Day 1 Breakout Room Discussions

This is the group’s time to freely brainstorm ideas. You are welcome to work towards the objectives in whatever way you want. If you prefer guidance or get stuck, prompts are provided to continue the discussion.

Day 1: Breakout Discussion #1A – Design EREN Project With NEON data

Objective: Develop research questions that could be answered with an EREN approach using NEON data.

Prompts:

  1. Keeping in mind the EREN project approach, how might you use NEON data or link NEON data to your research? 
  2. What is a novel research question that connects your group’s ideas from prompt 1? If you have a project already in mind (perhaps an idea that you may have shared in the workshop application), this is a good place to share. 
  3. Think about how your project can be designed to be inclusive of institutions, faculty and students from a range of backgrounds. 

Additional prompts (optional): 

  • What variables would you study and which ones would come from NEON?
  • What resources would you need in order to conduct the research with undergrads?

Day 1: Breakout Discussion #1B – Refining Ideas For Projects 

Objective: Refine a research question and begin development of research project design. Prepare a Reportback.

Prompts:

  1. As a group, work to refine your overarching question(s) and hypotheses and determine the NEON data you will use (if applicable).
  2. Begin consideration of your methods and the resources that would be needed to conduct your research project. You may want to consider some/all of the following questions:
    • What simple measurements would gain explanatory power if repeated over large spatial scales?
    • What different questions can you ask when you collect data across multiple sites versus a single site?
    • Could undergraduate students collect some of these data?
    • Can faculty be trained to supervise this data collection?
    • Is it possible to standardize the data collection methods so that they will be consistent across sites?
    • How much does it cost to collect these data at one site?
    • Is participation accessible to many different students, faculty and institutions?
    • Does the research question connect to environmental concerns of local communities and/or environmental justice?
  3. One goal coming out of this workshop is to identify project leaders (potential Lead Scientists) for emerging research projects. Working with interested participants, these project leaders would coordinate continued project development over the next 11 months to prepare for the in-person June 2022 meeting. Project leaders will be invited to meet with current EREN Lead Scientists after the workshop to learn more about the role, which includes a stipend for support. 
    • Is your group able to identify any potential project leader(s) for the research project you’ve described?
  4. As a group, agree on what you will share for the Reportback, and fill in your group’s slide in the shared Google Slides presentation to briefly describe your top 1 – 2 research project idea(s) and why you are most excited about them.

Additional prompts (optional): 

  • How to move forward over the upcoming academic year to position the group for a productive in-person workshop in June 2022
  • Tomorrow, your group will be working on designing a teaching module that is aligned with your top research project idea from today. To prepare for this, think about concepts and skills that students struggle with in your courses that are related to your research project.

Day 2 Breakout Room Discussions

This is the group’s time to freely brainstorm ideas. You are welcome to work towards the objectives in whatever way you want. If you prefer guidance or get stuck, prompts are provided to continue the discussion.

Day 2: Breakout Discussion #2 – Initiating Design of Teaching Modules Using EREN/NEON Data

Objective: Initiate design of a teaching module using EREN/NEON data, guided by the Project EDDIE approach, and linked to research project ideas.

Prompts:

  1. Identify skills/concepts that challenge students: With your group’s top research project idea from yesterday in mind, make a short list of the skills and/or concepts that students could learn from participating in the research project. Then highlight the top 1 or 2 of those skills and concepts that students find most challenging to understand or engage with in your courses.
  2. Identify dataset attributes related to the challenging skills/concepts: With 1 or 2 of the most challenging skills/concepts from the first prompt in mind, how might the dataset from the new research idea be used to address this/these learning challenge(s)? For example, would students learn best by collecting measurements to create the dataset, analyzing the dataset, or both? What rows/columns in the dataset would be targeted?
  3. Start designing the teaching activity: Now, having identified the student learning challenge(s) and the attributes of datasets that will be used to address these challenges, begin brainstorming about the design of your activity. You may want to consider some of the questions below as you think through the design:
    • What is the student audience for your teaching module (e.g., student level, majors/nonmajors)?
    • What are the specific learning goals of the activity? Avoid word-smithing your goals at this stage, but it is likely to be helpful to compose 1 – 2 learning goals specific to your concepts/skills and dataset before proceeding to design specifics.
    • What would students do with the dataset to reach the learning goals?
    • Do you require a lab course to accomplish your learning goals, or could they be accomplished in a lecture setting?
  4. Moving forward with EREN, what is needed (e.g., data management and analysis skills, statistical knowledge, campus resources) to empower you and your group to implement both your research project and teaching module?
  5. As a group, agree on what you will share for the Reportback, and fill in your group’s slide in the shared Google Slides presentation to briefly describe your teaching activity, including the challenging skills/concepts it can be used to teach, and the attributes of the dataset(s) it will use.

Additional prompts (optional): 

  • Discuss how to design the teaching module to be inclusive
  • Link your student learning objectives to the 4DEE framework 
  • Discuss whether your teaching module is adaptable to having multiple entry points for students