Skip to main content

2017 Networking Topics

Now Available – a product of the 4th Life Discovery – Doing Science education conference
Gibson, J. P. and T. Mourad. 2018. The growing importance of data literacy in life science education. American Journal of Botany 105(12): 1953–1956.  https://doi.org/10.1002/ajb2.1195

Participants will select one table topic.  In small groups of up to 10 persons, each table will generate ideas and recommendations related to the topic.

 

  1. Essential data skills.   
    What are the essential data collection and analysis skills students should develop through the course of a biology education?  What is the appropriate scaffolding of teaching data skills to STEM students? Are there specific skills that should be established by certain points/years in school?

Can we develop a plan for scaffolding data skills? What would be reasonable skills and concepts  for students to understand by different points along the way?

 

  1. Prevent the “canned” lab.
    How do we prevent the use of data sets from becoming a modern version of the “canned” lab? (keep datasets in active learning process)

As data sets become more available, how can we promote development and use of activities that don’t become ineffective over time? How do we keep these data focused activities based on active learning and inquiry?

 

  1. Balance between  data, theory and software
    How do we strike a balance between students understanding data, appreciating the theory  and learning the software to analyze them? (how to understand what the model is doing vs plug and chug Math)

What are effective strategies to promote deeper thinking about quantitative models and data analysis so that we are teaching more than just the skill of calculations and promoting understanding of biological principles

 

  1. Assess competencies in data literacy
    What are some issues and effective ways to assess competencies in data literacy? What are common problems and misconceptions students encounter in data literacy? What are effective strategies for identifying and correcting these problems?

How can we assess student learning of data skills, identify and describes misconceptions, and then effectively correct any problems?

 

  1. NGSS: Math & Computational thinking.
    The Next Generation Science Standards includes mathematics and computational thinking in its Science and Engineering Practices guidelines. How are K12 teachers expected to interpret wording like the following: “Mathematical and computational thinking in 9-12 builds on K-8 experiences and progresses to using algebraic thinking and analysis, a range of linear and nonlinear functions including trigonometric functions, exponential and logarithms, and computational tools for statistical analysis to analyze, represent, and model data. Simple computational simulations are created and used based on mathematical models of basic assumptions.”

What resources will teachers need to accomplish these objectives?  How do these objectives that have been articulated for Grades 9-12 educators relate to needs and expectations for higher education courses? Further, how can higher education teachers then take students to a higher level of data skills to achieve outcomes articulated in the Vision & Change report?

 

  1. Help students recognize pseudoscience                                 
    How can we use data to help students recognize pseudoscience (i.e. bad science) and differentiate it from valid science? How can students learn to evaluate data and determine if they support the claims they are being used to make?  data that don’t hold up against the claims they are being used to make? (particularly for non-majors – able to look at sources but may not be able to critically assess data).

How can we help students understand what data and results mean when presented with  research? How can we promote stronger data skills to help students be more critical consumers of data they are presented with in daily life?