Title: Revealing Test Answer Behavior Patterns Through Quartile Analysis
Stream: Assessment and Learning Analytics
Presentation Type: Oral Presentation
Authors:
LeAnne Schmidt, Notre Dame Preparatory and Marist Academy, United States
Kathryn Dirkin, Central Michigan University, United States
Abstract:
In recognition that characterizing a dataset by the mean homogenizes the data causing it to lose the informative high and low values, this study examined grammar assessments of eighth grade students through the benefit of quartile analysis to preserve the descriptive information on behavior of the highest- and lowest-level learners. Inspired by Peng's Learning Portrait Model, each response cell in the assessment was coded for characteristic patterns and individually analyzed on the basis of frequency. The data revealed that increased inconsistency was true of the low quartile, whereas consistent results were more frequent in the high quartile. Specifically, the trend of consistency/inconsistency was more predictive than accuracy/inaccuracy in student responses. Additionally, the lowest assessment score rose 25% from the first of three testing situations to the last, despite the fact that assessment content was unique and discrete among the assessments, indicating that familiarity with the application became an additional factor in the final assessment, not just increased content knowledge. Understanding the "learning portrait" of student responses presented an illuminating manner for recognizing data trends. Focus on quartile patterns over a mean score generated a picture of the likely behavior among students at different performance levels. By exploring these differences, educators and developers can strategically intervene and guide underperforming students in their choices during the assessment process to improve future results. Further study addressed the impact of Digital Application Literacy and the persistence of students at each quartile to resolve their inaccurate responses.
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