2024/11/11 by Karl Sigfrid, Sigfrid, Karl, Ellinor Fackle‐Fornius +3
Economics, Econometrics and Finance · Social Sciences · #Economic Growth and Productivity #Economic Policies and Impacts #FOS: Computer and information sciences #Intergenerational and Educational Inequality Studies #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2411.07028
openalex publication_date 2024/11/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
An intelligent tutoring system (ITS) aims to provide instructions and exercises tailored to the ability of a student. To do this, the ITS needs to estimate the ability based on student input. Rather than including frequent full-scale tests to update our ability estimate, we want to base estimates on the outcomes of practice exercises that are part of the learning process. A challenge with this approach is that the ability changes as the student learns, which makes traditional item response theory (IRT) models inappropriate. Most IRT models estimate an ability based on a test result, and assume that the ability is constant throughout a test. We review some existing methods for measuring abilities that change throughout the measurement period, and propose a new method which we call the Elo-informed growth model. This method assumes that the abilities for a group of respondents who are all in the same stage of the learning process follow a distribution that can be estimated. The method does not assume a particular shape of the growth curve. It performs better than the standard Elo algorithm when the measured outcomes are far apart in time, or when the ability change is rapid.