PreprintArticleVersion 1Preserved in Portico This version is not peer-reviewed
How Should I Teach From This Month Onwards? A State-Space Model That Helps Drive Whole Classes to Achieve End-Of-Year National Standardized Test Learning Targets
Ulloa, O.; Araya, R. How Should I Teach from This Month Onward? A State-Space Model That Helps Drive Whole Classes to Achieve End-of-Year National Standardized Test Learning Targets. Systems2022, 10, 167.
Ulloa, O.; Araya, R. How Should I Teach from This Month Onward? A State-Space Model That Helps Drive Whole Classes to Achieve End-of-Year National Standardized Test Learning Targets. Systems 2022, 10, 167.
Ulloa, O.; Araya, R. How Should I Teach from This Month Onward? A State-Space Model That Helps Drive Whole Classes to Achieve End-of-Year National Standardized Test Learning Targets. Systems2022, 10, 167.
Ulloa, O.; Araya, R. How Should I Teach from This Month Onward? A State-Space Model That Helps Drive Whole Classes to Achieve End-of-Year National Standardized Test Learning Targets. Systems 2022, 10, 167.
Abstract
Every month teachers face the dilemma of what exercises should their students practice, and what their consequences are on long-term learning. Since teachers prefer to pose their own exercises, this generates a large number of questions, each one attempted by a small number of students. Thus, we couldn’t use models based on big data such as deep learning. Instead, we developed a simple to understand state-space model that predicts end-of-year national test scores. We used 2,386 online fourth-grade mathematic questions designed by teachers and each attempted by some of the 500 students in 24 low socioeconomic schools. We found that the state-space model predictions improved month-by-month and that in most months it outperformed linear regression models. Moreover, the state-space estimator provides for each month a direct mechanism to simulate different practice strategies and compute their impact on the end-of-year standardized national test. We built iso-impact curves based on two critical variables: the number of questions solved correctly in the first attempt and the total number of exercises attempted. This allows the teacher to visualize the trade-off between asking students to do exercises more carefully or doing more exercises. To the best of our knowledge, this model is the first of its kind in education. It is a novel tool that supports teachers drive whole classes to achieve long-term learning targets.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
