Data Science Education Research Articles

DSLA 2023: Facing the dangers of data illiteracy

Abstract The Data Science and Literacy Act (DSLA) of 2023 aims to boost data literacy and data science education across the USA. Gregg M. Gascon, Katherine K. Wallman, Sridhar Ravula, Joseph Cappelleri, Julia Lee, Kristian Lum, Chaitra H. Nagaraja, Sudeshna Paul, Dooti Roy and Edward Wu explain why this opportunity demands our attention

Data Flourishing: Developing Human‐Centered Data Science through Communities of Ethical Practice

ABSTRACTData Science is a burgeoning area in the iField. But Data Science practices have far outstripped the field's ethical safeguards. We argue that Data Science graduate education programs must address this critical problem. In this theoretical and conceptual paper, we posit an ordinary macroethics that we call data flourishing. We contend that this macroethics is most appropriately developed through a holistic, human‐centered data science (HCDS)‐based pedagogy that concentrates on cultivating communities of ethical practice (COEPs) through social learning. We favor embedding this macroethics throughout iField programs' graduate data science curricula and by extension, the entire data science education enterprise. This paper aligns with the 2023 ASIS&T annual meeting theme of translating research into practice, particularly the subthemes of “improving decision‐making” and “understanding the power of information to develop human happiness, equality, and wellbeing.”

Data Science Education in iSchools across the Asian-Pacific Region

In this poster, we report preliminary findings of our investigation of data science education in iSchools around the world. For our analysis, we randomly sampled 20 iSchools within the regions of Asian-Pacific, European, and North American. We report here the findings from the 20 iSchools in the Asian Pacific region: these iSchools come from 9 different countries/areas, including China (n=9), Korea (n=3), New Zealand (n=2), Australia (n=1), Malaysia (n=1), Thailand (n=1), Indonesia (n=1), Philippines (n=1), and Taiwan, China (n=1). Of 20 iSchools, 15 had DS course information, with an average of 8.13 per program, range 1-21, and mode 4. The majority of the programs offered DS courses at both the undergraduate and graduate levels (n=11, 73.33%), three (20.00%) offered at the graduate level only, one (6.67%) only offered at the undergraduate level.
 In terms of the course coverage, there were 30 unique topics, with 15 topics included in multiple iSchools’ curriculum, and the remaining 15 were uniquely offered by one iSchool. The most common course topics were Data Structure or Data Organization (n=10, 66.67%), Data Mining & Data Warehouse (n=9, 60.00%), Data Visualization (n=6, 40.00%), Introduction to Data Science (n=6, 40.00%), Machine Learning (n=6, 40.00%), and NLP Text Analysis/Text Mining (n=5, 33.33%). Other repeated topics include Research Data Management, Big Data, AI, Programming, Data governance, Data Analytics, and more. Examples for unique course topics were blockchain, Data Asset Management, Data Security, UX, and more. Further coding and analysis for all three regions are in progress.

Data Science Curriculum

With an increase in technology use and rapid generation of information and data, graduate-level programs with data science curricula have grown exponentially to match the demand for information professionals with data-related skills. However, a gap has formed between skills learned in school and skills information professionals need post-graduation. Previous research indicates a skills gap among data scientists who lack LIS training in areas like data curation, preservation, etc., which data science programs should address (Ortiz-Repiso et al., 2018). This study, as part of a broader project funded by IMLS (Grant # RE-250066-OLS-21), attempts to address current and future workforce demands in data librarianship and analytics, employer skill demand, and acquisition of skills.
 This poster reports on part of the larger project - content analysis of 294 distinct graduate program curricula involving significant data science coursework in 43 LIS schools and iSchools across North America. After collecting over 1,000 data science/librarianship-related courses from institutional websites, we coded them into seven categories using their titles and descriptions. Data analysis, curation, governance, and discovery were the most popular subjects, followed by data professions, miscellaneous courses, and data assessment. In conjunction with job ad analysis and surveys of stakeholders in data science and librarianship education by others, our study could serve as input for graduate programs/curricula design. Results from our study will increase understanding of the nature and structure of data science curricula in iSchools and LIS departments while giving students requisite skills to meet future needs as data science professionals/data librarians.

Motivation, inclusivity, and realism should drive data science education

Data science education provides tremendous opportunities but remains inaccessible to many communities. Increasing the accessibility of data science to these communities not only benefits the individuals entering data science, but also increases the field's innovation and potential impact as a whole. Education is the most scalable solution to meet these needs, but many data science educators lack formal training in education. Our group has led education efforts for a variety of audiences: from professional scientists to high school students to lay audiences. These experiences have helped form our teaching philosophy which we have summarized into three main ideals: 1) motivation, 2) inclusivity, and 3) realism. To put these ideals better into practice, we also aim to iteratively update our teaching approaches and curriculum as we find ways to better reach these ideals. In this manuscript we discuss these ideals as well practical ideas for how to implement these philosophies in the classroom.

Teaching Data Science with Literate Programming Tools

This paper presents a case study on using Emacs and Org-mode for literate programming in undergraduate computer and data science courses. Over three academic terms, the author mandated these tools across courses in R, Python, C++, SQL, and more. The onboarding relied on simplified Emacs tutorials and starter configurations. Students gained proficiency after undertaking initial practice. Live coding sessions demonstrated the flexible instruction enabled by literate notebooks. Assignments and projects required documentation alongside functional code. Student feedback showed enthusiasm for learning a versatile IDE, despite some frustration with the learning curve. Skilled students highlighted efficiency gains in a unified environment. However, the uneven adoption of documentation practices pointed to a need for better incorporation into grading. Additionally, some students found Emacs unintuitive, desiring more accessible options. This highlights a need to match tools to skill levels, potentially starting novices with graphical IDEs before introducing Emacs. The key takeaways are as follows: literate programming aids comprehension but requires rigorous onboarding and reinforcement, and Emacs excels for advanced workflows but has a steep initial curve. With proper support, these tools show promise for data science education.

Automated Assessment of Knowledge and Skills at the National Russian Therapy Olympiad with International Participation in 2023

The development of modern systems and technologies creates new requirements for the education system every day, provides new opportunities for the development of educational methods, consumer coverage and data science. The experience gained as a result of the transfer of the Therapy Olympiad in 2021 to a remote format proved its success and prompted the development of the Olympiad in this direction. On March 21–22, 2023, the III National Russian Therapy Olympiad with international participation in online format was organized. As a result, 2,120 students, residents and schoolchildren from Russia, Uzbekistan, Kazakhstan, Belarus, Kyrgyzstan, Tajikistan, Azerbaijan, and India took part in the Olympiad. This article analyzes the Olympiad in order to study the possibility of conducting an automated assessment of knowledge and skills formed by students of 4–6 courses of specialty and residents of the therapeutic profile and other medical specialties. The following evaluation tools were used: knowledge testing using a distance learning system and assessment of practical skills using a Dimedus computer virtual on-screen patient simulator. The analysis of the results showed that the base of the formed test mini-cases and clinical cases is valid, reliable and can be used for automated assessment of knowledge and skills of students in specialty and residency programs of therapeutic profile. Statistical data and feedback data showed the involvement in the process and the interest of students in the chosen assessment format.

The Role of Data Scientists in Modern Enterprises - Experience from Data Science Education

"Data Scientist" has often been considered as the sexiest job of the 21st century. As a consequence, the spectrum of data science education programs has increased significantly in recent years, and there is a high demand for data scientists at many companies. However, what training is required to become a data scientist? What is the role of data scientists in current enterprises? Is the training well-aligned to the practical needs of a job? In this article, we will address these questions by evaluating a survey of people who were trained in a continuing education program in data science in Switzerland. Our study sheds lights on the practical aspects of the data science education and how this newly-gained knowledge can successfully be applied in an enterprise. One of the highlights from the point of view of the database community is the important role of SQL in data science.

Active-Learning Class Activities and Shiny Applications for Teaching Support Vector Classifiers

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.

Learning and Evidence Analytics Framework Bridges Research and Practice for Educational Data Science

research-article Free Access Share on Learning and Evidence Analytics Framework Bridges Research and Practice for Educational Data Science Authors: Hiroaki Ogata Kyoto University, Japan Kyoto University, JapanSearch about this author , Rwitajit Majumdar Kyoto University, Japan Kyoto University, JapanSearch about this author , Brendan Flanagan Kyoto University, Japan Kyoto University, JapanSearch about this author Authors Info & Claims Communications of the ACMVolume 66Issue 7July 2023pp 72–74https://doi.org/10.1145/3589660Published:22 June 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF

Students’ Experience and Perspective of a Data Science Program in a Two-Year College

Two-year colleges provide the opportunity for students of all ages to try new subjects, change careers, upskill, or begin exploring higher education, at affordable rates. Many might begin their exploration by taking a course at a local two-year college. Currently, not many of these institutions in the U.S. offer data science courses. This article introduces the perspective lens of students who have gone through the Montgomery College Data Science Certificate Program. We found that, contrary to many other educational fields at the College, data science students tend to come from diverse backgrounds and career paths. A common theme emerged that all students learned valuable skills and applications such as coding in various programming languages and approaches to machine learning. Other meaningful themes included an appreciation of course accessibility, especially catered toward busy professionals who might only be able to take evening courses. Students appreciated learning that data science and ethics are intertwined. Finally, it was evident that going through the data science program positively impacted the lives and careers of these students. The implications of the themes of these student experiences are discussed as they relate to data science education. Supplementary materials for this article are available online.

Teaching students to R3eason, not merely to solve problem sets: The role of philosophy and visual data communication in accessible data science education.

Much guidance on statistical training in STEM fields has been focused largely on the undergraduate cohort, with graduate education often being absent from the equation. Training in quantitative methods and reasoning is critical for graduate students in biomedical and science programs to foster reproducible and responsible research practices. We argue that graduate student education should more center around fundamental reasoning and integration skills rather than mainly on listing 1 statistical test method after the other without conveying the bigger context picture or critical argumentation skills that will enable student to improve research integrity through rigorous practice. Herein, we describe the approach we take in a quantitative reasoning course in the R3 program at the Johns Hopkins Bloomberg School of Public Health, with an error-focused lens, based on visualization and communication competencies. Specifically, we take this perspective stemming from the discussed causes of irreproducibility and apply it specifically to the many aspects of good statistical practice in science, ranging from experimental design to data collection and analysis, and conclusions drawn from the data. We also provide tips and guidelines for the implementation and adaptation of our course material to various graduate biomedical and STEM science programs.

Teaching Data Science through Storytelling: Improving Undergraduate Data Literacy

This study proposes and evaluates the OCEL.AI (Open Collaborative Experiential Learning. AI) paradigm that aims at broadening participation in data science education and enhancing undergraduate students’ data literacy. The core of the paradigm is the “Tell Stories” approach. This approach applies the 5W+1H (Who, What, When, Where, Why, and How) conceptual schema of stories as a transdisciplinary language for data science education for STEM and non-STEM majors. Accordingly, this study reported findings from the OCEL.AI project that implemented and evaluated the paradigm. A field experiment, in addition to classroom observations, was conducted to compare the learning outcomes of students in data science competence, appreciation, career motivation, life-long willingness to learn, and self-efficacy in data science between the treatment group and the control group. The results showed that the OCEL.AI paradigm improved undergraduates’ data science competence and career motivation despite majors or gender.

A New Era of Learning: Considerations for ChatGPT as a Tool to Enhance Statistics and Data Science Education

ChatGPT is one of many generative artificial intelligence (AI) tools that has emerged recently, creating controversy in the education community with concerns about its potential to be used for plagiarism and to undermine students’ ability to think independently. Recent publications have criticized the use of ChatGPT and other generative AI tools in the classroom, with little focus on the potential benefits. This article focuses on the potential of ChatGPT as an educational tool for statistics and data science. It encourages readers to consider the history of trepidation surrounding introducing new technology in the classroom, such as the calculator. We explore the possibility of leveraging ChatGPT’s capabilities in statistics and data science education, providing examples of how ChatGPT can aid in developing course materials and suggestions for how educators can prompt students to interact with ChatGPT responsibly. As educators, we can guide the use of generative AI tools in statistics and data science classrooms so that students and educators can leverage the benefits of this technology.

A Study on the Introduction, Status and Implications of The “Accreditation System for Mathematics, Data Science and AI Education Programs” in Japanese Higher Education

A Study on the Introduction, Status and Implications of The “Accreditation System for Mathematics, Data Science and AI Education Programs” in Japanese Higher Education

Data Science and Machine Learning Teaching Practices with Focus on Vocational Education and Training

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.

Preface to Clinical Neurosurgery Volume 69, Proceedings of the Congress of Neurological Surgeons 2022 Annual Meeting

The 2022 Annual Meeting of the Congress of Neurological Surgeons was held in San Francisco, CA, from October 8-12, 2022. Volume 69 of Clinical Neurosurgery represents the official compilation of the 2022 CNS Annual Meeting. This volume highlights invited scientific manuscripts from the plenary sessions and the 2022 CNS Presidential Address from Dr Nicholas C. Bambakidis. In addition, you will find bibliographic information on the 2022 Honored Guest, Dr Robert F. Spetzler. The annual meeting leadership team included Dr Ashok R. Asthagiri, Annual Meeting Chair; Dr Lola B. Chambless, Scientific Program Chair; and Dr Garni Barkhoudarian, Vice Scientific Program Chair. This year's meeting, themed “Neurosurgery Connected,” was embodied by the spirit of its participants. After years of meeting cancellations and/or limitations, this meeting was highly attended and had a record number of abstract submissions. International attendance additionally increased, and members from around the world were greeted and welcomed at a special reception. The CNS was thrilled to partner with The Japanese Congress of Neurological Surgeons, Neurological Society of India, and the Brazilian Society of Neurosurgery. The scientific and lectureship content of this year's annual meeting were as diverse as they were connecting. We heard from the likes of military pilot, Vernice “FlyGirl” Armour, actor William Shatner, and athletes, Carl Lewis and Dee Caffari, all touching on themes of connectivity, creativity, perseverance, and leadership. All speakers had seen and explored new frontiers, many being the first of their kind to achieve such feats. Dr Warren R. Selman opened the meeting by addressing our connections to each other and trainees through mentorship. Honored guest, Dr Robert F. Spetzler, shared his vast experience in vascular neurosurgery, and his trainees from all over the world expressed their gratitude having learned from his wisdom. Not to mention, the countless podium talks, seminars, symposia, and sessions. New sessions in data science and medical education featured speakers from multiple disciplines including neurosurgeons and those working in the fields of technology. A lunch symposium focused on diversity addressed the “Changing Face of Neurosurgery,” and students from the CNS Foundation Pathways Program were welcomed to the meeting to learn about our field. This meeting embodied what it means for us to be connected as a neurosurgical community. It has been a pleasure and honor to serve as Editor of Clinical Neurosurgery this past year. I would like to thank the Congress of Neurological Surgeons staff and the staff at Neurosurgery Publications, particularly Caitlyn Trautwein, Journal Editor. I would like to extend a special thank you to Dr Douglas Kondziolka, Editor-in-Chief, Neurosurgery Publications, for all his support and dedication to this volume. I welcome Dr Alexander A. Khalessi as the new President of the Congress of Neurological Surgeons and look forward to seeing you all in Washington, DC, in 2023.

Resisting Techno-Orientalism and Mimicry Stereotypes in and Through Data Science Education

Resisting Techno-Orientalism and Mimicry Stereotypes in and Through Data Science Education

Research on the teaching of classes on probability distributions and hypothesis testing in high school mathematics departments and universities

Data is a prevalent concept in today's world. We are in an era of Big Data and the field of data science is possibly more important than it ever has been. Associate Professor Chieko Matsumoto, School of Education, University of Fukui, Japan, is an expert in data science who is conducting research on statistics and data science education in upper secondary schools and universities, with a focus on the concept of statistical hypothesis testing. Matsumoto's goal is to quantify what is lacking in the current mathematics curriculum in order to put forward recommendations to overcome challenges facing statistics and data science education. She and her team want to develop teaching materials and practical guidance on statistics and data science education. Their research approach and methods are developed with a focus on probability distributions and hypothesis testing. The researchers will conduct a questionnaire survey of upper secondary school teachers to clarify the various teaching problems and teaching difficulties that teachers encounter when teaching statistical hypothesis testing. The team is yet to obtain permission to conduct a large-scale questionnaire and so the researchers have been conducting small-scale interviews to establish an understanding of teaching difficulties faced by upper secondary school teachers.

Synthesizing forecasts to inform decision‐making and advance ecological theory

Synthesizing forecasts to inform decision‐making and advance ecological theory