Making Undergraduate Geoscience Quantitative

Manduca, Cathryn A.; Baer, Eric M.D.; Hancock, Greg; MacDonald, Roderick; Patterson, Sam P.; Savina, Mary; Wenner, Jennifer M.

doi:10.1029/2008eo160001

Cited by 54 publications

(46 citation statements)

References 7 publications

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“…The five teaching ideas also established the basis of several subsequent workshops on QL in the geosciences (held at Carleton College in 2004, 2005, and 2006 and form the pedagogical underpinnings of the Teaching Quantitative Literacy Web site (Manduca et al 2008). Below, we discuss the educational basis for each of the five ideas and ways that the geosciences provide ample opportunity to use these ideas to increase QL.…”

Section: Lessons Learned From Mathematics: Five Ideas For Teaching Qlmentioning

confidence: 99%

“…Geoscience faculty recognize the need and opportunity for the geosciences to contribute to the development of a quantitatively literate citizenry (e.g., Macdonald and Bailey 2000;Manduca et al 2001Manduca et al , 2005Manduca et al , 2008Wenner et al 2004Wenner et al , 2005Wenner et al , 2006aHancock and Manduca 2005). Traditionally, many students enroll in introductory geoscience courses because they wish to avoid disciplines they perceive as quantitative and mathematical (e.g., physics, mathematics and chemistry) and may identify the geosciences as largely descriptive or remedial (e.g., Shea 1990;Corbett 2000).…”

Section: Introductionmentioning

confidence: 99%

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The Case for Infusing Quantitative Literacy into Introductory Geoscience Courses

Wenner¹,

Baer²,

Manduca³

et al. 2009

Numeracy

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We present the case for introductory geoscience courses as model venues for increasing the quantitative literacy (QL) of large numbers of the college-educated population. The geosciences provide meaningful context for a number of fundamental mathematical concepts that are revisited several times in a single course. Using some best practices from the mathematics education community surrounding problem solving, calculus reform, pre-college mathematics and five geoscience/math workshops, geoscience and mathematics faculty have identified five pedagogical ideas to increase the QL of the students who populate introductory geoscience courses. These five ideas include techniques such as: place mathematical concepts in context, use multiple representations, use technology appropriately, work in groups, and do multiple-day, in-depth problems that place quantitative skills in multiple contexts. We discuss the pedagogical underpinnings of these five ideas and illustrate some ways that the geosciences represent ideal places to use these techniques. However, the inclusion of QL in introductory courses is often met with resistance at all levels. Faculty who wish to include quantitative content must use creative means to break down barriers of public perception of geoscience as qualitative, administrative worry that enrollments will drop and faculty resistance to change. Novel ways to infuse QL into geoscience classrooms include use of web-based resources, shadow courses, setting clear expectations, and promoting quantitative geoscience to the general public. In order to help faculty increase the QL of geoscience students, a community-built faculty-centered web resource (Teaching Quantitative Skills in the Geosciences) houses multiple examples that implement the five best practices of QL throughout the geoscience curriculum. We direct faculty to three portions of the web resource: Teaching Quantitative Literacy, QL activities, and the 2006 workshop website -Infusing Quantitative Literacy into Introductory Geoscience Courses. These portions of the website are designed to give geoscience faculty the resources they need to infuse quantitative content into their entry-level courses, thereby building the QL of the students who enroll. The infusion of QL in the introductory geoscience classroom allows faculty to realistically represent the quantitative nature of the science to the students who may need it most. Ultimately, the inclusion of pedagogically sound quantitative activities and exercises will serve to increase QL of our educated citizenry.

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Section: Lessons Learned From Mathematics: Five Ideas For Teaching Qlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Case for Infusing Quantitative Literacy into Introductory Geoscience Courses

Wenner¹,

Baer²,

Manduca³

et al. 2009

Numeracy

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“…Introductory Earth science courses, for example, have a history of being perceived as "easy" in the United States, and have acquired a reputation of requiring qualitative rather than quantitative skills (Manduca et al 2008). Gilbert et al (2012) surveyed 1057 students in introductory geoscience courses across seven universities and found 73% (N=765) enrolled to fulfill a degree requirement.…”

Section: Foundations Of Mathematics Self-efficacy Attitudes and Anxmentioning

confidence: 99%

The Quantitative Reasoning for College Science (QuaRCS) Assessment 2: Demographic, Academic and Attitudinal Variables as Predictors of Quantitative Ability

Follette¹,

Buxner²,

Dokter³

et al. 2017

Numeracy

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In this article, we explore the ability of demographic and attitudinal variables to predict student scores on the Quantitative Reasoning for College Science (QuaRCS) Assessment. Variables measured by the assessment include: students' academic choices and plans, attitudes and perceptions regarding mathematics, self-reported effort level, and basic demographics such as age, race/ethnicity, gender and disability status. As in previously published numeracy studies, we find significant score deviations according to gender, race/ethnicity, and disability status; however, the effect size of these correlations pale in comparison to the effect size of affective/ attitudinal variables on QuaRCS score. A large number of variables with significant effects on QuaRCS score make the data well-suited to dimension reduction, and Factor Analyses reveal that a majority of affective variables can be collapsed into three underlying factors, which we call numerical self-efficacy, numerical relevancy and academic maturity. These three composite variables alone account for 32.4% of the variance in QuaRCS score. Two additional affective variables -self-reported effort and calculator usage -add 15.9% to the regression model. Together, these five variables account for nearly half of the variance in QuaRCS score. In contrast, academic and basic demographic variables, account for only 0.3% and 0.1% of the remainder, respectively. Furthermore, most demographic variables (including race and gender) do not have a significant effect on the regression model once affective variables have been accounted for. Kate Follette is a NASA Sagan Postdoctoral Fellow at the Kavli Institute for Particle Astrophysics and Cosmology at Stanford University. This work was begun as a graduate student at the University of Arizona, and was supported by an NSF Transforming Undergraduate Education in STEM grant (NSF-DUE # 114-0398) and an NSF Graduate Research Fellowship. She taught general education astronomy courses as an adjunct instructor at Pima Community College from 2009-2014, and will be an assistant professor at Amherst College beginning January 2017. Her science research focuses on finding and characterizing young extrasolar planets, as well as the disks of gas and dust from which they form.Sanlyn Buxner is an assistant research professor in the Department of Teaching, Learning and Sociocultural Studies at the University of Arizona. She teaches introductory science and research methods courses and supports institutional assessment of graduate and undergraduate programs. Her research includes examining science literacy and quantitative literacy in undergraduate science students and studying the impact of research and industry experiences for K12 teachers on classroom practice and student outcomes.This article is available in Numeracy: http://scholarcommons.usf.edu/numeracy/vol10/iss1/art5Erin Dokter is an associate professor of practice in the Office of Instruction and Assessment at the University of Arizona, where she serves as the Coordinator for th...

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“…Appropriate responses to these questions might demonstrate familiarity with the scope of collective knowledge of the hydrology community, but still do not demonstrate the ability to apply that understanding to answer questions or make quantitative predictions, as called for by Manduca et al (2008). Dooge (1986) posits that hydrology involves developing both explanatory theories (i.e., the relationship between the laws and processes represented in the response to Q1 and Q2) and predictive models.…”

Section: Key Questions and Tasks For The Assessment Toolmentioning

confidence: 99%

Assessing student understanding of physical hydrology

Marshall

Castillo

Cardenas

2013

Hydrol. Earth Syst. Sci.

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Abstract. Our objective is to devise a mechanism to characterize and assess upper division and graduate student thinking in hydrology. We accomplish this through development and testing of an assessment tool for a physical hydrology class. The instrument was piloted in two sections of a physical hydrology course. Students were asked to respond to two questions that probed understanding and one question that assessed their ability to apply their knowledge, both prior to and after the course. Student and expert responses to the questions were classified into broad categories to develop a rubric to score responses. Using the rubric, three researchers independently blind-coded the full set of pre-and post-artifacts, resulting in 89% inter-rater agreement on the pre-tests and 83% agreement on the post-tests. The majority of responses made by students at the beginning of the class were characterized as showing only recognition of hydrology concepts from a non-physical perspective; post surveys indicated that the majority had moved to a basic understanding of physical processes, with some students achieving expert understanding. Our study has limitations, including the small number of participants who were all from one institution and the fact that the rubric was still under development. Nevertheless, the high inter-rater agreement from a group of experts indicates that the process we undertook is potentially useful for assessment of learning and understanding physical hydrology.

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Making Undergraduate Geoscience Quantitative

Cited by 54 publications

References 7 publications

The Case for Infusing Quantitative Literacy into Introductory Geoscience Courses

The Case for Infusing Quantitative Literacy into Introductory Geoscience Courses

The Quantitative Reasoning for College Science (QuaRCS) Assessment 2: Demographic, Academic and Attitudinal Variables as Predictors of Quantitative Ability

Assessing student understanding of physical hydrology

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