Data Science Landscape in Preservice Teacher Education

British Journal of Educational Technology, 2022

Data science is often described as the integration of computational and digital technologies, statistical and mathematical knowledge, and disciplinary expertise. It is a rapidly growing methodological approach for educational practice (Estrellado et al., 2020) and research (McFarland et al., 2021) and as a topic of study at the collegiate level (National Academies of Science, Engineering, & Medicine, 2018). Recently, aggressive efforts have begun in many places to promote data science as a topic for students to study as part of primary and secondary education. 1 Yet, despite calls to support data science learning opportunities for younger-aged learners, it is still unclear what should constitute a data science education for these grade levels and how data science would be integrated into already densely-packed curricula. There are some natural affinities between pre-collegiate data science education and mathematics or computer science education. This is an approach that has been explored in recent years that has led to novel and noteworthy programs and curricula (eg, Gould et al., 2018; Krishnamurthi et al., 2020). While we remain interested in and receptive to the expansion and continued refinement of standalone courses, papers in this special issue demonstrate that now is the time to not only explore how data science education might take shape within such courses, but also across K-12 disciplines-for example, in social studies, science and even art education settings.

International Journal of Data Mining & Knowledge Management Process, 2020

Companies desires for making productive discoveries from big data have motivated academic institutions offering variety of different data science (DS) programs, in order to increases their graduates' ability to be data scientists who are capable to face the challenges of the new age. These data science programs represent a combination of subject areas from several disciplines. There are few studies have examined data science programs within a particular discipline, such as Business (e.g. Chen et al.). However, there are very few empirical studies that investigate DS programs and explore its curriculum structure across disciplines. Therefore, this study examines data science programs offered by American universities. The study aims to depict the current state of data science education in the U.S. to explore what discipline DS programs covers at the graduate level. The current study conducted an exploratory content analysis of 30 DS programs in the United States from a variety of disciplines. The analysis was conducted on course titles and course descriptions level. The study results indicate that DS programs required varying numbers of credit hours, including practicum and capstone. Management schools seem to take the lead and the initiative in lunching and hosting DS programs. In addition, all DS programs requires the basic knowledge of database design, representation, extraction and management. Furthermore, DS programs delivered information skills through their core courses. Moreover, the study results show that almost 40 percent of required courses in DS programs is involved information representations, retrieval and programming. Additionally, DS programs required courses also addressed communication visualization and mathematics skills.

Journal of Learning Analytics, 2016

Educational data science (EDS) is an emerging, interdisciplinary research domain that seeks to improve educational assessment, teaching, and student learning through data analytics. Teachers have been portrayed in the EDS literature as users of pre-constructed data dashboards in educational technologies, with little consideration given to them as active producers of data analytics. This article presents the case study results of an EDS program at a large university in Midwestern U.S.A. in which faculty and instructors were provided with access to institutional data and data analytics technologies in order to explore questions related to their classroom and departmental environments. Semi-structured interviews of program participants were conducted to examine the participants' experiences as practitioner researchers in EDS. The analysis showed that participants were motivated to participate to improve their learning and educational environments through data analytics, as opposed to developing a research agenda in EDS; that participants experienced a range of barriers related to data literacy; and that participant community support in addition to administrative support are vital to teacher-focused EDS programs. This study adds to a small but growing body of research in EDS that considers teachers as producers and not just consumers of data analytics.

Technology Innovations in Statistics Education, 2013

Proceedings of the Association for Information Science and Technology , 2021

Addressing the data skills gap, namely the superabundance of data and the lack of human capital to exploit it, this paper argues that iSchools and Library and Information Science programs are ideal venues for data science education. It unpacks two case studies: the LIS Education and Data Science for the National Digital Platform (LEADS-4-NDP) project (2017-2019), and the LIS Education and Data Science-Integrated Network Group (LEADING) project (2020-2023). These IMLS-funded initiatives respond to four national digital platform challenges: LIS faculty prepared to teach data science and mentor the next generation of educators and practitioners, an underdeveloped pedagogical infrastructure, scattered and inconsistent data science education opportunities for students and current information professionals, and an immature data science network. LEADS and LEADING have made appreciable collaborative, interdisciplinary contributions to the data science education community; these projects comprise an essential part of the long-awaited and much-needed national digital platform.

Computer

ata science is a burgeoning discipline that aims to develop methodologies and tools for analyzing large data sets to uncover insights that further research goals and facilitate decision making. Since its beginnings in the 1990s, the discipline has had origins in data mining and in the 2000s, with advances in computing, influences from big data analytics. Indeed, data, information collection, and the knowledge discovery pipeline describe what daily work in the field looks like. However, data science is also an interdisciplinary effort that does not exist independently or in isolation; instead, it should rightly be defined by its fruitful exchange of multiple knowledge areas: mathematics, statistics, computer science, communication, and relevant application domains. C.F. Jeff Wu, in his 1985 paper, first used the term data science. 1 Later, in his 1997 inauguration speech as the H.C. Carver Chair at the University of Michigan, Wu suggested that statistics should be equivalent to "data science" and emphasized the need for large-scale computing and interdisciplinary training. With the recent and ongoing proliferation of data across all facets of life-generated by social media, smart devices, streaming services, real-time systems, and more-the desire to involve computation to effectively analyze data sets at scale to reduce uncertainty in decision making distinguishes data science today from its previous incarnations.

Proceedings of the 49th ACM Technical Symposium on Computer Science Education

Data science keeps growing in popularity as an introductory computing experience, in which students answer real-world questions by processing data. Armed with carefully prepared pedagogical datasets, computing educators can contextualize assignments and projects in societally meaningful ways, thereby benefiting students' long-term professional careers. However, integrating data science into introductory computing courses requires that the datasets be sufficiently complex, follow appropriate organizational structure, and possess ample documentation. Moreover, the impact of a data science context on students' motivation remains poorly understood. To address these issues, we have created an open-sourced manual for developing pedagogical datasets (freely available at https:// think.cs.vt.edu/pragmatics). Structured as a collection of patterns, this manual shares the expertise that we have gained over the last several years, collecting and curating a large collection of real-world datasets, used in a dozen of universities worldwide. We also present new evidence confirming the efficacy of integrating data science in an introductory computing course. As a significant extension of our ongoing work, this study not only validates existing positive assessment, but also provides fine-grained nuance to the potential of data science as a motivational educational element.

Cogent Education, 2016

Educators by definition are now required to utilize a variety of student data to shape the decisions they make and design the lessons they teach. As accountability standards become more stringent and as teachers face increasingly diverse student populations within their classrooms, they often struggle to adequately meet the needs of all learners. Using student data, rationales for instructional decisions become grounded in best practices. Unfortunately, some administrators and teachers lack the confidence and/or training needed to successfully engage with and interpret data results. This may be especially true for early career educators and those just entering the field. Indeed, for novice teachers to be successful in the current accountability culture, they must possess, understand, and effectively utilize data literacy skills, something quite difficult to accomplish without adequate training. The research in this article explored how pre-service educators determined what worked in a data literacy intervention and the potential impact this had on their instructional decision-making process. Implications for instructor professional development are offered for consideration.

Journal of computational science education, 2021

This article reports on the efforts of the Computer Science Education Collaborative during the period between 2018-2020 to develop and implement a new computer science licensure program for preservice teachers seeking a license to teach computer science in grades 7-12 in Vermont. We present a brief review of the literature related to computer science teacher education and describe the process of developing the computer science education minor and major concentration at the University of Vermont. As a form of reflection, we discuss the program development process and lessons learned by the collaborative that might be informative to other institutes of higher education involved in CS teacher education program design and implementation. Finally, we describe next steps for developing in-service licensure programs for teachers seeking computer science professional development or licensure in grades 7-12.

2021 ASEE Midwest Section Conference Proceedings

This paper introduces the background and establishment of the first Research Experience for Teachers (RET) Site in Arkansas, supported by the National Science Foundation. The Arkansas Data Analytics Teacher Alliance (AR-DATA) program partners with school districts in the Northwest Arkansas region to promote research-driven high school analytics curriculum and education to reach underserved students, such as those from rural areas. At least thirty 9 th-12 th grade mathematics, computer science, and pre-engineering teachers will participate in AR-DATA and work with faculty mentors, graduate students, curriculum coaches, and industry experts in a six-week RET Summer Program and academic-year follow up to develop and disseminate learning modules to enhance current curriculum, attain new knowledge of data analytics and engineering applications, and benefit professionally through the RET program activities. The learning modules developed will reflect current cutting-edge analytics research, as well as the development needs of next-generation analytics workforce.