EDDIE hosts a number of webinars that broadly focus around the topic of teaching with large datasets. Topics include discussions about identifying barriers and solutions, the suite of pedagogical tools, developing teaching modules, and implementing the teaching modules in the classroom.
Registration is required for each webinar and registration forms can be found by visiting the web page for the webinar of interest. If you plan on attending an EDDIE webinar for the first time, please review the webinar technology page
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Upcoming EDDIE Webinars
Lessons learned from integrating EDDIE modules into a semester-long undergraduate Environmental Data Science course
Apr 29 2020 Presenter: R. Quinn Thomas, Virginia Tech
Wednesday, April 29th, 2020 - 10 PST | 12 CST | 1 EST - An overview of a semester-long undergraduate class in environmental data science that harmonized Project EDDIE and Macrosystems EDDIE modules using the R programming language.
Browse the EDDIE Webinar Archives
Learn to integrate NEON data into your undergraduate classroom via Macrosystems EDDIE and other resources
Wednesday, March 4th, 2020 - 8 PST | 10 CST | 11 EST - Participants will learn how to integrate the breadth of NEON data into their classrooms through Macrosystems EDDIE teaching modules, other NEON teaching modules, and through independent use of NEON data.
Using EDDIE modules for the first time - What you need to know
Wednesday, February 21, 2019 - 12 PST | 2 CST | 3 EST - Looking to implement an EDDIE module? This webinar discusses what you need to know when implementing an EDDIE module for the first time. Jen Klug will discuss the strategies, tips, and EDDIE module adaptations generated from the Project EDDIE Faculty Mentoring Network.
Webinar: Using Time Series Data in the Classroom
March 6th, 2019 11 PST | 1 CST | 2 EST - This webinar will discuss these challenges and present potential solutions. First, we will demonstrate Seismic Canvas, a downloadable waveform display and printing application that offers significant exploration and analysis functionality. Second, we will discuss ways that cloud computing can used to create predefined environments to eliminate the challenge of installations and environment setup on a local machine.
Webinar: How to Adapt EDDIE Modules to Meet Students' Needs
February 14th, 2019 10 PST | 12 CST | 1 EST - This webinar will focus on how to adapt existing EDDIE modules to meet your specific course objectives. Jen will share her experience and that of her colleagues in reducing the level of difficulty for entry level non-science major classes and adding extra activities for upper-level courses. In addition, she will discuss her experiences taking materials written by others and adapting them to her specific course environment. The webinar will focus on Jen's experience with the Lake Mixing, Lake Ice Phenology, Lake Modeling, and Nutrient Loading modules but the content will be applicable to all modules.
Webinar: Developing students' quantitative reasoning skills using large authentic datasets and Project EDDIE modules
February 7, 2019 8 PST | 10 CST | 11 EST Rebekka will describe how students make connections as they learn new material and why working with large authentic datasets can play an important role in addressing misconceptions. Catherine will describe how EDDIE modules are designed to help students improve quantitative reasoning using large datasets. They will both provide further insight into student and instructor experiences using these large-dataset modules.
Webinar: Developing and Implementing EDDIE Teaching Modules Using High-frequency Datasets to Address Scientific Concepts and Quantitative Reasoning Skills
January 15th, 2019 11 PST | 1 CST | 2 EST - Join us for the first EDDIE webinar of 2019. Nick will talk about his experiences with developing and using his EDDIE Stream Discharge Module that utilizes student collected data. He will also discuss his experience and tips when implementing modules (e.g., Soil Respiration) designed by other authors. Finally, Nick will compare modules designed using student collected data or pre-existing data.