. However, the nature of the task precludes traditional Excel programming approaches. We use Excel's new "dynamic array" capabilities (which are available only in Excel 365) to automate the creation and updating of the reports needed to manage the hyflex course. We show how to program the reports, and we discuss the importance of taking an iterative approach to creating effective, error-free cell formulas. To help the reader acquire genuine access to the dynamic array functions, we provide a practical tutorial on the principles and new concepts of dynamic arrays, explain how they relate to legacy array functions, and present selected dynamic array functions including SEQUENCE, FILTER, and XLOOKUP.

Central to the institutionalization of learning analytics is the need to understand and improve student learning. Frameworks guiding the implementation of learning analytics flow from and perpetuate specific understandings of learning. Crucially, they also provide insights into how learning analytics acknowledges and positions itself as entangled in institutional data ecosystems, and (increasingly) as part of a data ecology driven by a variety of data interests. The success of learning analytics should therefore be understood in terms of data flows and data interests informing the emerging and mutually constitutive interrelationships and interdependencies between different stakeholders, interests and power relations. This article analyses several selected frameworks to determine the extent to which learning analytics understands itself as a "data ecosystem" with dynamic interdependencies and interrelationships (human and non-human). Secondly, as learning analytics increasingly becomes part of broader "data ecologies," we examine the extent to which learning analytics takes cognizance of the reality, the potential and the risks of being part of a broader data ecology. Finally, this article examines the "different data interests" vested in learning analytics and critically considers implications for student data sovereignty. The research found that most of the analyzed frameworks understand learning analytics as a data ecosystem, with very little evidence of a broader data ecological understanding. The vast majority of analyzed frameworks consider student data as valuable resource without considering student data ownership and their data rights for self-determination.

There is an increased focus on fostering integrity in research by through creating an open culture where research integrity dilemmas can be discussed. We describe a pilot intervention study that used Moral Case Deliberation (MCD), a method that originated in clinical ethics support, to discuss research integrity dilemmas with researchers. Our research question was: can moral case deliberation in research groups help to navigate research integrity dilemmas? We performed 10 MCDs with 19 researchers who worked in three different research groups from three different disciplinary fields at a university in the Netherlands. We analyzed the dilemmas and values discussed, sent out a survey questionnaire to assess self-perceived moral competencies, and conducted in-depth interviews. We found research integrity dilemmas pertained to authorship disputes, supervision of junior co-workers, and questionable handling of data. Participants perceived the majority of moral competencies to a higher degree during the MCD when compared to perceiving them in daily practice afterward. Interviewees told us that they felt most comfortable discussing dilemmas among peers with whom they were not closely affiliated. We conclude that MCD sessions could be relevant in navigating research integrity dilemmas, but that revisions to ensure commitment and safety are required.

Libraries at academic institutions have been involved in research data management services (RDMS) for more than ten years. Institutions have adapted their service models in response to funder, journal, and federal mandates that have consequently increased RDMS demand. However, institutions that hope to start or grow their services may have difficulty extrapolating from thepublished literature, as few studies recount RDMS development strategies.

The COVID-19 pandemic has fast-tracked the development of digital applications and inspired everyone to adapt to the technologies to curb the spread of outbreak. As this crisis intensifies, the rapid usage of digital devices and apps has echoed the serious concerns about civil liberties, privacy, and data protection. Considering the situation, this research aimed to explore the internet using habits of the youths of West Bengal, a state in eastern India, during COVID-19. Besides, the paper explored their experiences of using various digital applications, the fundamental digital literacy and how safely they protect data from breaches. Thus, the paper presents the results by conducting an online survey among the youths in West Bengal. The result, from 215 participants, highlighted that the increased use of these digital applications has not matched the demand for digital privacy literacy among the young generation of the state. While this pandemic has raised their concerns over digital privacy and data protection, yet they do not undertake any strong protection mechanisms to safeguard them digitally. Besides, this paper suggests suitable plans to raise awareness among this generation and form a healthy digital citizenship with a proper regulatory framework as it is the need of the hour.

According to a national poll of high school students that the Data Quality Campaign (DQC) and the Kentucky Student Voice Team developed in 2022, students are not getting the information they need. State, district, and local education leaders must take actions to help students, their communities, and schools by sharing information about context, proximity, and framing of data. To make access to usable data a reality, statewide longitudinal data systems must be reoriented so that everyone--not just students--will have robust access to the information they need to answer their most pressing education and workforce questions and make decisions that support students.

The various empirical models built for enrollment management, operations, and program evaluation purposes may have lost their predictive power as a result of the recent collective impact of COVID restrictions, widespread social upheaval, and the shift in educational preferences. This statistical artifact is known as model drifting, data-shift, covariate-shift. Succinctly, these events drove changes in the stationarity of the target variable and the predictors. The result is a student body with unknown performance qualities entirely distinct from previous cohorts. This study explains and illustrates: (1) how to test for academic model drift in academe; and (2) sets forth two methods used to repair vitiated student-body performance properties. Formally, it frames the data-drift outcome as a One Class problem which allows the deployment of two well-known One-Class algorithms: Support Vector Machines and Isolated Random Forests. The study shows their use in reconstructing a representative sample of the student-body

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.

The research aims to determine the factors affecting PISA 2018 reading skills using the Random Forest and MARS methods and to compare their prediction abilities. This study used the information from 5713 students, 2838 (49.7%) male and 2875 (50.3%) female, in the PISA 2018 Turkey. The analysis shows the MARS method performed better than the Random Forest method. In both methods, the most significant factor affecting reading skills in Turkey is "the number of books in the house." The variables the MARS method finds significant are "students' perception of difficulty, motivation for reading skills, father's educational status, reading pleasure, bullying experience of the student, mother's educational status, attitude towards school, classical artifacts at home, supplementary school books at home, competition at school, competitive power, cooperation perception at school, reading frequency, self-efficacy, poetry books at home, anxiety about reading skills, and teacher support." However, the other variables had no relationship to prediction. This study is expected to serve as a model for the use of data mining in educational research.