Comprehension Accuracy Research Articles

CRISPR-Cas9 and its Application in the Fields of Medics

Gene editing technology has become a prominent objective within the fields of medical research, the clustered regularly interspaced short palindromic repeats-associated protein 9 (CRISPR-Cas9) is one particular technology that has already been practiced in a set of clinical trials, which produced a satisfying result that have demonstrated its potential to be applied to a great range of treatments. CRISPR-Cas9 was derived from bacterial defense system, and has been applied in the field of medical research. For instance, the research practice of using CAR-T cells by CAR-T Therapy, and genetically modifying DNA strands to remove cancerous or mutated sequences. This research has great chance to promote treatments associated with genetic modification and provide new hope for patients in the future. Nevertheless, this technology is still immature, due to its potential drawbacks. Most researchers are still focusing on practices of CRISPR-Cas9 on animal models or isolated somatic cells. This article draws attention on different treatments of CRISPR-Cas9 and provides evaluation and suggestions as a reference for future practices of this technology. Overall, there are two discoveries of advancement to the CRISPR-Cas9 mechanism: modification to the guide RNA that improves the accuracy, and applying high-fidelity Cas9 protein variants to enhance its comprehensive accuracy.

Runoff simulation of the Kaidu River Basin based on the GR4J-6 and GR4J-6-LSTM models

Study regionThe Kaidu River Basin originates from the southern slope of the Tienshan Mountains in the Xinjiang Uygur Autonomous Region, China. Study focusAccurate runoff simulation and prediction significantly affect flood control, drought resilience, and water resource allocation decisions. This study establishes the GR4J-6 model (modèle du Génie Rural à 4 paramètres Journalier-6, including a snowmelt module) and integrates it with the LSTM (Long Short-Term Memory) model to construct the hybrid GR4J-6-LSTM model and enhance the simulation accuracy of snowmelt runoff. A case study is conducted in the Kaidu River Basin to demonstrate the applicability of these models in cold and arid regions. The accuracy of the GR4J-6, LSTM, and GR4J-6-LSTM models is evaluated using Nash-Sutcliffe efficiency (NSE), Kling-Gupta Efficiency (KGE), and Root Mean Squared Error (RMSE) metrics. In addition, the contributions of each feature variable in the models are analyzed using the SHapley Additive exPlanations (SHAP) method to enhance the reliability of the results. New hydrological insights for the regionThe GR4J-6 model demonstrated good applicability in the Kaidu River Basin, with NSE, KGE, and RMSE values of 0.69, 0.79, and 39.39 m3/s during the validation period, respectively. The hybrid model GR4J-6-LSTM exhibited the highest comprehensive accuracy among all the models, with NSE, KGE, and RMSE values of 0.84, 0.87, and 28.79 m3/s, respectively. In the LSTM model, temperature and precipitation were found to significantly influence the simulated runoff, indicating that higher temperature and precipitation lead to increased runoff. In the GR4J-6-LSTM model, Tmin (minimum temperature) and the hydrological feature variable Qsim exhibited a strong positive correlation with simulated runoff, as Tmin and Qsim increased, they promoted stronger flow production. This study provides a framework for runoff simulation in snowmelt river basins, offering a reference for projecting extreme hydrological events under climate change.

A Matter of Memory? Age-Invariant Relative Clause Disambiguation and Memory Interference in Older Adults.

Past research suggests that Working Memory plays a role in determining relative clause attachment bias. Disambiguation preferences may further depend on Processing Speed and explicit memory demands in linguistic tasks. Given that Working Memory and Processing Speed decline with age, older adults offer a way of investigating the factors underlying disambiguation preferences. Additionally, older adults might be subject to more severe similarity-based memory interference given their larger vocabularies and slower lexical access. Nevertheless, memory interference and sentence disambiguation have not been combined in studies on older adults before. We used a self-paced reading paradigm under memory load interference conditions. Older (n=30) and Younger (n=35) readers took part in the study online; reading times were recorded and measures of comprehension accuracy and load recall were collected. This setup allowed for the implicit measurement of attachment biases and memory interference effects interactively. Results show that similarity-based interference affected both age groups equally, but was more pronounced in NP2-biased structures, which took participants generally longer to read. Attachment preferences did not differ by group and were unaffected by Working Memory span. However, accuracy on recall prompts was predicted by Working Memory span in both groups. Findings of greater interference in syntactically dispreferred structures support unified processing models where parsing constraints naturally interact. The lack of age differences on our measures further aligns with research finding age-invariant implicit language processing.

Enhancing Large Language Model Comprehension of Material Phase Diagrams through Prompt Engineering and Benchmark Datasets

Large Language Models (LLMs) excel in fields such as natural language understanding, generation, complex reasoning, and biomedicine. With advancements in materials science, traditional manual annotation methods for phase diagrams have become inadequate due to their time-consuming nature and limitations in updating thermodynamic databases. To overcome these challenges, we propose a framework based on instruction tuning, utilizing LLMs for automated end-to-end annotation of phase diagrams. High-quality phase diagram images and expert descriptions are collected from handbooks and then preprocessed to correct errors, remove redundancies, and enhance information. These preprocessed data form a golden dataset, from which a subset are used to train LLMs through hierarchical sampling. The fine-tuned LLM is then tested for automated phase diagram annotation. Results show that the fine-tuned model achieves a cosine similarity of 0.8737, improving phase diagram comprehension accuracy by 7% compared to untuned LLMs. To the best of our knowledge, this is the first paper to propose using LLMs for the automated annotation of phase diagrams, replacing traditional manual annotation methods and significantly enhancing efficiency and accuracy.

Measuring the Effects of Cognitive Capacity on Sentence Comprehension: Evidence From Elementary School-Age Children.

Our aim was to (a) develop a sentence comprehension measure that distinguished between cognitive capacity and syntactic knowledge in school-age children and (b) examine the relationship between comprehension performance and cognitive variables (working memory capacity and retrieval from long-term memory). We developed and administered a picture selection sentence comprehension task to 122 school-age children representing varied cognitive abilities. We evaluated comprehension accuracy and response time in two syntactically identical conditions but with different cognitive demands incorporated in picture foils-one with low demand using superfluous adjectives and another with high demand using contrastive adjectives. Children also completed tasks measuring working memory capacity and long-term memory retrieval. Comprehension accuracy was significantly lower, and response times were longer in the high-cognitive demand condition compared to the low-demand condition. Errors frequently involved incorrect attribute selection in the high-demand condition that included contrastive adjectives in picture foils, while reversal errors prevailed in the low-demand condition, which included superfluous adjectives. Accuracy correlated positively with the memory variables. Hierarchical regression analysis showed that after adjusting for comprehension in the low-cognitive demand condition (38.60% variance), memory variables accounted for 4.50% additional variance in the high-demand condition with only working memory capacity as the unique predictor. The significant role of working memory capacity in comprehending sentences with high cognitive demand indicated the recruitment of active attention and verbal rehearsal. Data support the newly developed measure's potential for assessing cognitive skills integral to sentence comprehension in school-age children. https://doi.org/10.23641/asha.26767063.

Improving AI-assisted video editing: Optimized footage analysis through multi-task learning

In recent years, AI-assisted video editing has shown promising applications. Understanding and analyzing camera language accurately is fundamental in video editing, guiding subsequent editing and production processes. However, many existing methods for camera language analysis overlook computational efficiency and deployment requirements in favor of improving classification accuracy. Consequently, they often fail to meet the demands of scenarios with limited computing power, such as mobile devices. To address this challenge, this paper proposes an efficient multi-task camera language analysis pipeline based on shared representations. This approach employs a multi-task learning architecture with hard parameter sharing, enabling different camera language classification tasks to utilize the same low-level feature extraction network, thereby implicitly learning feature representations of the footage. Subsequently, each classification sub-task independently learns the high-level semantic information corresponding to the camera language type. This method significantly reduces computational complexity and memory usage while facilitating efficient deployment on devices with limited computing power. Furthermore, to enhance performance, we introduce a dynamic task priority strategy and a conditional dataset downsampling strategy. The experimental results demonstrate that achieved a comprehensive accuracy surpassing all previous methods. Moreover, training time was reduced by 66.33%, inference cost decreased by 59.85%, and memory usage decreased by 31.95% on the 2-task dataset MovieShots; on the 4-task dataset AVE, training time was reduced by 95.34%, inference cost decreased by 97.23%, and memory usage decreased by 61.21%.

Constructive retrieval: Benefits for learning, motivation, and metacognitive monitoring

AimsWe tested whether combining constructive learning prompts with retrieval practice (constructive retrieval) positively affects delayed learning outcomes (factual knowledge, comprehension), metacognitive accuracy, and motivation. Sample & methodsUndergraduate students (N = 152) learned from a video-recorded lecture, and then engaged in follow-up learning activities involving retrieval practice, restudy, elaboration, or a combination of retrieval practice with elaboration. We ensured that initial retrieval success was high and that all groups had the same expectations about the nature of content to be tested. We assessed both factual knowledge and comprehension, as well as the respective indicators of metacognitive-monitoring accuracy (metamemory, metacomprehension). We assessed self-efficacy and situational interest as motivational variables. ResultsIn a one-week-delayed posttest, elaboration improved factual knowledge and comprehension. This pattern held whether learners just elaborated or additionally engaged in retrieval practice. Having students self-generate their own examples during retrieval practice improved comprehension, but merely letting students retrieve experimenter-provided examples did not. Students' self-reported mental effort mediated the factual knowledge benefits of retrieval practice. The number of students’ self-generated elaborations mediated comprehension benefits. Students engaging in constructive retrieval while learning revealed the highest metacognitive-monitoring accuracy in factual knowledge and comprehension. We found no differences between conditions regarding situational interest and self-efficacy. Motivation to reuse the respective learning strategies was higher in all conditions compared to restudy. ConclusionConstructive retrieval seems advantageous when considering its benefits on learning outcomes and metacognition. When working on retrieval tasks, students only profited from self-generated, but not from provided, examples with respect to comprehension.

How does background knowledge affect second language reading? An eye movement study

AbstractThere is broad consensus that a reader's background knowledge on a reading topic affects both their reading processes and comprehension in their first language and also in a second language. However, it is unclear whether a reader's background knowledge specifically affects reading comprehension accuracy and reading rate. The extent to which background knowledge facilitates second language reading when compared to a reader's L2 linguistic knowledge is also unclear. Moreover, the mental process accounting for the interaction between general background knowledge, type of linguistic knowledge such as vocabulary or writing system, and L2 reading abilities also need to be identified. Using texts in Mandarin Chinese, this paper investigates these problems with an eye‐movement study administered to 40 L2 Chinese learners with Indo‐European L1s. Results illustrate that an L2 reader's background knowledge about the text can positively impact both their reading comprehension and reading rate; however, the influence on the latter could be topic‐dependent. In more challenging topics, the contribution of background knowledge to reading comprehension could outweigh any single type of linguistic knowledge, even if the target language uses a more cognitively demanding writing system. The connectionist account proposed by the construction–integration theory is suggested to be currently the best theoretical explanation for the mental process behind developing second‐language reading abilities. Pedagogical implications are also considered based on these findings.

Online evidence for pseudo-relative effects on Italian RC attachment resolution

ABSTRACT This study investigates the resolution of relative clause attachment ambiguities, focussing on the availability of Pseudo-Relative Small Clauses (PRs) in Italian. The PR-first hypothesis posits that, when controlling for other factors, PRs are preferred over relative clauses due to their structural simplicity, leading to an apparent preference for “high attachment” in PR-licensing languages. Through a self-paced reading experiment, this study examines how PR availability affects ambiguity resolution in Italian, and how it interacts with locality principles during online sentence processing. We find a significant slowdown in the LA condition with PR-licensing verbs, also tied to lower comprehension accuracy, and unclear evidence for an advantage of LA in non-PR contexts. Overall, our results add support to the PR-first hypothesis for Italian, while opening new questions about the interplay of structural ambiguity, locality, and language-specific properties.

Comprehensive accuracy analysis for large closed-loop deployable structures based on matrix structure analysis and linear-complementarity-based contact analysis of spatial joint clearances

The surface accuracy of the deployable structure is crucial in determining the electromagnetic performance of the large satellite antenna. This paper proposes a comprehensive accuracy analysis framework for deployable structures considering clearances of spatial joints, geometric deviations, elastic deformations, external loads, and preloads, all of which can affect surface accuracy during the assembly process. Surface accuracy is calculated by combining the global equilibrium analysis of the deployable structure with the local equilibrium analysis of clearance-affected joints. First, a global elastostatic equilibrium analysis of the deployable structure is performed based on a unified mathematical formulation that describes the elasticity of beams and joints. Then, the local equilibrium analysis for the clearance-affected joints is transformed into a quadratic optimization problem through a linear-complementarity-based method. This method avoids the necessity for a combinatorial search for several traditional discontinuous contact configurations. Considering both global and local equilibrium in iterative analysis, the surface accuracy of the antenna is calculated. This integration avoids prior artificial assumptions about contact configurations of the joints. Finally, the proposed method is validated by comparing it with simulations and experimental results.

Intelligent Chinese Typesetting Model Based on Information Importance Can Enhance Text Readability

In today’s era of information overload, efficiently extracting valuable information from a large volume of textual data has become a crucial challenge in reading. This study aimed to explore the enhancement of Chinese readability in the digital domain through intelligent typesetting method. This study involved two experiments. The purpose of the first experiment was to achieve the machine learning-based assessment of the importance of individual words in Chinese articles and to automate typesetting based on the importance of words. For the second experiment, readability tests and eye-tracking reading tests were performed and the reading performance and reading attention between intelligent typesetting and general typesetting style was compared. This work proposed three Chinese typesetting methods that distinguished the importance of Chinese text information, based on font size and color. The results showed that first, when reading Chinese text, visual attention was more likely to be drawn to larger font sizes, darker brightness, or warmer-colored characters. Second, intelligent Chinese typography that distinguishes information importance through font size, color brightness, and color hue can enhance Chinese reading comprehension accuracy and subjective evaluation. It was concluded that using the TextRank model to distinguish importance of Chinese vocabulary and intelligent typesetting methods based on visual features of font could obviously improve text readability. Specifically, readability achieved through intelligent typesetting method distinguishing information importance through font color brightness surpasses that of general typesetting significantly. These intelligent typesetting methods can be widely applied in Chinese reading scenarios, such as web pages, e-books, information visualization, and other Chinese reading contexts.

Prosodic and Visual Cues Facilitate Irony Comprehension by Mandarin-Speaking Children With Cochlear Implants.

This study investigated irony comprehension by Mandarin-speaking children with cochlear implants, focusing on how prosodic and visual cues contribute to their comprehension, and whether second-order Theory of Mind is required for using these cues. We tested 52 Mandarin-speaking children with cochlear implants (aged 3-7 years) and 52 age- and gender-matched children with normal hearing. All children completed a Theory of Mind test and a story comprehension test. Ironic stories were presented in three conditions, each providing different cues: (a) context-only, (b) context and prosody, and (c) context, prosody, and visual cues. Comparisons were conducted on the accuracy of story understanding across the three conditions to examine the role of prosodic and visual cues. The results showed that, compared to the context-only condition, the additional prosodic and visual cues both improved the accuracy of irony comprehension for children with cochlear implants, similar to their normal-hearing peers. Furthermore, such improvements were observed for all children, regardless of whether they passed the second-order Theory of Mind test or not. This study is the first to demonstrate the benefits of prosodic and visual cues in irony comprehension, without reliance on second-order Theory of Mind, for Mandarin-speaking children with cochlear implants. It implies potential insights for utilizing prosodic and visual cues in intervention strategies to promote irony comprehension.

Are Personal and Reflexive Pronouns Dissociated in Agrammatic Comprehension? An Individual Participant Meta-Analysis With Clinical Implications.

This study had three objectives: (a) to verify if Grodzinsky et al.'s (1993) findings of worse comprehension of personal than reflexive pronouns can be replicated in a larger meta-analysis of individual participant data, (b) to examine if the heterogeneity found in the patterns of pronoun comprehension in agrammatism can be attributed to task effects, and (c) to evaluate the risk of bias in the reviewed studies. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic literature search was performed to identify studies examining the personal-reflexive pronoun dissociation in agrammatic comprehension. Seven studies met the search criteria and were included in the meta-analysis. For each participant, individual accuracy scores for the comprehension of personal and reflexive pronouns were extracted in addition to information on the study methods. Individual accuracy data were analyzed using the Fisher's exact test and the binomial test. The risk of bias in the studies was assessed using an adapted version of the Newcastle-Ottawa Quality Assessment Scale. The meta-analysis had three main findings: (a) The majority of the persons with agrammatic aphasia (89%) had no dissociation between the comprehension of personal and reflexive pronouns; (b) 8% revealed a pattern consistent with a neuropsychological dissociation, faring worse on the comprehension of personal than reflexive pronouns; and (c) 2% performed worse on reflexive than personal pronouns. The type of the task used affected pronoun comprehension accuracy and accounted for the heterogeneity in the patterns of pronoun comprehension attested across the different participants. Taken together, the meta-analysis did not support a dissociation between personal and reflexive pronoun comprehension in agrammatic comprehension. When confirmed, the dissociation was driven by task effects. The clinical implications of these findings were discussed together with implications to minimize the risk of bias in future examinations of the topic.

Individual tree detection and crown delineation in the Harz National Park from 2009 to 2022 using mask R–CNN and aerial imagery

Forest diebacks pose a major threat to global ecosystems. Identifying and mapping both living and dead trees is crucial for understanding the causes and implementing effective management strategies. This study explores the efficacy of Mask R–CNN for automated forest dieback monitoring. The method detects individual trees, delineates their crowns, and classifies them as alive or dead. We evaluated the approach using aerial imagery and canopy height models in the Harz Mountains, Germany, a region severely affected by forest dieback. To assess the model's ability to track changes over time, we applied it to images from three separate flight campaigns (2009, 2016, and 2022). This evaluation considered variations in acquisition dates, cameras, post-processing techniques, and image tilting. Forest changes were analyzed based on the detected trees' number, spatial distribution, and height. A comprehensive accuracy assessment demonstrated the Mask R–CNN's robust performance, with precision scores ranging from 0.80 to 0.88 and F1-scores from 0.88 to 0.91. These results confirm the model's ability to generalize across diverse image acquisition conditions. While minor changes were observed between 2009 and 2016, the period between 2016 and 2022 witnessed substantial dieback, with a 64.57% loss of living trees. Notably, taller trees appeared to be particularly affected. This study highlights Mask R–CNN's potential as a valuable tool for automated forest dieback monitoring. It enables efficient detection, delineation, and classification of both living and dead trees, providing crucial data for informed forest management practices.

Coupling Fault Diagnosis Based on Dynamic Vertex Interpretable Graph Neural Network.

Mechanical equipment is composed of several parts, and the interaction between parts exists throughout the whole life cycle, leading to the widespread phenomenon of fault coupling. The diagnosis of independent faults cannot meet the requirements of the health management of mechanical equipment under actual working conditions. In this paper, the dynamic vertex interpretable graph neural network (DIGNN) is proposed to solve the problem of coupling fault diagnosis, in which dynamic vertices are defined in the data topology. First, in the date preprocessing phase, wavelet transform is utilized to make input features interpretable and reduce the uncertainty of model training. In the fault topology, edge connections are made between nodes according to the fault coupling information, and edge connections are established between dynamic nodes and all other nodes. Second the data topology with dynamic vertices is used in the training phase and in the testing phase, the time series data are only fed into dynamic vertices for classification and analysis, which makes it possible to realize coupling fault diagnosis in an industrial production environment. The features extracted in different layers of DIGNN interpret how the model works. The method proposed in this paper can realize the accurate diagnosis of independent faults in the dataset with an accuracy of 100%, and can effectively judge the coupling mode of coupling faults with a comprehensive accuracy of 88.3%.

Music training influences online temporal order processing during reading comprehension

Numerous studies have demonstrated the influence of musical expertise on spoken language processing; however, its effects on reading comprehension remain largely unexplored. This study aims to investigate the role of musical expertise in sentence comprehension, particularly concerning the processing of temporal order. Using two self-paced reading experiments, we examined individuals' responses to two-clause sentences connected by the temporal connectives “before” or “after”. “After” sentences consistently presented events in their actual order of temporal occurrence, while “before” sentences described events in reverse temporal order. In both experiments, our analyses of reading times consistently uncovered a significant temporal order effect, with words immediately following the temporal connectives being processed slower in “before” sentences compared to “after” sentences. This suggests the presence of immediate online processing costs associated with “before” sentences. Notably, these processing costs were found to be attenuated in individuals with musical expertise compared to those without. However, analyses of comprehension accuracy showed no advantage of musicians over non-musicians. Specifically, in Experiment 1, the two groups showed no difference in comprehension accuracy, while in Experiment 2, musicians exhibited lower accuracy rates compared to non-musicians in both “before” and “after” sentences. These results suggest that musical expertise may attenuate online processing costs associated with complex linguistic constructs, but could not promote reading accuracy. We concluded that music training is associated with a restricted effect on written sentence comprehension.

The Application of English Grammar Teaching Based on the Thematic Contexts in Junior High Schools

Grammar is an important part of the language. It directly affects the accuracy and appropriateness of language comprehension and expression. English Curriculum Standards for Compulsory Education (2022 Edition) offers suggestions for grammar teaching. Students are encouraged to explore grammar in the context based on their understanding of the theme. However, teachers may neglect the role of thematic meaning in grammar teaching, also they fail to create the context in teaching practice. As a result, students have difficulty applying grammar properly in their lives. Based on the concept of three-dimensional dynamic grammar and teaching examples, the paper explores the strategies and specific methods of grammar teaching in thematic contexts. Through relevant literature on the thematic context and the application of some progressive activities in thematic contextualization, it is made clear that the connotation of grammar learning is to perceive grammar forms, explore grammar rules, understand grammatical meanings and further express them in correct ways. The conclusions are summarized as follows. English grammar teaching based on the thematic contexts helps to stimulate students' interest in grammar learning, while students’ ability to transfer knowledge, their cultural awareness and the quality of thinking are all enhanced.

Spatial Trueness Evaluation of 3D-Printed Dental Model Made of Photopolymer Resin: Use of Special Structurized Dental Model.

(1) Background: Various 3D printers are available for dental practice; however, a comprehensive accuracy evaluation method to effectively guide practitioners is lacking. This in vitro study aimed to propose an optimized method to evaluate the spatial trueness of a 3D-printed dental model made of photopolymer resin based on a special structurized dental model, and provide the preliminary evaluation results of six 3D printers. (2) Methods: A structurized dental model comprising several geometrical configurations was designed based on dental crown and arch measurement data reported in previous studies. Ninety-six feature sizes can be directly measured on this original model with minimized manual measurement errors. Six types of photo-curing 3D printers, including Objet30 Pro using the Polyjet technique, Projet 3510 HD Plus using the Multijet technique, Perfactory DDP and DLP 800d using the DLP technique, Form2 and Form3 using the SLA technique, and each printer's respective 3D-printable dental model materials, were used to fabricate one set of physical models each. Regarding the feature sizes of the simulated dental crowns and dental arches, linear measurements were recorded. The scanned digital models were compared with the design data, and 3D form errors (including overall 3D deviation; flatness, parallelism, and perpendicularity errors) were measured. (3) Results: The lowest overall 3D deviation, flatness, parallelism, and perpendicularity errors were noted for the models printed using the Objet30 Pro (overall value: 45 μm), Form3 (0.061 ± 0.019 mm), Objet30 Pro (0.138 ± 0.068°), and Projet 3510 HD Plus (0.095 ± 0.070°), respectively. In color difference maps, different deformation patterns were observed in the printed models. The feature size proved most accurate for the Objet30 Pro fabricated models (occlusal plane error: 0.02 ± 0.36%, occlusogingival direction error: -0.06 ± 0.09%). (4) Conclusions: The authors investigated a novel evaluation approach for the spatial trueness of a 3D-printed dental model made of photopolymer resin based on a structurized dental model. This method can objectively and comprehensively evaluate the spatial trueness of 3D-printed dental models and has a good repeatability and generalizability.

Sentence processing in bilingual children

Abstract Research in sentence processing in bilingual children is emergent but incomplete as very few studies examine the processing of structurally complex sentences or bilingual children’s real-time interpretation of sentences. One underexplored linguistic feature which can offer insights in this direction are garden-path sentences, i.e., sentences with temporary syntactic ambiguity. These are difficult to process for monolingual children as incremental processing results in an initial misinterpretation and the need for reanalysis. Studies on bilingual children’s processing of garden-path sentences have used paradigms with limited ecological validity and which are not informative about one’s interpretation while listening. This study bridges this gap by investigating the processing of garden-path sentences in bilingual children with the visual-world eye-tracking paradigm. It further explores the role of referential context in the visual stimuli to aid disambiguation. Monolingual and bilingual children aged 8–11 years completed a task similar to Trueswell et al. (1999). The results showed similar difficulty with revising garden-path sentences as evidenced by comprehension accuracy for both groups but only the monolinguals showed real-time garden-path effects in the gaze data. We interpret these findings as a manifestation of slower sentence processing in bilingual children. Both groups made limited use of the referential context to facilitate processing.

Enhancing English comprehension: a UbD mind walker intervention for remote upper-grade elementary students

This preliminary study examines the efficacy of the Mind Walker Reading Programme in improving word knowledge and, consequently, improving reading comprehension skills for three upper-grade elementary students in a remote setting. The programme is based on the understanding by design (UbD) Model developed by Wiggins and McTighe (The understanding by design guide to creating high-quality units, ASCD, Alexandria, 2011; The understanding by design guide to advanced concepts in creating and reviewing units. ASCD, Alexandra, 2012), emphasizing defined reading comprehension outcomes and appropriate indicators for assessment. To assess the Programme’s impact and the retention of acquired skills, pre- and post-tests were administered, with a delayed post-test conducted over an 11-week period. Analysis of the test results, collected from the city government’s test pool, reveals a significant improvement in reading comprehension accuracy among participants using the UbD Mind Walker reading tool. This study highlights the effectiveness of the UbD Mind Walker programme in enhancing English language proficiency and supporting sustainable learning for elementary school students, particularly in remote areas with limited educational resources.