Data Science Curriculum Research Articles

Philosophy within Data Science Ethics Courses

There is wide agreement that ethical considerations are a valuable aspect of a data science curriculum, and to that end, many data science programs offer courses in data science ethics. There are not always, however, explicit connections between data science ethics and the centuries-old work on ethics within the discipline of philosophy. Here, we present a framework for bringing together key data science practices with ethics topics. The ethics topics were collated from 16 data science ethics courses with public-facing syllabi and reading lists. We encourage individuals who are teaching data science ethics to engage with the philosophical literature and its connection to current data science practices, which are rife with potentially morally charged decision points.

Investigating influential factors in group learning: personality traits, COVID-19 lockdowns and back-to-school policies

Extensive research has probed the impacts of personality traits on student satisfaction, academic anxiety, and performance, with particular attention paid to their implications during the COVID-19 pandemic. Notably, a conspicuous gap is discernible in the existing literature concerning investigations that scrutinize the influence of personality on group learning among different COVID-19 lockdown phases (e.g., pre, during, and post-lockdowns). This study endeavors to fill this gap by undertaking an examination of the fluctuations in group performance within data science curricula. This inquiry considers student cohorts comprising diverse personality traits across distinct phases of lockdowns. Through the investigation of 84 student groups, our research has unearthed intriguing correlations and patterns linking group performance with four influential factors, including personality traits, the lockdown phases (i.e., pre, during and post-lockdowns), the joint factor of lockdowns and personality traits, and the implemented COVID-19 policies (e.g., mask-wearing and social distancing) during the early-stage of back-to-schools. The interesting findings revealed from our studies have the potential to propel the advancement of our comprehension within the field of educational psychology and may serve as catalysts for heightened scrutiny of student personality traits, thereby motivating educators to refine their pedagogical approaches in collaborative learning experiences.

텍스트 분석을 통한 숙련 수요와 교과 역량 간의 매칭 분석

The purpose of this study is to analyze the matching between job skill demands of industries and curriculum competencies provided by universities, focusing on data science majors, using textual data on online job postings and university curricula, and to provide useful implications for university curriculum program development and industry response. We collected curriculum texts and job posting texts from K-MOOCs, Job Korea, and PersonIn, derived and categorized competency and skill keywords, and calculated the cosine similarity between competencies and skills for analysis. The analysis shows that data science curriculum competencies are largely well matched to IT occupational skills and are in line with labor market occupational skill types. The “data security” and “market analysis” competencies have a high similarity to occupational skills, while the “statistical modeling” competency has a low similarity. In addition, the “database” and “manufacturing SI” skills have a high similarity to the curriculum competencies, suggesting that the university is doing a good job of meeting these skills. This study shows that analyzing the matching of competencies and skills based on textual data analysis can help universities adjust their curricula to meet the needs of the labor market, and has implications for analyzing industry skill needs, analyzing job mismatches, and linking education and industrial policies.

Engaging elementary students in data science practices

PurposeThis 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/approachThis 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.FindingsFindings 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/valueData 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.

Cybersecurity and Data Science Curriculum for Secondary Student Computing Programs

Cybersecurity and Data Science Curriculum for Secondary Student Computing Programs

What Is Common to Transportation and Health in Machine Learning Education? The Domain Neglect Bias

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> 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. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Background:</i> 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. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Research Question:</i> 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? <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Methodology:</i> 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. <p xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>Findings:</i> 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.

Is designed data collection still relevant in the big data era? – A discussion

Is designed data collection still relevant in the big data era? – A discussion

Three Principles for Modernizing an Undergraduate Regression Analysis Course

As data have become more prevalent in academia, industry, and daily life, it is imperative that undergraduate students are equipped with the skills needed to analyze data in the modern environment. In recent years there has been a lot of work innovating introductory statistics courses and developing introductory data science courses; however, there has been less work beyond the first course. This article describes innovations to Regression Analysis taught at Duke University, a course focused on application that serves a diverse undergraduate student population of statistics and data science majors along with nonmajors. Three principles guiding the modernization of the course are presented with details about how these principles align with the necessary skills of practice outlined in recent statistics and data science curriculum guidelines. The article includes pedagogical strategies, motivated by the innovations in introductory courses, that make it feasible to implement skills for the practice of modern statistics and data science alongside fundamental statistical concepts. The article concludes with the impact of these changes, challenges, and next steps for the course. Portions of in-class activities and assignments are included in the article, with full sample assignments and resources for finding data in the supplemental materials. Supplementary materials for this article are available online.

Data Science Education – A Scoping Review

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?

Teaching Visual Accessibility in Introductory Data Science Classes with Multi-Modal Data Representations

Although there are various ways to represent data patterns and models, visualization has been primarily taught in many data science courses for its efficiency. Such vision-dependent output may cause critical barriers against those who are blind and visually impaired and people with learning disabilities. We argue that instructors need to teach multiple data representation methods so that all students can produce data products that are more accessible. In this paper, we argue that accessibility should be taught as early as the introductory course as part of the data science curriculum so that regardless of whether learners major in data science or not, they can have foundational exposure to accessibility. As data science educators who teach accessibility as part of our lower-division courses in two different institutions, we share specific examples that can be utilized by other data science instructors.

Systemic racism in data practices

Systemic racism in data practices

R-miss-tastic: a unified platform for missing values methods and workflows

Missing values are unavoidable when working with data. Their occurrence is exacerbated as more data from different sources become available. However, most statistical models and visualization methods require complete data, and improper handling of missing data results in information loss or biased analyses. Since the seminal work of Rubin (1976), a burgeoning literature on missing values has arisen, with heterogeneous aims and motivations. This led to the development of various methods, formalizations, and tools. For practitioners, however, it remains a challenge to decide which method is most appropriate for their problem, in part because this topic is not systematically covered in statistics or data science curricula. To help address this challenge, we have launched the `R-miss-tastic` platform, which aims to provide an overview of standard missing values problems, methods, and relevant implementations of methodologies. Beyond gathering and organizing a large majority of the material on missing data (bibliography, courses, tutorials, implementations), `R-miss-tastic` covers the development of standardized analysis workflows. Indeed, we have developed several pipelines in R and Python to allow for hands-on illustration of and recommendations on missing values handling in various statistical tasks such as matrix completion, estimation, and prediction, while ensuring reproducibility of the analyses. Finally, the platform is dedicated to users who analyze incomplete data, researchers who want to compare their methods and search for an up-to-date bibliography, and teachers who are looking for didactic materials (notebooks, recordings, lecture notes).

Data Science Curriculum in the iField.

Many disciplines, including the broad Field of Information (iField), have been offering Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate-level and undergraduate-level DS education? Answers to these questions will not only distinguish an iField approach to DS education but also define critical components of DS curriculum. The results will inform individual DS programs in the iField to develop curriculum to support undergraduate and graduate DS education in their local context.

INTEGRATING THE HUMANITIES INTO DATA SCIENCE EDUCATION

Despite growing calls to develop data science students’ ethical awareness and expand human-centered approaches to data science education, introductory courses in the field remain largely technical. A new interdisciplinary data science program aims to merge STEM and humanities perspectives starting at the very beginning of the data science curriculum. Existing literature suggests that humanities integration can make STEM courses more appealing to a wider range of students, including women and students of color, and enhance student learning of essential concepts and foundational reasoning skills, such as those collectively known as data acumen. Cultivating students’ data acumen requires a more inclusive vision of how the knowledge and insights generated through computational methods and statistical analysis relates to other ways of knowing.

Preparing High School Teachers to Integrate AI Methods into STEM Classrooms

In this experience report, we describe an Artificial Intelligence (AI) Methods in Data Science (DS) curriculum and professional development (PD) program designed to prepare high school teachers with AI content knowledge and an understanding of the ethical issues posed by bias in AI to support their integration of AI methods into existing STEM classrooms. The curriculum consists of 5-day units on Data Analytics, Decision trees, Machine Learning, Neural Networks, and Transfer learning that follow a scaffolded learning progression consisting of introductions to concepts grounded in everyday experiences, hands-on activities, interactive web-based tools, and inspecting and modifying the code used to build, train and test AI models within Google Colab notebooks. The participants in the PD program were secondary school teachers from the Southwest and North-east regions of the United States who represented a variety of STEM disciplines: Biology, Chemistry, Physics, Engi-neering, and Mathematics. We share findings on teacher outcomes from the implementation of two one-week PD workshops during the summer of 2021 and share suggestions for improvements provided by teachers. We conclude with a discussion of affordances and challenges encountered in preparing teachers to integrate AI education into disciplinary classrooms.

Urban Data Science Education: A Key Actor towards Improving Data-Driven Policy-Making for Solving Urban Problems

&#x0D; This paper explains the benefits of Urban Data Science Education in improving data driving policy-making and highlights the impact of vital use of available open data using a practical demonstration from the Summer-Winter School (SWS) at CEPT University Ahmedabad India. Good Urban data policies can be an important driver for smart city research and the implementation of good data management practices. Many authors are also becoming interested in the data revolution that is enhancing the way we study and understand cities. It now becomes appropriate to respond to this need in an evidence-informed manner by working with stakeholders to encourage a more resilient approach via education for the younger enthusiast, leading to more transparent, improved, sustainable and safer urban cities. However, the major challenge we foresee is the inability to utilize the large amount of urban data generated daily. With the drive to mobilize more education towards urban data science and analytics, generated data can be effectively utilized to make informed and evidence-based decisions. This paper points to a practical methodology for reproducibility, the key steps taken, as it views the inclusion of an Urban Data Science curriculum as priceless, and a decision that will bring into notice the importance of engaging the younger generation in scholastic learning on data principles and analytics. Data along with spatial enhancement can be the driving factor towards improving data-driven policy-making for solving urban problems in smart cities.&#x0D; &#x0D;

Content and computing outline of two undergraduate Bayesian courses: Tools, examples, and recommendations

Undergraduate Bayesian education is an area that has started getting attention lately. As many educational innovations and articles are published and increasingly more teaching and learning materials are shared, statistics educators might be interested in incorporating Bayesian statistics in their undergraduate statistics and data science curriculum. In this paper, we share a succinct overview of two undergraduate Bayesian courses we have been teaching, with a comparison analysis to present the similarities and differences in our approaches. We dive deeper into various choices of Markov chain Monte Carlo estimation methods of Bayesian models with a working example and discuss their pros and cons for different learning objectives of computing that aspiring Bayesian educators might have in mind. Furthermore, we share challenges and opportunities for course development and curriculum design. The paper is suitable for aspiring Bayesian educators who are interested in learning ways to introduce Bayesian statistics to undergraduate students.

Data Detectives: A Data Science Program for Middle Grade Learners

Data science is a highly interdisciplinary field that comprises various principles, methodologies, and guidelines for the analysis of data. The creation of appropriate curricula that use computational tools and teaching activities is necessary for building skills and knowledge in data science. However, much of the literature about data science curricula focuses on the undergraduate university level. In this study, we developed an introductory data science curriculum for an out of school enrichment program aimed at middle grade learners (ages 11–13). We observed how the participants in the program (n = 11) learned data science practices through the combination of nonprogramming activities and programming activities using the language R. The results revealed that participants in the program were able to investigate statistical questions of their creation, perform data analysis using statistics and the creation of data visuals, make meaning from their results, and communicate their findings. These results suggest that a series of learner-centered nonprogramming and programming activities using R can facilitate the learning of data science skills for middle-school age students.

Data Science Curricula on the Viewpoint of Data Science Process

Data Science Curricula on the Viewpoint of Data Science Process

Using Team-Based Learning to Teach Data Science

ABSTRACT Data science is collaborative and its students should learn teamwork and collaboration. Yet it can be a challenge to fit the teaching of such skills into the data science curriculum. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative learning to actively engage students in doing data science. A consequence of this teaching method is helping students achieve the workforce-relevant data science learning goals of effective communication, teamwork, and collaboration. We describe the essential elements of TBL: accountability structures and feedback mechanisms to support students collaborating within permanent teams on well-designed application exercises to do data science. The results of our case study of using TBL to teach a modern, introductory data science course indicate that the course effectively taught reproducible data science workflows, beginning R programming, and communication and collaboration. Students also reported much room for improvement in their learning of statistical thinking and advanced R concepts. To help the data science education community adopt this appealing pedagogical strategy, we outline steps for deciding on using TBL, preparing and planning for it, and overcoming potential pitfalls when using TBL to teach data science.