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International Journal of Science Education Volume 36, 2014 - Issue 4
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Original Articles

Quantitative Reasoning in Environmental Science: A learning progression

Robert Lee MayesDepartment of Teaching and Learning, Georgia Southern University, PO Box 8134, Statesboro30460, GA, USACorrespondence[email protected]
,
Jennifer Harris ForresterElementary and Early Childhood Education, University of Wyoming, Laramie, WY, USA
,
Jennifer Schuttlefield ChristusDepartment of Chemistry, University of Wisconsin Oshkosh, Oshkosh, WI, USA
,
Franziska Isabel PetersonSecondary Education, University of Wyoming, Laramie, WY, USA
,
Rachel BonillaDepartment of Mathematics, Georgia Southern University, Statesboro, GA, USA
&
Nissa YestnessNREL, Colorado State University, Fort Collins, CO, USA
Pages 635-658 | Published online: 29 Jul 2013

Citations (18)

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Read on this site (3)

Joseph Dauer, Robert Mayes, Kent Rittschof & Bryon Gallant. (2021) Assessing quantitative modelling practices, metamodelling, and capability confidence of biology undergraduate students. International Journal of Science Education 43:10, pages 1685-1707.
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Miwa A. Takeuchi, Pratim Sengupta, Marie-Claire Shanahan, Jennifer D. Adams & Maryam Hachem. (2020) Transdisciplinarity in STEM education: a critical review. Studies in Science Education 56:2, pages 213-253.
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Julia D. Plummer, Christopher Palma, Alice Flarend, KeriAnn Rubin, Yann Shiou Ong, Brandon Botzer, Scott McDonald & Tanya Furman. (2015) Development of a Learning Progression for the Formation of the Solar System. International Journal of Science Education 37:9, pages 1381-1401.
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Articles from other publishers (15)

Gabriel Jaime Posada-Hernández, Mauricio López-Bonilla, Diego Alejandro Uribe-Suarez & Luis Fernando Cardona-Palacio. (2023) Analysis of the added value for the quantitative reasoning competency at the Luis Amigó Catholic University in 2021. Revista de Investigación, Desarrollo e Innovación 13:2, pages 329-344.
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Beth A. Covitt, Kristin L. Gunckel, Alan Berkowitz, William W. Woessner & John Moore. (2023) Employing a Groundwater Contamination Learning Experience to Build Proficiency in Computational Modeling for Socioscientific Literacy. Journal of Science Education and Technology.
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Alicia C. Alonzo. 2023. International Encyclopedia of Education(Fourth Edition). International Encyclopedia of Education(Fourth Edition) 544 559 .
Beth A. Covitt & Charles W. Anderson. (2022) Untangling Trustworthiness and Uncertainty in Science. Science & Education 31:5, pages 1155-1180.
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Yarden Gliksman, Shir Berebbi & Avishai Henik. (2022) Math Fluency during Primary School. Brain Sciences 12:3, pages 371.
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Yarden Gliksman, Shir Berebbi, Ronen Hershman & Avishai Henik. (2022) BGU‐MF : Ben‐Gurion University Math Fluency test . Applied Cognitive Psychology 36:2, pages 293-305.
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Johnson Enero Upahi & Umesh Ramnarain. (2021) Evidence of Foundational Knowledge and Conjectural Pathways in Science Learning Progressions. Science & Education 31:1, pages 55-92.
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Stephanie M. Gardner, Elizabeth Suazo-Flores, Susan Maruca, Joel K. Abraham, Anupriya Karippadath & Eli Meir. (2021) Biology Undergraduate Students’ Graphing Practice in Digital Versus Pen and Paper Graphing Environments. Journal of Science Education and Technology 30:3, pages 431-446.
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Elizabeth H. SchultheisMelissa K. Kjelvik. (2020) Using Messy, Authentic Data to Promote Data Literacy & Reveal the Nature of Science. The American Biology Teacher 82:7, pages 439-446.
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Hui Jin, Jamie N. Mikeska, Hayat Hokayem & Elia Mavronikolas. (2019) Toward coherence in curriculum, instruction, and assessment: A review of learning progression literature. Science Education 103:5, pages 1206-1234.
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Melissa K. Kjelvik & Elizabeth H. Schultheis. (2019) Getting Messy with Authentic Data: Exploring the Potential of Using Data from Scientific Research to Support Student Data Literacy. CBE—Life Sciences Education 18:2, pages es2.
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Robert Mayes. 2019. Interdisciplinary Mathematics Education. Interdisciplinary Mathematics Education 113 133 .
SeoungHey Paik, Geuron Song, Sungki Kim & Minsu Ha. (2017) Developing a Four-level Learning Progression and Assessment for the Concept of Buoyancy. EURASIA Journal of Mathematics, Science and Technology Education 13:8.
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Tasos Hovardas. (2016) A learning progression should address regression: Insights from developing non-linear reasoning in ecology. Journal of Research in Science Teaching 53:10, pages 1447-1470.
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Andrey Deryabin, Alexandr Popov & Pavel Gluhov. (2021) Дата-грамостность и наука о данных: Образовательные подходы и решения (Data-gravity and Data Science: Educational Approaches and Solutions). SSRN Electronic Journal.
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