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Even though working with data is as important as coding for understanding and dealing with complex problems across multiple fields, it has received very little attention in the context of Computational Thinking. This paper discusses an approach for bridging the gap between Computational Thinking with Data Science by employing and studying classification as a higher-order thinking process that connects the two. To achieve that, we designed and developed an online constructionist gaming tool called SorBET which integrates coding and database design enabling students to interpret, organize, and analyze data through game play and game design. The paper presents and discusses the results of a pilot study that aimed to investigate the data practices secondary students develop through playing and modifying SorBET games, and to determine the impact of game modding on student critical engagement with CT. According to the results, students developed and used certain data practices such as data interpretation and data model design to become better players or to design an interesting classification game. Moreover, game modding process motivated students to question the original games' content, leading them to develop a critical stance towards the game data model and representations.

Students believe mathematics is best learned by memorization; however, endorsing memorization as a study strategy is associated with a decrease in learning (Schoenfeld, 1989). When the world changed with the onset of the COVID-19 global pandemic, instruction transitioned to fully remote instruction where many assignments and examinations became open textbook, open note, and even open Internet. In this new world, did students change their beliefs about the role of memorization in learning? Did academic performance change? And did the relationship between memorization beliefs and academic performance change? The current study takes advantage of data collected in an online interactive statistics textbook used by courses before (in-person) and after (remote) the declaration of the COVID-19 pandemic at three institutions, each representing a part of the California Master Plan for Higher Education (e.g., University of California, California State University, and California Community Colleges). Results from 2668 students who used the textbook showed that the UC institution had lower memorization belief scores compared to both the CSU and CCC institutions. Even when controlling for institution and chapter of the textbook, lower memorization belief scores were related to higher performance. Surprisingly, there were no significant differences in either memorization beliefs nor performance before and after transitioning to online remote instruction due to the pandemic. Although much of educational research is conducted in one institution, this kind of research can identify differences across institutional contexts to understand how learning can be affected by different teaching formats, including in-person and online/distance, brought on by disruptive social changes such as a global pandemic.

The development of data science curricula has gained attention in academia and industry. Yet, less is known about the pedagogical practices and tools employed in data science education. Through a systematic literature review, we summarize prior pedagogical practices and tools used in data science initiatives at the higher education level. Following the Technological Pedagogical Content Knowledge (TPACK) framework, we aim to characterize the technological and pedagogical knowledge quality of reviewed studies, as we find the content presented to be diverse and incomparable. TPACK is a universally established method for teaching considering information and communication technology. Yet it is seldom used for the analysis of data science pedagogy. To make this framework more structured, we list the tools employed in each reviewed study to summarize technological knowledge quality. We further examine whether each study follows the needs of the Cognitive Apprenticeship theory to summarize the pedagogical knowledge quality in each reviewed study. Of the 23 reviewed studies, 14 met the needs of Cognitive Apprenticeship theory and include hands-on experiences, promote students' active learning, seeking guidance from the instructor as a coach, introduce students to the real-world industry demands of data and data scientists, and provide meaningful learning resources and feedback across various stages of their data science initiatives. While each study presents at least one tool to teach data science, we found the assessment of the technological knowledge of data science initiatives to be difficult. This is because the studies fall short of explaining how students come to learn the operation of tools and become proficient in using them throughout a course or program. Our review aims to highlight implications for practices and tools used in data science pedagogy for future research.

The focus of ethics in learning analytics (LA) frameworks and guidelines is predominantly on procedural elements of data management and accountability. Another, less represented focus is on the duty to act and LA as a moral practice. Data feminism as a critical theoretical approach to data science practices may offer LA research and practitioners a valuable lens through which to consider LA as a moral practice. This paper examines what data feminism can offer the LA community. It identifies critical questions for further developing and enabling a responsible stance in LA research and practice taking one particular case -- algorithmic decision-making -- as a point of departure.

With the development of technology allowing for a rapid expansion of data science and machine learning in our everyday lives, a significant gap is forming in the global job market where the demand for qualified workers in these fields cannot be properly satisfied. This worrying trend calls for an immediate action in education, where these skills must be taught to students at all levels in an efficient and up-to-date manner. This paper gives an overview of the current state of data science and machine learning education globally and both at the high school and university levels, while outlining some illustrative and positive examples. Special focus is given to vocational education and training (VET), where the teaching of these skills is at its very beginning. Also presented and analysed are survey results concerning VET students in Slovenia, Serbia, and North Macedonia, and their knowledge, interests, and prerequisites regarding data science and machine learning. These results confirm the need for development of efficient and accessible curricula and courses on these subjects in vocational schools.

In this article, we presented our findings regarding an online project-based learning course, delivered to 64 students from the Federal University of Sao Paulo, Brazil, during the COVID-19 pandemic, in the second semester of 2021. The course had the goal of teaching Project Management by means of a competition (the Data Science Olympics). Our goal was to investigate the systemic impacts of the competition on learning. Data was collected by means of a questionnaire and from comments posted on the teams' websites. We followed a convergent parallel mixed methods approach. We analyzed the data using a causal loop diagram to connect the insights gained with quantitative and qualitative results. Our findings were as follows: (1) The use of competition in a project-based learning centered course helped the students to develop project management and data science skills, and fostered metacognition and knowledge sharing opportunities. (2) The Data Science Olympics increased the students' intrinsic motivation to learn. (3) The project-based teaching practices (scaffolding the students' learning, giving meaningful feedback to the students, and managing the activities) facilitated the students' learning. (4) The problems the students faced throughout the Project (dropouts, communication problems, lack of commitment, difficulty scheduling online team meetings) impacted negatively on the students' motivation.

Purpose: This study is part of a participatory design research project and aims to develop and study pedagogical frameworks and tools for integrating computational thinking (CT) concepts and data science practices into elementary school classrooms. Design/methodology/approach: This paper describes a pedagogical approach that uses a data science framework the research team developed to assist teachers in providing data science instruction to elementary-aged students. Using phenomenological case study methodology, the authors use classroom observations, student focus groups, video recordings and artifacts to detail ways learners engage in data science practices and understand how they perceive their engagement during activities and learning. Findings: Findings suggest student engagement in data science is enhanced when data problems are contextualized and connected to students' lived experiences; data analysis and data-based decision-making is practiced in multiple ways; and students are given choices to communicate patterns, interpret graphs and tell data stories. The authors note challenges students experienced with data practices including conflict between inconsistencies in data patterns and lived experiences and focusing on data visualization appearances versus relationships between variables. Originality/value: Data science instruction in elementary schools is an understudied, emerging and important area of data science education. Most elementary schools offer limited data science instruction; few elementary schools offer data science curriculum with embedded CT practices integrated across disciplines. This research assists elementary educators in fostering children's data science engagement and agency while developing their ability to reason, visualize and make decisions with data.

QR decomposition is widely used for solving the least squares problem. However, existing materials about it may be too abstract for non-mathematicians, especially STEM students, and/or require serious background in linear algebra. The paper describes theoretical background and examples of GNU Octave compatible MATLAB scripts that give relatively simple but complete explanations about how to use QR decomposition for the least squares problem solution. Only basic knowledge of linear algebra and calculus are required. Both Givens rotations and Householder reflections usage for the linear least squares problem were considered. It was shown that the algorithm based on Givens rotations is even easier to program than explicit formation of the normal equations with subsequent usage of Gaussian elimination.

Theorists and educators increasingly highlight the importance of computational thinking in STEM education. While various scaffolding strategies describe how to best support this skillset (i.e., paired programming, worked examples), less research has focused on the design and development of these digital tools. One way to support computational thinking and data science is through block coding and other ways that visualize the coding process. However, less is known about the learning experience design of these tools. Based on this gap, this work-in-progress study compared the learning experience design of novices and those with more advanced understanding of computational thinking. Results found differences emerge in the perceived dynamic interaction and scaffolding constructs of learning experience design. Implications for theory and practice are discussed.

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

Educational curricula in data analysis are increasingly fundamental to statistics, data science, and a wide range of disciplines. The educational literature comparing coding syntaxes for instruction in data analysis recommends utilizing a simple syntax for introductory coursework. However, there is limited prior work to assess the pedagogical elements of coding syntaxes. The study investigates the paradigms of the dplyr, data.table, and DTwrappers packages for R programming from a pedagogical perspective. We enumerate the pedagogical elements of computer programming that are inherent to utilizing each package, including the functions, operators, general knowledge, and specialized knowledge. The merits of each package are also considered in concert with other pedagogical goals, such as computational efficiency and extensions to future coursework. The pedagogical considerations of this study can help instructors make informed choices about their curriculum and how best to teach their selected methods.

The rapidly increasing capacity of technology to collect, organize, and manage data has spurred changes in the practice of statistics: new methods of collecting data, large data sets, new forms of data, different ways to visualize and represent data, and recognition of the importance of being able to understand and to communicate data-based arguments and findings from the perspective of data consumers and data producers. Using a narrative review based on Delphi methods, we asked leading members of the statistics education community to describe trends they have observed in the field and to identify interesting and relevant papers related to those trends. We received 24 responses and over 200 suggestions for papers. Our analysis included papers published in journals, book chapters, conference proceedings, handbooks, and curricular documents. We focused on future directions for statistics education research, and thus included articles based on opinion or principles if the arguments made a strong case supported by evidence as to why the idea was needed. From our analysis of 50 papers in this review, we suggest four emerging themes in statistics education research, challenging what should be taught and suggesting new ways of thinking about the teaching and learning of statistics: Data Science, Visibilizing Statistical Concepts, Social Statistics, and New Contexts for Learning. The review focuses on articles from 2017-mid 2022 and highlights the relevance and importance of each theme. Our choice of a particularly important paper for each theme is annotated in the references.

As the use of data in courses that incorporate statistical methods has become more prevalent, so has the need for tools for working with such data, including those for data creation and adjustment. While numerous tools exist that support faculty who teach statistical methods, many are focused on data analysis or theoretical concepts, and there remains a need for tools that assist faculty when making data-based examples and assignments. In this article, we describe a free application, available both online and through an R package, which we developed for instructors of courses that incorporate introductory statistical methods. The app treats the process of coming up with data for examples and assignments synergistically, beginning with either data creation (through random number generation) or adjustment (when the user has data) and ending with an assessment of the data's suitability for the desired analysis. The app enables the quick creation of many versions of variables for multiple versions of assignments and is compatible with file types associated with popular interdisciplinary software such as Excel, R, and SPSS. We highlight the app's features and discuss survey feedback provided by faculty that demonstrates the desire and need for and effectiveness of such an app. Supplementary materials for this article are available online.

Missing data can significantly influence results of epidemiological studies. The National Health and Nutrition Examination Survey (NHANES) is a popular epidemiological dataset. We examined recent practices related to the prevalence and the reporting of the amount of missing data, the underlying mechanisms, and the methods used for handling missing data in recent doctoral dissertations and subsequent publications using NHANES data as a case study. We also explored missing data handling coverage in top-selling applied statistical textbooks. Thirty-seven doctoral dissertations (published from 2007 to 2017) and 17 subsequent journal articles were included in the analysis. Overall, 29 (78.4%) dissertations did not explicitly state whether they had missing data. Five (62.5%) dissertations reporting missing data did not report an assumed mechanism of missingness. Only one subsequent journal article reported the missing data percent for key variables. 28 (75.7%) dissertations and 16 (94.1%) journal articles reported the use of NHANES sample weights. Of the top-selling 20 applied statistics/biostatistics textbooks examined, 14 did not mention imputation. This sample reflects poor rigor in analysis, reporting, and handling of missing data found among recent graduates and poor coverage in textbooks. Checklist utilization and improvement in statistical training on missing data handling are needed. Supplementary materials for this article are available online.

Purpose: Knowing the students' readiness for the fourth industrial revolution (4IR) is essential to producing competent, knowledgeable and skilled graduates who can contribute to the skilled workforce in the country. This will assist the Higher Education Institutions (HEIs) to ensure that their graduates own skill sets needed to work in the 4IR era. However, studies on students' readiness and preparedness for the 4IR in developing countries such as the Sultanate of Oman are still lacking. Therefore, this study investigates students' readiness level and preparedness for the 4IR. The findings of this study will benefit the HEIs policymakers, administration, faculties, departments, industries and society at large since they will be informed of the student's readiness and preparedness toward industry 4.0. Design/methodology/approach: The authors adopted the measures from the same context as previous studies in this study. The questionnaire was divided into three sections; the first part described the purpose and introduction of the search with the surety to keep the data confidential. The second part consisted of demographical information like gender, education. The last parts consisted of four subsections, question items in these parts are based on the related previous study. Characteristics consisted of 14 items, knowledge consisted of 18 items related to 4IR technologies, Organizational Dimension comprised of four items related to academic programs, curriculum and training. Preparedness contained two items. The participants have rated all the items in 5-Likert scale. Findings: Results from structural equation modeling showed that students' characteristics, knowledge of 4IR technologies and organizational dimensions significantly impact their preparedness for the 4IR. The study also found that organizational dimensions have the highest impact on students' preparedness. Furthermore, the organizational dimension significantly influences students' knowledge of 4IR technology. Moreover, students' characteristics related to 4IR are significantly affected by their knowledge of 4IR technology and organizational dimension. The findings suggest that HEIs are responsible for increasing the adoption of 4IR, and therefore organizational dimensions such as the academic programs, training, technological infrastructure and others are all critical for preparing students for a better future and should be given a priority. Research limitations/implications: This study has used academic programs and training to measure the organizational dimension. However, other important factors should be considered, such as technological infrastructure and leadership and governance of HEIs. Second, the current research depends on quantitative data, so future research should implement a mixed methodology (questionnaires, depth interviews, document analysis and focus group) to understand the factors affecting students' readiness for 4IR clearly. Finally, although the 4IR has numerous benefits, it also has challenges in its implementation, so future studies should focus on challenges encountered by different stakeholders in implementing 4IR-related technologies. Practical implications: The curriculum must include mandatory courses related to IT infrastructure design, user experience programming, electronic measurement and control principles, and programming for data science. HEIs should also foster interdisciplinary knowledge by integrating IT, Engineering, Business and Sciences. Furthermore, the HEIs should develop their infrastructure to have smart campuses, labs, classrooms and libraries to make HEIs a space where knowledge can be generated and innovative solutions can be proposed. This entails HEIs offering necessary hardware, software and technical support because if the HEIs improve their technological resources, students will be capable of using 4IR-related technologies effectively. Originality/value: The advancement of technology has resulted in the emergence of the Fourth Industrial Revolution (4IR), such as artificial intelligence, blockchain, robotics, cloud computing, data science, virtual reality and 3D printing. It is essential to investigate students' readiness for 4IR. However, there is no study as per researchers' knowledge talked about students readiness in HEIs in the Arab world. This study could be a basis for more research on students' perception of the 4IR covering students from various backgrounds and levels.

This paper demonstrates how the innovative application of a Collective Intelligence approach enhanced Local Skills Improvement Planning information for employers, education and skills training organisations and regional economic policy organisations. This took place within a Knowledge Transfer Partnership between a Chamber of Commerce and a University. This aimed to develop and deploy regional business intelligence for enhanced policy and decision-making in enterprise and economic development. The project converged knowledge from several research centres including economics, entrepreneurship and innovation, data science, and Artificial Intelligence. The paper presents a project case study which provides two contributions to applied knowledge. Firstly, it demonstrates how a Collective Intelligence (CI) approach can be applied to achieve rapid results in resolving the real-world problem of local skills information availability. Useful real-time data was gathered from employers in three sectors on skills requirements, supply and training. This was analysed using Artificial Intelligence tools, then shared publicly via an automated Internet portal, providing a scalable model for wider use. Secondly, it explores and evaluates how the knowledge exchange (KE) process can function effectively and quickly in applying CI-based innovation in practical ways which create new value, within a Knowledge Transfer Partnership between a University and Chamber of Commerce.environment.

Personalized educational interventions have been shown to facilitate successful and inclusive statistics, mathematics, and data science (SMDS) in higher education through timely and targeted reduction of heterogeneous training disparities caused by years of cumulative, structural challenges in contemporary educational systems. However, the burden on the institutions and instructors to provide personalized training resources to large groups of students is also formidable, and often unsustainable. We present Individualized Pathways and Resources to Adaptive Control Theory-Inspired Scientific Education (iPRACTISE), a free, publicly available web app that serves as a tool to facilitate personalized trainings on SMDS and related topics through provision of personalized training recommendations as informed by computerized assessments and individuals' training preferences. We describe the resources available in iPRACTISE, and some proof-of-concept evaluation results from deploying iPRACTISE to supplement in-person and online classroom teaching in real-life settings. Strengths, practical difficulties, and potentials for future applications of iPRACTISE to crowdsource and sustain personalized SMDS education are discussed.

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.

Support vector classifiers are one of the most popular linear classification techniques for binary classification. Different from some commonly seen model fitting criteria in statistics, such as the ordinary least squares criterion and the maximum likelihood method, its algorithm depends on an optimization problem under constraints, which is unconventional to many students in a second or third course in statistics or data science. As a result, this topic is often not as intuitive to students as some of the more traditional statistical modeling tools. In order to facilitate students' mastery of the topic and promote active learning, we developed some in-class activities and their accompanying Shiny applications for teaching support vector classifiers. The designed course materials aim at engaging students through group work and solidifying students' understanding of the algorithm via hands-on explorations. The Shiny applications offer interactive demonstration of the changes of the components of a support vector classifier when altering its determining parameters. With the goal of benefiting the broader statistics and data science education community, we have made the developed Shiny applications publicly available. In addition, a detailed in-class activity worksheet and a real data example are also provided in the online supplementary materials.

Effective undergraduate statistical education requires training using real-world data. Textbook datasets seldom match the complexities and messiness of real-world data and finding these datasets can be challenging for educators. Consulting and industrial datasets often have nondisclosure agreements. Academic datasets often require subject area expertise beyond those of a general education or lack connections to real-world applications. Many governments, including the United States, now require the release of data from projects they directly complete or fund though grants and contracts. We show how statistical educators may find datasets and incorporate them into courses. Specifically, we use two examples from the U.S. Geological Survey (USGS) and one example from the ecology literature. We demonstrate the use of these datasets in an upper-level analysis of variance (ANOVA) class. In addition to describing how we found the datasets, we describe how to include them into course work and the course's student assessments. We have used these datasets over multiple semesters and included student feedback from these courses. Although our examples focus on an ANOVA class, the general methods for finding data shared here could be used for statistical classes ranging from high school to graduate education. Supplementary materials for this article are available online.

In this article, we consider how to make data more meaningful to students through the choice of data and the activities we use them in drawing upon students lived experiences more in the teaching of statistics and data science courses. In translating scholarship around culturally relevant pedagogy from the fields of education and mathematics education we develop the idea of culturally relevant data. We see this development as a key ingredient to implementing culturally relevant pedagogy in teaching data-intensive courses leveraging the centrality of context through data in both statistics and data science to engage students particularly from historically marginalized groups in STEM. We provide suggestions as to ways of finding or creating culturally relevant data and using them in implementing culturally relevant pedagogy to support the learning and flourishing of students in statistics and data science courses. We also present findings from pilot work we have done in implementing these data in statistics courses. Finally, we discuss lingering questions and possible next steps for research in this area. Supplementary materials for this article are available online.

With increased focus on data literacy and data science education in K-12, little is known about what makes a data set preferable for use by classroom teachers. Given that educational designers often privilege authenticity, the purpose of this study is to examine how teachers use features of data sets to determine their suitability for authentic data science learning experiences with their students. Design/methodology/approach Interviews with 12 practicing high school mathematics and statistics teachers were conducted and video-recorded. Teachers were given two different data sets about the same context and asked to explain which one would be better suited for an authentic data science experience. Following knowledge analysis methods, the teachers' responses were coded and iteratively reviewed to find themes that appeared across multiple teachers related to their aesthetic judgments. Findings Three aspects of authenticity for data sets for this task were identified. These include thinking of authentic data sets as being "messy," as requiring more work for the student or analyst to pore through than other data sets and as involving computation. Originality/value Analysis of teachers' aesthetics of data sets is a new direction for work on data literacy and data science education. The findings invite the field to think critically about how to help teachers develop new aesthetics and to provide data sets in curriculum materials that are suited for classroom use.

Much of the mathematics that children experience in school neglect the skills increasingly needed for citizenship, particularly the power of complex data to investigate and make sense of the world. We draw on the relatively new field of data science as a multi-disciplinary approach to investigate problems through analysis of massive, non-standard, incongruous and/or messy data. Our exploratory qualitative study had as its research question: What can children learn about citizenship when they engage with data science? The case study in this paper illustrated ways that children's learning about citizenship were enriched through an age-appropriate data science investigation. The study analysed classroom video from a Year 4 classroom (aged 9-10) over six lessons that integrated curricula in digital technologies, health, and mathematics. In these lessons, the children generated and analysed non-standard data and debated social, well-being and privacy issues as they considered their activities in cyberspace. The video data were analysed using a framework based on critical citizenship education literature that examined dimensions of power, collective engagement, individual responsibility and action. Three key findings emerged. First, the case study highlighted skills in citizenship education developed through data science, positioning children as agents and advocates. Second, the study showed how a complex data investigation in citizenship education was achievable with primary children through meaningful curriculum integration. This is important given that problems that citizens address are typically interdisciplinary. Finally, the findings revealed a gap between data science skills and those developed in the mathematics curriculum, and recommend ways that the maths curriculum could be updated.

Data reported from memory can be unreliable. A simple activity lets students experience this firsthand.

Understanding data structures is fundamental for mastering algorithms. In order to solve problems and tasks, students must be able to choose the most appropriate data structure in which the data is stored and that helps in the process of the solution. Of course, there is no single correct solution, but in many cases, it is an important step to find the most efficient data structure to solve the task. In this paper, I will present the most common data representations and data structures that can help get to know them, to master them, and then to reuse them for solving different tasks.

This article discusses the key elements of the Data Science Technology course offered to postgraduate students enrolled in the Master of Data Science program. This course complements the existing curriculum by providing the skills to handle the Big Data platform and tools, in addition to data science activities. We tackle the discussion about this course based on three main requirements, which are related to the need to exploit the key skills from two dimensions, namely, Data Science and Big Data, and the need for a cluster-based computing platform and its accessibility. We address these requirements by presenting the course design and its assessments, the configuration of the computing platform, and the strategy to enable flexible accessibility. In terms of course design, the offered course contributes to several innovative elements and has covered multiple key areas of the data science body of knowledge and multiple quadrants of the job and skills matrix. In the case of the computing platform, a stable deployment of a Hadoop cluster with flexible accessibility, triggered by the pandemic situation, has been established. Furthermore, through our experience with the implementation of the cluster, it has shown the ability of the cluster to handle computing problems with a larger dataset than the one used for the semesters within the scope of the study. We also provide some reflections and highlight future improvements.

Mass adoption of advanced information technologies is fueling a need for public servants with the skills to manage data-driven public agencies. Public employees typically acquire data skills through graduate research methods courses, which focus primarily on research design and statistical analysis. What data skills are currently taught, and what content should Master of Public Administration (MPA) programs include in their research method courses? We categorized research method course content in 52 syllabi from 31 MPA programs to understand how data skills are taught in public administration. We find that most graduate programs rely on research methods more suited for academic and policy research while lacking the data skills needed to modernize public agencies. Informed by these results, this work presents the "Data Science Literacy Framework" as a guide for assessing and planning curriculum within MPA programs.

Integration of computational data science (CDS) into the university curriculum offers several advantages for students, faculty and the institution. This article discusses the benefits to students of introducing CDS into the university curriculum with a focus on developing skills in cheminformatics, data analysis, structure--activity relationships, modelling and simulation. Moreover, CDS can enable students to engage with complex chemical and toxicological data in new and dynamic ways, helping them to develop a more nuanced understanding of the potential hazards and risks associated with different chemicals and substances. On the other hand, it can foster greater collaboration between students and faculty and with external partners in industry and government. This can lead to the development of more effective and efficient toxicological testing methods and tools to screen chemicals for potential hazards and aid the development of environmentally friendly chemicals. Overall, the integration of CDS into the university curriculum will help prepare the next generation of scientists giving them a competitive edge to make considerable contributions to green chemistry, designing safer chemicals and non-animal testing methods. It will enable them to tackle modern challenges facing society including identifying safer and more sustainable chemicals and predicting the health and environmental impacts of novel chemical substances.

Preparing data-literate citizens and supporting future generations to effectively work with data is challenging. Engaging students in Knowledge Building (KB) may be a promising way to respond to this challenge because it requires students to reflect on and direct their inquiry with the support of data. Informed by previous studies, this research explored how an analytics-supported reflective assessment (AsRA)-enhanced KB design influenced 6th graders' KB and data science practices in a science education setting. One intact class with 56 students participated in this study. The analysis of students' Knowledge Forum discourse showed the positive influences of the AsRA-enhanced KB design on students' development of KB and data science practices. Further analysis of different-performing groups revealed that the AsRA-enhanced KB design was accessible to all performing groups. These findings have important implications for teachers and researchers who aim to develop students' KB and data science practices, and general high-level collaborative inquiry skills.

Contribution: This article presents evidence that electrical engineering, computer science, and data science students, participating in introduction to machine learning (ML) courses, fail to interpret the performance of ML algorithms correctly, since they fail to consider the application domain. This phenomenon is referred to as the domain neglect bias and is discussed in this article from the cognitive psychology perspective. Background: ML algorithms are common components in electrical engineering, computer science, and data science curricula. Learning ML is challenging not only due to its complex mathematical and algorithmic aspects, but also due to: (1) the complexity of using these algorithms in the context of real-life situations correctly and (2) the comprehension of related social and ethical issues. Research Question: To what extent do students fail to correctly interpret the performance of ML algorithms due to the domain neglect bias and how can this bias be explained? Methodology: A questionnaire containing two questions was designed, in which the participants were asked to compare the performance of ML algorithms in the contexts of different application domains. The questions were designed to trigger the domain neglect bias which in turn, allowed quantification of its prevalence. Findings: One third of the students failed to recognize the outperforming ML algorithm due to the domain neglect bias. Educators should note this bias and develop new pedagogical methods for teaching ML performance in order to mitigate the bias.

The perceived importance of data in society has led to a surge in interest towards data science education. This article seeks to build on existing literature concerned with the sociopolitical, cultural, and ethical dimensions of data science education by considering the salience of two interrelated concepts discussed in Asian and Asian American studies and postcolonial studies: techno-Orientalism and the Filipino mimicry stereotype. These concepts describe the positioning of Asians and Asian Americans, and Filipinos in particular, as performance machines who are adept at copying the ideas of dominant groups but incapable of innovation themselves. This article situates techno-Orientalism and the Filipino mimicry stereotype within broader conversations around postcolonial data politics and then argues for the possibility of including these concepts in data science education curricula. Finally, this article discusses Gaskins's techno-vernacular creativity as one path for resisting techno-Orientalist and mimicry discourses.

This article reports on a study investigating how computational essays can be used to help students in higher education STEM take up disciplinary epistemic agency--cognitive control and responsibility over one's own learning within the scientific disciplines. Computational essays are a genre of scientific writing that combine live, executable computer code with narrative text to present a computational model or analysis. The study took place across two contrasting university contexts: an interdisciplinary data science and modeling course at a large research university in the Midwestern United States, and a third-semester physics course at a large research university in Scandinavia. Over the course of a semester, computational essays were simultaneously and independently used in both courses, and comparable datasets of student artifacts and retrospective interviews were collected from both student populations. These data were analyzed using a framework that operationalized the construct of disciplinary epistemic agency across the dimensions of programming, inquiry, data analysis and modeling, and communication. Based on this analysis, we argue that computational essays can be a useful tool for fostering disciplinary epistemic agency within higher education science due to their combination of adaptability and disciplinary authenticity. However, we also argue that educational contexts, scaffolding, expectations, and student backgrounds can constrain and influence the ways in which students choose to take up epistemic agency.

Artificial intelligence (AI) has been widely adopted in higher education. However, the current research on AI in higher education is limited lacking both breadth and depth. The present study fills the research gap by exploring faculty members' perception on teaching AI and data science related courses facilitated by an open experiential AI platform. Specifically, two focus groups are conducted among computer science and non-computer science faculty members to gauge their perception on the integration of AI in an experiential learning platform to teach data science, as well as their perception on AI powered data science curriculum in higher education. Findings reveal three major themes which are defining data science, assembling interdisciplinary teams, and building platform for connection. The study has both theoretical and practical implications.

This study presents topic modeling based bibliometric characteristics of the articles related to the flipped classroom. The corpus of the study consists of 2959 articles published in the Scopus database as of the end of 2021. In addition to the bibliometric characteristics of the field, research interests and trends were also revealed with the study, which was based on the topic modeling-based bibliometric analysis method. According to the results of the study, an increase in the number of publications has been observed since 2015. Nearly one-third of the studies are of United States origin. According to the findings of the topic analysis in which the research interests and trends in the studies were revealed, the articles in this field were gathered under 16 topics. Considering the number of publications of the topic, it was seen that the three most voluminous topics were "Performance and perception", "Nursing education" and "Effectiveness and motivation", respectively. It is thought that the results of the study will provide a general perspective to the researchers in this field and provide important outputs in the context of monitoring the issues that may become prominent in the future.

Library and librarian involvement in digital humanities (DH) has grown over the past few years. However, it is unclear whether current library and information studies (LIS) programs are properly preparing students for this type of work. This study analyzed course offerings at Canadian ALA-accredited LIS programs. While Canadian ALA-accredited LIS programs offer DH-relevant courses, the number of courses offered and their range/scope vary greatly among institutions. Although many are teaching the technical skills required by the field of DH librarianship, collaboration and project management training remain elusive in most programs.

Mathematics education is like many scientific disciplines witnessing an increase in scientific output. Examining and reviewing every paper in an area in detail are time-consuming, making comprehensive reviews a challenging task. Unsupervised machine learning algorithms like topic models have become increasingly popular in recent years. Their ability to summarize large amounts of unstructured text into coherent themes or topics allows researchers in any field to keep an overview of state of the art by creating automated literature reviews. In this article, we apply topic modeling in the context of mathematics education and showcase the use of this data science approach for creating literature reviews by training a model of all papers (n = 336) that have been presented in Thematic Working Groups related to technology in the first eleven Congresses of the European Society for Research in Mathematics Education (CERME). We guide the reader through the stepwise process of training a model and give recommendations for best practices and decisions that are decisive for the success of such an approach to a literature review. We find that research in this period revolved around 25 distinct topics that can be grouped into four clusters: digital tools, teachers and their resources, technology experimentation, and a diverse cluster with a strong focus on student activity. Finally, a temporal analysis of these topics reveals a correlation between technology trends and research focus, allowing for reflection on future research in the field.

In recent years, big data has become ubiquitous in our day-to-day lives. Therefore, it is imperative for educators to integrate nontraditional (big) data into statistics education to ensure that students are prepared for a big data reality. This study examined graduate students' expressions of uncertainty while engaging with traditional and nontraditional big data investigation activities. We first suggest a theoretical framework based on integrated insights from statistics education and data science to analyze and describe novices' reasoning with the various uncertainties that characterize both traditional and big data--the Variability, Data, and Phenomenon (VDP) framework. We offer a case study of graduate students' participation in the integrated modeling approach (IMA) learning trajectory, illustrating the utility of the VDP framework in accounting for the different types of articulated uncertainties. We also discuss the teaching implications of the VDP.

As the field of data science evolves with advancing technology and methods for working with data, so do the opportunities for re-conceptualizing how we teach undergraduate statistics and data science courses for majors and non-majors alike. In this paper, we focus on three crucial components for this re-conceptualization: Developing research questions, professional ethics, and team collaborations. We share vignettes from two teams of undergraduate statistics or data science majors at two different stages of their development (novice and expert) while they worked on a DataFest data challenge. These vignettes shed light on opportunities for re-conceptualizing introductory courses to give more attention to issues of the process of developing focused research questions when given a complex data set, professional ethics and bias, and how to collaborate effectively with others. We provide some implications for teaching and learning as well as an example activity for educators to use in their courses.

Growing societal concern about a crisis in the wellbeing of young people has prompted a range of responses from governments and corporations, predicated on an ideal of the resilient, self-reliant individual. Behavioural economists, data scientists and educational technology companies now offer a variety of psychological interventions based on psychometric data, aimed at 'equipping' individual students with the necessary skills and character to enable them to withstand the pressures of contemporary life. As a consequence, the critical importance of mutually supportive interpersonal relationships continues to be neglected in mainstream approaches to Social and Emotional Learning (SEL). This article draws on Fromm's theory of "social character" and Zuboff's analysis of 'life in the hive' to challenge the assumptions about human behaviour underpinning data science and its application in digital tools for social and emotional learning and self-managed wellbeing. To improve students' wellbeing, we need to begin with an understanding of why they are more likely to thrive within a network of mutually supportive social relationships than a digital 'hive'.

When pressing societal challenges (e.g., COVID-19, access to clean water) are sidelined in science classrooms, science education fails to leverage the knowledge and experiences of minoritized students in school, thus reproducing injustices in society. Our conceptual framework for "justice-centered STEM education" engages all students in multiple STEM subjects, including data science and computer science, to explain and design solutions to pressing societal challenges and their disproportionate impact on minoritized groups. In the first part of this article, we extend our conceptual framework by articulating the affordances of justice-centered STEM education for one minoritized student group that has been traditionally denied meaningful STEM learning experiences: multilingual learners (MLs). Justice-centered STEM education with MLs leverages the assets that MLs bring to STEM learning, including their transnational knowledge and experiences as well as their rich repertoire of meaning-making resources, thus refuting deficit narratives of these students. To illustrate the affordances of justice-centered STEM education with MLs, we draw on examples from our instructional unit that engages students in the pressing societal challenge of the COVID-19 pandemic. In the second part of the article, we report on an initial inquiry into how 14 undergraduate pre-service teachers made sense of our conceptual framework after participating in lessons from our COVID-19 instructional unit. Findings indicated that pre-service teachers perceived both opportunities and obstacles of justice-centered STEM education with MLs. We close by discussing what it might take to prepare the next generation of teachers to disrupt systemic injustices in and out of school.

Being able to communicate effectively is an essential skill for statisticians and data scientists. Despite this, communication skills are not frequently taught or emphasized in statistics and data science courses. In this article, we describe a series of four workshops that were developed to enhance the written communication skills of statistics graduate students. We also present results of a survey administered prior to the first workshop and after the last workshop to assess changes in attitudes towards writing and obtain student feedback to improve the workshops. Lastly, we discuss potential modifications of these workshops including ways to use these workshops or elements from them for undergraduates or in statistics and data science courses. Supplementary materials for this article are available online.

Despite rapid growth in the data science workforce, people of color, women, those with disabilities, and others remain underrepresented in, underserved by, and sometimes excluded from the field. This pattern prevents equal opportunities for individuals, while also creating products and policies that perpetuate inequality. Thus, it is critical that, as statistics and data science educators of the next generation, we center accessibility and inclusion throughout our curriculum, classroom environment, modes of assessment, course materials, and more. Though some common strategies apply across these areas, this article focuses on providing a framework for developing accessible and inclusive course materials (e.g., in-class activities, course manuals, lecture slides, etc.), with examples drawn from our experience co-writing a statistics textbook. In turn, this framework establishes a structure for holding ourselves accountable to these principles.

We present a two-week active learning chemical engineering hackathon event specifically designed to teach undergraduate chemical engineering students of any skill level data science through Python and directly apply this knowledge to a real problem provided by industry. The event is free and optional to the students. We use self-evaluation surveys to measure improvements in both data science knowledge and team soft skills, finding that participants report a substantial increase in self-confidence with Python, a popular coding language with support for data science tools. The reported improvement is found to be statistically significant, to a greater extent for women who have traditionally been undersupported in computing.