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Interdisciplinary STEM Education for Millennial Students

Workshop Sessions Resources

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1. Interdisciplinary Biology, Mathematics, and Physics Education

NEXUS:   1. Random diffusion versus directed movement of particles in the onion cell    BioQUEST_OnionWorkshop_June21_2014

NEXUS: 2. Dimensions and Scaling: How Big is a Worm?

Joe Redish and Kim Moore (University of Maryland) 

Any attendees wishing to use their own laptops for data analysis should download and install the ImageJ software (with Manual tracking plug-in). Here are some download instructions.
The lab documents/files we will use are these:

http://umdberg.pbworks.com/w/file/fetch/70833121/Lab%205_Onion%20Cells.pdf

http://umdberg.pbworks.com/w/page/61378642/Log-Log_Plots_WhyDoWeLikeThem

http://umdberg.pbworks.com/w/file/fetch/69416036/TechnicalDocument_IntroToVideoCapture.pdf

http://umdberg.pbworks.com/w/file/fetch/69180490/TechnicalDocument_IntroToImageJ.pdf

http://umdberg.pbworks.com/w/file/fetch/69180489/ImageJ%20Quick%20Reference%20Sheet.pdf

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2. BIRDD: Beagle Investigation Returns with Darwinian Data  

Kristin Jenkins (University of Wisconsin-Madison) and Sam Donovan (University of Pittsburgh)

BIRDD Session Resources

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3. BEEPOP: The Population Dynamics of the Honey Bee in the Hive and in the Wild

Joe Watkins (University of Arizona)

BEEPOP Resources  

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4. Designing Active Learning Exercises for Mathematical Biology and Ecology Classes

        Steve Adolph (Harvey Mudd College)

Designing Active Learning Exercises

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5. Problem Based Learning in Biochemistry and Integrated Biology and Chemistry

Hal White and Deborah Allen (University of Delaware)

BAMBED 36(4),262-273 (2008) P(X)nL Pedagogies of Engagement

30. Generating Discussion during Examinations

56. Brain Respiration Fuels Problem-solving Activity

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6. Modeling Phage in a Predator-Prey System

Pak-Wing Fok (University of Delaware)

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7. Big Data on the Chesapeake: Technology, Quantitative Reasoning,  and Collaboration for Interdisciplinary Learners

Stacey Kiser (Lane Community College) and Meegie Wheat, Michelle Fisher and Ethel Stanley (Three Rivers College)

Big Data on the Chesapeake 2014-Final

Group Posters

Blue_crabs_decline-why

BQ_Blue_Crab_Salinity

BlueCrabsandPollution

POSTER_Bioquest2014_WkshpIV_Chesapeake

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8. The Biological ESTEEM Project (Excel Simulations and Tools for Exploratory, Experiential Mathematics): Hidden Markov Model and Evolutionary Bioinformatics

Anton Weisstein (Truman State University)

Building Mathematical Models and Biological Insight (Weisstein 2011 PREPRINT)

Strategic Simulations and Post-Socratic Pedagogy (Jungck & Calley 1985)

Ten Equations that Changed Biology (Jungck 1997)

The Case of the Protective Protein (Weisstein 2010)

Using PharmacoKinetics to Introduce Biomathematical Modeling (Koch-Noble 2011 PREPRINT)

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9. Exploring the Human Transcriptome

Claudia Neuhauser (University of Minnesota) NUMB3R5 COUNT: Numerical Undergraduate Mathematical Biology Education

RNA seq Presentation

RNA_Seq Worksheet docx 508 KB

RNA_Seq Worksheet  pdf 595 KB

RNAseqWang2008BioQUEST2014SMALL  xls 4.27 MB

The Numbers Count Project from the University of Minnesota  was initially funded by HHMI and focuses on biological problem solving.  Faculty workshop and classroom resources include biological data (Excel files, images, and FASTA format text files), introductory mathematical modules for biology and chemistry, concepts in statistics mapped to individual modules, our workshop resources (materials, tools, data, powerpoints, and participant products), course materials for Calculus and Freshman Statistics, and links to sites focusing on datamodeling toolsand visualization.

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10. a. MathBench

Kaci Thompson and Karen Nelson, University of Maryland

MathBench Biology Modules developed at the University of Maryland introduce students to the mathematical underpinnings of what they learn in introductory biology courses. Their goal is to integrate quantitative approaches and mathematics more deeply into the undergraduate curriculum in a way that (1) reinforces biological concepts, (2) increases math literacy, and (3) prepares students for more complicated mathematical approaches in upper-level courses.

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10. b. Bringing Data-intensive Approaches into Ecology and Conservation Courses

Leslie Ries, SeSync, University of Maryland,  Keryn Bromberg Gedan, University of Maryland, and Gina Wimp, Georgetown University

 SeSync The National Socio-Environmental Synthesis Center (SESYNC)—funded through a National Science Foundation grant to the University of Maryland—is dedicated to solving society’s most challenging and complex environmental problems.

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