Analysis of International Students Knowledge Acquisition in Data Science Education at Private Liberal Arts University

In accordance with theAI Strategy 2019 issued by the Cabinet Office, Kyushu Institute of Information Sciences has established and is operating the KIIS Mathematical and Data Science and AI Education Program at the literacy level and the applied basic level. In particular, the literacy level can be completed by all students, and is recommended for all students to acquire the basic data science knowledge necessary after employment.In this paper, we took up the subject of "Exercise of Information Literacy," which is positioned asan introductory course for data science education, and examined the possibility of its application to data science education for existing courses, using the report assignments in the lectures and the results of questionnaires.The results show that,in both departments, the final survey displayedhigher marks than the initial survey, indicating thateach student was able to master the lecture content and basic skills during the lecture.Thisbrought about a certain level of educational effects in both humanities and sciences.Differences in data science backgroundsexistbetween humanities and science students, and it is important to fully take these into account in educational practices.

This article examined the factors influencing the college choice of African American male undergraduate students at two private liberal arts colleges in the South. Utilizing Hossler and Gallagher's (1987) College Choice Model as the theoretical framework, the study explored these students' experiences, perceptions, and attitudes toward the college selection process. The research investigated explicitly how predisposition, search, and choice factors impact the decision-making process for African American males at these institutions. Employing a qualitative methodology, data were collected through in-depth interviews with 20 participants. The findings revealed that family and community influences, financial considerations, campus environment, and diversity significantly shape college choice. The study contributes to a deeper understanding of the factors affecting college choice for African American males at private liberal arts colleges in the South. Additionally, it offers insights that could inform the development of policies and practices to enhance access, diversity, and educational equity within higher education.

There is a plethora of studies concerning the academic performance of students after they transfer from 2‐year to 4‐year colleges or universities. These studies, however, have predominately included transfers to public colleges and universities. Researchers have not included the private liberal arts college in such investigations. With the continued increases in community college enrollment, a number of private liberal arts colleges are interested in exploring the viability of this market as a means to increase or stabilize enrollment. This study examined the grade point average declines (transfer shock) experienced by community college transfer students at 2 private liberal arts colleges. The sample included 216 students who transferred from a community college to either Benedictine College (Kansas) or Saint Vincent College (Pennsylvania) over a 9‐semester time period. Results indicate that the transfer shock experienced at these private liberal arts colleges was at the lower end of magnitude in comparison to studies that have used samples of public college and university transfer students.

Introduction and Background In 1997, The Chronicle of Higher Education reported the story of Charlotte Dickerson who had transferred from Miami-Dade Community College (MDCC) to Smith College, an elite women's college in Northampton, Massachusetts (Geraghty, 1997). At the time, Charlotte was one of a handful of Miami-Dade students who had made the transfer to Smith. The success of Dickerson and the other students led Smith and Miami-Dade to formalize a transfer agreement between the two institutions. Later, Santa Monica College (SMC) entered into a similar agreement with Smith.1 Both transfer agreements allow students the opportunity to transfer from either community college to Smith and receive credit for the courses they have taken, as long as those courses have been designated as appropriate for transfer by the institutions involved. Although most transfer or articulation agreements involve two public institutions from the same state, these two specific agreements are unique because they involve public community colleges and an elite out-of-state private college.2 There is no way of knowing how widespread the practice of having articulation agreements across state lines is, especially between public and private colleges, but we can conclude that the vast majority of the research on transfer agreements between community colleges and baccalaureate-granting institutions focuses on relationships between institutions within the same state and, more specifically, on arrangements between public institutions. Cejda's (1999) research on the transfer patterns at a private liberal arts college offers an exception. His research, however, suggests that the concept of transfer can be only loosely applied to the students and college he studied. The students in his research used community colleges more for reverse transfer or as a safety valve to pick up isolated credits than they did as a stepping stone to a baccalaureate education. The transfer agreements between Smith and Santa Monica and Miami-Dade are of interest because of their potential to provide very bright community colleges students with an opportunity to attend an elite school as well as their potential to add to the richness and diversity of institutions like Smith College. The three institutions involved in this arrangement are somewhat unique. For example, both Miami-Dade and Santa Monica are extraordinarily successful in terms of the numbers of their students that transfer to their respective state systems of higher education and to local private institutions. Santa Monica regularly publicizes the fact that it is the premier transfer spot to the University of California and California State University systems. Miami-Dade boasts that one of seven upper division students in the Florida State University System began his or her postsecondary career at Miami-Dade. Smith College is not a typical postsecondary institution either. It is both a women's college and an elite institution with a great degree of selectivity. Nonetheless, we believe that these institutions and the transfer agreements they have constructed have something important to teach other institutions. In particular, transfer agreements such as these have the potential to create win-win situations for community colleges and private liberal arts colleges. Transfer agreements with private institutions, for example, enhance the reputation of community colleges, help them to look beyond traditional venues of cooperation, and, most importantly, provide opportunities for community college students to transfer to elite institutions that may have previously been out of reach. There are also tangible benefits for liberal arts colleges in considering such a relationship with community colleges. Specifically, liberal arts colleges can use such agreements to attract bright, capable, proven students from diverse backgrounds and can supplement applicant pools, which is particularly important for private four-year colleges in today's competitive market. …

Aim/Purpose: This study aimed to evaluate the extant research on data science education (DSE) to identify the existing gaps, opportunities, and challenges, and make recommendations for current and future DSE. Background: There has been an increase in the number of data science programs especially because of the increased appreciation of data as a multidisciplinary strategic resource. This has resulted in a greater need for skills in data science to extract meaningful insights from data. However, the data science programs are not enough to meet the demand for data science skills. While there is growth in data science programs, they appear more as a rebranding of existing engineering, computer science, mathematics, and statistics programs. Methodology: A scoping review was adopted for the period 2010–2021 using six scholarly multidisciplinary databases: Google Scholar, IEEE Xplore, ACM Digital Library, ScienceDirect, Scopus, and the AIS Basket of eight journals. The study was narrowed down to 91 research articles and adopted a classification coding framework and correlation analysis for analysis. Contribution: We theoretically contribute to the growing body of knowledge about the need to scale up data science through multidisciplinary pedagogies and disciplines as the demand grows. This paves the way for future research to understand which programs can provide current and future data scientists the skills and competencies relevant to societal needs. Findings: The key results revealed the limited emphasis on DSE, especially in non-STEM (Science, Technology, Engineering, and Mathematics) disciplines. In addition, the results identified the need to find a suitable pedagogy or a set of pedagogies because of the multidisciplinary nature of DSE. Further, there is currently no existing framework to guide the design and development of DSE at various education levels, leading to sometimes inadequate programs. The study also noted the importance of various stakeholders who can contribute towards DSE and thus create opportunities in the DSE ecosystem. Most of the research studies reviewed were case studies that presented more STEM programs as compared to non-STEM. Recommendations for Practitioners: We recommend CRoss Industry Standard Process for Data Mining (CRISP-DM) as a framework to adopt collaborative pedagogies to teach data science. This research implies that it is important for academia, policymakers, and data science content developers to work closely with organizations to understand their needs. Recommendation for Researchers: We recommend future research into programs that can provide current and future data scientists the skills and competencies relevant to societal needs and how interdisciplinarity within these programs can be integrated. Impact on Society: Data science expertise is essential for tackling societal issues and generating beneficial effects. The main problem is that data is diverse and always changing, necessitating ongoing (up)skilling. Academic institutions must therefore stay current with new advances, changing data, and organizational requirements. Industry experts might share views based on their practical knowledge. The DSE ecosystem can be shaped by collaborating with numerous stakeholders and being aware of each stakeholder’s function in order to advance data science internationally. Future Research: The study found that there are a number of research opportunities that can be explored to improve the implementation of DSE, for instance, how can CRISP-DM be integrated into collaborative pedagogies to provide a fully comprehensive data science curriculum?

Emerging data driven economy including industry, research and business, requires new types of specialists that are capable to support all stages of the data lifecycle from data production and input to data processing and actionable results delivery, visualisation and reporting, which can be jointly defined as the Data Science professions family. Data Science is becoming a new recognised field of science that leverages the Data Analytics methods with the power of the Big Data technologies and Cloud Computing that both provide a basis for effective use of the data driven research and economy models. Data Science research and education require a multi-disciplinary approach and data driven/centric paradigm shift. Besides core professional competences and knowledge in Data Science, increasing digitalisation of Science and Industry also requires new type of workplace and professional skills that rise the importance of critical thinking, problem solving and creativity required to work in highly automated and dynamic environment. The education and training of the data related professions must reflect all multi-disciplinary knowledge and competences that are required from the Data Science and handling practitioners in modern, data driven research and the digital economy. In modern conditions with the fast technology change and strong skills demand, the Data Science education and training should be customizable and delivered in multiple forms, also providing sufficient lab facilities for practical training. This paper discusses aspects of building customizable and interoperable Data Science curricula for different types of learners and target application domains. The proposed approach is based on using the EDISON Data Science Framework (EDSF) initially developed in the EU funded Project EDISON and currently being maintained by the EDISON Community Initiative.

The Information Processing Society of Japan (IPSJ) published a curriculum standard for university-level education majoring in data science (DS) (An English translation of the IPSJ Data Science Curriculum Standard is available at: https://www.ipthree.org/wp-content/uploads/IPSJ-DS-Curriculum_202104_en.pdf). In this paper, we shall report strategy and development of the DS curriculum standards. Data science education and the development of data scientists are recognised to be quite important in both social and business contexts. The IPSJ DS curriculum standard is developed by integrating various related initiatives and has the following unique features. (1) The DS curriculum standard covers a wide range of related fields to ensure international compatibility through mapping to the ACM Data Science curriculum and the European EDISON Data Science Framework. (2) It collaborates with the IPSJ Data Scientist certification (under development) by referring to the Data Scientist skill checklist (assistant level) developed by the Data Scientist Society of Japan. (3) It clarifies the knowledge and skills required of students majoring in data science. (4) It assigns a time to each educational content so that the curriculum size becomes approximately 675 class hours (60 credits in the Japanese credit system). (5) It collaborates with the Model Curriculum for Mathematics, Data Science and AI Education (literacy level) developed by the Japan Inter-University Consortium for Mathematics and Data Science, supported by the Japanese government.KeywordsData scienceCurriculum developmentStatisticsArtificial intelligenceComputer scienceData managementSoftware engineeringData scientist certification

The increasing datafication of African societies has led to a proliferation of data science-related training opportunities. These trainings provide young people with the opportunity to learn the skills to work on Data science, with some focused specifically on women and girls. While this is encouraging and brings new opportunities for women and girls to participate in the knowledge economy, it is important to understand the wider context of data science training in Africa, in particular, how women and girls experience their (data science) education, and how this knowledge can impact their lives, sustain livelihoods and bring empowerment. Through a review of the literature, as well as an examination of different pedagogical approaches and practices used by various formal and informal training programs in Africa, we examined the experience of women and girls. We conducted a mapping of the training and networks that have been set up to provide knowledge and skills and to empower women in data science. We highlight some of the facilitators that have positively contributed to a greater participation of women and girls in data science education, while also revealing some of the barriers and structural impediments to fair access to training for women in data science.

Surveying the Ongoing History CrisisQuantitative and Qualitative Evidence from Across the Midwest Kevin Mason (bio) Following the alarm bells sounded by Middle West Review editor-in-chief Jon Lauck in the Fall 2022 issue editorial titled "The Ongoing History Crisis," the need for a more robust and comprehensive data set to gauge the current trends related to faculty jobs across the region stood out. With added data from a more comprehensive survey a stark reality emerged: across the 104 institutions surveyed as of December 31, 2022—out of 629 Higher Learning Commission-accredited institutions that were identified and contacted—a reduction of 290 full-time jobs in the field of history occurred within responding institutions over the past fifteen years across the twelve-state region. Twenty-eight responding institutions did not experience a reduction in full-time staffing over the past fifteen years, while seventy-one respondents lost positions. Meant to provide comprehensive representation of all institutions throughout the region, the survey focused on several meaningful data points considered in Lauck's original editorial: current full-time faculty (tenure and non-tenure), current adjunct faculty, institutional trend in full-time staffing numbers, presence of a major in history, presence of a graduate program in history, and an opportunity to offer qualitative evidence and comments. Institutions surveyed ranged from small liberal arts colleges and community colleges to the largest R1 universities throughout the region. Although many other indicators of program health, especially individual program enrollment data, would help to further illuminate the crisis, the design of the survey instrument favored the ability to respond quickly and easily in the hopes of increasing the number of respondents. Pairing Lauck's initial data set with responses received from eighty-one additional colleges and universities across the region, a clearer depiction of the crisis emerged. [End Page 183] Small institutions with five or fewer faculty members represent fiftyseven survey respondents. Constituted primarily of private liberal colleges, small state institutions, and community colleges, this group currently employs 145 full-time faculty members in history. These schools also rely most heavily (per capita) on adjunct instructors, with 105 such positions currently included in offerings at the responding institutions. The small-school group also represents the greatest balance between programs who experienced a reduction in full-time positions over the past fifteen years and those who did not, with thirty-one respondents indicating a reduction while twenty-two did not lose any positions. Thirty-five of the programs surveyed currently offer a major in history, eleven represent community colleges, and one program does not offer a history-oriented major. Two of the respondents provide graduate offerings in or related to history. Nineteen respondents were institutions employing between six and ten full-time faculty in history. Consisting primarily of small state institutions, two private liberal arts colleges, and one public city university, the grouping currently employs 141 full-time faculty members in history, as well as thirty-seven adjunct positions. Fourteen of the respondent institutions reported a reduction in full-time faculty over the past fifteen years for a total of sixty-three jobs lost, while four indicated stable staffing (one respondent did not include trend information). Each of the surveyed schools currently offers a major in history, and ten provide graduate offerings in or related to history. Eight respondents were institutions employing between eleven and fifteen full-time faculty in history. Consisting of seven state institutions and one private university, the grouping currently employs 105.5 full-time faculty members in history, as well as ten adjunct positions. Virtually all schools in the category reported a reduction in full-time faculty over the past fifteen years, totaling forty-one jobs lost. Each of the surveyed schools currently offers a major in history and graduate offerings in or related to history. Eighteen respondents were institutions employing more than sixteen full-time faculty in history. Consisting of fifteen state universities and three private liberal arts colleges, the grouping currently employs 522.5 full-time faculty in history, as well as twenty-five adjunct positions. Sixteen of the respondent institutions reported a reduction in full-time faculty over the past fifteen years, totaling 119 jobs lost. Each of the...

Large-scale medical data sets are vital for hands-on education in health data science but are often inaccessible due to privacy concerns. Addressing this gap, we developed the Health Gym project, a free and open-source platform designed to generate synthetic health data sets applicable to various areas of data science education, including machine learning, data visualization, and traditional statistical models. Initially, we generated 3 synthetic data sets for sepsis, acute hypotension, and antiretroviral therapy for HIV infection. This paper discusses the educational applications of Health Gym's synthetic data sets. We illustrate this through their use in postgraduate health data science courses delivered by the University of New South Wales, Australia, and a Datathon event, involving academics, students, clinicians, and local health district professionals. We also include adaptable worked examples using our synthetic data sets, designed to enrich hands-on tutorial and workshop experiences. Although we highlight the potential of these data sets in advancing data science education and health care artificial intelligence, we also emphasize the need for continued research into the inherent limitations of synthetic data.

The presence of data science has been profound in the scientific community in almost every discipline. An important part of the data science education expansion has been at the undergraduate level. We conducted a systematic literature review to (a) portray current evidence and knowledge gaps in self-proclaimed undergraduate data science education research and (b) inform policymakers and the data science education community about what educators may encounter when searching for literature using the general keyword “data science education.” While open-access publications that target a broader audience of data science educators and include multiple examples of data science programs and courses are a strength, substantial knowledge gaps remain. The undergraduate data science literature that we identified often lacks empirical data, research questions, and reproducibility. Certain disciplines are less visible. We recommend that we should (a) cherish data science as an interdisciplinary field; (b) adopt a consistent set of keywords/terminology to ensure data science education literature is easily identifiable; (c) prioritize investments in empirical studies.

Although data science education research has been accumulating, the meaning of data science itself in such research is varied, as there is no common definition. Thus, the purpose of this paper is to clarify the several characteristics that make data science education unique. Through a review of previous studies on data science education, it was clarified that the definition of ‘data science in data science education’ is multifaceted, moreover, there is no mention of the meaning of ‘data science education’. This paper attempts to explore this challenge and discusses the statistical inquiry cycle, PPDAC cycle (Problem – Plan – Data – Analysis - Conclusion) (Wild & Pfannkuch, 1999) as a theoretical framework for this purpose. As a result, the several characteristics are as follows: trans-scientific problems (Problem), interdisciplinary approach (Plan), big data (Data), technology use & teamwork (Analysis), and fruitful disagreement (Conclusion).

Transdisciplinary teaching practices for data science education: A comprehensive framework for integrating disciplines

In the early 1980s, private liberal arts colleges were headed for serious trouble. Financial distress and declining enrollments made many closures seem inevitable. Yet these gloomy forecasts failed to materialize. Enrollments did not decline and are projected to remain stable during the next decade. Why? One answer may be institutional transformation. This article examines five private liberal arts colleges that inaugurated fundamental changes in management and organization during the 1980s, thus averting anticipated crises and apparently ensuring a healthy future.

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 and practitioner research that considers teachers as producers and not just consumers of data analytics.