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Introductory Biology

  • Image from a clicker-based case study on muscular dystrophy and the effect of mutations on the processes in the central dogma.

    A clicker-based case study that untangles student thinking about the processes in the central dogma

    Learning Objectives
    Students will be able to:
    • explain the differences between silent (no change in the resulting amino acid sequence), missense (a change in the amino acid sequence), and nonsense (a change resulting in a premature stop codon) mutations.
    • differentiate between how information is encoded during DNA replication, transcription, and translation.
    • evaluate how different types of mutations (silent, missense, and nonsense) and the location of those mutations (intron, exon, and promoter) differentially affect the processes in the central dogma.
    • predict the molecular (DNA size, mRNA length, mRNA abundance, and protein length) and/or phenotypic consequences of mutations.
  • A crossbill feeds on a pinecone

    Coevolution or not? Crossbills, squirrels and pinecones

    Learning Objectives
    1. Define coevolution.
    2. Identify types of evidence that would help determine whether two species are currently in a coevolutionary relationship.
    3. Interpret graphs.
    4. Evaluate evidence about whether two species are coevolving and use evidence to make a scientific argument.
    5. Describe what evidence of a coevolutionary relationship might look like.
    6. Distinguish between coadaptation and coevolution.
  • Simplified Representation of the Global Carbon Cycle, https://earthobservatory.nasa.gov/Features/CarbonCycle/images/carbon_cycle.jpg

    Promoting Climate Change Literacy for Non-majors: Implementation of an atmospheric carbon dioxide modeling activity as...

    Learning Objectives
    • Students will be able to manipulate and produce data and graphs.
    • Students will be able to design a simple mathematical model of atmospheric CO2 that can be used to make predictions.
    • Students will be able to conduct simulations, analyze, interpret, and draw conclusions about atmospheric CO2 levels from their own computer generated simulated data.