The first step to solving a reading delay is knowing it exists.

Decades of research have found that all children learn to read by developing the same core skills. This applies to children with dyslexia, English language learners and those reading well beyond grade level. Per the science of reading, some kids just need more targeted and explicit instruction — more time building whichever specific reading skill (and there are several) isn’t growing as fast.

But to do that, teachers need to figure out which skill is lacking. It takes a particular kind of training and the right tools — and until recently, many Connecticut schools lacked both.

Read more HERE

Many children with dyslexia describe school, revision and work as a constant uphill struggle, alongside a significant mental health impact: 59% of dyslexic children report being bullied or teased, and 70% say it makes them feel bad about themselves. These challenges often continue into adulthood, with 97% of dyslexic adults reporting mental health issues at work and 33% taking time off because of it.

We believe this can change. We are calling for more inclusive, dyslexia-friendly classrooms and workplaces, and we continue to train educators and employers to provide better support. As part of this, we are inviting people to take part in a Virtual Everest Challenge to raise awareness and support.

See it HERE

This article focuses on a case study of intervention-resistant dyslexia in a 13-year-old boy with severe reading difficulties despite seven years of intensive, evidence-based phonics instruction. The neuropsychological evaluation revealed a complex profile of dyslexia accompanied by unexpected deficits in processing speed, verbal working memory, retention, and executive functions. These co-occurring cognitive weaknesses are suggested to contribute to the lack of response to intervention. The study highlights the need for increased awareness and further research on neurocognitive factors affecting progress in children with severe dyslexia who do not benefit from standard interventions.

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Persistent homology, a method from topological data analysis, extracts robust, multi-scale features from data. It produces stable representations of time series by applying varying thresholds to their values (a process known as a filtration). We develop novel filtrations for time series and introduce topological methods for the analysis of eye-tracking data, by interpreting fixation sequences as time series, and constructing “hybrid models” that combine topological features with traditional statistical features. We empirically evaluate our method by applying it to the task of dyslexia detection from eye-tracking-while-reading data using the Copenhagen Corpus, which contains scanpaths from dyslexic and non-dyslexic L1 and L2 readers.1 Our hybrid models outperform existing approaches that rely solely on traditional features, showing that persistent homology captures complementary information encoded in fixation sequences. The strength of these topological features is further underscored by their achieving performance comparable to established baseline methods. Importantly, our proposed filtrations outperform existing ones.

Read more HERE