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Jeff Ford; Rachel Erickson; Ha Le; Kaylee Vick; Jillian Downey – PRIMUS, 2024
In this study, we analyzed student participation and success in a college-level Calculus I course that utilized standards-based grading. By measuring the level to which students participate in this class structure, we were able to use a clustering algorithm that revealed multiple groupings of students that were distinct based on activity…
Descriptors: Calculus, Mathematics Instruction, Mathematics Achievement, Grades (Scholastic)
Kuang, Huan; Sahin, Fusun – Large-scale Assessments in Education, 2023
Background: Examinees may not make enough effort when responding to test items if the assessment has no consequence for them. These disengaged responses can be problematic in low-stakes, large-scale assessments because they can bias item parameter estimates. However, the amount of bias, and whether this bias is similar across administrations, is…
Descriptors: Test Items, Comparative Analysis, Mathematics Tests, Reaction Time
Ferdiani, Rosita Dwi; Manuharawati; Khabibah, Siti – European Journal of Educational Research, 2022
This study aimed to describe the creative thinking process of students with active learning styles in proposing and solving problems on geometry material. The research instruments were Honey and Mumford's Learning Style Questionnaire (LSQ), problem-solving and submission test sheets, and interview guidelines. The LSQ questionnaire was distributed…
Descriptors: Cognitive Style, Foreign Countries, Geometry, Mathematics Instruction
Pavel Chernyavskiy; Traci S. Kutaka; Carson Keeter; Julie Sarama; Douglas Clements – Grantee Submission, 2024
When researchers code behavior that is undetectable or falls outside of the validated ordinal scale, the resultant outcomes often suffer from informative missingness. Incorrect analysis of such data can lead to biased arguments around efficacy and effectiveness in the context of experimental and intervention research. Here, we detail a new…
Descriptors: Bayesian Statistics, Mathematics Instruction, Learning Trajectories, Item Response Theory