Unsupervised machine learning to classify language dimensions to constitute the linguistic complexity of mathematical word problems
Author:
Affiliation:
1. Department of Mathematics Education, IPN-Leibniz Institute for Sciene and Mathematics Education, Kiel, GERMANY
2. Department for Mathematics Education, Bielefeld University, Bielefeld, GERMANY
Abstract
Publisher
Modestum Ltd
Subject
Education,General Mathematics
Link
https://www.iejme.com/download/unsupervised-machine-learning-to-classify-language-dimensions-to-constitute-the-linguistic-12588.pdf
Reference98 articles.
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2. Abedi, J., & Gándara, P. (2006). Performance of english language learners as a subgroup in large-scale assessment: Interaction of research and policy. Educational Measurement: Issues and Practice, 25(4), 36-46. https://doi.org/10.1111/j.1745-3992.2006.00077.x
3. Abedi, J., & Herman, J. (2010). Assessing english language learners’ opportunity to learn mathematics: Issues and limitations. Teachers College Record, 112(3), 723-746. https://doi.org/10.1177/016146811011200301
4. Abedi, J., & Lord, C. (2001). The language factor in mathematics tests. Applied Measurement in Education, 14(3), 219-234. https://doi.org/10.1207/S15324818AME1403_2
5. Abedi, J., Leon, S., Wolf, M. K., & Farnsworth, T. (2008). Detecting test items differentially impacting the performance of ell students. In M. K. Wolf, J. L. Herman, J. Kim, J. Abedi, S. Leon, N. Griffin, & P. L. Bachman (Eds.), Providing validity evidence to improve the assessment of English language learners (pp. 55-81). National Center for Research on Evaluation, Standards, and Student Testing.
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