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Introductory Biology

  • This collage contains original images taken by the course instructor. The images show a microscopic view of stomata on the underside of a Brassica rapa leaf (A), B. rapa plants in their growth trays (B), a flowering B. rapa plant (C), and different concentrations of foliar protein (D). Photos edited via Microsoft Windows Photo Editor and Phototastic Collage Maker.

    A flexible, multi-week approach to plant biology - How will plants respond to higher levels of CO2?

    Learning Objectives
    Students will be able to:
    • Apply findings from each week's lesson to make predictions and informed hypotheses about the next week's lesson.
    • Keep a detailed laboratory notebook.
    • Write and peer-edit the sections of a scientific paper, and collaboratively write an entire lab report in the form of a scientific research paper.
    • Search for, find, and read scientific research papers.
    • Work together as a team to conduct experiments.
    • Connect findings and ideas from each week's lesson to get a broader understanding of how plants will respond to higher levels of CO2 (e.g., stomatal density, photosynthetic/respiratory rates, foliar protein concentrations, growth, and resource allocation).
    Note: Additional, more specific objectives are included with each of the four lessons (Supporting Files S1-S4)
  • Adult female Daphnia dentifera. Daphnia spp. make a great study system due to their transparent body and their ease of upkeep in a lab.

    Dynamic Daphnia: An inquiry-based research experience in ecology that teaches the scientific process to first-year...

    Learning Objectives
    Students will be able to:
    • Construct written predictions about 1 factor experiments.
    • Interpret simple (2 variables) figures.
    • Construct simple (2 variables) figures from data.
    • Design simple 1 factor experiments with appropriate controls.
    • Demonstrate proper use of standard laboratory items, including a two-stop pipette, stereomicroscope, and laboratory notebook.
    • Calculate means and standard deviations.
    • Given some scaffolding (instructions), select the correct statistical test for a data set, be able to run a t-test, ANOVA, chi-squared test, and linear regression in Microsoft Excel, and be able to correctly interpret their results.
    • Construct and present a scientific poster.
  • photo credit John Friedlein. Author (SRB) helps a student troubleshooting RStudio in the workshop session of class.
  • Figure 2. ICB-Students come to class prepared to discuss the text
  • “Phenology of a Dawn Redwood” – Images collected by students for this lesson pieced together illustrating a Metasequoia glyptostroboides changing color and dropping its leaves in the fall of 2017 on Michigan State University campus.

    Quantifying and Visualizing Campus Tree Phenology

    Learning Objectives
    The Learning Objectives of this lesson span across the entire semester.
    • Observe and collect information on phenological changes in local trees.
    • Become familiar with a database and how to work with large datasets.
    • Analyze and visualize data from the database to test their hypotheses and questions.
    • Develop a research proposal including empirically-driven questions and hypotheses.
    • Synthesize the results of their analysis in the context of plant biodiversity and local environmental conditions.
  • Image from http://www.epa.gov/airdata/ad_maps.html

    Air Quality Data Mining: Mining the US EPA AirData website for student-led evaluation of air quality issues

    Learning Objectives
    Students will be able to:
    • Describe various parameters of air quality that can negatively impact human health, list priority air pollutants, and interpret the EPA Air Quality Index as it relates to human health.
    • Identify an air quality problem that varies on spatial and/or temporal scales that can be addressed using publicly available U.S. EPA air data.
    • Collect appropriate U.S. EPA Airdata information needed to answer that/those questions, using the U.S. EPA Airdata website data mining tools.
    • Analyze the data as needed to address or answer their question(s).
    • Interpret data and draw conclusions regarding air quality levels and/or impacts on human and public health.
    • Communicate results in the form of a scientific paper.