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

Introduction: The 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. Methods: We 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. Results: The 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. Discussion: While 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.

How you read affects what you gain: Individual differences in the functional organization of the reading system predict intervention gains in children with reading disabilities.

There is now considerable evidence regarding the types of interventions that are effective at remediating reading disabilities on average. It is generally unclear, however, what predicts the magnitude of individual-level change following a given intervention. We examine new predictors of intervention gains that are theoretically grounded in computational models of reading and focus on individual differences in the functional organization of the reading system. Specifically, we estimate the extent to which children with reading disabilities (n=118 3rd-4th graders) rely on two sources of information during an oral word reading task - print-speech correspondences and semantic imageability - before and after a phonologically-weighted intervention. We show that children who relied more on print-speech regularities and less on imageability pre-intervention had better intervention gains. In parallel, children who over the course of the intervention exhibited greater increases in their reliance on print-speech correspondences and greater decreases in their reliance on imageability had better intervention outcomes. Importantly, these two factors were differentially related to specific reading task outcomes, with greater reliance on print-speech correspondences associated with pseudoword naming, while (lesser) reliance on imageability related to word reading and comprehension. We discuss the implications of these findings for theoretical models of reading acquisition and educational practice.

Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach.

BACKGROUND: Accurately predicting which patients with chronic heart failure (CHF) are particularly vulnerable for adverse outcomes is of crucial importance to support clinical decision making. The goal of the current study was to examine the predictive value on long term heart failure (HF) hospitalisation and all-cause mortality in CHF patients, by exploring and exploiting machine learning (ML) and traditional statistical techniques on a Dutch health insurance claims database. METHODS: Our study population consisted of 25,776 patients with a CHF diagnosis code between 2012 and 2014 and one year and three years follow-up HF hospitalisation (1446 and 3220 patients respectively) and all-cause mortality (2434 and 7882 patients respectively) were measured from 2015 to 2018. The area under the receiver operating characteristic (ROC) curve (AUC) was calculated after modelling the data using Logistic Regression, Random Forest, Elastic Net regression and Neural Networks. RESULTS: AUC rates ranged from 0.710 to 0.732 for 1-year HF hospitalisation, 0.705-0.733 for 3-years HF hospitalisation, 0.765-0.787 for 1-year mortality and 0.764-0.791 for 3-years mortality. Elastic Net performed best for all endpoints. Differences between techniques were small and only statistically significant between Elastic Net and Logistic Regression compared with Random Forest for 3-years HF hospitalisation. CONCLUSION: In this study based on a health insurance claims database we found clear predictive value for predicting long-term HF hospitalisation and mortality of CHF patients by using ML techniques compared to traditional statistics.

Theory-driven classification of reading difficulties from fMRI data using Bayesian latent-mixture models.

Decades of research have led to several competing theories regarding the neural contributors to impaired reading. But how can we know which theory (or theories) identifies the types of markers that indeed differentiate between individuals with reading disabilities (RD) and their typically developing (TD) peers? To answer this question, we propose a new analytical tool for theory evaluation and comparison, grounded in the Bayesian latent-mixture modeling framework. We start by constructing a series of latent-mixture classification models, each reflecting one existing theoretical claim regarding the neurofunctional markers of RD (highlighting network-level differences in either mean activation, inter-subject heterogeneity, inter-region variability, or connectivity). Then, we run each model on fMRI data alone (i.e., while models are blind to participants' behavioral status), which enables us to interpret the fit between a model's classification of participants and their behavioral (known) RD/TD status as an estimate of its explanatory power. Results from n=127 adolescents and young adults (RD: n=59; TD: n=68) show that models based on network-level differences in mean activation and heterogeneity failed to differentiate between TD and RD individuals. In contrast, classifications based on variability and connectivity were significantly associated with participants' behavioral status. These findings suggest that differences in inter-region variability and connectivity may be better network-level markers of RD than mean activation or heterogeneity (at least in some populations and tasks). More broadly, the results demonstrate the promise of latent-mixture modeling as a theory-driven tool for evaluating different theoretical claims regarding neural contributors to language disorders and other cognitive traits.

Individual differences in learning the regularities between orthography, phonology and semantics predict early reading skills.

Statistical views of literacy development maintain that proficient reading requires the assimilation of myriad statistical regularities present in the writing system. Indeed, previous studies have tied statistical learning (SL) abilities to reading skills, establishing the existence of a link between the two. However, some issues are currently left unanswered, including questions regarding the underlying bases for these associations as well as the types of statistical regularities actually assimilated by developing readers. Here we present an alternative approach to study the role of SL in literacy development, focusing on individual differences among beginning readers. Instead of using an artificial task to estimate SL abilities, our approach identifies individual differences in children's reliance on statistical regularities as reflected by actual reading behavior. We specifically focus on individuals' reliance on regularities in the mapping between print and speech versus associations between print and meaning in a word naming task. We present data from 399 children, showing that those whose oral naming performance is impacted more by print-speech regularities and less by associations between print and meaning have better reading skills. These findings suggest that a key route by which SL mechanisms impact developing reading abilities is via their role in the assimilation of sub-lexical regularities between printed and spoken language -and more generally, in detecting regularities that are more reliable than others. We discuss the implications of our findings to both SL and reading theories.

Adult dyslexic readers benefit less from visual input during audiovisual speech processing: fMRI evidence.

The aim of the present fMRI study was to investigate whether typical and dyslexic adult readers differed in the neural correlates of audiovisual speech processing. We tested for Blood Oxygen-Level Dependent (BOLD) activity differences between these two groups in a 1-back task, as they processed written (word, illegal consonant strings) and spoken (auditory, visual and audiovisual) stimuli. When processing written stimuli, dyslexic readers showed reduced activity in the supramarginal gyrus, a region suggested to play an important role in phonological processing, but only when they processed strings of consonants, not when they read words. During the speech perception tasks, dyslexic readers were only slower than typical readers in their behavioral responses in the visual speech condition. Additionally, dyslexic readers presented reduced neural activation in the auditory, the visual, and the audiovisual speech conditions. The groups also differed in terms of superadditivity, with dyslexic readers showing decreased neural activation in the regions of interest. An additional analysis focusing on vision-related processing during the audiovisual condition showed diminished activation for the dyslexic readers in a fusiform gyrus cluster. Our results thus suggest that there are differences in audiovisual speech processing between dyslexic and normal readers. These differences might be explained by difficulties in processing the unisensory components of audiovisual speech, more specifically, dyslexic readers may benefit less from visual information during audiovisual speech processing than typical readers. Given that visual speech processing supports the development of phonological skills fundamental in reading, differences in processing of visual speech could contribute to differences in reading ability between typical and dyslexic readers.

Increased Response to Altered Auditory Feedback in Dyslexia: A Weaker Sensorimotor Magnet Implied in the Phonological Deficit.

Purpose: The purpose of this study was to examine whether developmental dyslexia (DD) is characterized by deficiencies in speech sensory and motor feedforward and feedback mechanisms, which are involved in the modulation of phonological representations. Method: A total of 42 adult native speakers of Dutch (22 adults with DD; 20 participants who were typically reading controls) were asked to produce /bep/ while the first formant (F1) of the /e/ was not altered (baseline), increased (ramp), held at maximal perturbation (hold), and not altered again (after-effect). The F1 of the produced utterance was measured for each trial and used for statistical analyses. The measured F1s produced during each phase were entered in a linear mixed-effects model. Results: Participants with DD adapted more strongly during the ramp phase and returned to baseline to a lesser extent when feedback was back to normal (after-effect phase) when compared with the typically reading group. In this study, a faster deviation from baseline during the ramp phase, a stronger adaptation response during the hold phase, and a slower return to baseline during the after-effect phase were associated with poorer reading and phonological abilities. Conclusion: The data of the current study are consistent with the notion that the phonological deficit in DD is associated with a weaker sensorimotor magnet for phonological representations.