Differential Motivation in Remote Educational Assessment: Person-Based Filtering Versus Response-Based Filtering

Publication Date


Document Type



Large-scale educational assessments are often considered low-stakes, increasing the possibility of confounding true performance level with low motivation. These concerns are amplified in remote testing conditions. To remove the effects of low effort levels in responses observed in remote low-stakes testing, several motivation filtering methods can be used to purify the data. We estimated scores from assessment data collected remotely in Spring 2021 six ways, applying examinee-based filtering methods (filtering examinees based on total time) and response-based filtering methods (filtering responses using the effort-moderated IRT model), varying the thresholds selected to separate solution behavior (SB) responses from rapid-guessing behavior (RGB). We compared the 2021 scores (estimated six ways) to those obtained in previous cohorts tested in person. The results support the use of motivation filtering regardless of which method is used.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

This document is currently not available here.