Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics
Item
Title
Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics
Abstract/Description
If we wish to embed assessment for accountability within instruction, we need to better understand the relative contribution of different types of learner data to statistical models that predict scores and discrete achievement levels on assessments used for accountability purposes. The present work scales up and extends predictive models of math test scores and achievement levels from existing literature and specifies six categories of models that incorporate information about student prior knowledge, socio-demographics, and performance within the MATHia intelligent tutoring system. Linear regression, ordinal logistic regression, and random forest regression and classification models are learned within each category and generalized over a sample of 23,000+ learners in Grades 6, 7, and 8 over three academic years in Miami-Dade County Public Schools. After briefly exploring hierarchical models of this data, we discuss a variety of technical and practical applications, limitations, and open questions related to this work, especially concerning to the potential use of instructional platforms like MATHia as a replacement for time- consuming standardized tests.
Author/creator
Date
In publication
Volume
6
Issue
2
Pages
153-174
Resource type
Research/Scholarly Media
Resource status/form
Published Text
Scholarship genre
Empirical
Keywords
ISSN
1929-7750
Citation
Zheng, G., Fancsali, S. E., Ritter, S., & Berman, S. (2019). Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics. Journal of Learning Analytics, 6(2), Article 2. https://doi.org/10.18608/jla.2019.62.11
Rights
Copyright (c) 2019 Journal of Learning Analytics
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