Learning Progress Research Articles

Classifying Learning Speed Using Brain Networks and Psychological States: Unraveling the Interdependence Between Learning Performance, Psychological States, and Brain Functions.

Introduction The progression of performance learning (PL) may have complex relationships beyond mere concurrent occurrences and may influence each other. This study aimed to classify the speed of PL using a random forest based on brain network and stress state information and to identify the factors necessary for PL. In addition, this study also aimed to clarify the complex interdependent relationships between PL, psychological state, and brain function through these factors, using covariance structure analysis. Methods A total of 20 healthy individuals participated in a choice reaction time task, and brain function was measured using near-infrared spectroscopy (NIRS). Participants were divided into high-PL and low-PL groups based on the median difference in correct responses. Results Random forest analysis identified the left orbitofrontal area, right premotor cortex, right frontal pole, left frontal pole, left dorsolateral prefrontal cortex, and depression and anxiety as key factors. Covariance structure analysis revealed that depression and anxiety affected PL through the frontal pole and prefrontal cortex, suggesting a complex interplay between psychological state, brain function, and learning. Conclusions These findings suggest that psychological states influence brain networks, thereby affecting learning performance. Tailoring rehabilitation programs to address psychological states and providing targeted feedback may improve learning outcomes. The study provides insights into the theoretical and practical applications of understanding the brain's role in PL, as well as the importance of addressing psychological factors to optimize learning and rehabilitation strategies.

Impact of Progression on Secondary Schools Learners’ Performance in Limpopo Province, South Africa

Progression is good for promoting learners from one stage to another and for career development. Secondary schools in Limpopo Province, South Africa, use progression. The study investigated the impact of progression on Grade 12 learners’ performance in Capricorn District, South Africa. A phenomenological design was employed to identify the impact of the progression of learners. Study participants were purposively selected from three schools because they had valuable information regarding the impact of learners’ progression on learning outcomes in Capricorn District. Data were collected through semi-structured interviews, document analysis and focus group interviews. Data were analysed thematically. The results show that stakeholders' perceptions suggest that the progression of learners resulted in poor performance in Grade 12 final examinations in the study area. Teachers' lack of skills in dealing with academic challenges led to low pass rates. The findings have implications for the implementation of progression policies in secondary schools

2024 updated European Heart Rhythm Association core curriculum for physicians and allied professionals: a statement of the European Heart Rhythm Association of the European Society of Cardiology.

Heart rhythm management is a continuously evolving sub-speciality of cardiology. Every year, many physicians and allied professionals (APs) start and complete their training in cardiac implantable electronic devices (CIEDs) or electrophysiology (EP) across the European Heart Rhythm Association (EHRA) member countries. While this training ideally ends with an EHRA certification, the description of the learning pathway (what, how, when, and where) through an EHRA core curriculum is also a prerequisite for a successful training. The first EHRA core curriculum for physicians was published in 2009. Due to the huge developments in the field of EP and device therapy, this document needed updating. In addition, a certification process for APs has been introduced, as well as a recertification process and accreditation of EHRA recognized training centres. Learning pathways are more individualized now, with Objective Structured Assessment of Technical Skills (OSATS) to monitor learning progression of trainees. The 2024 updated EHRA core curriculum for physicians and APs describes, for both CIED and EP, the syllabus, OSATS, training programme and certification, and recertification for physicians and APs and stresses the importance of continued medical education after certification. In addition, requirements for accreditation of training centres and trainers are given. Finally, suggested reading lists for CIED and EP are attached as online supplements.

Mediation/moderation effects of engagement, foreign language enjoyment, and ambiguity tolerance in metaverse-based foreign language learning

This study investigates how engagement (E), foreign language enjoyment (FLE), and ambiguity tolerance (AT) exert mediation/moderation in metaverse-based foreign language learning (FLL). Featuring augment/simulation-based experiences and self-fulfillment/external-control-oriented mechanics, metaverse provides virtualized interactive circumstances involving individuals’ embodied presence/behaviors, aligning with FLL that emphasizes social interaction. Based on the quantified survey data, partial least squares structural equation modeling (PLS-SEM) analyses investigate the significance and positivity of the mediation/moderation relations. According to the results, E exerts fully positive mediation in the effect of intrinsic motivation (IM) on learning effectiveness (LE), FLE exerts partially positive mediation in the effect of classroom social climate (CSC) on E, and AT exerts negative moderation in the positive effect of E on LE. Notably, FLE exerts insignificant mediation in the effect of growth mindset (GM) on E. Therefore, efficient metaverse-based FLL requires synergies of affective factors, i.e., intrinsic motivation, perceptions of classroom social climate, moderate degrees of ambiguity tolerance, and engagement, for sustainable and long-term language learning progress in virtual interactive experiences. At the theoretical level, the findings extend the FLL-related models and advance the understanding of FLL. At the practical level, the findings provide references for more efficient metaverse implementations in FLL.

“Once upon a time… a beach sand grain”: a bed-time story and scientific outreach activity for young children to increase sediment literacy

Abstract Purpose Learning science in early years can cultivate children’s curiosity and enjoyment in exploring the world around them, laying the foundation for the progression of science learning and ultimately increasing science literacy. Here, we present an example of a tailored preschool scientific activity designed to enhance literacy about sediments and illustrate their importance to both humans and nature. Methods The activity centres around a captivating story detailing the journey of a sand grain from the mountains to the sea. This storytelling experience is enriched with hands-on observation of various sand grains, informative cards on key topics, and culminates in a creative colouring activity. Results To date, the activity has been repeated five times, engaging 110 children (from 2 to 10 years). It has yielded positive outcomes with both preschool and primary school students, as they were actively engaged in the story and delighted in handling and observing the magnified sand grains. Conclusions The activity was successfully implemented for preschool and primary school students, fostering engagement with the story and the sand samples. However, while the immediate engagement was evident, the impact on sediment literacy remains to be measured. Future structured evaluations are needed to assess the long-term effectiveness of such initiatives in enhancing sediment literacy among young learners.

Enhancing alzheimer’s diagnosis through optimized brain lesion classification in MRI with attention-driven grid feature fusion

This paper explores cognitive interface technology, aiming to tackle current challenges and shed light on the prospects of brain-computer interfaces (BCIs). It provides a comprehensive examination of their transformative impact on medical technology and patient well-being. Specifically, this study contributes to addressing challenges in classifying brain lesion images arising from the complex nature of lesions and limitations of traditional deep learning approaches. It introduces advanced feature fusion models that leverage deep learning algorithms, including the African vulture optimization (AVO) algorithm. These models integrate informative features from multiple pre-trained networks and employ innovative fusion techniques, including the attention-driven grid feature fusion (ADGFF) model. The ADGFF model incorporates an attention mechanism based on the optimized weights obtained using AVO. The objective is to improve the overall accuracy by providing fine-grained control over different regions of interest in the input image through a grid-based technique. This grid-based technique divides the image into vertical and horizontal grids, simplifying the exemplar feature generation process without compromising performance. Experimental results demonstrate that the proposed feature fusion strategies consistently outperform individual pre-trained models in terms of accuracy, sensitivity, specificity, and F1-score. The optimized feature fusion strategies, particularly the GRU-ADGFF model, further enhance classification performance, outperforming CNN and RNN classifiers. The learning progress analysis shows convergence, indicating the effectiveness of the feature fusion strategies in capturing lesion patterns. AUC-ROC curves highlight the superior discriminatory capabilities of the ADGFF-AVO strategy. Five-fold cross-validation is employed to assess the performance of the proposed models, demonstrating their accuracy, and few other accuracy-based measures. The GRU-ADGFF model optimized with AVO consistently achieves high accuracy, sensitivity, and AUC values, demonstrating its effectiveness and generalization capability. The GRU-ADGFF model also outperforms the majority voting ensemble technique in terms of accuracy and discriminative ability. Additionally, execution time analysis reveals good scalability and resource utilization of the proposed models. The Friedman rank test confirms significant differences in classifier performance, with the GRU-ADGFF model emerging as the top-performing method across different feature fusion strategies and optimization algorithms.

Improving Word Recognition Skills of Children Learning to Read in a Second Language Through Podcasts

<p style="text-align:justify">Failure in visual word recognition brings about frustration and failure in reading which impinge on the achievement and reading progress of learners. In order to contribute towards assisting every child to achieve minimum proficiency in reading, this investigation examined the impact of podcasts on the abilities of second language learners in sight word recognition. The study which is quasi-experimental, made use of the pre-test post-test nonequivalent groups method. The study participants who were purposively sampled from a privately owned primary school in Enugu urban, South East, Nigeria comprise thirty elementary two learners. Pre-and post-tests were administered to the participants who were grouped into intervention and control groups. The intervention group listened to podcasts in word recognition skills while the control group learned word recognition through the usual method. Statistical tools such as means, standard deviation and analysis of covariance (ANCOVA) were utilized to analyze the data collected. The research question was answered through Standard Deviation and the learners’ mean achievement scores, while the hypothesis, which was set at <0.05 level of significance, was tested with the ANCOVA. The results of the investigation showed that podcasts facilitated improvement in the word recognition skills of English as a second language (ESL) learners. The study concluded that the use of podcast proves to be an efficient instructional media capable of promoting achievement in word recognition skills of learners in an ESL classroom. On the basis of the results, exploring the use of podcasts in the teaching of word recognition skills among ESL learners was recommended.</p>

Universal Ensemble-Embedding Graph Neural Network for Direct Prediction of Optical Spectra from Crystal Structures.

Optical properties in solids, such as refractive index and absorption, hold vast applications ranging from solar panels to sensors, photodetectors, and transparent displays. However, first-principles computation of optical properties from crystal structures is a complex task due to the high convergence criteria and computational cost. Recent progress in machine learning shows promise in predicting material properties, yet predicting optical properties from crystal structures remains challenging due to the lack of efficient atomic embeddings. Here, Graph Neural Network for Optical spectra prediction (GNNOpt) is introduced, an equivariant graph-neural-network architecture featuring universal embedding with automatic optimization. This enables high-quality optical predictions with a dataset of only 944 materials. GNNOpt predicts all optical properties based on the Kramers-Krönig relations, including absorption coefficient, complex dielectric function, complex refractive index, and reflectance. The trained model is applied to screen photovoltaic materials based on spectroscopic limited maximum efficiency and search for quantum materials based on quantum weight. First-principles calculations validate the efficacy of the GNNOpt model, demonstrating excellent agreement in predicting the optical spectra of unseen materials. The discovery of new quantum materials with high predicted quantum weight, such as SiOs, which host exotic quasiparticles with multifold nontrivial topology, demonstrates the potential of GNNOpt in predicting optical properties across a broad range of materials andapplications.

Self-supervised learning for CT image denoising and reconstruction: a review.

This article reviews the self-supervised learning methods for CT image denoising and reconstruction. Currently, deep learning has become a dominant tool in medical imaging as well as computer vision. In particular, self-supervised learning approaches have attracted great attention as a technique for learning CT images without clean/noisy references. After briefly reviewing the fundamentals of CT image denoising and reconstruction, we examine the progress of deep learning in CT image denoising and reconstruction. Finally, we focus on the theoretical and methodological evolution of self-supervised learning for image denoising and reconstruction.

Teaching methods emphasizing phonological forms enhance L2 vocabulary learning

The present study aims to measure the effects of the teaching of second language (L2) phonological forms on L2 receptive vocabulary learning. Two teaching methods were compared in a pre- and delayed post-test to evaluate their impact on L2 word learning. Participants ( n = 127; mean age = 12;6, i.e. 12 years and 6 months) were randomly divided in two groups that followed either an explicit teaching method focused on L2 phonological forms, or a communicative teaching method focused on meaning, in which L2 phonological forms were taught implicitly. The teaching methods in the two groups aimed to foster the skills and the learning of phonological forms involved in the development of receptive vocabulary. The two teaching methods trained the same skills and relied on the same vocabulary. They both targeted the phonological forms of two difficult phonemic contrasts in French as a foreign language. The two teaching sequences took place during mandatory lessons in French as a foreign language for six weeks (12 lessons), in a Swiss state school. Generalized mixed models were fitted to the data to test for differences across teaching methods in their impact on L2 word learning. Overall, the results indicate that participants made significant progress in word learning, with no significant differences between the two teaching methods. Pronunciation, discrimination, retention in verbal working memory, and the mastery of phoneme–grapheme correspondences are significant factors of vocabulary learning in French as foreign language. The teaching of L2 phonological representations and the training of their processing facilitated the learning of words in L2 French. However, the teaching of vocabulary in French as a foreign language rarely involves a focus on phonological representations.

Structural graph federated learning: Exploiting high-dimensional information of statistical heterogeneity

With the recent progress in graph-federated learning (GFL), it has demonstrated a promising performance in effectively addressing challenges associated with heterogeneous clients. Although the majority of advances in GFL have been focused on techniques for elucidating the intricate relationships among clients, existing GFL methods have two limitations. First, current methods comprising the use of low-dimensional graphs fail to accurately depict the associations between clients, thereby compromising the performance of GFL. Second, these methods may disclose additional information when sharing client-side hidden representations. This paper presents a structural GFL (SGFL) framework and a suite of novel optimization methods. SGFL addresses the limitations of existing GFL approaches with three original contributions. Firstly, our approach advocates the dynamic construction of federated learning (FL) graphs by leveraging the high-dimensional information inherent among clients, while enabling the discovery of hierarchical communities within clients. Secondly, we present SG-FedX, a novel federated stochastic gradient optimization algorithm that mitigates the effects of heterogeneity by intelligently using a global representation. Furthermore, SG-FedX introduces a strict sharing mechanism that protects client privacy more effectively by refraining from sharing client information beyond the model parameters. Our comparative evaluations, conducted against ten representative FL algorithms under challenging non-independently-and-identically-distributed settings, demonstrated the superior performance of SG-FedX. It was noted that, in the cross-dataset scenarios, SG-FedX outperformed the second-best baseline by 8.12% and 7.91% in personalization and generalization performance, respectively.

Highly textured CMOS-compatible hexagonal boron nitride-based neuristor for reservoir computing

Significant advancements in artificial neural networks (ANNs) have driven the rapid progress of artificial intelligence and machine learning. While current feedforward neural networks primarily handle static data, recurrent neural networks (RNNs) are designed for dynamical systems. However, RNNs demand extensive training on specific tasks, limiting their scalability and affordability for edge computing. Physical reservoir computing (RC) offers an alternative approach by mapping inputs into high-dimensional states, allowing for pattern analysis within a fixed reservoir. Unlike RNNs, RC is well-suited for temporal and sequential data processing with rapid speed and low training costs. This makes RC suitable for hardware implementation across various research domains. Nonetheless, existing demonstrations of RC remain constrained to small-scale device arrays. As electronic synapse arrays aim to approach very large-scale and highly complex hardware as in the human brain, managing heat dissipation becomes a formidable challenge. In this work, we successfully developed the neuristors based on textured h-BN films, prepared using a CMOS-compatible technique, and constructed a physical RC system based on as-fabricated devices. Our approach leverages vertically aligned BN to provide aligned diffusion paths for the reproducible migration process of metal ions from the electrodes and offers a potential solution for thermal management in electronic devices. This achievement highlights the promising potential of our neuristors for future high-density and energy-efficient neuromorphic computing.

Linking Education Data in Wales (UK) – Methodology and Lessons Learned

There are separate data collections across education phases in Wales (UK). Each use a different unique learner identifier. The matched education dataset project used advanced data linking methods to produce a set of pseudo identifiers for each learner that can be matched back to the original datasets to undertake specific, de-identified analysis. The first phase of the project involved data cleaning, preparation, and the creation of new derived variables. The second phase established the linking methodology, developing and advanced data linking techniques and algorithms including frequency matching and phonetic string comparators. At each stage of the project the data linking was sequential, ranging from exact to more fuzzy matching. During the code development the approach was uniquely tailored to each data set and constantly fine-tuned to ensure the highest possible match rate while reducing potential for false matches. The resulting linked education data sets are used in a number of ways for statistical and research purposes to support the formation of evidence-based policies. This has included research into raising the compulsory education age and the evaluation of learner journeys during the pandemic. Robust linked data facilitates analysis examining the progression of learners through the education system in Wales. There is a broad scope of future analysis planned and the matched education outputs will be used extensively in the evaluation of learner journeys post pandemic to inform Welsh Government policy. The matched education dataset project involved researching and upskilling in data linkage methodology, we hope to disseminate the lessons learned widely.

Student`s Understanding toward Rural Development Development after Learning from Local Visit by the Faculty of Education Souphanouvong University; During Academic Year of 2018 - 2020

This research aimed to investigate the students’ understanding toward rural development after learning from local visit at rural areas by Faculty of Education Souphanouvong University, which is a subject that belongs to the political science curriculum of Faculty of Education, Souphanouvong University. The research was to gain insights regarding the understanding level by using the pretest and posttest to assess their learning progress, then compared means of those scores with the variable of their residence origins, The sampling students focused on 58 students (27 Women students included) of political science department, the instrument of this research was an objective test with multiple choice with IOC = 1.00 and difficulty level ranged between 0.40-0.86; and the reliability was of 0.84; there are two steps of assessment including of before and after local visiting with the same questionnaire The research findings revealed that 1). The students` understand about rural development policy after local visit was significant higher with the compared mean of posttest remark after local visit was significant different Sig .05, and 2). The students` understand about rural development policy after local visit compared to residence origins were not significant different.

THE APPLICATION OF PROBLEM BASED LEARNING (PBL) MODEL TO IMPROVE STUDENTS' LEARNING OUTCOMES ON SOCIAL STUDIES SUBJECTS IN GRADE IV OF ELEMENTARY SCHOOL II TANJUNGSARI

This research was conducted using the classroom action research (CAR) method. The progress in learning and storytelling skills is still below the Minimum Completeness Criteria and success indicators. The study included 142 students from SDN 2 Tanjungsari, ranging from grade I to grade VI. 36 sixth-grade students were part of the sample, with an equal number of males and females. The researcher collected data using tests and observations. The research instruments were observation sheets and test sheets. The analysis techniques used were qualitative and quantitative analysis. The results of this study showed that the students' learning outcomes in cycle I had a mastery level of 27.78%. In cycle II, there was an improvement in student learning outcomes with a mastery level of 52.78%. In cycle III, the mastery level reached 83.33%. This indicates that the students' learning outcomes had met the applied mastery criteria of 80%. The study also observed students' learning activities. In cycle I, the students' activities scored 19 with a percentage of 1.58%, which was still categorized as poor. In cycle II, there was an improvement in students' learning activities with a score of 27 and a percentage of 2.25%, categorized as moderate. In cycle III, students' learning activities had a score of 41 with a percentage of 3.42%, which was categorized as good. Research using Problem-Based Learning can enhance learning outcomes and student engagement in Social Studies for fourth-grade students at SDN 2 Tanjungsari in Indramayu District.

Research on the Intelligent System of English Teaching from the Perspective of Intercultural Education Based on Deep Learning

In today’s globalized world, cross-cultural communication skills are crucial for English learners. However, traditional English teaching often overlooks cultural differences among students, leading to difficulties in practical communication. The development of deep learning technology has provided new possibilities for English teaching, which can analyze students’ cultural background and learning needs through intelligent systems, and provide more personalized teaching plans. This study focuses on the application of intelligent system in English teaching from the perspective of intercultural education. By analyzing the characteristics of cross-cultural education and the advantages of deep learning technology, an intelligent system model of English teaching based on deep learning is proposed. The model can provide individualized English teaching programs according to students’ cultural backgrounds, learning needs and learning progress. A recommendation algorithm based on a cross-cultural education model comparison is proposed. For each student, retrieve similar teaching cases from the database based on their cultural background and learning needs. Compare the similarity between current students and cases in the database using similarity calculation algorithms. Based on the sustainable development teaching model of college English, the MAP algorithm is analyzed. The experimental results show that the intelligent English teaching system based on deep learning has a remarkable effect on improving students’ English achievement and intercultural communication ability. The model is trained and tested by the measured data. The error of the test results is less than 5%. The results of the experiment prove that the construction of a Chinese college English sustainable development education model can effectively enhance China’s cultural soft power.

EXPLORING STUDENT ENGAGEMENT WITH FORMATIVE ASSESSMENT AND ICT TOOLS

This study investigates the potential of Information and Communication Technology (ICT) tools to enhance formative assessment (FA) practices in English as a Foreign Language (EFL) classrooms. While previous research has focused primarily on teacher feedback in writing courses, this study emphasizes a broader scope of FA. Utilizing a cross-sectional, quantitative analysis, the research examines how ICT tools support FA's goal of facilitating the learning process from students' perspectives, thereby informing effective teaching practices and improving learning outcomes. Participants included 173 university EFL students in Japan, divided into English majors and non-English majors. The study employed a revision of an existing survey for language learning with digital tools and a self-developed survey based on an existing framework for FA. Correlational analysis indicated that participants generally perceived ICT tools as supportive of all five FA principles. Despite issues like connectivity problems, students found the tools valuable for making the learning progress visible and enhancing peer communication. The study underscores the importance of understanding and practicing FA beyond feedback, such as clarifying learning goals and fostering peer learning, to enhance learning. These findings suggest that integrating ICT tools into FA practices can significantly benefit EFL instruction by promoting student engagement and self-regulated learning.

Survey of source code vulnerability analysis based on deep learning

Amidst the rapid development of the software industry and the burgeoning open-source culture, vulnerability detection within the software security domain has emerged as an ever-expanding area of focus. In recent years, the rapid advancement of artificial intelligence, particularly the notable progress in deep learning for pattern recognition and natural language processing, has catalyzed a surge in research endeavors exploring the integration of deep learning for the enhancement of vulnerability detection techniques. In this paper, we investigate contemporary deep learning-based source code analysis methods, with a concentrated emphasis on those pertaining to static code vulnerability detection. We categorize these methods based on various representations of source code employed during the preprocessing stage, including token-based and graph-based representations of source code, and further subdivided based on the types of deep learning algorithms or graph representations employed. We summarize the basic processes of model training and vulnerability detection under these different representation formats. Furthermore, we explore the limitations inherent in current approaches and provide insights into future trends and challenges for research in this field.

The Implementation of Love Sensory Stimulation Method in Teaching Autistic Students

This research aimed to find depth insight into the implementation of love sensory in teaching English to autistic students and subsequently the impact of its implementation on their learning achievement. The research subjects are students who have problems understanding instructions, interacting, and communicating. The data collected in this research is qualitative and was collected through observation and interviews. There are some findings found such as the behavior of autistic students that is seen in the teaching and learning process such as difficulty in understanding instructions and interacting, and ignoring their learning environment are specific symptoms of Autistic Syndrome Disorder. The symptoms shown by autistic students are the cause of failure in the learning process. However, the implementation of love sensory stimulation can change the behavior of autistic students. This method can help autistic students adapt and learn together with normal students. So, autistic students’ progress in learning and have achieved their learning goals. The expectation is to provide contributions to parties in need, including teachers who teach autistic students, parents, and future researchers. Based on the findings can be concluded that autistic students can develop in their learning process and interact in their social environment if they are guided in the right way.

Exploration in 4-year-old children is guided by learning progress and novelty.

Humans are driven by an intrinsic motivation to learn, but the developmental origins of curiosity-driven exploration remain unclear. We investigated the computational principles guiding 4-year-old children's exploration during a touchscreen game (N = 102, F = 49, M = 53, primarily white and middle-class, data collected in the Netherlands from 2021-2023). Children guessed the location of characters that were hiding following predictable (yet noisy) patterns. Children could freely switch characters, which allowed us to quantify when they decided to explore something different and what they chose to explore. Bayesian modeling of their responses revealed that children selected activities that were more novel and offered greater learning progress (LP). Moreover, children's interest in making LP correlated with better learning performance. These findings highlight the importance of novelty and LP in guiding children's exploration.