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Educational Paths to Mathematics pp 33–65Cite as

Connecting Place and Community to Mathematics Instruction in Rural Schools

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Part of the book series: Advances in Mathematics Education ((AME))

Abstract

This chapter uses a mixed-method approach to address practices of mathematics education in the context of various rural schools in the US. It reports on connections of mathematics instruction to place and community by developing issues of relevance, sustainability and social-class interaction. Special attention is paid to place-based education and the university-intending students, to rural insufficiency and rural affordance and to the egalitarian local/elite cosmopolitan continuum.

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Notes

  1. 1.

    This work draws on the efforts of a talented team of colleagues including Aimee Howley, Craig Howley, Daniel Showalter, John Hitchcock, and Jerry Johnson. This paper makes use of Howley et al. (2010, 2011), and Klein et al. (2013) but focuses more acutely on mixed-methods results than do those papers.

  2. 2.

    Descriptions are modified from those given in Howley et al. (2010).

  3. 3.

    The ACT is a college readiness assessment administered in the United States to students in secondary school wishing to enter post-secondary education. It is used by many institutions of higher education in the United States, along with the similar SAT, as part of college admissions decisions and sometimes placement into mathematics and English courses. The ACT reports an overall composite score (1–36), with 36 being the highest score, as well as subject-specific composite scores (also 1–36) for English, Mathematics, Reading, Science Reading, and Writing.

  4. 4.

    The word “college” used throughout this chapter, refers to post-compulsory graduation. It may be used synonymously with “university” as is the practice in the United States.

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Author information

Authors and Affiliations

  1. Ohio University, Athens, OH, USA

    Robert Klein

Authors
  1. Robert Klein
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Corresponding author

Correspondence to Robert Klein .

Editor information

Editors and Affiliations

  1. Fachbereich Erziehungswissenschaft und Psychologie, Freie Universität Berlin, Berlin, Germany

    Uwe Gellert

  2. Facultat de Formació del Professorat Departament de Didàctica de les Ciències Experimentals i la Matemàtica, Universitat de Barcelona, Barcelona, Spain

    Joaquim Giménez Rodríguez

  3. Management de l’Information et des Opérations, ESCP Europe, Paris, France

    Corinne Hahn

  4. Department of Sciences of Pre-School Education and of Educational Design, University of the Aegean, Rhodes, Greece

    Sonia Kafoussi

Appendices

Appendices

Appendix A

Factor loadings A–D for exploratory factor analysis with polychoric factor analyses reported in parentheses

 

Item

Factor 1

Factor 2

Factor 3

Factor 4

A1.

Connecting mathematics to students’ everyday lives can help prepare them for study at 4-year colleges.

.782 (.69)

−.019

−.014

−.178

A2.

Connecting mathematics to students’ everyday lives can help prepare them for study at 2-year colleges

.769 (.82)

−.072

−.074

−.112

A3.

Using students’ daily life experiences as part of instruction improves the learning of lower-level mathematics.

.698 (.84)

−.020

−.037

.109

A4.

Using students’ daily life experiences as part of instruction improves the learning of higher-level mathematics.

.652 (.78)

−.038

.021

−.103

A5.

Connecting mathematics to students’ everyday lives can help prepare them for coursework in a 4-year university or college.

.639 (.69)

−.044

.096

−.096

A6.

Students are more motivated to learn mathematics when they see it as being relevant to their daily lives.

.581 (.74)

.007

−.028

.018

A7.

Connecting mathematics to the local community will improve instruction for all students.

.558 (.63)

.022

.159

.078

A8.

My remedial students like it when mathematics instruction is tied to their daily lives.

.546 (.63)

.089

.077

.259

A9.

My advanced students like it when mathematics instruction is tied to their daily lives.

.517 (.59)

.086

.022

.091

A10.

The mathematics I teach is directly applicable to my students’ everyday lives.

.366 (.37)

.074

.189

−.164

A11.

Twenty-first century jobs will demand high level mathematical skills of all students.

.321 (.35)

.275

.068

.034

B1.

All students can do higher-level mathematics.

.057

.725 (.76)

−.007

−.093

B2.

Some students will never be ready for college-level mathematics.

−.014

.665 (.75)

−.134

−.167

B3.

Schools should prepare all students to go to college.

.001

.621 (.68)

.188

.135

C1.

My colleagues support efforts to connect mathematics instruction to the local community.

.075

−.091

.710 (.77)

.031

C2.

My administration supports community-connected mathematics instruction.

.056

.048

.683 (.722)

.107

C3.

Parents of my students support community-connected mathematics instruction.

.137

.007

.675 (.698)

.094

C4.

My community offers opportunities for applying mathematics topics.

−.061

.066

.539 (.701)

−.121

C5.

Students who stay in the area will apply their math skills to address local needs.

.157

.031

.418 (.54)

−.088

C6.

My students are accustomed to the sorts of projects that connect mathematics to everyday life.

.199

−.074

.389 (.498)

−.141

C7.

Students with strong math skills are likely to settle in this area.

−.085

.154

.258 (.34)

−.066

D1.

It’s easier to connect lower-level mathematics content to daily life than higher-level mathematics content.

.082

.023

.079

.530 (.61)

D2.

There are barriers that make it difficult to connect mathematics to students’ everyday lives.

−.037

.033

−.147

.463 (.59)

D3.

The mathematics of daily life matters more to the future of some of my students than it does to others.

−.007

−.067

.024

.416 (.55)

D4.

My typical student will need only basic mathematics in his/her daily life.

−.169

.007

−.066

.310 (.366)

D5.

Activities that connect math to the local community take up instructional time needed to address standards.

.009

.008

−.099

.305 (.32)

D6.

Algebra I (or the equivalent) is where you start to notice which students will be ready for college.

−.003

−.103

.099

.303 (.38)

D7.

Some students receive better academic opportunities because of his/her family’s local prominence.

.001

−.032

−.085

.132 (.22)

  1. Note: Extraction method: Maximum likelihood. Rotation method: Oblimin with Kaiser normalization
  2. a. Rotation converged in seven iterations

Appendix B

Factor correlation matrix

Factor

A

B

C

D

A

1.000

.120

.463

−.055

B

.120

1.000

.267

−.245

C

.463

.267

1.000

−.201

D

−.055

−.245

−.201

1.000

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Klein, R. (2015). Connecting Place and Community to Mathematics Instruction in Rural Schools. In: Gellert, U., Giménez Rodríguez, J., Hahn, C., Kafoussi, S. (eds) Educational Paths to Mathematics. Advances in Mathematics Education. Springer, Cham. https://doi.org/10.1007/978-3-319-15410-7_3

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