Windward students have demonstrated continued enthusiasm about participation. Many reported they enjoyed learning about the neural basis of reading.
Nicole Landi, PhD, leads the Haskins research team of the PLOW study and oversees the EEG lab research. She reported the following findings from the first year.
1. There is significant individual variability in reading and reading-related skills (e.g., phonological awareness) in terms of single time point measures.
2. There are medium-size correlations between EEG profiles and reading/reading-related skills.
3. There appears to be significant variability in EEG profiles, which we hope will be predictive of children’s outcomes longitudinally.
The Haskins team of scientists are presently scoring and entering data from the spring/summer and will begin multi-time point analyses this fall. Over the course of the past year, Dr. Landi has presented on the Windward/Haskins Collaborative Project at several regional and national conferences and co-published a paper “What’s the Promise of In-School Neuroscience?” in International Dyslexia Association’s The Examiner, Volume 8, Issue 3. The study has also received widespread media attention from Education Week, The Journal News, the SeeHearSpeak podcast, and more. Several grant proposals to further fund the project, including an NIH proposal that was well-received, will be revised and resubmitted this fall.
Timeline of the Predicting Literacy Outcomes at The Windward School Study
· Windward installed EEG labs at both the Manhattan campus and Westchester campuses
· Haskins researchers established IRB protocol and began designing the study
· 22 Windward students, or “Junior scientists”, volunteered to participate in the study
· EEG and behavioral data collection began, led by Haskins scientists
· Reconfigured study and assessments to online data collection model
· Began collecting data virtually on behavioral measures of reading (and related skills) from Windward student participants
· Data collection has been highly successful, with minimal data loss and limited attrition