Pre- and in-service teachers attribution beliefs for students’ success and struggle in mathematics: first insights
Abstract This study investigates the attributional beliefs of pre-service and in-service elementary and middle school mathematics teachers regarding students’ success and struggles in mathematics. Utilizing a qualitative case study approach, semi-structured interviews were conducted with 28 in-servi...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-07-01
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| Series: | BMC Psychology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s40359-025-03038-8 |
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| Summary: | Abstract This study investigates the attributional beliefs of pre-service and in-service elementary and middle school mathematics teachers regarding students’ success and struggles in mathematics. Utilizing a qualitative case study approach, semi-structured interviews were conducted with 28 in-service and pre-service teachers to explore their beliefs. Data were analyzed through content analysis, categorizing attributions into four main types: genetic, social, personal, and educational. The findings reveal that participants, selected through a convenience sampling method, expressed attributions related to both student success and struggle, reflecting the multifaceted nature of influences on students’ mathematical performance. Pre-service teachers mentioned a broad range of attributions, including personal, social, and educational factors. In-service teachers tended to emphasize personal and genetic attributions, highlighting interest and innate ability as significant factors. While social and educational attributions, including family support and teacher quality, were discussed, they were less frequently highlighted by in-service teachers. This study highlights the importance of understanding teachers’ beliefs, as these beliefs have the potential to shape instructional decisions and expectations. The findings suggest that future research should involve observational data and include larger sample size. |
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| ISSN: | 2050-7283 |