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Attitudes play an important role in students’ academic achievement and retention, yet quality tools to measure them are not readily available in the new field of data science. Through funding from the National Science Foundation, we are developing a family of instruments that measure attitudes toward data science in the context of an introductory, college-level course. This poster will showcase preliminary results discussing pilot instruments to assess instructors’ attitudes toward teaching data science and an inventory which captures classroom characteristics. These instruments, based on Expectancy-Value Theory, will enable data science education researchers to evaluate pedagogical innovations and measure instructional effectiveness relating to student attitudes. We invite instructors of data science courses to join in this discussion and to use these instruments for their own data science education research projects. 

Data Science is one of the fastest growing fields with unmet demand from employers. Many academic institutions have taken on the task of creating programs to meet both current and future needs and demands. Data science, as a field, integrates aspects of computer science, statistics, and subject matter expertise which encourages cross-disciplinary conversations and collaboration. In this talk, we present results from a broad survey of instructors of introductory college-level data science courses for undergraduates. In addition, we explore the alignment of these findings with the recommendations of various professional organizations. We conducted a national survey on topics covered in introductory, college-level data science courses. With responses from computer scientists, statisticians, and allied fields, these results represent a wide array of instructors of data science. The survey identifies topics commonly covered, the amount of time spent on each, common and divergent definitions of data science, and course materials used. These results will be presented. We will then discuss the alignment of these results through a rigorous review and synthesis of recommendations from various professional organizations. These include Association for Computing Machinery's Computing Competencies for Undergraduate Data Science Curricula[1], the National Academies of Science, Engineering, and Medicine’s Data Science for Undergraduates: Opportunities and Options[2], the Park City Math Institute's report Curriculum Guidelines for Undergraduate Programs in Data Science[3], and the American Statistical Association’s Two-Year College Data Science Summit Final Report[4] and Curriculum Guidelines for Undergraduate Programs in Statistical Science[5]. We will also explore alignment with ABET’s accreditation of data science.[6] 

Introductory data science courses are appearing at colleges, universities, and high schools around the country and the world. What topics do we cover in these courses, and how and why are these decisions made? How do we consider the background knowledge of our students and how they hope to utilize their skills after this course (whether professionally, additional courses, or as an engaged citizen)? In addition, the course is being taught by computer scientists, statisticians, business analysts, mathematicians, journalists, etc. Each of these disciplines approaches the topics differently. What upskilling is required of instructors to prepare them to integrate material from academic disciplines in which they were not trained into the course? How much, if any, cross-disciplinary collaboration, and discussion occurs or should occur in designing this course? Participants in this birds-of-a-feather will share their decision processes and choices about introductory data science courses that they teach or are designing. This includes choices made about the content as well as whether and how upskilling occurs. They will review and refine a list of current data science topics created based on national surveys of data science instructors as well as a review of curriculum guidelines. Close attention will be paid to differing language between data science instructors from different academic backgrounds. We welcome new and experienced data science instructors, educators planning on or interested in teaching such a course. 

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. 

Attitudes matter in statistics and data science education, but previous instruments have been limited in scope, resulting in many unanswered questions. This paper discusses the Surveys of Motivational Attitudes toward Statistics and Data Science, a family of instruments designed to provide a broad understanding of university-level student and instructor attitudes as well as learning environment characteristics. Based on Expectancy Value Theory, a meta-model explains the interrelationships among the instruments, and an iterative design process is followed for survey development. Psychometric results from data collections using instruments developed thus far are presented. This is the first time a cohesive, synergistic set of instruments has been designed to work together to give a broader understanding of the state of statistics and data science education. 

The Student Survey of Motivational Attitudes toward Statistics is a new instrument designed to measure affective outcomes in statistics education. This instrument is grounded in the established Expectancy-Value Theory of motivation and is being developed using a rigorous process. This paper provides an overview of the four pilot studies that have been conducted during the survey development process. Additionally, a description of the methods used for analyzing the data and the way the results are used to holistically make decisions about revisions to the survey is included. Brief confirmatory factor analysis results are included from two pilot studies to demonstrate that substantial progress has been made on the development. Once finalized (Spring 2023), the survey will be made freely available. 

The Motivational Attitudes in Statistics and Data Science Education Research group is developing a family of validated instruments: two instruments assessing students’ attitudes toward statistics or data science, two instruments assessing instructors’ attitudes toward teaching statistics or data science, and two sets of inventories to measure the learning environment in which the students and instructor interact. The Environment Inventories measure the institutional structures, course characteristics, and enacted classroom behaviors of both the students and instructors, all of which interact with the student and instructor background. This paper will discuss our proposed theoretical framework for the learning environment and its development. 

The Survey of Attitudes Toward Statistics (SATS) is a widely used family of instruments for measuring attitude constructs in statistics education. Since the development of the SATS instruments, there has been an evolution in the understanding of validity in the field of educational measurement emphasizing validation as an on-going process. While a 2012 review of statistics education attitude instruments noted that the SATS family had the most validity evidence, two types of challenges to the use of these instruments have emerged: challenges to the interpretations of scale scores and challenges using the SATS instruments in populations other than undergraduate students enrolled in introductory statistics courses. A synthesis of the literature and empirical results are used to document these challenges.