Related Experiment Video
Updated: Oct 26, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
Are Faculty Changing? How Reform Frameworks, Sampling Intensities, and Instrument Measures Impact Inferences about
Gena C Sbeglia1, Justin A Goodridge1, Lucy H Gordon1
1Department of Ecology and Evolution, Stony Brook University, Stony Brook, NY 11794.
Abstract:
Although recent studies have used the Classroom Observation Protocol for Undergraduate STEM (COPUS) to make claims about faculty reform, important questions remain: How should COPUS measures be situated within existing reform frameworks? Is there a universal sampling intensity that allows for valid inferences about the frequency of student-centered instruction within a semester or across semesters of a course? These questions were addressed using longitudinal COPUS observations (128 classes, three faculty, 4 years). COPUS behaviors were used to categorize classes into didactic, interactive lecture, or student-centered instructional styles. Sampling intensities (one to 11 classes) were simulated (1000 times) within a course and across semesters. The sampling intensities required for generating valid inferences about 1) the presence of student-centered instruction and 2) the proportion of instructional styles in a course and through time were calculated. Results indicated that the sampling intensity needed to characterize courses and instructors varied and was much higher than previously recommended for instructors with: 1) rare instances of student-centered classes, 2) variability in instructional style, and 3) longitudinal changes in instructional patterns. These conditions are common in early reform contexts. This study highlights the risks of broad, decontextualized sampling protocol recommendations and illustrates how reform frameworks, sampling intensities, and COPUS measures interact to impact inferences about faculty change.
More Related Videos
06:05The Participant-Reported Implementation Update and Score PRIUS: A Novel Method for Capturing Implementation-Related Data Over Time
Published on: February 19, 2021
10:17Improving Student Outcomes with an Adaptable Molecular Cloning Course-Based Undergraduate Research Experience
Published on: November 15, 2024
Related Concept Videos
Surveys
Reliability and Validity
Systematic Error: Methodological and Sampling Errors
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...
Random Sampling Method
Bias
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
Comparing Experimental Results: Student's t-Test