Use of Open Educational Resources in an Introductory Statistics Course

Two weeks ago, I presented my dissertation study in the convention of the American Educational Research Association (AERA) in Washington DC. This is a rewarding experience for educators and practioners to network and know the trend in the field.

Here is the abstract of my study. Why did I conduct my study in introductory statistics courses? Introductory statistics/ quantitative research methodology is generally a required course for college and graduate students to take. I have seen multiple students in college or graduate school struggling in such courses. These students were anxious about exams, comprehension of abstract concepts, and how they can interpret academic reports or research findings and successfully apply research methodology into their own studies. Some students may attribute their statistics anxiety to their prior performance and experience in math courses. Accordingly, these students may postpone their time to take courses or consider statistics as calculation which brings boredom or no value to them.

In my study, I specifically focused on student motivation (e.g., interest, enjoyment, perceived value and usefulness), statistics anxiety, and their effects on the use of open educational resources (OERs). I not only measured student motivation for statistics, but also identified their statistics anxiety. OER provides a variety of resources that users can reuse or reproduce without worrying about copyright issue. I closely worked with instructors to search for OERs used in courses in consideration of the weekly topic and student motivation and anxiety toward statistics. Based on students’ concerns about abstract concepts in statistics, for example, we selected a regression simulation to delineate linear regression (e.g., slope, residual, statistics estimates). This simulation helped instructors easily explain some abstract concepts in a visual way. Users can also manually add dots in the left pane (see Figure 1)

Figure 1. Linear Regression

and understand how they form a linear regression line and calculate its residuals. In addition, some OERs may be customized by different purposes (e.g., instruction, enrichment, or remediation of prior knowledge). According to students’ feedback about the use of OER, they perceived that OER used in statistics courses can clarify their abstract concepts and improve their individual skills used in statistics learning. Also, the use of OER to explain some abstract concepts brings a positive learning environment for them. Through this study, I can specifically identify how OERs can be effectively customized in terms of student motivation and statistics anxiety to meet diverse needs in introductory statistics courses.