Assessing a Novel Asynchronous EEG-Learning Curriculum via Quantitative and Qualitative Methods
Christopher Traner1
1Neurology, Epilepsy, Yale School of Medicine
Objective:

To assess curriculum and identify key characteristics that make online asynchronous learning tools in EEG education both useful and challenging for learners in Neurology.  

Background:

The COVID-19 pandemic accelerated the need to adopt online education resources. As Neurology residency programs become taxed as to the demands of meeting learners’ needs across the spectrum of training, time allocated to EEG reading has often decreased and students arrive to Neurology residency with little or no EEG knowledge. Therefore, given the need for these tools, a modular online curriculum aimed at addressing a variety of learning styles was created by experts. The goal of this open source curriculum was to allow residents the ability to learn EEG concepts and reading in a self-paced manner than can supplement existing EEG curriculum and rotations across the world.

Design/Methods:

A modular based curriculum was written and designed by a variety of epilepsy experts at US Academic Medical Centers. This curriculum was hosted on an open-source website www.eegplatform.com. With IRB approval, visitors to the website are anonymously tracked via mouse-clicks and timing throughout the use of the curriculum to assess what aspects of each module are being used and which are not. Post-module surveys are optionally completed by learners to provide instant feedback. This approach allows for quantitative analysis of the curriculum in a real-time fashion. Additionally, with IRB approval the twelve PGY-2 residents at Yale Neurology were able to complete this curriculum during their neurophysiology rotations and then participate in an one-on-one interview which was recorded, transcribed and coded for themes that were noted across the spectrum of learners.

Results:

Data from >50 learners have been collected for the quantitative study and are being analyzed at this time. Additionally, interviews with residents are being completed and coded. Data will be fully analyzed with conclusions by December 2022.

Conclusions:
NA
10.1212/WNL.0000000000201784