Pre- and Post-COVID Concerns in an Introductory Statistics Course and Their Correlates With Expectancy-Value-Cost Theory

• In Event: Innovation in Introductory Statistics Instruction: Student Attitudes, Motivations, and Peer Support as Key to Learning

Abstract

Introductory statistics is often a gateway course not only for students majoring in STEM, but also for students pursuing other majors and careers. This study explores (1) What concerns students hold about their introductory statistics course and whether those concerns differ based on student demographic characteristics (i.e., gender; race/ethnicity); (2) How those concerns changed with the onset of the pandemic and whether subgroups (i.e., gender, URM) were differently affected by the onset of the pandemic; (3) Whether students’ concerns relate to their achievement motivation (i.e., expectancies, utility-value, and cost); (4) Whether course concerns are related to course outcomes, and (5) How course concerns, achievement motivation (expectancy, utility value, cost), and course outcomes interrelate. The evidence may help both instructors and curriculum developers identify potential barriers to success. A total of 1,278 undergraduate students (74.4% female; 39.4% underrepresented racial minority students) who used an interactive online textbook as part of an introduction to statistics and data science course reported their concerns prior to entering the course via an open-ended survey question and their perceptions of utility value, success expectations, and cost via a questionnaire during the course. In total, 21 common course-based concerns were identified. The most common concern voiced by the students was the R programming/coding requirement, followed by understanding concepts, time management/falling behind, performance/not doing well, and lack of prior knowledge. For each of these five concerns, a higher percentage of female than male students voiced it, with the largest discrepancies in a concern for understanding concepts and time management. There were no significant differences in the overall number of concerns voiced by students based on URM status. Among the five most common concerns, more URM students voiced a concern about understanding concepts and performance/not doing well, and fewer about the R programming/coding requirement. A lower percentage of students voiced each of the above concerns post-COVID than had voiced them pre-COVID. Two other concerns rose post-COVID: mental health (increase from 2.2 to 6.3%) and access to resources/availability of help (increase from 0 to 2.6%). Mental health concerns rose most steeply among URM students: Only 1.9% of URM students in the pre-COVID cohort mentioned their mental health as a concern, whereas 7.6% of URM students in the post-pandemic cohort mentioned it. Overall, the number of concerns students voiced were significantly negatively related to their course success expectancies mid-course, significantly positively related to students’ perceived costs mid-course, and not significantly related to their values. The number of course concerns students voiced was not significantly related to their average quiz scores or their final course grade. However, SEM modeling revealed that course concerns indirectly related to their course outcomes via course expectancies and perceptions of course costs. Understanding student concerns in introductory statistics could (1) help identify particular pain points among students, (2) help instructors allay students’ unfounded fears, and (3) guide instructional designers and policymakers as they develop interventions.

Authors

• Claudia C. Sutter, University of California - Los Angeles

• Mary C Tucker, University of California - Los Angeles

• Paige Louisa Solomon, University of California - Los Angeles

• Ana Leandro-Ramos

• Karen B. Givvin, University of California - Los Angeles