Author(s):
Measuring Student/Instructor Attitudes and the Learning Environment in Statistics and Data Science
Need: Statistics and data science are two of the fastest growing academic fields, leaving institutions of higher education challenged to meet the demands of teaching skills needed to engage with data of increasing volume, variety and velocity. Attitudes matter in education! Research shows that student attitudes are associated with their future choices, content retention, and ability to master data-based skills. Instructor attitudes and the learning environment impact students’ attitudes and content mastery. However, there has been a dearth of quality instruments assessing such attitudes and the learning environment in undergraduate statistics and the emerging field of data science.
Guiding Question/Inquiry: The Motivational Attitudes toward Statistics and Data Science Education Research (MASDER) team (DUE #2013392) sought to develop instruments that could assess students, instructors, and the learning environment concurrently.
Outcomes:
1) The MASDER team developed six validated instruments to measure and understand students’ attitudes toward statistics and data science (SDS), instructors’ attitudes toward teaching introductory SDS courses, and the salient learning environment and pedagogical characteristics. The instruments are called the Surveys of Motivational Attitudes toward Statistics (SOMAS) or toward Data Science (SOMADS) with an additional first letter indicating whether the survey is for students (S), or instructors (I), or environment (E).
2) Theoretical models were developed to guide all aspects of survey design. Expectancy-Value Theory was used as a framework for the student and instructor attitudinal surveys. The environment model was developed by the MASDER team and includes demographic and characteristic items on the students, instructors, institution, course and learning environment as well as the instructor’s pedagogy and the student-teacher relationship. The team developed a meta-model to describe the connections between students, instructors, and the learning environment.
3) The team has additionally developed a Data Science Topics survey designed to collect information on what content is being taught in introductory data science courses. This survey can be used to understand national trends regarding content of data science courses.
4) The MASDER website (portal.attitudes.com) was developed as a sustainable infrastructure for facilitating data collection and dissemination. Educators and researchers can create accounts, enter instructor and course information, and generate Qualtrics links to surveys for each participating course.
5) An instructor receives a custom report, automatically generated using R, comparing their own students’ attitudes to the national sample of students.
6) Deidentified data resulting from the survey administration will be made publicly available at the end of the grant for other researchers to explore.
Broader Impacts: Use of these instruments will lead to better instruction and improved student attitudes toward SDS as well as evidence-based training and professional development material. This will impact millions of students across the US who are taught SDS annually and lead to improved data literacy and a more competitive workforce with the skills needed to engage with data in its many forms.
Coauthors
Alana Unfried, California State University Monterey Bay, Seaside, CA; Marjorie Bond, Pennsylvania State University, State College, PA; April Kerby-Helm, Winona State University, Winona, MN; Michael Posner, Villanova University, Villanova, PA; Douglas Whitaker, Mount Saint Vincent University, Halifax, Nova Scotia, Canada
