Comprehension Accuracy Research Articles

Filtering Photon Cloud Data in Forested Areas Based on Elliptical Distance Parameters and Machine Learning Approach

The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) was successfully launched. Due to its small spot size, multibeam configuration, high sampling rate, and strong immunity to terrain slopes, it has been regarded as a powerful tool for forest resources surveying and managing. However, the ICESat-2 photon cloud data contain considerable background photons, which discretely distribute in the background space of signal photons. Therefore, it is necessary to filter these noise photons. In this study, photons are divided into three categories: signal photons, noise photons far away from signal photons, and noise photons adjacent to signal photons. Based on the existing research, forward and backward elliptical distances were used to express the spatial relationship between two photons, and backward local density (BLD) was used to describe the density distribution of the photons. However, the single statistical parameter cannot clearly distinguish three types of photon cloud. Therefore, forward local density (FLD) and neighboring forward local density difference (NFLDD) also were defined to describe the density distribution of the photons. Finally, by combining the support vector machine (SVM), the above three density parameters were used to classify the photons by signal and noise photons. The proposed method was validated with photon cloud data acquired by the Simulated Advanced Terrain Laser Altimeter System (MATLAS), the Multiple Altimeter Beam Experimental Lidar (MABEL), and the ICESat-2 systems over different forested areas. The results demonstrated that the proposed method can well remove the noise photons and retain the signal photons without depending on any statistical assumptions or thresholds. The comprehensive accuracy of the three test sites was 0.99, 0.98, and 0.99, respectively, which was higher than those of the existing method. In addition, the total errors corresponding to the three test sites were about 0.4%, 0.5%, and 1.0% respectively, which were lower than those of the existing method.

Stereolithography 3D Printer for Micromodel Fabrications with Comprehensive Accuracy Evaluation by Using Microtomography

Micromodels are important for studying various pore-scale phenomena in hydrogeology. However, the fabrication of a custom micromodel involves complicated steps with cost-prohibitive equipment. The direct fabrication of micromodels with a 3D printer can accelerate the fabrication steps and reduce the cost. A stereolithography (SLA) 3D printer is one of the best options because it has sufficient printing performance for micromodel fabrication and is relatively inexpensive. However, it is not without drawbacks. In this report, we explored the capability of an SLA 3D printer for micromodel fabrication. Various parameters affecting the printing results, such as the effects of geometries, dimensions, printing axis configurations, printing thickness resolutions, and pattern thicknesses were investigated using microtomography for the first time. Eventually, the most optimal printing configuration was then also discussed. In the end, a complete micromodel was printed, assembled, and used for fluid displacement experiments. As a demonstration, viscous and capillary fingerings were successfully performed using this micromodel design.

A Traditional Chinese Medicine Syndrome Classification Model Based on Cross-Feature Generation by Convolution Neural Network: Model Development and Validation.

BackgroundNowadays, intelligent medicine is gaining widespread attention, and great progress has been made in Western medicine with the help of artificial intelligence to assist in decision making. Compared with Western medicine, traditional Chinese medicine (TCM) involves selecting the specific treatment method, prescription, and medication based on the dialectical results of each patient’s symptoms. For this reason, the development of a TCM-assisted decision-making system has lagged. Treatment based on syndrome differentiation is the core of TCM treatment; TCM doctors can dialectically classify diseases according to patients’ symptoms and optimize treatment in time. Therefore, the essence of a TCM-assisted decision-making system is a TCM intelligent, dialectical algorithm. Symptoms stored in electronic medical records are mostly associated with patients’ diseases; however, symptoms of TCM are mostly subjectively identified. In general electronic medical records, there are many missing values. TCM medical records, in which symptoms tend to cause high-dimensional sparse data, reduce algorithm accuracy.ObjectiveThis study aims to construct an algorithm model compatible for the multidimensional, highly sparse, and multiclassification task of TCM syndrome differentiation, so that it can be effectively applied to the intelligent dialectic of different diseases.MethodsThe relevant terms in electronic medical records were standardized with respect to symptoms and evidence-based criteria of TCM. We structuralized case data based on the classification of different symptoms and physical signs according to the 4 diagnostic examinations in TCM diagnosis. A novel cross-feature generation by convolution neural network model performed evidence-based recommendations based on the input embedded, structured medical record data.ResultsThe data set included 5273 real dysmenorrhea cases from the Sichuan TCM big data management platform and the Chinese literature database, which were embedded into 60 fields after being structured and standardized. The training set and test set were randomly constructed in a ratio of 3:1. For the classification of different syndrome types, compared with 6 traditional, intelligent dialectical models and 3 click-through-rate models, the new model showed a good generalization ability and good classification effect. The comprehensive accuracy rate reached 96.21%.ConclusionsThe main contribution of this study is the construction of a new intelligent dialectical model combining the characteristics of TCM by treating intelligent dialectics as a high-dimensional sparse vector classification task. Owing to the standardization of the input symptoms, all the common symptoms of TCM are covered, and the model can differentiate the symptoms with a variety of missing values. Therefore, with the continuous improvement of disease data sets, this model has the potential to be applied to the dialectical classification of different diseases in TCM.

Source identification of mine water inrush based on the exponential whitenization function and the grey situation decision model

Coal mining at deep levels can cause mine water inrush and groundwater contamination, making it important to accurately and rapidly identify the water inrush source. In this study, 52 water samples were extracted from three types of aquifers in the Linhuan mining area, China. The water sample components Na+ + K+, Ca2+, Mg2+, HCO3−, Cl−, and SO42−, measured in the experiment, were used as evaluation variables, and the piecewise function equation was established by using the exponential whitening function. Finally, combined with water sample data and the CRITIC weighted grey situation decision-making method, the comprehensive membership degree was obtained, and the water inrush source was identified according to the principle of the maximum membership degree. The comprehensive accuracy of the model was 92.3%. The traditional grey situation decision-making method uses the linear whitening function to determine the membership value, ignoring that the value of the whitening function outside the adjacent level is 0, which improves the weight of the adjacent level, causes the loss of effective information, and reduces the discrimination rate. The exponential whitenization function in this paper will solve this problem and further improves the grey situation decision-making method to discriminate the water inrush source, which would also be beneficial regarding the prevention and control of mine water inrush and groundwater contamination.

Text reading in English as a second language: Evidence from the Multilingual Eye-Movements Corpus

AbstractResearch into second language (L2) reading is an exponentially growing field. Yet, it still has a relatively short supply of comparable, ecologically valid data from readers representing a variety of first languages (L1). This article addresses this need by presenting a new data resource called MECO L2 (Multilingual Eye Movements Corpus), a rich behavioral eye-tracking record of text reading in English as an L2 among 543 university student speakers of 12 different L1s. MECO L2 includes a test battery of component skills of reading and allows for a comparison of the participants’ reading performance in their L1 and L2. This data resource enables innovative large-scale cross-sample analyses of predictors of L2 reading fluency and comprehension. We first introduce the design and structure of the MECO L2 resource, along with reliability estimates and basic descriptive analyses. Then, we illustrate the utility of MECO L2 by quantifying contributions of four sources to variability in L2 reading proficiency proposed in prior literature: reading fluency and comprehension in L1, proficiency in L2 component skills of reading, extralinguistic factors, and the L1 of the readers. Major findings included (a) a fundamental contrast between the determinants of L2 reading fluency versus comprehension accuracy, and (b) high within-participant consistency in the real-time strategy of reading in L1 and L2. We conclude by reviewing the implications of these findings to theories of L2 acquisition and outline further directions in which the new data resource may support L2 reading research.

A novel combined model based on VMD and IMODA for wind speed forecasting

Wind energy, a highly popular renewable clean energy, has been increasingly valued by the international community and been leaping forward. However, the original wind speed signal characterized by intermittent fluctuations impose heavy burdens on wind speed forecasting of wind farms. This study proposed a wind speed forecasting method by complying with a model integrating the Variational Mode Decomposition (VMD) and the Improved Multi-Objective Dragonfly Optimization Algorithm (IMODA). First, the VMD was adopted to decompose the original wind speed signal, as an attempt to obtain multiple sub-sequences (IMFs) exhibiting stable frequency domain. Second, to simplify the calculation, the sample entropy (SE) was adopted for the sequence recombination, and the respective recombined sub-sequence of the wind speed was forecasted by using four advanced neural networks. Lastly, the IMODA algorithm was adopted to fuse the forecasting results of the neural network, and the results of the optimal wind speed were forecasted. To verify the effectiveness and adaptability of the algorithm, the wind farm data in four different regions were forecasted. As indicated from the results, this algorithm could outperform other algorithms in the comprehensive forecasting accuracy and the model calculation time, and it could be effectively applied for the wind speed forecasting in wind farms.

Reading comprehension in aphasia: the relationship between linguistic performance, personal perspective, and preferences

ABSTRACT Background Reading is an important everyday activity. Extensive individual variability exists in both typical readers and readers with aphasia in their reading ability, preferences, and practice. Although reading comprehension difficulties in people with aphasia are typically assessed at single word, sentence, and text level, there is limited information about how performance at these levels corresponds to their enjoyment and practice of reading. Aims This study aimed to improve our understanding of reading in people with aphasia by examining: 1) the relationship between performance on assessments of word, sentence, and paragraph comprehension; 2) the relationship between reported reading difficulty, feelings about reading, and reading activity; and 3) the relationship between reading comprehension scores and personal perception of reading. Methods & Procedures Participants included 74 adults with a single symptomatic stroke resulting in aphasia; they did not have to report or present with reading difficulties. Participants completed the Comprehensive Assessment of Reading in Aphasia (CARA). Statistical analysis used Spearman correlations to investigate the relationship between measures. Illustrative case studies provided evidence of individual variability. Outcomes & Results Significant positive correlations were found between comprehension accuracy in single word and sentence, single word and paragraph, and sentence and paragraph assessments. There were also significant positive correlations among reported reading difficulty, feelings about reading, and reading activity. There were no significant correlations between reading comprehension scores and personal perception of reading. Conclusions There is no straightforward relationship between reading comprehension difficulties and individual perception of reading. This emphasises the importance of considering both when discussing and agreeing goals, selecting a treatment approach, and evaluating the outcome of intervention.

A Preliminary Study of the Eye-Gaze Patterns and Reading Comprehension Skill of Students on the Autism Spectrum

ObjectivesTo identify, using eye-tracking technology, if readers on the autism spectrum (AS) would exhibit longer mean fixation times during question-answering but similar levels of comprehension accuracy to typically developing (TD) individuals.MethodsThe eye-gaze behavior of two school-aged autistic children and two TD children with similar reading abilities, age, gender, and grade level, was tracked while they read age-appropriate passages and answered related comprehension questions.ResultsBoth participants on the AS displayed longer mean fixations than TD participants during question-answering. Despite the longer fixations during question-answering, the accuracy of the participants on the AS was equal to or superior to those of the TD participants.ConclusionsLonger mean fixation durations during question-answering did not result in lower reading comprehension scores.

A study of the effects of mobile media on L2 text processing: Beyond offline comprehension accuracy measures

Recent research on the processing effects of mobile media has incorporated offline research methods to demonstrate that reading on mobile media is as effective as reading on computers or paper in accuracy of text comprehension. The present study uses an eye-tracking methodology to compare the effects of reading on mobile media (mobile phone and tablet) and traditional media (paper and computer) on cognitive processing behaviours as well as on accuracy. Chinese L2 learners of English (n = 156) read an English novel, The Elephant Man, in one of the four reading conditions (mobile phone: 41, tablet: 39, computer: 38, and paper: 38) while their eye movements were recorded. Between-condition comparisons showed that mobile-assisted reading performance was equally accurate, fast and efficient to process texts, and fast to respond to posttests. Statistically significantly shorter mean fixation duration of mobile-assisted than computer-assisted reading suggests slightly more efficiency in cognitive processing. The overall findings of the present study add to other strong evidence that mobile-assisted reading is effective in text processing. We discuss the implications of our results and provide directions for future research and insights into mobile-assisted language learning, including online learning, e.g., during the COVID-19 global pandemic.

Research and Implementation of English Grammar Check and Error Correction Based on Deep Learning

English as a universal language in the world will get more and more attention, but English is not our mother tongue, and there exist differences in culture and thinking. English grammar is the most difficult problem to solve. There are many English learners, and the number of English teachers is limited, and it is inevitable to use Internet technology to solve the problem of lack of resources. The article uses deep learning technology to propose an ASS grammar detection model, which can quickly and efficiently detect grammatical errors. The research results show the following. (1) This study selects data from the GEC evaluation task and analyzes the four modules of article, noun, verb, and preposition through algorithms under different models. The results indicate the accuracy of the four modules. The recall rate has been improved to a certain extent, the accuracy rate of nouns is the highest, which can reach 63.99%, the accuracy rate of prepositions is improved to a lesser extent, and the inspection accuracy rate after improvement is 12.79%. (2) In the experiment to verify the effectiveness of the ASS grammar detection model, compared with the detection effect of the ordinary model, the accuracy of the ASS comprehensive inspection has been greatly improved. The comprehensive accuracy of the ordinary detection model is 28.01%, and the ASS model’s comprehensive accuracy rate of the inspection was 82.82%, and the accuracy rate was increased by 54.81%. The result shows that the performance of the ASS inspection model has been improved by leaps and bounds compared with the traditional model. (3) After transforming and upgrading the ASS model, the three models and other models obtained were run on the test set and the mixed test set, respectively. The results show that the accuracy, precision, recall, and F1 score of ASS model are the highest in the test set, which are 98.71%, 98.83%, 98.64%, and 98.73%, respectively, the Bayesian network check model has the lowest accuracy rate of 51.74%, and the ROC curve value and AUC value of the ASS model are both the largest. The accuracy of the ASS model on the mixed test set is also the highest, reaching 98.01%. The JaSt model on the mixed test set has a significant downward trend, with the accuracy rate dropping from 92.16% to 56.68%. It can be concluded that the ASS model can accurately and efficiently monitor grammatical errors.

The Interaction of Silent Reading Rate, Academic Vocabulary, and Comprehension Among Students in Grades 2–12

AbstractThis study examined silent reading rates (SRRs) in relation to students’ estimated academic vocabulary grade levels (EVGLs) and comprehension accuracy (Comprehension Items Correct; compIC). Analyses were based on data from 288,934 students in grades 2‐12 who completed an adaptive silent reading assessment that yielded measures of the three variables of interest. Silent reading rate was measured while students read five 150‐ to 300‐word passages. Each student’s initial passage difficulty was aligned with their EVGL. Each passage was followed by five comprehension questions, such that in total, students could answer up to 25 comprehension items correctly. Two‐level Multilevel Models (MLMs) were fitted to evaluate SRR in relation to EVGL, compIC, and their interactions. The final MLM included the random intercept and three random slopes for the two level‐1 predictors (school‐mean‐centered EVGL as the focal predictor and school‐mean‐centered compIC as the moderator) and their interactions. Results indicated that: (a) the fixed effect of higher EVGL on SRR was positive and significant, (b) the fixed effect of higher compIC on SRR was negative and significant, and (c) there was a significant interaction indicating that the relationship between school‐mean‐centered EVGL and SRR grew stronger as school‐mean‐centered compIC increased. These results suggest that vocabulary knowledge and SRR increase in concert among students with good comprehension, whereas SRRs measured in the absence of good comprehension are less meaningful and may indicate inadequate skills or insufficient motivation to fully comprehend what is being read.

The effect of Japanese proficiency and lexical accessing speed on the comprehension of indirect speech act in L2 Japanese

This study examined the comprehension accuracy scores and response time of indirect speech act by 63 native Chinese speakers learning Japanese, and discussed the effect from Japanese proficiency, as well as the speed of lexical access. The results showed that: (1) Japanese proficiency effects the accuracy rate of indirect speech act, the higher the Japanese proficiency, the higher the accuracy rate. However, there is no clear linear relationship between the learners' Japanese proficiency and the indirect speech act's response time. (2) The lexical accessing speed effects the learners' response time of indirect speech act, the faster the accessing speed is, the shorter the indirect speech acts' response time. The lexical accessing speed also affects the comprehension accuracy rate of the high-level learners. The faster the lexical accessing speed of the high-level learners, the higher the comprehension accuracy rate.

SOCNet: A Lightweight and Fine-Grained Object Recognition Network for Satellite On-Orbit Computing

High-quality remote sensing images not only provide opportunities for deep learning-based image interpretation, but also challenge satellite edge devices for storage, processing and downlinking to the ground server. Satellite on-orbit object recognition is an effective measure to solve this challenge. However, there is an insurmountable gap between the extremely limited computing and storage resources of satellites and the demand for those of deep learning. To solve this problem, a fast and lightweight intelligent satellite-on-orbit computing network (SOCNet) is proposed. First, the overall network architecture based on the idea of flat multi-branch feature extraction is proposed to accelerate model inference and reduce the network depth. Second, we propose the idea of exchanging a larger receptive field for network depth and combine the idea of depthwise separable convolution to further reduce the amount of parameters. We design a feature extraction method of coupled fine-coarse-grained for efficient feature extraction. Finally, global average pooling is used for feature fusion to further reduce network parameters amount and computational complexity. We have performed multiple sets of ablation experiments and some state-of-the-art comparison experiments on the server and carried out experimental verification on the NVIDIA Jetson TX2 mobile device to mimic the resource-constrained environment on the satellite. SOCNet with parameters of 0.21 MB, model size of 1.22 MB and computation cost of 50.81 MB floating point operations has achieved 99% comprehensive accuracy, 2.45 ms latency and 408 images per second throughput on 30 types of aircraft datasets with the 224 × 224 remote sensing images.

Gingival biotype - comparative analysis of different evaluation methods

Background/Aim. Gingival biotype can have a significant impact on the outcome of the periodontal therapeutic procedures and the predictability of their aesthetic outcome. There is a strong correlation between the types of biotype and the potential gingival recession after restorative, periodontal, and implant surgical procedures. Therefore, accurate identification of gingival biotypes before initiating these procedures is one of the significant predictive factors for their success. The aim of this study was to evaluate the reliability of accurate gingival biotype determination with the use of the visual method, periodontal, and trans-gingival probing compared to the direct measurement method. Methods. This prospective study involved 33 patients indicated for apical root resection in the intercanine sector of the upper jaw. Gingival biotype identification was performed in all patients using the following techniques: 1) visual method; 2) periodontal probe technique; 3) trans-gingival probing; and 4) direct measurement after flap elevation. Statistical analysis of the obtained data was performed to assess the diagnostic accuracy of the visual method, periodontal probing method, and trans-gingival probing method in relation to the direct measurement method, used as a gold standard, to discriminate the gingival thickness biotype (thin versus thick). Results. The overall accuracy of the tested diagnostic procedures compared to direct gingival biotype measurement was 66.7% for the visual method, 78.8% for periodontal probing, and 97.0% for trans-mucosal probing. Conclusion. The periodontal probing method can be recommended for gingival biotype determination as a routine method since its sensitivity and overall accuracy are higher compared to the visual method. The trans-gingival method, in terms of sensitivity and comprehensive accuracy, almost completely coincides with the direct method, but it is more invasive compared to the periodontal probing method, and it has to be conducted in local anesthesia.

Who "it" is influences what "it" does: Discourse effects on children's syntactic parsing.

Children often interpret first noun phrases (NP1s) as agents, which improves comprehension of actives but hinders passives. While children sometimes withhold the agent-first bias, the reasons remain unclear. The current study tests the hypothesis that children default to the agent-first bias as a "best guess" of role assignment when they face uncertainty about sentence properties. Thus, rather than always relying on early-arriving cues, children can attend to different sentence cues across communicative contexts. To test this account, we manipulated interpretive uncertainty by varying cues to the discourse status of initial arguments (referring to new vs. given entities) and measured interpretation accuracy for active (where the agent-first bias predicted verb morphology) and passive sentences (where the two conflicted). Across three experiments, we found that children relied on the agent-first bias more when new discourse entities were signaled by definite NP1s, reference to unmentioned entities, and novel words. This, in turn, led to higher accuracy for actives relative to passives. In contrast, when given entities were implied through pronoun NP1s, reference to mentioned entities, and known words, children avoided the agent-first bias and instead assigned roles using more reliable but later-arriving verb morphology. This led to similar comprehension accuracy across constructions. These findings suggest that children simultaneously interpret relations between sentences (e.g., discourse continuity) and within sentences (e.g., role assignment), such that commitments to the former can influence parsing cues for the latter.

Reading Comprehension and Processing Time When People With Aphasia Use Text-to-Speech Technology With Personalized Supports and Features.

Person-centered approaches promote consistent use of supportive technology and feelings of empowerment for people with disabilities. Feature personalization is an aspect of person-centered approaches that can affect the benefit people with aphasia (PWA) derive from using text-to-speech (TTS) technology as a reading support. This study's primary purpose was to compare the comprehension and processing time of PWA when performing TTS-supported reading with preferred settings for voice, speech output rate, highlighting type, and highlighting color versus unsupported reading. A secondary aim was to examine initial support and feature preference selections, preference changes following TTS exposure, and anticipated functional reading activities for utilizing TTS technology. Twenty PWA read passages either via written text or text combined with TTS output using personally selected supports and features. Participants answered comprehension questions, reevaluated their preference selections, and provided feedback both about feature selections and possible future TTS technology uses. Comprehension accuracy did not vary significantly between reading conditions; however, processing time was significantly less in the TTS-supported condition, thus suggesting TTS support promoted greater reading speed without compromising comprehension. Most participants preferred the TTS condition and several anticipated benefits when reading lengthy and difficult materials. Alterations to initial settings were relatively rare. Personalizing TTS systems is relevant to person-centered interventions. Reading with desired TTS system supports and features promotes improved reading efficiency by PWA compared with reading without TTS support. Attending to client preferences is important when customizing and implementing TTS technology as a reading support.

The effect of frequency of augmented input on the auditory comprehension of narratives for persons with Wernicke’s aphasia

ABSTRACT Background Augmented input refers to the support of any form of linguistic or visual strategy to enhance understanding of language. Previous research for persons with aphasia predominantly focused on the various types of augmented input that can be used to support comprehension. Aims The purpose of this study was to determine and compare the effect of varying amounts of augmented input on the accuracy of auditory comprehension for persons with Wernicke’s aphasia Method & Procedures A within-subject design was conducted with nine participants with Wernicke’s aphasia. Based on previous studies performed by the authors, the participants reviewed a relevant high-content image, listened to three narratives in three conditions, namely 0%, 50%, and 100%, augmented input with active partner pointing (AI-PP), and then responded to comprehension items based on the narratives. Outcomes & Results Of the nine participants, four gave more accurate responses to comprehension items in the 50% AI-PP condition, three gave more accurate responses in the 100% AI-PP condition, and two participants scored the same in either the 50% and 100% or the 0% and 50% conditions. Furthermore, participants did significantly better in the 50% than 0% augmented input condition when answering inferential rather than factual questions. Conclusion The main clinical implication is that supporting narrative auditory comprehension with augmented input, used as pretask and during-task stimulation, seems to improve the auditory comprehension of narratives for some persons with Wernicke’s aphasia. Moreover, providing augmented input for narratives seems to have a significant effect on the auditory comprehension of inferential questions for some persons with Wernicke’s aphasia. Continuous research is necessary to determine what types and frequency of augmented input will enhance auditory comprehension for persons with aphasia, specifically Wernicke’s aphasia.

Diagnosis and Classification Decision Analysis of Overheating Defects of Substation Equipment Based on Infrared Detection Technology

Substation equipment is not only the main part of the power grid but also the essential part to ensure the development of the national economy and People's Daily life of one of the important infrastructure. How to ensure its normal operation and find the sudden failure has become a hot issue to be solved urgently. For thermal fault diagnosis needs to classify and identify different power equipment first, this paper designed an SVM infrared image classifier, which can effectively identify three types of common power equipment. The classifier extracts HOG features from the infrared images of power equipment processed by the above segmentation and combines them with SVM multiclassification to achieve the purpose of improving the recognition accuracy. The experiment uses the classifier to identify three kinds of equipment, and the results show that the comprehensive recognition accuracy of the classifier is more than 95.3%, which is better than the traditional classification method and meets the demand for classification accuracy. In this paper, the traditional method of relative temperature difference is improved by using the temperature data of the infrared image, which can automatically judge the thermal failure level of electric power equipment. Experiments show that the diagnosis system designed in this paper can classify faults and give treatment suggestions while judging whether there are thermal faults for three types of power equipment, which verifies the feasibility and effectiveness of the substation infrared diagnosis technology designed in this paper.

Mobile-assisted or paper-based? The influence of the reading medium on the reading comprehension of English as a foreign language

The issues of whether mobile-assisted English as a foreign language (EFL) reading is effective have become increasingly important for EFL teachers and policy-makers. However, empirical studies investigating EFL students’ reading comprehension performance on mobile phones and students’ perceptions of these reading formats are lacking. The present research employed questionnaire surveys and semistructured interviews to investigate whether differences exist between mobile-assisted and paper-based EFL reading in terms of participants’ reading comprehension accuracy, reading speed, and reading strategy used (i.e. general, problem-solving or support strategies) and explore EFL learners’ perceptions of mobile-assisted EFL reading. A sample of 81 first-year college students participated in a quasiexperiment, 6 participants participated in a semistructured interview, and a sample of an additional 10 students participated in a pilot study. The analyses, including qualitative descriptions, mixed-design analysis of variance (ANOVA), paired-sample t-tests, and independent-sample t-tests, indicated that participants achieved superior reading comprehension accuracy when reading from printed paper compared to reading from mobile phones. Superior reading speed from paper was observed only when participants were instructed to read and complete the comprehension test the first time, while no difference in speed between the two reading media was observed the second time. Additionally, participants’ use of reading strategies during mobile-assisted EFL reading was not as effective as that during paper reading. Regarding participants’ preferences, most participants (79%) preferred reading from paper over mobile phones because hard copies provided a better reading experience and promoted active reading engagement. However, participants held positive attitudes toward mobile-assisted EFL learning and wanted to continue to read from mobile phones to assist their foreign language learning due to their convenient built-in functions and portability. The implications for the use of mobile devices to assist language pedagogical practice are discussed.

Language activation in dual language schools: the development of subject-verb agreement in the English and Spanish of heritage speaker children

ABSTRACT Cross-linguistically, monolingual children produce target-like inflected verbs much earlier than they can reliably distinguish between singular and plural subject-verb agreement morphology in comprehension (i.e. Johnson, V., J. de Villiers, and H. Seymour. 2005. “Agreement Without Understanding? The Case of Third Person Singular /s/.” First Language 25: 317-333; Pérez-Leroux, A. T. 2005. “Number Problems in Children.” In Proceedings of the 2005 Annual Conference of the Canadian Linguistics Association, edited by C. Gurski, 1-12. https://cla-acl.artsci.utoronto.ca/wp-content/uploads/actes-2005/Perez-Leroux.pdf). In heritage speakers, Spanish agreement morphology shows optionality due to reduced input, especially when children transition to English schooling (i.e. Montrul, S. 2004. The Acquisition of Spanish: Morphosyntactic Development in Monolingual and Bilingual L1 Acquisition and Adult L2 Acquisition. Amsterdam: John Benjamins; Jacobson, P. 2012. “The Effects of Language Impairment on the use of Direct Object Pronouns and Verb Inflections in Heritage Spanish Speakers: A Look at Attrition, Incomplete Acquisition and Maintenance.” Bilingualism: Language and Cognition 15 (1): 22–38). We investigate how simultaneous heritage bilingual children’s interpretation and use of subject-verb agreement in English and Spanish may be modulated by increased input and activation of Spanish in dual language education (DLE). 125 participants aged 3–7 (42 heritage children, 40 English monolinguals, 39 Spanish-dominant children) took part in a fill-in-the-blanks task and a picture-matching task. In English, bilingual comprehension accuracy surpassed that of the monolinguals. In Spanish, bilingual production lagged behind that of the Spanish-dominant children and language output was found to be a greater predictor of productive accuracy than increased activation of Spanish in DLE. The implications of these results for theories of bilingualism and DLE are discussed.