Low-Attaining Secondary School Mathematics Students' Perspectives on Recommended Teaching Strategies.

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Title: Low-Attaining Secondary School Mathematics Students' Perspectives on Recommended Teaching Strategies.
Authors: Hodgen, Jeremy1 (AUTHOR), Foster, Colin2 (AUTHOR) c.foster@lboro.ac.uk, Brown, Margaret3 (AUTHOR), Martin, David4 (AUTHOR)
Source: International Journal of Science & Mathematics Education. Aug2024, Vol. 22 Issue 6, p1325-1343. 19p.
Subject Terms: *Student attitudes, *Secondary school students, *Psychology of students, *Mathematics education, *Mathematics students, Mathematics
Geographic Terms: England
Abstract: Recent research syntheses have identified several potentially high-leverage teaching strategies for improving low-attaining secondary school students' learning of mathematics. These strategies include the structured use of representations and manipulatives and an emphasis on derived facts and estimation. This paper reports on 70 semi-structured interviews conducted with low-attaining students in Years 9–10 (ages 13–15) in England. The interviews addressed the students' perceptions of learning mathematics and the teaching strategies that they experienced and believed were most helpful. Many students reported rarely using number lines, not spontaneously estimating answers and being unfamiliar with derived facts. During the interviews, with minimal direction, students often showed that they were well able to make use of these strategies; however, they did not report making spontaneous use of them independently. We conclude that many of the most well-evidenced and recommended strategies to support low-attaining students in mathematics appear to be unfamiliar and unvalued, and we discuss how this might be addressed. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Science & Mathematics Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Low-Attaining Secondary School Mathematics Students' Perspectives on Recommended Teaching Strategies.
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Science+%26+Mathematics+Education%22">International Journal of Science & Mathematics Education</searchLink>. Aug2024, Vol. 22 Issue 6, p1325-1343. 19p.
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  Data: Recent research syntheses have identified several potentially high-leverage teaching strategies for improving low-attaining secondary school students' learning of mathematics. These strategies include the structured use of representations and manipulatives and an emphasis on derived facts and estimation. This paper reports on 70 semi-structured interviews conducted with low-attaining students in Years 9–10 (ages 13–15) in England. The interviews addressed the students' perceptions of learning mathematics and the teaching strategies that they experienced and believed were most helpful. Many students reported rarely using number lines, not spontaneously estimating answers and being unfamiliar with derived facts. During the interviews, with minimal direction, students often showed that they were well able to make use of these strategies; however, they did not report making spontaneous use of them independently. We conclude that many of the most well-evidenced and recommended strategies to support low-attaining students in mathematics appear to be unfamiliar and unvalued, and we discuss how this might be addressed. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Science & Mathematics Education is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s10763-023-10420-8
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        Text: English
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              Text: Aug2024
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