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INTRODUCTION: Stoma formation affects an individual in various ways, including physical, emotional, social, and cognitive functions. Diverse studies report ways of an individual lives with new stoma formation. However, the comprehensive understanding of the entire process by the patient, which includes knowledge before the surgical procedure, as well as the subsequent acceptance, adaptation, and challenges to living with a stoma is lacking. MATERIALS AND METHODS: In-depth interview session were conducted with 12 colorectal cancer patients who have undergone surgical procedures for intestinal stoma formation. The patterns and themes within the data were identified by thematic analysis, involving data familiarisation and coding followed by themes' generation and refinement of the themes. RESULTS: Four themes and 9 subthemes were identified, which revealed the sufficiency of stoma-related information and understanding prior to surgery as well as positive acceptance of self and family members reflected through their reactions and support. Nonetheless, the challenges were anticipated which highlights the complications of the stoma itself, obstacles surrounding social life, and financial burdens. CONCLUSION: This study provided valuable insights into the experiences of individuals living with a stoma following colorectal cancer surgery. The themes and subthemes highlight the need to address social stigma as well as financial issues to alleviate the burden of stoma-related expenses. Increasing public awareness and improving financial assistance could be a way to enhance the overall quality of life for patients living with stoma. [ABSTRACT FROM AUTHOR]

Knowing the factors affecting students’ learning achievement in digital learning is a crucial educational issue nowadays. However, recent research has paid less attention to how an individual’s internal factors (prior knowledge) influence their learning achievement through cognitive engagement, and previous studies generally employed students’ self-reported data, which are subjective. This study investigated the relationships between students’ prior knowledge, cognitive engagement, and learning achievement in digital game-based learning by using eye-tracking technology to analyze their visual behaviors. A total of 55 university students volunteered to use the game to learn about programming, during which their visual behaviors were recorded by an eye tracker to investigate their cognitive engagement and visual transition patterns. Their prior knowledge of programming was assessed one week before the game started, while their learning achievement was tested immediately after the game ended. The results of the study showed that: (1) Students’ prior knowledge had a moderately positive predictive effect on their learning achievement; (2) Students’ learning concentration played a mediating role in the predictive effect of prior knowledge on learning achievement; (3) Students’ cognitive strategies moderated the predictive effect of prior knowledge on learning achievement; and (4) Groups of students with different prior knowledge and cognitive engagement adopted significantly different modes of visual transformation in the game. These findings further revealed the complex relationship between learners’ prior knowledge, cognitive engagement and learning achievement in the game environments, which would be a good reference for understanding individual differences in the game environment and for designing game-based adaptive learning systems. [ABSTRACT FROM AUTHOR]

Digital game-based learning (DGBL) and electronic books (E-books) can provide students with different benefits. Accordingly, one aim of this study was to incorporate an E-book into DGBL to develop a Digital Entertaining English Learning (DEEL). However, individual differences exist among learners, especially prior knowledge. To this end, the other aim of this study was to provide a complete understanding of the effects of prior knowledge on student learning in the context of DEEL. Hence, we conducted empirical investigation, where learners' reactions to the DEEL were analyzed with quantitative methods and the lag sequential analyses. An independent variable was prior knowledge while dependent variables were test performance, task performance and learning behavior. Regardless of test performance or task performance, results indicated that high prior knowledge learners (HPK) performed better than low prior knowledge learners (LPK). Regarding learning behavior, LPK more frequently used the grammar E-book than HPK, especially pictorial illustration. Additionally, LPK moved between the text description and pictorial illustration with a bidirectional approach while HPK viewed the text description and pictorial illustration with a unidirectional approach. Based on these findings, we developed a framework, which can be applied to personalize the DEEL so that learners' learning experience can be improved. [ABSTRACT FROM AUTHOR]

A precise radiotherapy plan is crucial to ensure accurate segmentation of glioblastomas (GBMs) for radiation therapy. However, the traditional manual segmentation process is labor‐intensive and heavily reliant on the experience of radiation oncologists. In this retrospective study, a novel auto‐segmentation method is proposed to address these problems. To assess the method's applicability across diverse scenarios, we conducted its development and evaluation using a cohort of 148 eligible patients drawn from four multicenter datasets and retrospective data collection including noncontrast CT, multisequence MRI scans, and corresponding medical records. All patients were diagnosed with histologically confirmed high‐grade glioma (HGG). A deep learning‐based method (PKMI‐Net) for automatically segmenting gross tumor volume (GTV) and clinical target volumes (CTV1 and CTV2) of GBMs was proposed by leveraging prior knowledge from multimodal imaging. The proposed PKMI‐Net demonstrated high accuracy in segmenting, respectively, GTV, CTV1, and CTV2 in an 11‐patient test set, achieving Dice similarity coefficients (DSC) of 0.94, 0.95, and 0.92; 95% Hausdorff distances (HD95) of 2.07, 1.18, and 3.95 mm; average surface distances (ASD) of 0.69, 0.39, and 1.17 mm; and relative volume differences (RVD) of 5.50%, 9.68%, and 3.97%. Moreover, the vast majority of GTV, CTV1, and CTV2 produced by PKMI‐Net are clinically acceptable and require no revision for clinical practice. In our multicenter evaluation, the PKMI‐Net exhibited consistent and robust generalizability across the various datasets, demonstrating its effectiveness in automatically segmenting GBMs. The proposed method using prior knowledge in multimodal imaging can improve the contouring accuracy of GBMs, which holds the potential to improve the quality and efficiency of GBMs' radiotherapy. [ABSTRACT FROM AUTHOR]

Interruptions are often pervasive and require attentional shifts from the primary task. Limited data are available on the factors influencing individuals' efficiency in resuming from interruptions during digital reading. The reported investigation--conducted using the InteRead dataset--examined whether individual differences in visuo-spatial working memory capacity (vsWMC) and prior knowledge could influence resumption lag times during interrupted reading. Participants' vsWMC capacity was assessed using the symmetry span (SSPAN) task, while a pre-test questionnaire targeted their background knowledge about the text. While reading an extract from a Sherlock Holmes story, they were interrupted six times and asked to answer an opinion question. Our analyses revealed that the interaction between vsWMC and prior knowledge significantly predicted the time needed to resume reading following an interruption. The results from our analyses are discussed in relation to theoretical frameworks of task resumption and current research in the field. [ABSTRACT FROM AUTHOR]

In aquaculture, the presence of dead fish on the water surface can serve as a bioindicator of health issues or environmental stressors. To enhance the precision of detecting dead fish floating on the water's surface, this paper proposes a detection approach that integrates data-driven insights with advanced modeling techniques. Firstly, to reduce the influence of aquatic disturbances and branches during the identification process, prior information, such as branches and ripples, is annotated in the dataset to guide the model to better learn the scale and shape characteristics of dead fish, reduce the interference of branch ripples on detection, and thus improve the accuracy of target identification. Secondly, leveraging the foundational YOLOv8 architecture, a DD-IYOLOv8 (Data-Driven Improved YOLOv8) dead fish detection model is designed. Considering the significant changes in the scale of dead fish at different distances, DySnakeConv (Dynamic Snake Convolution) is introduced into the neck network detection head to adaptively adjust the receptive field, thereby improving the network's capability to capture features. Additionally, a layer for detecting minor objects has been added, and the detection head of YOLOv8 has been modified to 4, allowing the network to better focus on small targets and occluded dead fish, which improves detection performance. Furthermore, the model incorporates a HAM (Hybrid Attention Mechanism) in the later stages of the backbone network to refine global feature extraction, sharpening the model's focus on dead fish targets and further enhancing detection accuracy. The experimental results showed that the accuracy of DD-IYOLOv8 in detecting dead fish reached 92.8%, the recall rate reached 89.4%, the AP reached 91.7%, and the F1 value reached 91.0%. This study can achieve precise identification of dead fish, which will help promote the research of automatic pond patrol machine ships. [ABSTRACT FROM AUTHOR]

As crucial water conservancy projects, ship locks play a key role in flood control, shipping, water resource allocation, and promoting regional economic development, making them an indispensable part of the modern water transportation system. Utilizing satellite remote sensing for lock extraction can significantly reduce manual workload and costs, assist in the daily dynamic maintenance of lock hubs, and provide more comprehensive data support for the construction and management of water transport infrastructure. In this context, this paper proposes a new method for ship lock object extraction. Leveraging fuzzy theory and prior knowledge of locks, the extraction of lock objects is achieved from Gaofen-1 (GF-1) high-resolution remote sensing images. The experimental results demonstrate that the proposed algorithm can effectively extract small lock objects in remote sensing images, achieving an average extraction accuracy of 80.9% in the study area. [ABSTRACT FROM AUTHOR]

Imagination-to-learn is a specific learning strategy that has been studied in many academic fields. The present study investigated whether imagination is beneficial overall for learning compared to conventional study strategies, while also identifying moderator factors affecting the global effect. A meta-analysis was conducted by scientifically rigorous experiments comparing the learning outcomes of students who were asked to form a mental image of the events described in learning material while reading (imagination condition) or were given no imagination instructions (control condition). A total of 21 experimental studies published on the PsycINFO, Web of Science, ProQuest Dissertations and Theses, ERIC and Google Scholar databases were included, yielding 70 pair-wise comparisons with N = 2625 participants. An overall positive effect of imagination-to-learn was found for both retention (g+ = 0.40, 95% CI [0.23, 0.58], z = 4.63, p <.001) and transfer (g+ = 0.51, 95% CI [0.22, 0.43, z = 3.43, p <.001]) performance. However, analysis of the funnel plots showed that publication bias was present in the reporting of learning outcomes. Analysis of the moderators indicated that the effect sizes differed significantly only with respect to learners' prior knowledge for transfer performance and their educational level for retention scores. Based on these findings, the present study provides important directions for future research and practices. [ABSTRACT FROM AUTHOR]

Due to time, budget, and/or technical constraints, geotechnical site investigation data from a specific site are often limited and sparse, leading to a long-lasting challenge in characterization of spatially varying geotechnical properties. During preliminary stages of site characterization, geotechnical data from neighboring sites or sites with similar geological conditions are often collected and used as valuable prior knowledge in geotechnical engineering practice. Nevertheless, existing methods for spatial variability characterization often rely solely on site-specific data and cannot effectively incorporate prior knowledge or existing databases. To address this issue, this study proposes a novel machine learning method that systematically combines sparsely measured data at a specific site with existing data from neighboring sites or sites with similar geological settings for characterization of property spatial variability in a data-driven manner. The proposed method starts with the construction of a dictionary that draws the dominant spatially varying patterns from a property measured at sites with similar geology under a dictionary learning framework. Leveraging the developed dictionary, the spatial variability of a property is interpreted from sparse site-specific measurements using Bayesian learning. The effectiveness of the proposed method is demonstrated using real data, and improved performance over existing methods is observed. [ABSTRACT FROM AUTHOR]

Multi-agent encirclement with collision avoidance constitutes a common challenge in the multi-agent confrontation domain, wherein the focus lies in the development of cooperative strategies among agents. Previous studies encountered difficulties in addressing the dynamic encirclement of faster prey in obstacles environment. This paper introduces a novel multi-agent deep reinforcement learning approach based on prior knowledge. It is dedicated to exploring the multi-agent encirclement with collision avoidance task involving slower multiple pursuers collaboratively encircling faster prey in an obstacles environment. Firstly, the utilization of the classic Apollonius circle theory as prior knowledge guides agent action selection, narrows the exploratory action space, and accelerates the learning of strategies. Subsequently, the variance descriptor restricts the motion direction of pursuers, thus ensuring that pursuers continuously narrow the encirclement until the prey is successfully encircled. Finally, experiments in an obstacles environment were conducted to validate the proposed method. The results indicate that our method can acquire an effective encirclement strategy, with an encirclement success rate exceeding that of previous methods by more than 10%, and simulation experiment results demonstrate the effectiveness and practicability of our method. [ABSTRACT FROM AUTHOR]

The combination of contextual side information and search is a powerful paradigm in the scope of artificial intelligence. The prior knowledge enables the identification of possible solutions but may be imperfect. Contextual information can arise naturally, for example in game AI where prior knowledge is used to bias move decisions. In this work we investigate the problem of taking quantum advantage of contextual information, especially searching with prior knowledge. We propose a new generalization of Grover’s search algorithm that achieves the optimal expected success probability of finding the solution if the number of queries is fixed. Experiments on small-scale quantum circuits verify the advantage of our algorithm. Since contextual information exists widely, our method has wide applications. We take game tree search as an example. [ABSTRACT FROM AUTHOR]

Tiger puffer is a commercially important fish cultured in high-density environments, and its accurate detection is indispensable for determining growth conditions and realizing accurate feeding. However, the detection precision and recall of farmed tiger puffer are low due to target blurring and occlusion in real farming environments. The farmed tiger puffer detection model, called knowledge aggregation YOLO (KAYOLO), fuses prior knowledge with improved YOLOv5 and was proposed to solve this problem. To alleviate feature loss caused by target blurring, we drew on the human practice of using prior knowledge for reasoning when recognizing blurred targets and used prior knowledge to strengthen the tiger puffer's features and improve detection precision. To address missed detection caused by mutual occlusion in high-density farming environments, a prediction box aggregation method, aggregating prediction boxes of the same object, was proposed to reduce the influence among different objects to improve detection recall. To validate the effectiveness of the proposed methods, ablation experiments, model performance experiments, and model robustness experiments were designed. The experimental results showed that KAYOLO's detection precision and recall results reached 94.92% and 92.21%, respectively. The two indices were improved by 1.29% and 1.35%, respectively, compared to those of YOLOv5. Compared with the recent state-of-the-art underwater object detection models, such as SWIPENet, RoIMix, FERNet, and SK-YOLOv5, KAYOLO achieved 2.09%, 1.63%, 1.13% and 0.85% higher precision and 1.2%, 0.18%, 1.74% and 0.39% higher recall, respectively. Experiments were conducted on different datasets to verify the model's robustness, and the precision and recall of KAYOLO were improved by approximately 1.3% compared to those of YOLOv5. The study showed that KAYOLO effectively enhanced farmed tiger puffer detection by reducing blurring and occlusion effects. Additionally, the model had a strong generalization ability on different datasets, indicating that the model can be adapted to different environments, and it has strong robustness.

Abstract The use of prior knowledge in the machine learning framework has been considered a potential tool to handle the curse of dimensionality in genetic and genomics data. Although random forest (RF) represents a flexible non-parametric approach with several advantages, it can provide poor accuracy in high-dimensional settings, mainly in scenarios with small sample sizes. We propose a knowledge-slanted RF that integrates biological networks as prior knowledge into the model to improve its performance and explainability, exemplifying its use for selecting and identifying relevant genes. knowledge-slanted RF is a combination of two stages. First, prior knowledge represented by graphs is translated by running a random walk with restart algorithm to determine the relevance of each gene based on its connection and localization on a protein-protein interaction network. Then, each relevance is used to modify the selection probability to draw a gene as a candidate split-feature in the conventional RF. Experiments in simulated datasets with very small sample sizes $$(n \le 30)$$ ( n ≤ 30 ) comparing knowledge-slanted RF against conventional RF and logistic lasso regression, suggest an improved precision in outcome prediction compared to the other methods. The knowledge-slanted RF was completed with the introduction of a modified version of the Boruta feature selection algorithm. Finally, knowledge-slanted RF identified more relevant biological genes, offering a higher level of explainability for users than conventional RF. These findings were corroborated in one real case to identify relevant genes to calcific aortic valve stenosis.

Abstract The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability.

Background: The development of critical thinking skills in prospective mathematics teachers is essential for their future effectiveness in the classroom. Understanding how these individuals process and resolve problems that contain contradictory information provides insight into their critical thinking abilities. Previous research has highlighted the significant role of prior knowledge in problem-solving and critical thinking. Aim: This study aims to explore the critical thinking processes of prospective mathematics teachers when faced with problems that contain contradictory information. Specifically, it seeks to determine the influence of prior knowledge on their ability to navigate and resolve these complex problems. Methods: The study employed a sequential explanatory design. Initially, quantitative data from prerequisite skill and critical thinking tests (specifically, problems with contradictory information) were collected from 68 participants. Simple regression analysis informed the selection of six participants (two each with high, medium, and low prerequisite abilities) for the subsequent qualitative phase. In-depth interviews and problem-solving tasks were conducted, prompting participants to articulate their thought processes. Data analysis focuses on identifying patterns and themes in their use of prior knowledge and critical thinking strategies. Results: The findings reveal that prior knowledge plays a pivotal role in how prospective mathematics teachers approach and resolve problems with contradictory information. Those with a strong foundation in mathematical concepts and problem-solving strategies are better equipped to identify inconsistencies and develop logical solutions. Conversely, participants with limited prior knowledge struggle to reconcile conflicting information and often resort to less effective problem-solving methods. Conclusion: This study underscores the importance of prior knowledge in the development of critical thinking skills among prospective mathematics teachers. Educator preparation programs should emphasize the cultivation of a robust knowledge base and provide opportunities for students to engage in complex problem-solving tasks. By doing so, future teachers will be better prepared to navigate the challenges of the classroom and foster critical thinking in their own students.

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system. [ABSTRACT FROM AUTHOR]

Vocational high schools, receive special attention from the government to produce graduates who are skilled and in line with the demands of the 21st century. The COVID-19 pandemic has disrupted the education system, necessitating a shift from offline to online learning methods. This research focuses on developing digital simulator learning (DSL) media to improve the prior or initial knowledge and problem solving skills of vocational school students in the field of motorcycle engineering and business. The novelty of DSL media is that students not only learn through the reading process but can also see visually through the simulation process, and also simulate troubleshooting on vehicles. This study follows the analysis, design, development, implementation, and evaluation (ADDIE) model. The research results found that smart phone based DSL media affected increasing competence in initial knowledge of injection systems, while the effectiveness of DSL media was at a medium level. [ABSTRACT FROM AUTHOR]

The precise extraction of winter wheat planting structure holds significant importance for food security risk assessment, agricultural resource management, and governmental decision-making. This study proposed a method for extracting the winter wheat planting structure by taking into account the growth phenology of winter wheat. Utilizing the fitting effect index, the optimal Savitzky–Golay (S–G) filtering parameter combination was determined automatically to achieve automated filtering and reconstruction of NDVI time series data. The phenological phases of winter wheat growth was identified automatically using a threshold method, and subsequently, a model for extracting the winter wheat planting structure was constructed based on three key phenological stages, including seeding, heading, and harvesting, with the combination of hierarchical classification principles. A priori sample library was constructed using historical data on winter wheat distribution to verify the accuracy of the extracted results. The validation of fitting effect on different surfaces demonstrated that the optimal filtering parameters for S–G filtering could be obtained automatically by using the fitting effect index. The extracted winter wheat phenological phases showed good consistency with ground-based observational results and MOD12Q2 phenological products. Validation against statistical yearbook data and the proposed priori knowledge base exhibited high statistical accuracy and spatial precision, with an extracting accuracy of 94.92%, a spatial positioning accuracy of 93.26%, and a kappa coefficient of 0.9228. The results indicated that the proposed method for winter wheat planting structure extracting can identify winter wheat areas rapidly and significantly. Furthermore, this method does not require training samples or manual experience, and exhibits strong transferability. [ABSTRACT FROM AUTHOR]

High school students with better knowledge about back care have fewer problems, but conceptual errors can hinder the acquisition of essential knowledge necessary for developing healthy habits. This study analyzed secondary school students' declarative knowledge and misconceptions related to back care in daily activities. An exploratory cross-sectional study was conducted with 80 girls and 89 boys aged 14–18 years (M = 15.68, SD = 2.12). The Health Questionnaire on Back Care Knowledge in Activities of Daily Living was used to evaluate knowledge using the true answer model (TAM) and the misconception model (MM). Using the test–retest method, both models' reliability was confirmed (TAM = 0.75; MM = 0.77), while only a minimal measurement error was identified (TAM = −0.01; MM = −0.07). The average scores were 6.23 for the TAM and 2.29 for the MM. The results showed no significant differences in both models. The analysis indicated that students had the most accurate knowledge of the location and function of the spine, whereas misconceptions regarding anatomical understanding and body posture usage were common. An analysis of the results under Reassumption Theory emphasizes the significance of comprehending concepts such as load transmission and spinal stability to maintain back health, thus highlighting the need for improved education in these areas to address misconceptions and enhance overall back-care knowledge. [ABSTRACT FROM AUTHOR]

Learner control allows for greater autonomy and is supposed to benefit learning motivation, but it might be more advantageous for students with specific learner characteristics. The current study looks into the relationships between self-regulated learning, cognitive load, and performance within learner-controlled environments. The research was conducted in an asynchronous online setting, allowing for learner control. Cognitive load and self-regulated learning were measured using self-report questionnaires. Performance was assessed through case solutions. The participants were 97 graduate law students studying the civil code. Analysis based on structural equation modeling showed that both prior knowledge and self-regulated learning skills significantly contribute to the increase in germane cognitive load and are positively correlated with performance. The implications of these findings underscore the critical role of prior knowledge and self-regulated learning skills in shaping the cognitive processes involved in learning, ultimately impacting academic achievement. These results emphasize the need for careful consideration of learner-control options in asynchronous online environments. [ABSTRACT FROM AUTHOR]

Poetry, being a distinct literary art form, fosters meaningful literacy, but few studies focus on enhancing its writing quality. Using a 2 × 2 between-subject design, this study explored the effects of prior knowledge and peer assessment on the quality of English as a foreign language poetry writing. A total of 81 English majors participated in a 7-week online poetry writing task, generating 567 poems on seven themes. Literary experts evaluated the poems across seven aspects. Results revealed that peer assessment enhanced general writing quality, specifically for participants with high prior knowledge. Prior knowledge negatively influenced personal voice and organization, with the low prior knowledge group showing a stronger focus on personal expressions and the flow of the poem. Peer assessment positively influenced the use of poetry schemes, with the assessed group demonstrating better utilization compared to the non-assessed group. The findings guide teaching poetic knowledge, encourage communication among students, and ultimately improve the quality of L2 poetry writing. [ABSTRACT FROM AUTHOR]

Deep learning fromcollaboration occurs if the learner enacts interactive activities in the sense of leveraging the knowledge externalized by co-learners as resource for own inferencing processes and if these interactive activities in turn promote the learner's deep comprehension outcomes. This experimental study investigates whether inducing dyad members to enact constructive preparation activities can promote deep learning from subsequent collaboration while examining prior knowledge as moderator. In a digital collaborative learning environment, 122 non-expert university students assigned to 61 dyads studied a text about the human circulatory system and then prepared individually for collaboration according to their experimental conditions: the preparation tasks varied across dyads with respect to their generativity, that is, the degree to which they required the learners to enact constructive activities (note-taking, compare-contrast, or explanation). After externalizing their answer to the task, learners in all conditions inspected their partner's externalization and then jointly discussed their text understanding via chat. Results showed that more rather than less generative tasks fostered constructive preparation but not interactive collaboration activities or deep comprehension outcomes. Moderatedmediation analyses considering actor and partner effects indicated the indirect effects of constructive preparation activities on deep comprehension outcomes via interactive activities to depend on prior knowledge: when own prior knowledge was relatively low, self-performed but not partner-performed constructive preparation activities were beneficial. When own prior knowledge was relatively high, partner-performed constructive preparation activities were conducive while one's own were ineffective or even detrimental. Given these differential effects, suggestions are made for optimizing the instructional design around generative preparation tasks to streamline the effectiveness of constructive preparation activities for deep learning from digital collaboration. [ABSTRACT FROM AUTHOR]

Background: The field of automatic electrocardiogram (ECG) analysis has gained significant attention due to its potential for enhancing diagnostic accuracy and efficiency. An InceptionTime model enhanced with a lead-wise prior knowledge framework (LPKF) has been specifically developed to address the intricate challenge of multi-label ECG classification using the PTB-XL dataset. This model has demonstrated high performance, yielding superior Macro-AUC and F1 scores compared to other state-of-the-art studies. The objective of this study is to further assess the quality and interpretability of the LPKFenhanced InceptionTime model. Methods: The Grad-CAM technique has been adopted for analysis. The Grad-CAM method involves generating maps that highlight the regions of the ECG waveforms the model focuses on while making its predictions. This technique operates by computing the gradients of the target class scores with respect to the feature maps in the final convolutional layer. These gradients are then used to produce a localization map that indicates the most relevant regions contributing to the decision. Result: Grad-CAM visualizations of randomly selected ECG signals reveal the model's focus areas, which align well with clinical knowledge and provide clear, actionable insights into the decision-making process. Conclusion: The integration of Grad-CAM technique has verified the transparency and reliability of the LPKF-enhanced Inception Time approach, confirming its utility and effectiveness in clinical applications [ABSTRACT FROM AUTHOR]

The aspect sentiment triplet extraction (Triplet) aims at extracting aspect terms (AE), extracting aspect-oriented opinion terms (AOE), and discriminating aspect-level sentiment polarity (ASC) from the comments. To address the current study, the end-to-end framework-based approach suffers from the problem of contribution distribution among multiple components, while the pipeline framework-based approach is susceptible to error propagation. Moreover, the complexity of the model limits the detection of long-distance aspect terms and opinion terms. In this paper, we propose a framework based on multi-task shared cascade learning and machine reading comprehension (MRC), which is called Triple-MRC. The multi-task shared cascade learning can effectively avoid the problem of contribution distribution among components. The MRC approach leverages the prior knowledge from the question to reduce the error propagation between tasks and mitigate the limitation associated with model complexity. We conduct experiments on publicly available two benchmark datasets for the Triplet task. The experimental results demonstrate the superior performance of the Triple-MRC framework compared to the baseline model, which better achieves the Triplet task. Through the analysis of the comparison study, model training process, error analysis, ablation study, attention visualization, and case study, we have confirmed the effectiveness of introducing the multi-task shared cascade learning method and MRC method into the model. [ABSTRACT FROM AUTHOR]

Listening comprehension serves as a basic means for communication and participation in society. Unfortunately, especially low-performing students have difficulties understanding informational content presented in a listening format, even more so than with the comprehension of printed texts. Based on empirical findings that text features, such as global text cohesion, have proven to be effective for promoting reading comprehension, and cognitive processes of reading and listening to academic texts share commonalities, the question arises as to how much global cohesion can support students' listening comprehension. 140 ninth-grade students in German secondary schools listened to one of two informational listening texts which differed in their degree of global text cohesion (low vs. high in cohesion). Listening comprehension was assessed with a written test after listening. Regression analyses show that global text cohesion promotes listening comprehension and that the effect of cohesion remains significant and stable when controlling for topic-related prior knowledge and language-related background variables. Low-performing students profited more from the highly cohesive text than high-performing students. Thus, cohesion contributes to the comprehensibility of informational listening texts which can have implications for the construction of listening texts and listening comprehension instruction at school. [ABSTRACT FROM AUTHOR]

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AbstractActivation of prior knowledge is vital for reading comprehension. However, little is known about students’ perceptions of what prior knowledge they view as significant for improving their learning from textbook texts in civics. This study explores how Swedish ninth-grade students, aged 14–16, with diverse linguistic and educational backgrounds, engage with two grade-level civics textbook texts by drawing on their prior knowledge. A four-field model is used to closely study the possibilities with civics texts, focusing on students’ prior knowledge. Thematic content analysis of the students’ statements from two think-aloud sessions reveals the emergence of seven distinct categories of prior knowledge. For instance, parents’ narratives and teacher explanations are underlined as essential resources for students to enhance their comprehension of the content of civics texts, thereby supporting their civics learning. The findings suggest that challenges with civics texts may not be a result of a lack of prior knowledge but may rather be due to the limited opportunities the students receive to activate their prior knowledge when engaging with civics texts. The students report that they face challenges in activating their prior knowledge when they encounter language- and content-related difficulties with civics texts. This underscores the pivotal role of civics teachers in facilitating prior knowledge activation before, during, and after reading civics texts. The four-field model can be used as a tool for analyzing and understanding the intricacies of reading civics texts. Using the model could support civics teachers in designing instruction to minimize challenges and improve students’ learning in civics. [ABSTRACT FROM AUTHOR]

The quality of products from the causal mapping process and the effect of factors related to causal map quality are unlikely to be the same for students at different educational levels. However, there is a lack of studies that provide insights into causal maps constructed by primary school students to reveal appropriate strategies. This study conducted an analysis of 32 causal maps constructed by primary school students to explore the quality of these products as well as the effect of prior knowledge and causal reasoning process on causal map quality. The findings suggested that it is quite possible for primary school students to construct quality causal maps. The results also indicated that students with higher prior knowledge produced higher-quality causal maps regardless of the causal reasoning process used by these students. In contrast, the causal reasoning process was found to have no significant effect on students' causal map quality. [ABSTRACT FROM AUTHOR]

Background: Guided gaze is the instructor's gaze towards teaching materials to guide students' attention, and it plays a vital role in enhancing video‐based education. The duration of guided gaze, indicating how long instructors focus on teaching materials, varies based on the lecture design. Nevertheless, the impact of varying durations of guided gaze, especially concerning students' prior knowledge, remains inadequately understood. Objectives: This study investigates the influence of the instructor's guided gaze duration and students' prior knowledge on learning performance and affective experiences in video lectures. Methods: 145 fifth‐grade students participated and were divided into high and low prior knowledge groups based on a pre‐test. Within each group, students were randomly assigned to view one of three video lectures with different guided gaze durations (high vs. medium vs. low). Learning performance and affective experiences (learning experience, satisfaction, and emotions) were measured as dependent variables. Results and Conclusion: The results revealed that low guided gaze duration significantly improves learning performance for students with low prior knowledge. Conversely, high guided gaze duration negatively impacts learning experience, satisfaction, and positive emotions. Additionally, students with high prior knowledge reported higher learning experience and satisfaction. These findings highlight the interaction between guided gaze duration and prior knowledge in students' learning performance. Implications: Our findings provide valuable implications for the design of guided gaze duration in video lectures based on students' prior knowledge. By adjusting guided gaze duration appropriately, instructors can optimise students' learning performance and affective experiences. Lay Description: What is already known about this topic: Guided gaze is the instructor's gaze on teaching materials, guiding students' attention to relevant information.Guided gaze duration measures the amount of time instructors spend looking at teaching materials.Prior knowledge can modulate the impact of instructors' guidance on students' learning performance.It remains unclear whether guided gaze duration affects the learning outcomes of students with varied prior knowledge. What this paper adds: Guided gaze duration is divided into three levels (high, medium, and low), indicating the percentage of time the instructor looks at teaching materials.High guided gaze duration has negative effects on students' learning experience, satisfaction, and positive emotions.Students with high prior knowledge have higher learning performance, experience, and satisfaction.Low guided gaze duration improves learning performance for students with low prior knowledge. Implications for practice and/or policy: Instructors should use low guided gaze duration for students with low prior knowledge.Instructors should aim to reduce the use of high guided gaze duration. [ABSTRACT FROM AUTHOR]

In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. In response to the problems of insufficient semantic information and poor cross-modal fusion effect of traditional sentiment classification models, this paper proposes a composite hierarchical feature fusion method combined with prior knowledge. Firstly, the ALBERT (A Lite BERT) model and the improved ResNet model are constructed for feature extraction of text and image, respectively, and high-dimensional feature vectors are obtained. Secondly, to solve the problem of insufficient semantic information expression in cross-scene, a prior knowledge enhancement model is proposed to enrich the data characteristics of each modality. Finally, to solve the problem of poor cross-modal fusion effect, a composite hierarchical fusion model is proposed, which combines the temporal convolutional network and the attention mechanism to fuse the sequence features of each modality information and realizes the information interaction between different modalities. Experiments on MVSA-Single and MVSA-Multi datasets show that the proposed model is superior to a series of comparison models and has good adaptability in new scenarios. [ABSTRACT FROM AUTHOR]

Interleaved practice combined with comparison prompts can better foster students' adaptive use of subtraction strategies compared to blocked practice. It has not been previously investigated whether all students benefit equally from these teaching approaches. While interleaving subtraction tasks prompts students' attention to the different task characteristics triggering the use of specific subtraction strategies, blocked practice does not support students in detecting these differences. Thus, low-prior-knowledge students would benefit from interleaving rather than blocking as it guides them through the learning-relevant comparison processes. Because these comparison processes are cognitively demanding, students' need for cognition (NFC) could influence the effectiveness of interleaved practice. The present study investigates the role of students' prior knowledge and NFC for the effectiveness of interleaved and blocked practice. To this end, 236 German third-graders were randomly assigned to either an interleaved or blocked condition. Over 14 lessons, both groups were taught to use four number-based strategies and the written algorithm for solving subtraction problems. The interleaved learners were prompted to compare the strategies, while the blocked learners compared the adaptivity of one strategy for different mathematical tasks. A quadratic growth curve model showed that prior knowledge had a positive influence on students' development of adaptivity in the blocked but not in the interleaved condition. Students' NFC had a positive impact in the interleaved condition, while it had no influence in the blocked condition. However, the effects of prior knowledge and NFC did not differ significantly between the two conditions. [ABSTRACT FROM AUTHOR]

Pedagogical training is considered an efficient tool to train university teachers to understand and foster active learning. In Finland, pedagogical training courses are organized periodically at universities, and university teachers participate voluntarily to improve pedagogical knowledge and skills for teaching in higher education settings. This study aims to examine the effects of short online pedagogical training courses on the development of university teachers' conceptions of active learning from two perspectives: the role of prior knowledge and engaging their students during lectures. The effects of the training were measured through self-reported questionnaires completed by teachers at a Finnish university before and after the pedagogical course (N = 108). The results showed an increase in participants' perceptions of the importance of prior knowledge in the learning process, and a decrease in the idea of learning as remembering. Additionally, the awareness of developing engaging lectures increased by the end of the courses. These outcomes indicate the benefits of short pedagogical courses for pedagogical development, especially for university teachers who have not had any prior training in pedagogy. [ABSTRACT FROM AUTHOR]

Complex problem solving (CPS) is a new paradigm in solving problems and is one of the soft skills needed to face the industrial revolution 4.0. Reflective abstraction is associating and modifying pre-existing conceptions into new situations. This article reviews research on CPS and reflective abstraction. This research is needed to know the relationship between reflective abstraction and CPS. The systematic writing of this review was assisted by the Publish or Perish 7 application, Mendeley, and VOSviewer. A literature search was performed through the ScienceDirect and ERIC databases. Based on the search results with the term “complex problem solving” and several exclusion criteria, 58 articles were found, whereas with the word “reflective abstraction” there were 23 articles, totaling 81 papers. Based on the literature review, it was found that there is a relationship between CPS and reflective abstraction by obtaining common ground in the form of prior knowledge. CPS requires prior knowledge from reflective abstraction to integrate the most relevant information. To improve CPS, efforts and special attention can be made to build initial knowledge through reflective abstraction. This article contributes to further research and becomes a study for the themes of CPS and reflective abstraction in learning and education. [ABSTRACT FROM AUTHOR]

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Sleep associated memory consolidation and reactivation play an important role in language acquisition and learning of new words. However, it is unclear to what extent properties of word learning difficulty impact sleep associated memory reactivation. To address this gap, we investigated in 22 young healthy adults the effectiveness of auditory targeted memory reactivation (TMR) during non-rapid eye movement sleep of artificial words with easy and difficult to learn phonotactical properties. Here, we found that TMR of the easy words improved their overnight memory performance, whereas TMR of the difficult words had no effect. By comparing EEG activities after TMR presentations, we found an increase in slow wave density independent of word difficulty, whereas the spindle-band power nested during the slow wave up-states – as an assumed underlying activity of memory reactivation – was significantly higher in the easy/effective compared to the difficult/ineffective condition. Our findings indicate that word learning difficulty by phonotactics impacts the effectiveness of TMR and further emphasize the critical role of prior encoding depth in sleep associated memory reactivation.

The low numeracy skills in Indonesia are one factor affecting the quality of mathematics education, so reflective thinking is needed to improve this ability. This study aims to describe prospective mathematics teachers' reflective thinking process in solving numeracy problems at the critical reflection stage based on prior knowledge. This study used a qualitative, descriptive research methodology. The research participants were 34 prospective mathematics teachers at the State University of Malang, East Java. The instruments used are numeracy tests and interview guidelines. Data analysis includes data reduction, presentation, and conclusion drawing. The findings revealed notable differences in reflective thinking based on prior knowledge levels at the critical reflection stage. Subjects with high prior knowledge tried various solutions, were confident in their answers, and accurately drew conclusions and explained their reasons. Subjects with medium prior knowledge tried different methods but needed more confidence in their results. They could conclude but struggled to explain their reasons. Subjects with low prior knowledge used the same method, needed more confidence, and needed help explaining their reasons despite concluding. These findings imply creating a better training program for prospective mathematics teachers, emphasizing numeracy and reflective thinking growth.

Interruptions are often pervasive and require attentional shifts from the primary task. Limited data are available on the factors influencing individuals' efficiency in resuming from interruptions during digital reading. The reported investigation—conducted using the InteRead dataset—examined whether individual differences in visuo-spatial working memory capacity (vsWMC) and prior knowledge could influence resumption lag times during interrupted reading. Participants' vsWMC capacity was assessed using the symmetry span (SSPAN) task, while a pre-test questionnaire targeted their background knowledge about the text. While reading an extract from a Sherlock Holmes story, they were interrupted six times and asked to answer an opinion question. Our analyses revealed that the interaction between vsWMC and prior knowledge significantly predicted the time needed to resume reading following an interruption. The results from our analyses are discussed in relation to theoretical frameworks of task resumption and current research in the field.