Using embedded formative assessment to predict state summative test scores
| Autor: | Guoguo Zheng, April Galyardt, Yanyan Tan, Steven Ritter, Susan R. Berman, Stephen Fancsali |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
business.industry
05 social sciences 050301 education Sample (statistics) Standardized test Statistical model 02 engineering and technology Intelligent tutoring system Test (assessment) Formative assessment Summative assessment Accountability 0202 electrical engineering electronic engineering information engineering Mathematics education 020201 artificial intelligence & image processing business 0503 education |
| Zdroj: | LAK |
| DOI: | 10.1145/3170358.3170392 |
| Popis: | 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 on assessments used for accountability purposes. The present work scales up and extends predictive models of math test scores 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 and random forest 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. |
| Databáze: | OpenAIRE |
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