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ERIC Number: EJ1224157
Record Type: Journal
Publication Date: 2019
Pages: 22
Abstractor: As Provided
ISSN: EISSN-1929-7750
Using Instruction-Embedded Formative Assessment to Predict State Summative Test Scores and Achievement Levels in Mathematics
Zheng, Guoguo; Fancsali, Stephen E.; Ritter, Steven; Berman, Susan R.
Journal of Learning Analytics, v6 n2 p153-174 2019
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, 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.
Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: Elementary Education; Grade 6; Intermediate Grades; Middle Schools; Grade 7; Junior High Schools; Secondary Education; Grade 8
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Identifiers - Location: Florida
Identifiers - Assessments and Surveys: Florida Comprehensive Assessment Test