Abstract
This study reports how a validation argument for a learning trajectory (LT) is constituted from test design, empirical recovery, and data use through a collaborative process, described as a “trading zone” among learning scientists, psychometricians, and practitioners. The validation argument is tied to a learning theory about learning trajectories and a framework (LT-based data-driven decision-making, or LT-DDDM) to guide instructional modifications. A validation study was conducted on a middle school LT on “Relations and Functions” using a Rasch model and stepwise regression. Of five potentially non-conforming items, three were adjusted, one retained to collect more data, and one was flagged as a discussion item. One LT level description was revised. A linear logistic test model (LLTM) revealed that LT level and item type explained substantial variance in item difficulty. Using the LT-DDDM framework, a hypothesized teacher analysis of a class report led to three conjectures for interventions, demonstrating the LT assessment’s potential to inform instructional decision-making.
Highlights
Learning trajectory (LT)-based diagnostic assessments represent an alternative approach to traditional domain-sampling assessments (Briggs and Peck, 2015) in two fundamental ways: 1) they assess progress along a set of levels in an ordered sequence from less to more sophisticated, and 2) they are better positioned to formatively guide instructional modifications to improve student learning during, not after, instruction
Results for Q1 The student response data came from 929 assessments at the cluster or construct level that were administered from school year (SY) 2016–17 through SY 2019–20
The third model had a reduction of residual variance of 43% from the second model, suggesting that the different item types account for a significant amount of variance in item difficulty
Summary
Learning trajectory (LT)-based diagnostic assessments represent an alternative approach to traditional domain-sampling assessments (Briggs and Peck, 2015) in two fundamental ways: 1) they assess progress along a set of levels in an ordered sequence from less to more sophisticated, and 2) they are better positioned to formatively guide instructional modifications to improve student learning during, not after, instruction. A finer grain size can ensure the LT is sensitive to differences in student thinking as they move from naive to more sophisticated understanding of target concepts. Teachers, equipped with these fine-grained data, can interpret these data to make ongoing modifications to instruction in order to support and improve students’ learning
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
