Abstract: This meta-analysis examines the inconsistent findings across experimental studies that compared children’s learning outcomes with digital and paper books. We quantitatively reviewed 39 studies reported in 30 articles (n = 1,812 children) and compared children’s story comprehension and vocabulary learning in relation to medium (reading on paper versus on-screen), design enhancements in digital books, the presence of a dictionary, and adult support for children aged between 1 and 8 years. The comparison of digital versus paper books that only differed by digitization showed lower comprehension scores for digital books. Adults’ mediation during print books’ reading was more effective than the enhancements in digital books read by children independently. However, with story-congruent enhancements, digital books outperformed paper books. An embedded dictionary had no or negative effect on children’s story comprehension but positively affected children’s vocabulary learning. Findings are discussed in relation to the cognitive load theory and practical design implications.

Abstract: Artificial intelligence (AI) has given the electrocardiogram (ECG) and clinicians reading them super-human diagnostic abilities. Trained without hard-coded rules by finding often subclinical patterns in huge datasets, AI transforms the ECG, a ubiquitous, non-invasive cardiac test that is integrated into practice workflows, into a screening tool and predictor of cardiac and non-cardiac diseases, often in asymptomatic individuals. This review describes the mathematical background behind supervised AI algorithms, and discusses selected AI ECG cardiac screening algorithms including those for the detection of left ventricular dysfunction, episodic atrial fibrillation from a tracing recorded during normal sinus rhythm, and other structural and valvular diseases. The ability to learn from big data sets, without the need to understand the biological mechanism, has created opportunities for detecting non-cardiac diseases as COVID-19 and introduced challenges with regards to data privacy. Like all medical tests, the AI ECG must be carefully vetted and validated in real-world clinical environments. Finally, with mobile form factors that allow acquisition of medical-grade ECGs from smartphones and wearables, the use of AI may enable massive scalability to democratize healthcare.

Abstract: After reading Pulitzer-prize winning author Isabel Wilkerson’s 2010 masterpiece The Warmth of Other Suns, I anxiously anticipated the publication of her next book Caste: The Origins of Our Disconte...

Abstract: The first wave of the COVID-19 pandemic disrupted regular classes in spring 2020. Temporary school closures supposedly led to a considerable learning loss, particularly for low-achieving students. Schools in Baden-Württemberg, Germany, were closed for two months. Although distance learning was implemented, students spent less time learning. Additionally, teachers were faced with organizational and technological challenges of remote learning environments. The present study investigates the competencies of fifth-graders, using large-scale assessment results in reading and mathematics from annual mandatory tests in September (each n > 80,000). In line with studies from other countries, competence scores were slightly lower in 2020 compared with the three previous years (–0.07 standard deviations for reading comprehension, –0.09 for operations, and –0.03 for numbers). Low-achieving readers managed to attain pre-pandemic competence levels. On the other hand, low-achieving students seem to have a learning backlog regarding mathematics competencies (such as operations) that deserves attention in future education.

Abstract: A defining feature of language lies in its capacity to represent meaning across oral and written forms. Morphemes, the smallest units of meaning in a language, are the fundamental building blocks that encode meaning, and morphological skills enable their effective use in oral and written language. Increasing evidence indicates that morphological skills are linked to literacy outcomes, including word reading, spelling, and reading comprehension. Despite this evidence, the precise ways in which morphology influences the development of children’s literacy skills remains largely underspecified in theoretical models of reading and spelling development. In this paper, we draw on the extensive empirical evidence base in English to explicitly detail how morphology might be integrated into models of reading and spelling development. In doing so, we build on the perspective that morphology is multidimensional in its support of literacy development. The culmination of our efforts is the Morphological Pathways Framework—an adapted framework that illuminates precise mechanisms by which morphology impacts word reading, spelling, and reading comprehension. Through this framework, we bring greater clarity and specificity on how the use of morphemes in oral and written language supports the development of children’s literacy skills. We also highlight gaps in the literature, revealing important areas to focus future research to improve theoretical understanding. Furthermore, this paper provides valuable theoretical insight that will guide future empirical inquiries in identifying more precise morphological targets for intervention, which may have widespread implications for informing literacy practices in the classroom and educational policies more broadly.

Abstract: More than 50 000 papers have been published about COVID-19 since the beginning of 2020 and several hundred new papers continue to be published every day. This incredible rate of scientific productivity leads to information overload, making it difficult for researchers, clinicians and public health officials to keep up with the latest findings. Automated text mining techniques for searching, reading and summarizing papers are helpful for addressing information overload. In this review, we describe the many resources that have been introduced to support text mining applications over the COVID-19 literature; specifically, we discuss the corpora, modeling resources, systems and shared tasks that have been introduced for COVID-19. We compile a list of 39 systems that provide functionality such as search, discovery, visualization and summarization over the COVID-19 literature. For each system, we provide a qualitative description and assessment of the system's performance, unique data or user interface features and modeling decisions. Many systems focus on search and discovery, though several systems provide novel features, such as the ability to summarize findings over multiple documents or linking between scientific articles and clinical trials. We also describe the public corpora, models and shared tasks that have been introduced to help reduce repeated effort among community members; some of these resources (especially shared tasks) can provide a basis for comparing the performance of different systems. Finally, we summarize promising results and open challenges for text mining the COVID-19 literature.

Abstract: Students’ learning experiences and perceptions are markedly influenced by the use of digital technology during the COVID-19 pandemic. Exploring students’ perception of blended online learning, amid the adaptations of the higher education sector in the wake of uncertainty, has become more critical than ever. This paper reflects on the experience of learning and teaching the Research Methods and Techniques subject in the postgraduate programme of MA Urban Design at Cardiff University during COVID-19 in the UK. To do so, we designed and carried out an online survey to explore students’ perception of online teaching and learning activities, feedback and assessment, and digital platforms based on their experience during the subject delivery period in the 2020–2021 academic year. One of the significant findings of this paper was that students agreed with the impact of eye contact on their virtual learning experience but as long as this was aligned with their rights to see others, including their peers and instructors, rather than reciprocal rights to be seen. In addition, students felt that facilitating synchronous communication through effective interaction among diverse peers has been quite challenging in small-group online reading seminars. The majority of respondents also reported that attending live online lectures was more helpful than watching pre-recorded lectures. Online formative feedback and synchronous interim reviews also allowed students to reflect on their progress and develop their projects further before their summative assessment. The outcomes of this paper can effectively assist educators who consider delivering programmes, adopting a blended online learning environment design model, in the post COVID-19 era. The findings of this study can also provide guidance for further developments and improvements in using digital technology and blended online learning in urban design education and pedagogy.

Abstract: What was a book in early modern England? By combining book history, bibliography and literary criticism, Material Texts in Early Modern England explores how sixteenth- and seventeenth-century books were stranger, richer things than scholars have imagined. Adam Smyth examines important aspects of bibliographical culture which have been under-examined by critics: the cutting up of books as a form of careful reading; book destruction and its relation to canon formation; the prevalence of printed errors and the literary richness of mistakes; and the recycling of older texts in the bodies of new books, as printed waste. How did authors, including Herbert, Jonson, Milton, Nashe and Cavendish, respond to this sense of the book as patched, transient, flawed, and palimpsestic? Material Texts in Early Modern England recovers these traits and practices, and so crucially revises our sense of what a book was, and what a book might be.

Abstract: We investigated whether a passive brain–computer interface that was trained to distinguish low and high mental workload in the electroencephalogram (EEG) can be used to identify (1) texts of different readability difficulties and (2) texts read at different presentation speeds. For twelve subjects we calibrated a subject-dependent, but task-independent predictive model classifying mental workload. We then recorded EEG data from each subject, while twelve texts in blocks of three were presented to them word by word. Half of the texts were easy, and the other half were difficult texts according to classic reading formulas. From each text category three texts were read at a self-adjusted comfortable presentation speed and the other three at an increased speed. For each subject we applied the predictive model to EEG data of each word of the twelve texts. We found that the resulting predictive values for mental workload were higher for difficult texts than for easy texts. Predictive values from texts presented at an increased speed were also higher than for those presented at a normal self-adjusted speed. The results suggest that the task-independent predictive model can be used on single-subject level to build a highly predictive user model of the reader over time. Such a model could be employed in a system which continuously monitors brain activity related to mental workload and adapts to specific reader’s abilities and characteristics by adjusting the difficulty of text materials and the way it is presented to the reader in real time. A neuroadaptive system like this could foster efficient reading and text-based learning by keeping readers’ mental workload levels at an individually optimal level.

Abstract: Background: Assistive technology has been used to mitigate reading disabilities for almost three decades, and tablets with text-to-speech and speech-to-text apps have been introduced in recent year ...

Abstract: Lip reading aims to predict the spoken sentences from silent lip videos. Due to the fact that such a vision task usually performs worse than its counterpart speech recognition, one potential scheme is to distill knowledge from a teacher pretrained by audio signals. However, the latent domain gap between the cross-modal data could lead to a learning ambiguity and thus limits the performance of lip reading. In this paper, we propose a novel collaborative framework for lip reading, and two aspects of issues are considered: 1) the teacher should understand bi-modal knowledge to possibly bridge the inherent cross-modal gap; 2) the teacher should adjust teaching contents adaptively with the evolution of the student. To these ends, we introduce a trainable "master" network which ingests both audio signals and silent lip videos instead of a pretrained teacher. The master produces logits from three modalities of features: audio modality, video modality, and their combination. To further provide an interactive strategy to fuse these knowledge organically, we regularize the master with the task-specific feedback from the student, in which the requirement of the student is implicitly embedded. Meanwhile, we involve a couple of "tutor" networks into our system as guidance for emphasizing the fruitful knowledge flexibly. In addition, we incorporate a curriculum learning design to ensure a better convergence. Extensive experiments demonstrate that the proposed network outperforms the state-of-the-art methods on several benchmarks, including in both word-level and sentence-level scenarios.

Abstract: This paper discusses research on reading disorders during the period since their classification within the overarching category of neurodevelopmental disorders (Journal of Child Psychology and Psychiatry, 53, 2012, 593). Following a review of the predictors of learning to read across languages, and the role of language skills as critical foundations for literacy, profiles of reading disorders are discussed and putative causal risk factors at the cognitive, biological, and environmental levels of explanation considered. Reading disorders are highly heritable and highly comorbid with disorders of language, attention, and other learning disorders, notably mathematics disorders. The home literacy environment, reflecting gene‐environment correlation, is one of several factors that promote reading development and highlight an important target for intervention. The multiple deficit view of dyslexia (Cognition, 101, 2006, 385) suggests that risks accumulate to a diagnostic threshold although categorical diagnoses tend to be unstable. Implications for assessment and intervention are discussed.

Abstract: The purpose of this research is to find out the problems of online learning during the COVID-19 pandemic. This research method uses literature study or library research by taking reading sources from secondary data collected through textbooks scientific journals e-books and other sources relevant to the research problem. This type of research was analyzed qualitatively with an interactive model consisting of data collection data reduction drawing conclusions and verification. The results of this study indicate that online learning is one solution so that the learning process continues during the COVID-19 pandemic. But in this learning there are various problems experienced by various parties namely educational institutions educators (teachers and lecturers) students and parents of students. The problems that exist in educational institutions are the lack of availability of telecommunication technology infrastructure multimedia information and platforms that support the process of teaching and learning activities online from educators in the form of limitations in the use of it as well as the difficulty of forming student personality characteristics and applying learning media so that students can understand the material presented from students namely most students are not familiar with online learning and due to limited internet facilities from parents the lack of time available to accompany their children during online learning because not all parents can divide their time between work and mentoring children in house.

Abstract: In this article, we provide a cross-linguistic perspective on the universals and particulars in learning to read across seventeen different orthographies. Starting from the assumption that reading ...

Abstract: This study investigated the effectiveness of the Model of Reading Engagement (MORE), a content literacy intervention, on first graders’ science domain knowledge, reading engagement, and reading comprehension. The MORE intervention emphasizes the role of domain knowledge and reading engagement in supporting reading comprehension. MORE lessons included a 10-day thematic unit that provided a framework for students to connect new learning to a meaningful schema (i.e., Arctic animal survival) and to pursue mastery goals for acquiring domain knowledge. A total of 38 first-grade classrooms (N = 674 students) within 10 elementary schools were randomly assigned to (a) MORE at school (MS), (b) MORE at home, (MS-H), in which the MS condition included at-home reading, or (c) typical instruction. Since there were minimal differences in procedures between the MS and MS-H conditions, the main analyses combined the two treatment groups. Findings from hierarchical linear models revealed that the MORE intervention had a positive and significant effect on science domain knowledge, as measured by vocabulary knowledge depth (effect size [ES] = .30), listening comprehension (ES = .40), and argumentative writing (ES = .24). The MORE intervention effects on reading engagement as measured by situational interest, reading motivation, and task orientations were not statistically significant. However, the intervention had a significant, positive effect on a distal measure of reading comprehension (ES = .11), and there was no evidence of Treatment × Aptitude interaction effects. Content literacy can facilitate first graders’ acquisition of science domain knowledge and reading comprehension without contributing to Matthew effects. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Abstract: Objective To investigate the general population’s view on the use of artificial intelligence (AI) for the diagnostic interpretation of screening mammograms. Methods Dutch women aged 16 to 75 years were surveyed using the Longitudinal Internet Studies for the Social sciences panel, representative for the Dutch population. Attitude toward AI in mammography screening was measured by means of five items: necessity of a human check; AI as a selector for second reading; AI as a second reader; developer is responsible for error; and radiologist is responsible for error. Results Of the 922 participants included, 77.8% agreed with the necessity of a human check, whereas the item AI as a selector for a second reading was more heterogeneously answered, with 41.7% disagreement, 31.5% agreement, and 26.9% responding with “neither agree nor disagree.” The item AI as a second reader was mostly responded with “neither agree nor disagree” (37.1%) and “agree” (37.6%), whereas the two last items on developer’s and radiologist’ responsibilities were mostly answered with “neither agree nor disagree” (44.6% and 39.2%, respectively). Discussion Despite recent breakthroughs in the diagnostic performance of AI algorithms for the interpretation of screening mammograms, the general population currently does not support a fully independent use of such systems without involving a radiologist. The combination of a radiologist as a first reader and an AI system as a second reader in a breast cancer screening program finds most support at present. Accountability in case of AI-related diagnostic errors in screening mammography is still an unresolved conundrum.

Abstract: The increasing importance of digital reading and the prevalence of social media among adolescents necessitate further investigations into the effects of social media on the development of students' digital reading proficiency, which could foster proper behavior and attitudes regarding social media and help narrow achievement gaps in reading education. Thus, this study aimed to investigate the effects of information and communication technology (ICT)-based social media factors, categorized into use and attitudinal factors, on adolescents' digital reading performance and to capture the trajectory of the impacts on generations of adolescents over nine years. Data from 767,511 15-year-old students from 57 countries/regions in total were extracted from the four cycles of the Programme for International Student Assessment (PISA) database, i.e., PISA 2009, PISA 2012, PISA 2015 and PISA 2018. Hierarchical linear models were constructed to investigate significant student-, school- and country-level factors. The findings indicated that a) the effects of ICT-based social media use outside of school varied across different purposes and types of use and the influential patterns remained relatively unchanged; b) students’ use of ICT-based social media at school was negatively correlated with digital reading performance in general from PISA 2009 to PISA 2018; and c) students with positive attitudes towards ICT-based social media performed better on the digital reading assessment than those with negative attitudes.

Abstract: Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks but little is known about its ability to model human language processing. We compare Transformer- and RNN-based language models’ ability to account for measures of human reading effort. Our analysis shows Transformers to outperform RNNs in explaining self-paced reading times and neural activity during reading English sentences, challenging the widely held idea that human sentence processing involves recurrent and immediate processing and provides evidence for cue-based retrieval.

Abstract: This study aimed to investigate the effect of using authentic materials on English as a foreign language (EFL) learners’ reading comprehension, reading motivation, and reading anxiety. In this stud...