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Exploratory data analysis (EDA), data visualization, and visual analytics are essential for understanding and analyzing complex datasets. In this project, we explored these techniques and their applications in data analytics. The case discusses Tableau, a powerful data visualization tool, and Google BigQuery, a cloud-based data warehouse that enables users to store, query, and analyze large datasets. It also explored the benefits and applications of both tools and their integration with other platforms and services. The project offers an introductory insight into Tableau's functionalities, employing a data file from the US Census Bureau via Google BigQuery.

Many articles have been published in gifted education in recent years. This study aims to provide a comprehensive review of the evolution of academic studies in gifted education. In this context, the structural topic modeling (STM) method was used to analyze the topics and trends in the field. STM is a machine learning technique that utilizes natural language processing techniques based on text mining. It is a valuable methodology for identifying a text corpus's main topics and trends. The corpus used in this study is 5,127 articles from nine leading journals in giftedness without any year limitations. As a result of the analysis, five topics that prominently emerged in the literature were discovered. These are curriculum and instruction, social-emotional characteristics, thinking skills, identification and assessment tools, and equity and policies. The research topics and trends discovered due to the analysis are discussed within the literature framework, and recommendations are presented.

Assessment is an important component of learning which aims to determine student achievement in learning. However, the pandemic has changed how assessment is carried out, prompting teachers to look for new strategies in carrying out mathematics learning assessments under any conditions, whether in normal conditions or during a pandemic. This study described the strategies used by high school mathematics teachers in assessing mathematics learning during the pandemic towards the post-pandemic period. This type of research is descriptive exploratory research with a qualitative approach. The subjects in this study were three mathematics teachers, three principals, and three vice principals from a public senior high school in Yogyakarta City, Indonesia. Qualitative data were collected by means of questionnaires, interviews, and documentation. The collected data were analyzed qualitatively according to Bogdan and Biklen. The results of the study reveal that the mathematics teacher's strategy for carrying out assessments during a pandemic includes: i) determining the appropriate online assessment platform that suits the needs; ii) assessment based on student participation; iii) using various of online assessment methods; iv) prioritizing academic integrity and honesty in the assessment implementation; and v) carry out the management and reporting of data on the results of the assessment in accordance with established procedures.

In the current era, the E-Government phenomenon, which describes the state's provision of public services to its stakeholders through information and communication technologies, has a global character. This study aimed to examine the big data-based e-Government portal, which has many examples in the world and was created by utilizing digital technologies, on the example of the Turkish Republic of Northern Cyprus (TRNC), and whether the portal is user-oriented or not was examined by content analysis method in the context of big data features and citizen-oriented. The absence of any other study on the examination of the TRNC E-Government Portal, which was reorganized in 2021, gives the study a unique quality. At the end of the study, although progress was observed in the information and transaction stages of the TRNC E-Government Portal in the context of e-Government models, it has been determined that success has not yet been achieved in the interaction and integration stages, as well as in the context of citizen-oriented and big data features, it has been concluded that there is not enough level for a successful government portal.

Incorporating an evidence-based approach in STEM education using data collection and analysis strategies when learning about science concepts enhances primary students' discipline knowledge and cognitive development. This paper reports on learning activities that use the nature of viscosity and the power of informal statistical inference to build students' conceptual understanding of interpolation and extrapolation without imposing on them the demands of understanding the nonlinear mathematics used to explore the concepts at the tertiary level. An exploratory research strategy was adopted to investigate the way in which Year 5 students created and analysed graphical representations from data collected when performing viscosity experiments. The data representations produced by the students and their subsequent predictions were analysed using the Structure of Observed Learning Outcomes (SOLO) model as adapted specifically for graphical representations. The results illustrate that when provided with appropriate technological tools to scaffold student learning, in this case "TinkerPlots"™, development of students' appreciation of interpolation and extrapolation within meaningful data contexts across the STEM curriculum does not have to wait until the tertiary level.

The use of videos in teaching has gained impetus in recent years, especially after the increased attention towards remote learning. Understanding students' video-related behaviour through learning (and video) analytics can offer instructors significant potential to intervene and enhance course designs. Previous studies explored students' video engagement to reveal learning patterns and identify at-risk students. However, the focus has been mostly placed on single contexts, and therefore, limited insights have been offered about the differences and commonalities between different learning settings. To that end, the current paper explored student video engagement in three disparate contexts. Following a case study research approach, we uncovered the commonalities and differences of video engagement in the context of SPOC, MOOC, and an undergraduate university course. The findings offer a deeper and more comprehensive understanding of students' video-related engagement and shed light into several key aspects related to video analytics that should be considered during the design of video-based learning (e.g., learning objectives in relation to video type or context). Additionally, the three cases indicated the important role of the content type, the length, and the aim of the video on students' engagement. Further implications of the work are also discussed in the paper.

This study aims to analyse the impact of demographic and educational factors on in digital upskilling. We address the relationship between digital skills, critical thinking skills and the student learning experience in courses with embedded upskilling programs. Statistical analysis and exploratory research are used to analyse the data collected by the digital fitness app developed by a global company leading digital talent transformation. Our research identified the following demographic indicators that have influenced individuals' digital upskilling: Gender, Race, Residency, Pell-eligibility, Age, in addition to six academic indicators including department, major, class level, first-time freshman, High School GPA and ACT score. Discussion of findings and future research directions would lead to novel research articles focusing on longitudinal studies to assess digital skills. Also, this study shows the need for a new assessment tool to expand the scope and the sample size of the study. This article provides a valuable guidance for policy makers, higher-education institutions, educators, students, and employers, who are looking for digital talent or skill sets needed for digital transformation. The main contribution lays in data collection and analysis. This is the first study using a globally accepted digital talent assessment tool to collect data at a minority serving institution.

With the advent of readily accessible generative artificial intelligence (GAI), a concern exists within the academic community that research data collected in the context of conducting doctoral dissertation research is authentic. The purpose of the present study was to explore the role of GAI in the production of new research paying particular attention to the use of GAI in collecting new data for a doctoral dissertation. This study employed qualitative methodology examining how GAI, specifically ChatGPT, responded to interview questions from a previously published article by the researchers to determine how closely chatbots mimic responses from the actual study participants. The researchers found that data integrity in qualitative research may be at risk if higher education institutions do not set clear policies and specific parameters for how doctoral research data is obtained and validated in light of GAI.

A small subset of education studies analyzes school data collected seasonally (separating the summer from the school year). At first, this work was primarily known for documenting learning loss in the summers, but scholars have since recognized that observing how inequality changes between summer and school periods provides leverage for understanding how schools influence inequality. Results based on this analytic technique confirm current views in some ways, but in other ways the patterns challenge existing wisdom. For example, Black/White gaps in math and reading skills often grow faster when school is in versus out, consistent with the view that schools exacerbate racial inequality. But socioeconomic gaps produce the opposite pattern, suggesting that schools are compensatory across this dimension. In this review, I consider the logic behind seasonal research, the empirical patterns it has produced, and the kinds of new questions it motivates.

The digital era has significantly reshaped journalism, emphasizing the pivotal role of data-driven reporting. This review delves into the nexus of data journalism and journalism education, investigating dominant study characteristics, challenges, and gaps for future research. Examining 41 relevant articles through the Arksey and O'Malley procedure, the findings highlight key impediments to integrating data journalism into curricula: scarcity of technically proficient specialists, resource limitations, absence of concrete methodological approaches, and a reluctance to incorporate mathematical elements into journalism education. This exploration underscores the critical need for overcoming these challenges to foster a seamless integration of data-driven approaches within journalism education.

The prevalent paradigm for understanding what constitutes a 'good' data visualization, and what we are likely teaching business students, relates to a conventional wisdom of efficiency, clarity, transparency, and faithful representation of truth. Teaching about the ethics of visualizations seems to be largely absent from business school curricula. This paper suggests that business students who make and use visualizations should be taught to use a code of ethics for visualizations based not only on conventional wisdom, but also on ideas from the Machine Learning/AI and 'ethical visualizations' communities. Elaborating on Sheppard's code, the code suggested here incorporates ideas about bias, transparency, user agency, and privacy. Ethical awareness and practice become even more important as more data collection, processing, and visualization is shifted to Machine Learning and Generative AI. A threshold model is adapted to understand when visualization decisions require more scrutiny. Ideas from the code and the threshold model are used to analyze choices made by the CDC's COVID-19 Rapid Response Team for an early pandemic visualization that circulated widely beyond the initial report in which it appeared. This and other examples underscore the need for business professionals to consider their visualizations from the perspective of the code.

Knowledge tracing aims to model and predict students' knowledge states during learning activities. Traditional methods like Bayesian Knowledge Tracing (BKT) and logistic regression have limitations in granularity and performance, while deep knowledge tracing (DKT) models often suffer from lacking transparency. This paper proposes a Transformer-based framework that emphasizes both accuracy and interpretability. It captures the relationship between student behaviors and learning outcomes considering the associations between exam and exercise problems. We participated in the EDM Cup 2023 Contest using the proposed framework and achieved first place on the task of predicting students' performance on end-of-unit test problems using clickstream data from previous assignments. Furthermore, the framework provides meaningful insights by analyzing user actions and visualizing attention weight matrices. These insights enable targeted interventions and personalized support, enhancing online learning experiences. We have uploaded our code, saved models, and predictions to an OSF repository: https://osf.io/mdpzc/.

The prediction of student performance and the analysis of students' learning behaviour play an important role in enhancing online courses. By analysing a massive amount of clickstream data that captures student behaviour, educators can gain valuable insights into the factors that influence students' academic outcomes and identify areas of improvement in courses. In this study, we developed ClickTree, a tree-based methodology, to predict student performance in mathematical problems in end-unit assignments based on students' clickstream data. Utilising extensive clickstream data, we extracted a novel set of features at three levels, including problem-level, assignment-level and student-level, and we trained a CatBoost tree to predict whether a student will successfully answer a problem in an end-unit assignment or not. The developed method achieved an Area under the ROC Curve (AUC) of approximately 79% in the Educational Data Mining Cup 2023 and ranked second in the competition. Our results indicate that students who performed well in end-unit assignment problems engaged more with in-unit assignments and answered more problems correctly, while those who struggled had higher tutoring request rates. We also found that students face more difficulties with "check all that apply" types of problems. Moreover, Algebra II was the most difficult subject for students. The proposed method can be utilised to improve students' learning experiences, and the insights from this study can be integrated into mathematics courses to enhance students' learning outcomes.

Colleges and universities depend upon external approvals from accreditors, government agencies, and national organizations. These approvals require periodic review of institution-prepared reports and self-studies documenting the continued validity of offered degrees and programs, student support and success, institutional effectiveness, and continuous improvement. Campus leaders generally take one of two approaches to complete this work: deploying working groups and committees or relying on one or two institutional accreditation leaders. This 2024 AALHE Conference session introduced a third approach, a hybrid of the two dominant approaches, which begins with a campus-wide assets-based exercise for gathering information and evidence. Deploying the exercise decreases the time campus stakeholders must commit to these recurring external review processes; provides accreditation leaders with information and evidence that reflect the breadth of the institution's activities; addresses the problem of a narrow interpretation of assessment activities; and is more effective in the current higher education milieu where institutional effectiveness and continuous improvement are embedded across the breadth of institutionally unique activities.

The power of sport to affect social change is widely acknowledged. However, little empirical research has explored the use of sport in engaging young people in global education. This article fills the gap by exploring the role of sport using experiential learning tools as delivery methodology for global education in the context of Central Europe (Czechia). The study focuses on the experiential learning process of the study participants, a mixed intercultural team of Czech and Kenyan youth ambassadors participating in a Football for Development project with the aim of engendering cultural understanding, respecting diversity as positive and valuing differences as beneficial to society. The study benefited from a newly developed non-conventional data collection tool enhancing the research methodology and allowing for the facilitation of the participants' ability to discover and process the experience-related learning through reflexivity. By showing the changes in knowledge, skills, values and attitudes, our findings indicate how the sport for development programme contributes to creating a semi-conscious learning environment for global education.

Cooperative Extension professionals utilize proven qualitative techniques to collect and analyze information to make data-driven decisions that guide program direction and determine impact. While the process may not always look the same, it is indeed essential to ensure findings are credible and reflective of the data. A codebook is a valuable tool that promotes interrater reliability among teams and enhances the reliability of findings. This article provides an overview of the process used to analyze qualitative data and the steps followed to create a codebook. Examples of the analysis process and codebook development are provided from the 4-H multi-state Youth Retention Study.

Remote technology has been widely incorporated into health professions education. For procedural skills training, effective feedback and reflection processes are required. Consequently, supporting a self-regulated learning (SRL) approach with learning analytics dashboards (LADs) has proven beneficial in online environments. Despite the potential of LADs, understanding their design to enhance SRL and provide useful feedback remains a significant challenge. Focusing on LAD design, implementation, and evaluation, the study followed a mixed-methods two-phase design-based research approach. The study used a triangulation methodology of qualitative interviews and SRL and sensemaking questionnaires to comprehensively understand the LAD's effectiveness and student SRL and feedback uptake strategies during remote procedural skills training. Initial findings revealed the value students placed on performance visualization and peer comparison despite some challenges in LAD design and usability. The study also identified the prominent adoption of SRL strategies such as help-seeking, elaboration, and strategic planning. Sensemaking results showed the value of personalized performance metrics and planning resources in the LAD and recommendations to improve reflection and feedback uptake. Subsequent findings suggested that SRL levels significantly predicted the levels of sensemaking. The students valued the LAD as a tool for supporting feedback uptake and strategic planning, demonstrating the potential for enhancing procedural skills learning.

Classroom sensing systems can capture data on teacher-student behaviours and interactions at a scale far greater than human observers can. These data, translated to multi-modal analytics, can provide meaningful insights to educational stakeholders. However, complex data can be difficult to make sense of. In addition, analyses done on these data are often limited by the organization of the underlying sensing system, and translating sensing data into meaningful insights often requires custom analyses across different modalities. We present Edulyze, an analytics engine that processes complex, multi-modal sensing data and translates them into a unified schema that is agnostic to the underlying sensing system or classroom configuration. We evaluate Edulyze's performance by integrating three sensing systems (Edusense, ClassGaze, and Moodoo) and then present data analyses of five case studies of relevant pedagogical research questions across these sensing systems. We demonstrate how Edulyze's flexibility and customizability allow us to answer a broad range of research questions made possible by Edulyze's translation of a breadth of raw sensing data from different sensing systems into relevant classroom analytics.

As part of a study investigating research productivity of sociology PhD students in Australia, an application to the federal government's Department of Education, Skills and Employment (DESE) aimed to establish a baseline count of completed sociology doctorates by university. It was anticipated that university totals might be different from PhD completions within the main sociology teaching units. The government data furnished by this inquiry have proved to be problematic in several ways. The gaps in the data may be explained by the fact that universities report sociology PhDs to DESE in more generic rather than discipline-specific Field of Education (FoE) codes or as PhDs in other disciplines. DESE has either not required or been able to enforce consistent reporting. This raises questions about the time and cost value of these annual compliance efforts. It also limits the value of these data to researchers in charting current and potential future trends.

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.

Practitioner-focused educational doctoral programs have grown substantially in recent years. Dissertations in Practice (DiPs), which are the culminating research report and evaluation method in these programs, differ from traditional PhD dissertations in their focus on addressing a problem of practice and on connecting theories with practice. As part of our ongoing program evaluation, we reviewed DiPs from doctoral students who graduated from an online asynchronous Educational Doctoral program in Learning Design and Technologies at the University of South Carolina. Findings revealed that most students chose a pragmatic philosophical paradigm, adopted a mixed methods research design, reported an action research intervention implemented with populations in K-12 schools, used surveys and interviews as data sources, and analyzed data with descriptive/inferential statistics and thematic analysis. Implications for the program curriculum are discussed.

This research aimed to determine the trends related to blended learning studies conducted in science education through descriptive content analysis. This study was performed using the document review method. For this purpose, 120 studies on blended learning in science education were determined between 2005 and 2022 in the Web of Science (WoS) database. The studies were examined by considering themes such as keywords, country, purpose, method, sample, online system, and result. The research findings showed that more studies occurred between 2020 and 2022. The findings showed that studies mostly use quantitative research methods and experimental designs. Most of the studies were conducted with undergraduate-level sample groups. Findings revealed that the blended learning environment is designed more to teach physics subjects, and parametric tests were generally used for data analysis. The more preferred course management systems in the studies were the online systems designed by the schools. A considerable number of studies revealed that blended learning environments designed for science education have a positive effect on variables such as achievement, skill, and motivation.

Data collection, use, leveraging, and sharing as a business practice and advantage has proliferated over the past decade. Along with this proliferation of data collection is the increase in regulatory activity which continues to morph exponentially around the globe. Adding to this complexity are the increasing business disruptions, productivity and revenue losses, settlements, fines, and penalties which can amount to over $15 million, with many penalties now being ascribed to the organization's leadership, to include the Board of Directors (BoD), the CEO and members of the senior leadership team (SLT). Thus, boards of directors can no longer ignore and in fact must embrace data privacy as a critical part of doing business in the digital world. In fact, not embracing data privacy as a critical part of their strategy, not only puts their stakeholders and stockholders at risk, but also places the future success of their organization in jeopardy. Additionally, increasingly through legal, regulatory, and normative occurrences, Boards are being pressured into taking a more active role in the data privacy activities in their organizations. Therefore, it behooves the BoD to be proactive vice reactive toward their data privacy endeavors.

A staff study (SS) is an administrative tool used for decision-making, involving comprehensive data gathering and analysis to resolve issues. Particularly crucial in military settings where classified information limits resources, staff studies also demonstrate their value in interdisciplinary academic fields. This article emphasizes the significance of SS in education policy by exploring their application in developing English testing within the Royal Thai Navy (RTN). While staff studies share similarities with applied research in language education, their primary emphasis is on applying knowledge to solve problems in specific contexts. Staff study principles provide a logical framework for navigating contextual variables in problem-solving, rather than expanding knowledge. Overall, SSs offer innovative approaches to addressing unique challenges across various research contexts.