Science in the elementary grades is often deprioritized in comparison to ELA and mathematics. We wondered, how comprehensively, frequently, and consistently is science in elementary schools’ schedules? In this study, we reviewed daily schedules for 14 schools in 9 districts across the U.S. to qualitatively examine how science is represented on the daily instructional schedule. These schools were selected as “best case scenarios” recommended by district or state science leaders as places where science is taken seriously. We complement these schedule data with data from 21 interviews with teachers, science specialists, and school leaders to better understand how science actually appears in children’s daily instructional experiences. Our findings suggest that, in these schools, science is taught comprehensively (though not as comprehensively as ELA or
mathematics), has the potential for being taught frequently (even in the lower elementary grades), and is taught somewhat consistently (albeit usually in some kind of rotation with social studies). The paper closes with implications for how school schedules could be crafted to make science comprehensive, frequent, and consistent, as well as some pitfalls that could be avoided as schedules are developed.
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Generalizing Predictive Models of Reading Ability in Adaptive Mathematics Software
Recent research seeks to develop more comprehensive learner models for adaptive learning software. For example, models of reading comprehension built using data from students’ use of adaptive instructional software for mathematics have recently been developed. These models aim to deliver experiences that consider factors related to learning beyond performance in the target domain for instruction. We investigate the extent to which generalization is possible for a recently developed predictive model that seeks to infer students’ reading comprehension ability (as measured by end-of-year standardized test scores) using an introductory learning experience in Carnegie Learning’s MATHia intelligent tutoring system for mathematics. Building on a model learned on data from middle school students in a single school district in a mid-western U.S. state, using that state’s end-of-year English Language Arts (ELA) standardized test score as an outcome, we consider data from a school district in a south-eastern U.S. state as well as that state’s end-of-year ELA standardized test outcome. Generalization is explored by considering prediction performance when training and testing models on data from each of the individual school districts (and for their respective state’s test outcomes) as well as pooling data from both districts together. We conclude with discussion of investigations of some algorithmic fairness characteristics of the learned models. The results suggest that a model trained on data from the smaller of the two school districts considered may achieve greater fairness in its predictions over models trained on data from the other district or both districts, despite broad, overall similarities in some demographic characteristics of the two school districts. This raises interesting questions for future research on generalizing these kinds of models as well as on ensuring algorithmic fairness of resulting models for use in real-world adaptive systems for learning.
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- Award ID(s):
- 2000638
- NSF-PAR ID:
- 10445554
- Editor(s):
- Feng, Mingyu; Käser, Tanja; Talukdar, Partha
- Date Published:
- Journal Name:
- Proceedings of the 16th International Conference on Educational Data Mining
- Page Range / eLocation ID:
- 207-216
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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