Neuromatch Academy: a 3-week, online summer school in computational neuroscience

Neuromatch Academy (https://academy.neuromatch.io; (van Viegen et al., 2021)) was designed as an online summer school to cover the basics of computational neuroscience in three weeks. The materials cover dominant and emerging computational neuroscience tools, how they complement one another, and specifically focus on how they can help us to better understand how the brain functions. An original component of the materials is its focus on modeling choices, i.e. how do we choose the right approach, how do we build models, and how can we evaluate models to determine if they provide real (meaningful) insight. This meta-modeling component of the instructional materials asks what questions can be answered by different techniques, and how to apply them meaningfully to get insight about brain function

Data_Sheet_1_The impact of computer–assisted technology on literacy acquisition during COVID-19-related school closures: Group–level effects and predictors of individual–level outcomes.zip

IntroductionThe COVID-19 pandemic led to school closure and loss of in-person instruction during the 2019–2020 academic year across the United States, which had a profound impact on the reading development of beginning readers. In this study we tested if a research-informed educational technology (EdTech) program–GraphoLearn–could help alleviate the COVID-19 slide. We also sought to understand the profiles of children who benefitted most from this EdTech program.MethodsWe tested participants’ (N = 172 K-2 children) early literacy skills using a standardized measure (STAR) before and after playing GraphoLearn, and used the pre to post difference as the dependent variable. We first compared children’s STAR actual and expected growth. Then we conducted a multiple regression analysis with data about engagement with GraphoLearn included as predictors. Additional predictors were extracted from GraphoLearn performance at study onset to assess children’s letter-sound knowledge, rime awareness, and word recognition.ResultsThe difference between actual average reading growth and expected growth in a regular school year was not statistically significant. This suggests that children in our sample seem to be gaining reading skills as expected in a regular school year. Our multiple linear regression model (which accounted for R2 = 48% of reading growth) showed that older children, with higher baseline GraphoLearn word recognition, who played more units in a fixed number of days, made significantly more early literacy progress.DiscussionWhile lacking a control group, our preliminary results suggest that an EdTech program such as GraphoLearn may be a useful reading instructional tool during school shutdowns. In addition, our results suggest that practice with GraphoLearn was more effective and efficient when foundational instruction was already in place.</p