Abstract
Characterizing a dataset by the mean value homogenizes the data to lose the integrity of the highs and lows, however, a quartile analysis quantifies the tendencies of both high- and lowperforming participants for comparison. This study analyzed the grammar assessment responses of 8th grade students to determine patterns of response between the lowest and highest quartile. Using Peng’s Learning Portrait Model, each assessment cell was coded to show the accuracy of prior and subsequent answers. Analysis of these codes revealed that learners in the lowest quartile were significantly likely to respond inconsistently (variable accuracy, such as correct-incorrect-correct) and that learners in the highest quartile were significantly likely to respond consistently, whether correct or incorrect. Further, the baseline score increased over the course of seven months by 25% on unrelated content, suggesting that familiarity with the application software can account for that much of a student’s assessment score. Future explorations on the dynamics of online assessment and the persistence of students in resolving inaccuracies on digital assessments are encouraged.
Author Information
LeAnne J. Schmidt, Central Michigan University, United States
Kathryn Dirkin, Central Michigan University, United States
Paper Information
Conference: ERI2022
Stream: Assessment and Learning Analytics
This paper is part of the ERI2022 Conference Proceedings (View)
Full Paper
View / Download the full paper in a new tab/window
To cite this article:
Schmidt L., & Dirkin K. (2022) Revealing Test Answer Behavior Patterns Through Quartile Analysis ISSN: 2435-1202 – The IAFOR Conference on Educational Research & Innovation: 2022 Official Conference Proceedings https://doi.org/10.22492/issn.2435-1202.2022.9
To link to this article: https://doi.org/10.22492/issn.2435-1202.2022.9
Comments
Powered by WP LinkPress