Multiplatform software tool to disaggregate and adjust value-added learning scores
This is an Accepted Manuscript of an article published by Taylor & Francis in The Journal of Economic Education on 3/13/2018, available online: https://www.tandfonline.com/doi/full/10.1080/00220485.2018.1438863.
Abstract
In 2016, Walstad and Wagner released an article that suggested practitioners should disaggregate value-added learning scores into four categories: positive, negative, retained, and zero learning. Positive learning is said to occur when a student answers a question incorrectly on the pre-test and correctly on the post-test. Negative learning is said to occur when the student correctly answers the question on the pre-test but incorrectly on the post-test. Retained learning is said to occur when the student answers the question correctly on both exams and zero learning is said to occur when the student answers the question incorrectly on both exams. Smith and Wagner (2017 Smith, B. O., and J. Wagner. 2017. Adjusting for guessing and applying a statistical test to the disaggregation of value-added learning scores. Available at SSRN: https://ssrn.com/abstract=2941454 . [Google Scholar] ) improved on this work by adjusting the learning categories for guessing.