Numeracy and Quantitative Reasoning > Best Practices for Quantitative Reasoning Instruction

# Best Practices for Quantitative Reasoning Instruction

In her book Powerful Learning: What we Know about Teaching for Understanding, Darling-Hammond (2008: 5) argues that meaningful learning is accomplished through a number of key approaches including: "(1) creating ambitious and meaningful tasks, (2) engaging students in active learning, (3) drawing connections to students, (4) scaffolding the learning process, (5) assessing student learning continuously, (6) providing clear standards and constant feedback, and (7) encouraging strategic and metacognitive thinking."

Several pedagogical approaches are especially important for teaching QR which are described in more detail on this page including:

Of course, these approaches are frequently overlapping.

## A Video about Best Practices for Teaching QR

A video produced by the University of Colorado at Boulder Journalism and Mass Communications students that discusses the educational practices that contribute to quantitative illiteracy and proposes solutions. This video features an interview with Sanjoy Mahajan, author of Street-Fighting Mathematics.

## (1) Real World Applications and Active Learning, including Discovery Methods

There is a famous Chinese proverb that states, "What I hear, I forget; what I see, I remember; what I do, I understand." This proverb is a fundamental principle of active learning. Extensive research has shown that students learn more rapidly, retain knowledge longer, and develop superior critical thinking skills when they are actively involved in the learning process (see, e.g., Himes and Caffrey 2003, Kain 1999; Kenny 1998; King 1994).

Indeed, the premise that "students learn math by doing math, not by listening to someone talk about doing math" (Twigg 2005) provides the philosophical approach for NICHE (i.e., "students learn quantitative reasoning by doing quantitative reasoning"). Successful instruction in QL requires progressive pedagogy: "connecting content to real-life situations, lighter coverage of topics, an emphasis on understanding concepts rather than facts, integrating content across disciplinary boundaries" (Cuban 2001: 89). When theory and data analysis are combined in an active learning setting, students often come to understand that quantitative reasoning skills are relevant to social issues.

The importance of teaching QR skills within an applied (real-world) context has been emphasized by a number of educators. For example, Burkhardt (2008) argues in favor of Numeracy through Problem Solving (NTPS), which grew out of a concern that students see school mathematics as irrelevant to their present or future lives. Likewise, Karim and Wakefield (2007: 3) stress the importance of presenting real-world example before introducing more general theoretical concepts. These approaches to QR instruction are important, particularly since the contextualized use of data is central to QR and empirical research has failed to demonstrate that traditional remedial math courses improve student performance (see, e.g., Lagerlöf and Seltzer 2008; Pozo and Stull 2006).

Other researchers have demonstrated how active learning with real applications can improve statistical literacy skills among young children (Marshall and Swan 2006) as well as graphing skills college students (McFarland 2010). Moreover, at Macalester College, the economics and mathematics departments jointly offered a course which taught students fundamental quantitative skills within an applied context (e.g., sampling issues and the interpretation of polling data) (Bressoud 2009).

One approach to active learning that is valuable for teaching QR is the application of constructivist and discovery methods. Indeed, Killen (2006) contends:

It is now generally accepted that most people learn best through personally meaningful experiences that enable them to connect new knowledge to what they already believe or understand. Such constructivist views of learning have led to a redefinition of effective teaching. It is now more widely accepted that teachers have to deliberately help learners construct their own understanding, rather than simply tell them things that they are expected to memorize. Good teaching is no longer about helping students to accumulate knowledge that is passed onto them by teachers; it is about helping students to make sense of new information (no matter what its source), to integrate new information with their existing ideas and to apply their new understandings in meaningful and relevant ways.

These approaches see learning as a form of understanding constructed by the learner and focus on ways in which the individual learner makes sense of the subject matter (see, e.g., Brooks and Brooks 2001; Caine and Caine 1994; Cakin 2008; Hatano 1996; Leonard 2002; Tout and Schmitt 2002; Switzer 2004). At the same time, teaching QR does not always necessitate such approaches. For example, McLaughlin and Talbert (1993: 4) contend that "teachers need to learn when the interactive, constructivist forms of teaching are called for and when other less demanding, conventional strategies are appropriate."

## (2) Pairing QR Instruction with Writing, Storytelling and Critical Reading

Below is a video which features John Allen Paulos discussing stories and statistics:

Pairing quantitative constructs with language serves to (1) strengthen academic arguments; (2) strengthen quantitative literacy/reasoning; (3) interpret and improve public discourse; (4) encourage quantitative reasoning across the curriculum; and (5) prepare students for the workplace (Madison 2012).

Research has also shown that placing QR programs within the context of writing programs brings a number of benefits. For example, it improves writing instruction, challenges the notion that QR is only remedial math, and provides a route for the incorporation of QR into the curriculum (Grawe and Rutz 2009). Stressing the importance of connecting writing and QR, Lutsky (2008: 63) argues that "quantitative information may be used to help articulate or clarify an argument, frame or draw attention to an argument, make a descriptive argument, or support, qualify, or evaluate an argument. Quantitative analysis may also influence how arguments are marshaled and how exchanges of arguments are conducted."<a></a> Adding numbers to language does not only strengthen the latter, but the reverse is also true. Indeed, research has shown that pairing developmental mathematics with reading can enhance success in mathematics (Kirk and Lerma 2010).

## (3) Using Technology, including Computers

Computer skills (operating systems, spreadsheets, etc.) are also essential to QL/QR (Collison et al. 2008; Jabon 2006; Steen 2004; Vacher and Lardner 2010; Wiest et al. 2007). Indeed, the use of computers can actively engage students in QR work, promote logical thinking and help students master QL/QR skills that are central to the research process (Fuller 1998; Markham 1991; Persell 1992; Raymondo 1996). Interactive computer software; personalized, on-demand assistance; and mandatory student participation have also been recognized as key elements of successful math instruction (NCAT 2005). The integration of spreadsheets across the curriculum has also been shown to successfully promote QR engagement in a variety of fields (Vacher and Lardner 2000).

Moseley and colleagues (1999) found that various strategies of using information and communication technology promoted effective instruction in literacy and numeracy in primary schools in Great Britain. Indeed, research has shown that active learning using computers helps promote students' QR skills (Wilder 2009), and that computer literacy is itself seen as a QR skill (Wilder 2010). For instance, the interdisciplinary, technology-infused approach to QR adopted by DePaul University had a number of positive benefits (Jabon 2006); students mastered technology tools by undertaking realistic analyses, and the computer-based activities created an active, lively learning environment that was engaging for students.

## (4) Collaborative Instruction and Group Work

Interdisciplinary and collaborative approaches, including group work, are effective educational strategies for promoting mathematics and QR education. For example, in their research on teaching social science reasoning and quantitative literacy using collaborative groups, Caulfield and Hodges (2006: 52) reported, "Our data clearly reveal that most of our students work harder and learn more while working in groups." Indeed, Grouws and Cebulla (2000: 20) argue that "teachers must encourage students to find their own solution methods and give them opportunities to share and compare their solution methods and answers. One way to organize such instruction to have students work in small groups initially and then share ideas and solutions in a whole-class discussion."

More recently, Dingman and Madison (2010; see also Madison and Dingman 2010) taught a course that engaged students in collaborative small-group learning exercises in which they read and evaluated data from newspaper articles.

## (5) Pedagogy that is Sensitive to Students' Differences in Culture and Learning Styles

Considerable research has been written about how women and minorities experience mathematical and quantitative disadvantages. The strategies for overcoming this inequality remain a topic of considerable interest and debate. Tout and Schmitt (2002) note that in the United States, considerable effort has been directed towards fostering the success of females in mathematics and such approaches to teaching have "challenged the traditionally male-dominated domain of math education and promoted alternatives that in many cases are attractive not only to girls but to the many boys who struggle with learning mathematics in the class. Such approaches include working cooperatively, promoting discussion and idea sharing, and using hands-on materials." Even within the classroom, teachers need to be sensitive to the variation in students' abilities (see, e.g., Stern 2004).

Teaching quantitative reasoning in a manner that is sensitive to different cultures also has the potential for improving student learning. For example, Ubiratan D'Ambrosio, the Brazilian educator who coined the term "ethnomathematics," described it as the study of different forms of mathematics that arise in different cultural contexts. Zaslavsky (1994: 7) argues that the incorporation of ethnomathematical perspectives calls for "a complete turn-around from the way mathematics is now taught in many classrooms." In a nutshell, she argues that:

a) The entire mathematics curriculum must be restructured so that mathematical concepts and ethnomathematical aspects are synthesized. Rather than a curriculum emphasizing hundreds of isolated skills, mathematics education will embody real-life applications in the forms of projects based on themes and mathematical concepts.

b) Teachers at all levels must be well-grounded in mathematics and at the same time familiar with the interface between mathematics and other subject areas. They will need the initiative and time to work with other teachers, with parents and the community in planning lessons that are relevant specifically to their students. Preservice and inservice education should incorporate these perspectives.

c) The revised curriculum will require various methods of assessment – on-going assessment of projects, evaluation of portfolios, etc. Simplistic multiple-choice tests will be abolished or downplayed.

d) Research must be conducted and the results made valuable to teachers on the ways in which underserved and underrepresented students, particularly females and people of color, can best learn mathematics.

Likewise, Rowlands and Carson (2002: 52) argue that "only through the lens of formal, academic mathematics sensitive to cultural differences that the real value of the mathematics inherent in certain cultures and societies [can] be understood and appreciated." Indeed, "functional math has much in common with ethnomathematics. Both argue for an approach that covers a wide range of math skills embedded within social contexts and purposes that values personal ways of doing math" (Tout and Schmitt 2002).

"Acknowledging the cultural component of mathematics will enhance our appreciation of its scope and its potential to providing an interesting, artistic and useful view of the world" (Barton 1996: 299). Just as an ethnomathematical framework can improve mathematics education, so too can an ethnonumeracy1 approach improve students' understanding of quantitative reasoning skills. For example, integrating culturally relevant QR exercises is important. At the same time, Orey and Rosa (2007: 15): caution that ethnomathematical work in the schools is not a simplistic presentation of cultural examples or simply situating mathematics in cultural contexts. Rather it requires considerable background work, complete understanding, and pedagogical sophistication . . . For example, it is convenient to state that teachers may interpret an ethnomathematical approach by starting with the students' outside socio-cultural-economic realities, but the students may refuse to study their own realities because they consider them to be oppressive."

Another example has to do with cultural modes of communication. As communication is one key component of quantitative reasoning, an ethonumerical approach to QR instruction will need to respond to differences in language (particularly among non-native English speakers) and how words are used to describe numbers and data among different populations.

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## (6) Scaffolding the Learning Process and Providing Rich Feedback and Opportunities for Revision

Teaching QR for understanding involves a process whereby the instructor is an active facilitator of learning. Killen (2006: 21) notes that the teacher's goal should be "to encourage students to be both investigators and critics of the subjects they are studying, which providing them with sufficient scaffolding for them to be successful in their learning." Scaffolding entails "providing a student with enough help to complete a task and then gradually decreasing the help as the student becomes able to work independently" (Killen 2006: 7).

Moreover, throughout the learning process, faculty must be engaged in providing rich feedback to students and ensuring that students have ample opportunities to master the material, particularly if they are not successful the first time around. The Writing Across the Curriculum (WAC) model of instruction incorporates revision as an essential component in the process of learning how to write; this model is also critical for mastering quantitative reasoning skills.

## Notes

I I (Esther Wilder) use the term "ethnonumeracy" to refer to an appreciation for the cultural context of quantitative reasoning skills and understanding.

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