Fraction Magnitude Student Explanations: A Latent Class Analysis
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| Title: | Fraction Magnitude Student Explanations: A Latent Class Analysis |
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| Language: | English |
| Authors: | Lindy Crawford (ORCID |
| Source: | International Electronic Journal of Mathematics Education. 2026 21(1). |
| Availability: | International Electronic Journal of Mathematics Education. Suite 124, Challenge House 616 Mitcham Road, CR0 3AA, Croydon, London, UK. Tel: +44-208-936-7681; e-mail: iejme@iejme.com; Web site: https://www.iejme.com |
| Peer Reviewed: | Y |
| Page Count: | 14 |
| Publication Date: | 2026 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Elementary Education Grade 4 Intermediate Grades Grade 5 Middle Schools Grade 6 |
| Descriptors: | Fractions, Mathematics Instruction, Mathematical Concepts, Concept Formation, Knowledge Level, Elementary School Students, Grade 4, Grade 5, Grade 6, Mathematical Logic |
| Geographic Terms: | Texas, Oregon |
| Assessment and Survey Identifiers: | Wide Range Achievement Test |
| ISSN: | 1306-3030 |
| Abstract: | The study examined the types of explanations students provide for fraction magnitude problems. Student responses were coded into one of five explanation types: (a) absent, (b) faulty, (c) conceptual-partially developed, (d) algorithmic, and (e) conceptual-fully developed. When examining latent classes specific to students' explanations of their knowledge of fraction magnitude, a five-class model was the most tenable, conveying the presence of five distinct student profiles. The algorithmic class represented the largest percentage of student explanations and also revealed the strongest correlation with criterion measures. A combined algorithmic-conceptual class was not identified. |
| Abstractor: | As Provided |
| Entry Date: | 2026 |
| Accession Number: | EJ1505473 |
| Database: | ERIC |
| Abstract: | The study examined the types of explanations students provide for fraction magnitude problems. Student responses were coded into one of five explanation types: (a) absent, (b) faulty, (c) conceptual-partially developed, (d) algorithmic, and (e) conceptual-fully developed. When examining latent classes specific to students' explanations of their knowledge of fraction magnitude, a five-class model was the most tenable, conveying the presence of five distinct student profiles. The algorithmic class represented the largest percentage of student explanations and also revealed the strongest correlation with criterion measures. A combined algorithmic-conceptual class was not identified. |
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| ISSN: | 1306-3030 |
