Student Perceptions of Group Work and Group Formation Strategies

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.

ABSTRACTDemand for data science education is surging and traditional courses offered by statistics departments are not meeting the needs of those seeking training. This has led to a number of opinion pieces advocating for an update to the Statistics curriculum. The unifying recommendation is that computing should play a more prominent role. We strongly agree with this recommendation, but advocate the main priority is to bring applications to the forefront as proposed by Nolan and Speed in 1999. We also argue that the individuals tasked with developing data science courses should not only have statistical training, but also have experience analyzing data with the main objective of solving real-world problems. Here, we share a set of general principles and offer a detailed guide derived from our successful experience developing and teaching a graduate-level, introductory data science course centered entirely on case studies. We argue for the importance of statistical thinking, as defined by Wild and Pfannkuch in 1999 and describe how our approach teaches students three key skills needed to succeed in data science, which we refer to as creating, connecting, and computing. This guide can also be used for statisticians wanting to gain more practical knowledge about data science before embarking on teaching an introductory course. Supplementary materials for this article are available online.

In this era of technology and science, data skills are critical for full participation in the workforce and contemporary society. Alarmingly not all graduates exit college with what are nothing less than survival skills. The goals of the course curriculum innovation are to raise the awareness of data science, promote retention, increase the interest and curiosity, and boost essential data skills for all students on campus. These goals are similar to existing efforts on the design of introductory data science courses for majors and non-majors. The distinctiveness of this course curriculum resides in hands-on learning by examples, case studies, and team-based projects within a low-stakes format, where students from different disciplines early in their college careers collaborated to ethically solve problems in the repetition of data science life cycles. The Innovative Practice Category Work in Progress paper presents our experience in integrating introductory data science into a college-wide computer and information literacy course via collaborative practices of exemplar and project-based learning. The collaborative learning with wide interdisciplinary focus enriched our pedagogues and scalability. Its effectiveness has been measured in a poster session and student perceptions on 5-point Likert scales. Furthermore, integrating introductory data science into a computer and information literacy course served to optimize existing educational resources and facilitate multiple pathways in college. The methodology and studio-like training shown in the experience report can be expanded to upper-level data science courses.

Artificial Intelligence (AI) has impacted the world tremendously in the last decade, causing an increased demand for accessible AI education globally. Students benefit from studying AI earlier in the curriculum; however, AI courses can require a range of prerequisites, which can be structured differently in various educational contexts. In this paper, we study the curriculum structure of AI, Machine Learning (ML), and Data Science (DS) courses in Canadian Universities and compare it with that of US Research-1 institutions. There are many similarities between AI, ML, and DS courses in Canada and the US. For example, DS courses tend to be more accessible earlier in the CS curriculum compared to AI and ML. However, there are key differences between the two countries, with Canadian AI, ML, and DS courses generally being a part of a longer prerequisites chain, and Canadian CS departments offering fewer DS courses. Still, both Canadian and US institutions find innovative ways to introduce AI earlier in the curriculum, including via interdisciplinary courses and specialized courses with few prerequisites. This study corroborates earlier work in recognizing diversity in curricular frameworks in North America and recommends curricular revisions and early academic advising to ensure access to AI courses.

Teaching Data Science

This Full Paper in the Research-To-Practice Category illustrates what an online survey of students’ opinions reveals about students’ perceived learning and take-away from a course on Data Science. The course is offered at University of Vienna in the Business Analytics, Data Science, and Digital Humanities faculties as part of the masters’ programs. In this work, we outline the course structures, goals, modules, and present preliminary findings gained while teaching the course. Due to the global pandemic and resulting lock-downs, the course was held online, hence, we also analyze the effects of the current situation on the students.The results revealed that the structure of the course is appreciated by the students. Furthermore, the students liked the open source software taught in the course where they can create visual workflows with an intuitive, drag-and-drop style graphical interface, without the need for coding. The results also confirmed our hypothesis which showed that working in groups is more complex and difficult using online tools. We learn that the instructor-generated technique for forming the groups assigning to each group students with different backgrounds, lead to teams that are able to solve problems faster as they are more cognitively diverse. These findings confirm that the approach used in the Data Science course is viable for teaching computer science skills to non computer-scientist and can be used by other educational institutions.

A substantial fraction of students who complete their college education at a public university in the United States begin their journey at one of the 935 public 2-year colleges. While the number of 4-year colleges offering bachelor’s degrees in data science continues to increase, data science instruction at many 2-year colleges lags behind. A major impediment is the relative paucity of introductory data science courses that serve multiple student audiences and can easily transfer. In addition, the lack of predefined transfer pathways (or articulation agreements) for data science creates a growing disconnect that leaves students who want to study data science at a disadvantage. We describe opportunities and barriers to data science transfer pathways. Five points of curricular friction merit attention: 1) a first course in data science, 2) a second course in data science, 3) a course in scientific computing, data science workflow, and/or reproducible computing, 4) lab sciences, and 5) navigating communication, ethics, and application domain requirements in the context of general education and liberal arts course mappings. We catalog existing transfer pathways, efforts to align curricula across institutions, obstacles to overcome with minimally disruptive solutions, and approaches to foster these pathways. Improvements in these areas are critically important to ensure that a broad and diverse set of students are able to engage and succeed in undergraduate data science programs.

This study aims to analyze the factors that improve engineering students’ motivation for success in statistics and data science courses at higher education institutions by using four mathematical models. The distinctiveness of this study was exemplified by the innovative graphical depiction of those models. The impact of certain factors, such as the importance of recognition and enjoyment of the course, students’ self-concept, and future aspirations, on engineering students’ motivation for achieving success in statistics and data science courses was examined. The proposed models are expected to provide beneficial academic insights to students, instructors, administrators of higher education, and societies worldwide. This paper employed a quantitative methodology, including a sample consisting of 144 female and 101 male engineering students enrolled in various statistics and data science courses at higher education institutions in the United Arab Emirates (UAE). A comprehensive survey questionnaire was developed to gather quantitative data, which were mathematically modeled via factor and regression analyses. The four mathematical models analyzed six variables derived from the survey items. According to the results, models IV, II, I, and III had the most significant influence on motivation, in decreasing order. Model IV explained 94.4% of the variation in the motivation for achieving success in statistics and data science courses, while models II and I explained 75.5% and 71.4%, respectively. The study’s limitations stem from the fact that its findings might not apply universally and are dependent on the specific educational settings or cultural contexts in which the study was conducted.

An interactive environment for beginning Java programmers

This Innovative-Practice Full Paper presents the curriculum development of an introductory course in programming and data science for postdoctoral researchers (PDRs) in the biosciences. The use of computing software has become ubiquitous and a working knowledge of data science has become increasingly essential for researchers in all domains. However, curriculum development focusing on imparting foundational programming skills and fundamentals of data science for researchers in domains other than computing has been scarce. Thus, there is an unmet need for curriculum development involving computational thinking, programming, and the fundamentals of data science for this audience. Recognizing these growing needs and demands of researchers to learn programming and data science that can then be applied to their area of research or practice, we developed an introductory course in programming and data science for PDRs in biology and medicine. The primary goal of the course was to develop computational thinking skills in PDRs who hail from backgrounds that have traditionally not focused on inculcating computational thinking. This course covered the fundamental concepts of programming using either Python or R - languages that researchers outside the computing community use in numerous ways including the statistical analysis of large datasets that are becoming increasingly common in biomedical research. Further, PDRs enrolled in the course were introduced to some of the broad categories of problems in data science - exploratory data analysis, classification, regression, and clustering - along with relevant algorithms and how they can be applied to real-world datasets in their respective domains using packages or libraries in Python or R. We also report the feedback from the enrolled PDRs, lessons learned, and recommendations for instructors interested in designing similar curricula. Our course focusing on computing and data science education for postdoctoral scholars from a non-computing background demonstrates a promising model for incorporating computing education in other areas of study that do not traditionally have a focus on computing education as well as in continuing education.

In response to the industry demand for data science skills, universities have created new data science degrees and integrated new data science courses into existing degrees. While data science is now being taught at several universities, there is still limited consensus among instructors on the best way to teach data science. Interviews and surveys with data science instructors revealed that they find it difficult to accommodate diverse student cohorts. Students that enrol in data science courses or degrees have differences in background knowledge, are at various stages of their careers, have various levels of commitment and prefer different learning styles. Although the challenges of teaching data science to diverse student cohorts are often stressed, limited methodologies or guidelines have been developed in response. This paper presents the design of a scaffolding framework developed to teach data science programming skills to a diverse student cohort. The scaffolding framework outlined can be used by instructors to design a project-based data science course that progressively challenges the development of data science programming and self-scaffolding skills.

Introductory data science courses provide students with a computational foundation beyond traditional introductory courses. Even in the intro stats course, many students are using R to support their coursework rather than applet or “point-and-click” software systems. Introducing computation earlier in the statistics and data science curriculum enables students to work deeper and sooner with real data sets. Changes in early statistics and data science education have a ripple effect across the curriculum. As the introductory courses are modernized, the later courses must change too. The class described in this paper is a second-semester statistical modeling course with a modern, post-data science flair. Regression models are introduced separately (multiple regression, Poisson regression, logistic regression) before being generalized as the generalized linear model (GLM). In this class, learners studied the patterns and behaviors of these models through targeted labs leaning heavily on simulated data. This course emphasizes the development of statistical intuition through hands-on learning experiences, rather than a set of rules for each situation.

In the past 10 years, new data science courses and programs have proliferated at the collegiate level. As faculty and administrators enter the race to provide data science training and attract new students, the road map for teaching data science remains elusive. In 2019, 69 college and university faculty teaching data science courses and developing data science curricula were surveyed to learn about their curricula, computing tools, and challenges they face in their classrooms. Faculty reported teaching a variety of computing skills in introductory data science (albeit fewer computing topics than statistics topics), and that one of the biggest challenges they face is teaching computing to a diverse audience with varying preparation. The ever-evolving nature of data science is a major hurdle for faculty teaching data science courses, and a call for more data science teaching resources was echoed in many responses.

The growth of the data culture has led to calls for improving data literacy among primary and secondary students and their teachers. One approach to improving data literacy is to teach a course devoted to data science but, given the lack of consensus over the term “data science,” just what should an introductory data science course include? The author argues that at the secondary level, an introductory data science course should strive to teach data‐scientific thinking, which has statistical thinking at its core, blended with some computational thinking, and with a dash of mathematics.

Attitudes play an important role in students’ academic achievement and retention, yet we lack quality attitude measurement instruments in the new field of data science. This paper explains the process of creating Expectancy Value Theory-based instruments for introductory, college-level data science courses, including construct development, item creation, and refinement involving content experts. The family of instruments consist of surveys measuring student attitudes, instructor attitudes, and instructor and course characteristics. These instruments will enable data science education researchers to evaluate pedagogical innovations, create course assessments, and measure instructional effectiveness relating to student attitudes. We also present plans for pilot data collection and analyses to verify the categorization of items to constructs, as well as ways in which faculty who teach introductory data science courses can be involved.