Search Results (141)

The study undertook a comprehensive review and comparative analysis of natural language processing techniques for news article classification, with a particular focus on Java language libraries. The dataset comprised an excess of 200,000 items of news metadata sourced from The Huffington Post. The traditional algorithms based on mathematical statistics and deep machine learning were evaluated. The libraries chosen for tests were Apache OpenNLP, Stanford CoreNLP, Waikato Weka, and the Huggingface ecosystem with the Pytorch backend. The efficacy of the trained models in forecasting specific topics was evaluated, and diverse methodologies for the feature extraction and analysis of word-vector representations were explored. The study considered aspects such as hardware resource management, implementation simplicity, learning time, and the quality of the resulting model in terms of detection, and it examined a range of techniques for attribute selection, feature filtering, vector representation, and the handling of imbalanced datasets. Advanced techniques for word selection and named entity recognition were employed. The study compared different models and configurations in terms of their performance and the resources they consumed. Furthermore, it addressed the difficulties encountered when processing lengthy texts with transformer neural networks, and it presented potential solutions such as sequence truncation and segment analysis. The elevated computational cost inherent to Java-based languages may present challenges in machine learning tasks. OpenNLP model achieved 84% accuracy, Weka and CoreNLP attained 86% and 88%, respectively, and DistilBERT emerged as the top performer, with an accuracy rate of 92%. Deep learning models demonstrated superior performance, training time, and ease of implementation compared to conventional statistical algorithms. Full article

Core mathematics courses are fundamental to the academic success of engineering students in higher education. These courses equip students with skills and knowledge applicable to their specialized fields. However, first-year engineering students often face significant challenges in mathematics due to a range of factors, including insufficient preparation, mathematics anxiety, and difficulty connecting theoretical concepts to real-life applications. The transition from secondary to tertiary mathematics remains a key area of educational research, with ongoing discussions about effective pedagogical approaches for teaching engineering mathematics. This study utilized a belief survey to gain general insights into the attitudes of first-year mathematics students towards the subject. In addition, it employed the activity theory framework to conduct a deeper exploration of the experiences of first-year engineering students, aiming to identify contradictions, or “tensions,” encountered within a flipped-classroom learning environment. Quantitative data were collected using surveys that assessed students’ self-reported confidence, competence, and knowledge development. Results from Friedman’s and Wilcoxon’s Signed-Rank Tests, conducted with a sample of 20 participants in 10 flipped-classroom sessions, statistically showed significant improvements in all three areas. All of Friedman’s test statistics were above 50, with p-values below 0.05, indicating meaningful progress. Similarly, Wilcoxon’s Signed-Rank Test results supported these findings, with p values under 0.05, leading to the rejection of the null hypothesis. The qualitative data, derived from student questionnaire comments and one-to-one interviews, elucidated critical aspects of flipped-classroom delivery. The analysis revealed emerging contradictions (“tensions”) that trigger “expansive learning”. These tensions encompassed the following: student expectation–curriculum structure; traditional versus novel delivery systems; self-regulation and accountability; group learning pace versus interactive learning; and the interplay between motivation and anxiety. These tensions are vital for academic staff and stakeholders to consider when designing and delivering a first-year mathematics course. Understanding these dynamics can lead to more effective, responsive teaching practices and support student success during this crucial transition phase. Full article

Quantum computing, with its foundational principles of superposition and entanglement, has the potential to provide significant quantum advantages, addressing challenges that classical computing may struggle to overcome. As data generation continues to grow exponentially and technological advancements accelerate, classical machine learning algorithms increasingly face difficulties in solving complex real-world problems. The integration of classical machine learning with quantum information processing has led to the emergence of quantum machine learning, a promising interdisciplinary field. This work provides the reader with a bottom-up view of quantum circuits starting from quantum data representation, quantum gates, the fundamental quantum algorithms, and more complex quantum processes. Thoroughly studying the mathematics behind them is a powerful tool to guide scientists entering this domain and exploring their connection to quantum machine learning. Quantum algorithms such as Shor’s algorithm, Grover’s algorithm, and the Harrow–Hassidim–Lloyd (HHL) algorithm are discussed in detail. Furthermore, real-world implementations of quantum machine learning and quantum deep learning are presented in fields such as healthcare, bioinformatics and finance. These implementations aim to enhance time efficiency and reduce algorithmic complexity through the development of more effective quantum algorithms. Therefore, a comprehensive understanding of the fundamentals of these algorithms is crucial. Full article

This study aimed to investigate the use of a structured learning outcomes approach for fifth-grade mathematics instruction, with a focus on students with difficulties in learning mathematics, across two mainstream schools in Kuwait. Three special education teachers, across three classes, who worked with 30 focus students, participated in the study. Teachers implemented a structured approach to curriculum and pedagogy based on a focus on learning outcomes, simultaneously supported by a dedicated technology platform, with the aim of encouraging a focus on differentiation to meet individual learning needs. This study employed a mainly qualitative approach involving interviews to gauge teacher perceptions of the extent to which this approach supported them in thinking more effectively about individual learning needs. Links are made to the extant literature in this area, and recommendations are made for future research using this learning outcomes approach based on a wider sample of mainstream schools and classrooms. Full article

Readability formulas are mathematical functions that assess the ‘difficulty’ level of a given text. They play a crucial role in aligning educational texts with student reading abilities; however, existing models are often not tailored to specific linguistic or regional contexts. This study aims to develop and evaluate two novel readability formulas specifically designed for the Mexican Spanish language, targeting elementary education levels. The formulas were trained on a corpus of 540 texts drawn from official elementary-level textbooks issued by the Mexican public education system. The first formula was constructed using multiple linear regression, emulating the structure of traditional readability models. The second was derived through genetic programming (GP), a machine learning technique that evolves symbolic expressions based on training data. Both approaches prioritize interpretability and use standard textual features, such as sentence length, word length, and lexical and syntactic complexity. Experimental results show that the proposed formulas outperform several well-established Spanish and non-Spanish readability formulas in distinguishing between grade levels, particularly for early and intermediate stages of elementary education. The GP-based formula achieved the highest alignment with target grade levels while maintaining a clear analytical form. These findings underscore the potential of combining machine learning with interpretable modeling techniques and highlight the importance of linguistic and curricular adaptation in readability assessment tools. Full article

Word problem posing is a critical component of student mathematics learning. This study examined the effects of a problem-posing intervention designed to improve mathematics performance and sentence-writing conventions. Using a multiple baseline across participants design, three third-grade students with mathematics and writing difficulties received one-on-one intervention delivered after school at a university reading center. Data were collected from baseline, intervention, and maintenance phases. Visual analysis and Tau-U statistical analysis indicated that all three students showed improvements in problem solving, problem posing, total words written, words spelled correctly, and correct writing sequence. Post-intervention data suggested that students maintained the improvement over baseline. Discussion and implications for future practice and research were provided. Full article

The assessment of mathematical competence, particularly in real-world problem-solving contexts, has become increasingly crucial in high school educational evaluation. While traditional methods have shifted towards emphasizing problem-solving skills, they remain predominantly outcome-oriented, often failing to adequately capture the nuanced cognitive processes underlying students’ problem-solving behaviors. To address this gap, this study introduces a process-oriented assessment method leveraging eye-tracking technology. Fifty-three university students (primarily first- and second-year undergraduates) in China were recruited to solve six context-based mathematical problems of varying difficulty levels while wearing portable eye-tracking glasses, allowing for natural problem-solving behaviors in a paper-and-pencil test format. The study established a multidimensional model of eye movement features to evaluate problem-solving processes. Using China’s National College Entrance Examination (CNCEE) mathematics scores as the dependent variable, a Partial Least Squares Regression (PLSR) analysis achieved its best predictive performance (prediction R² of 0.271) based on multidimensional eye movement features when solving the most difficult problem. The first visual intake duration on problem-reading areas and key information regions emerged as significant contributors of the students’ CNCEE scores. These findings substantiate the potential of eye-tracking technology as a valuable tool for educational assessment, offering insights into the assessment of students’ mathematical competence and supporting the development of more comprehensive learning diagnosis and intervention strategies. Full article

Physics teaching faces challenges due to students’ limited understanding of fundamental concepts such as force and motion, as well as the restricted pedagogical strategies often employed by instructors and the limited variety of approaches to physical foundations. This difficulty is aggravated by the perception of physics as distant from everyday life and by the traditional approach focused on solving mathematical problems. Despite the importance of Newton’s second law, many students confuse the relationships between mass, force, and acceleration, which highlights the need to innovate in teaching practices toward active learning trends. To explore the state of teaching Newton’s second law, a systematic review of the literature was conducted using the PRISMA methodology, analyzing twenty-six articles from the Web of Science and Scopus databases. This revealed an increase in interest in teaching this law, especially in 2023. However, the limited number of studies (only 26) also indicates that research on this topic remains scarce and underexplored. Most studies focus on primary and secondary school students (43%) and employ quantitative methodologies (38%). Teaching strategies include problem-solving (40%), simulations (27%), practical activities (14%), and group discussions (12%). Furthermore, it was identified that Newton’s law is primarily represented in scalar form, with limited inclusion of vector approaches, which highlights the need to discuss didactic alternatives that consider both approaches. Full article

Dyscalculia is a specific learning disorder that affects numerical comprehension, arithmetic reasoning, and problem-solving skills, significantly impacting academic performance and daily life activities. Traditional teaching methods often fail to address the unique cognitive challenges faced by students with dyscalculia, highlighting the need for innovative educational approaches. Recent studies suggest that educational robotics and game-based learning can provide engaging and adaptive learning environments, enhancing numerical cognition and motivation in students with mathematical difficulties. The intervention was designed to improve calculation skills, problem-solving strategies, and overall engagement in mathematics. The study involved 73 secondary students, divided into three classes, among whom only a specific group had been diagnosed with dyscalculia. Data were collected through pre- and post-intervention assessment evaluating improvements in numerical accuracy, processing speed, and support motivation. Preliminary findings indicate that robotics and gamification create an interactive, less anxiety-inducing learning experience, facilitating conceptual understanding and retention of mathematical concepts. The results suggest that these tools hold promise as supplementary interventions for children with dyscalculia. Future research should explore long-term effects, optimal implementation strategies, and their integration within formal educational settings. Full article

Recognizing the impact of mathematical learning difficulties on student achievement, this research focuses on developing adaptive, technology-based solutions for those struggling with learning mathematics, including individuals with dyscalculia. Dyscalculia, a difficulty in understanding numbers and mathematics, can profoundly affect a child’s academic progress and self-confidence. Many interventions aim for broad effectiveness but often struggle to address individual learning differences. This research addresses this gap by employing Dynamic Bayesian Networks (DBNs) within intelligent tutoring systems to develop a personalized, gamified approach for improving mathematical skills in children with dyscalculia. We assessed 158 children aged 6–10 years using the Smartick Dyscalculia Assessment Tool to identify specific numerical cognition deficits. Based on these assessments, we have developed EDSense (Early Detection and Intervention for Insufficient Number Sense), an adaptive web-based learning tool. EDSense provides personalized support and targets skill refinement in mathematics learning. A pre-test and post-test design evaluates EDSense’s effectiveness and demonstrates significant improvements in numerical abilities. The findings highlight the crucial role of adaptive learning platforms in addressing dyscalculia. The EDSense platform demonstrates gamified, self-directed learning environments to enhance both engagement and learning outcomes by accommodating individual cognitive differences. We have proposed a technology-driven framework for personalized dyscalculia interventions, emphasizing early detection to support mathematical skill development. Full article

In this cohort study, we evaluated the cognitive and learning difficulties of school-age children perinatally infected with Chikungunya virus (CHIKV) on Reunion Island using the Evaluation of Cognitive Functions and Learning in Children (EDA) battery screening test compared to the healthy children cohort used for EDA development. Of the 19 infected children, 11 (57.9%) exhibited subnormal or abnormal scores, of whom 3 were classified as high risk, and 8 were classified as at risk for cognitive and learning difficulties. Children who had encephalopathy were at higher risk for displaying at least one difficulty than non-encephalopathic children (relative risk 2.13; 95% CI 1.05–4.33). The difficulties observed affected verbal functions, non-verbal functions, and learning abilities, such as phonology, lexical evocation and comprehension, graphism, selective visual attention, planning, visual–spatial reasoning, dictation and mathematics, as well as core executive functions, such as inhibitory control, shifting, and working memory. Neurocognitive dysfunctions could be linked to severe brain damage, as evidenced by severe white matter reduction mainly in the frontal lobes and corpus callosum and potentially in all functional networks involved in difficulties. These results should motivate further investigation of intellectual and adaptive functioning to diagnose intellectual deficiency and severe maladaptive behaviour in children perinatally infected with Chikungunya virus. Full article

This study examines the effectiveness of in-service training programs aimed at enhancing teachers’ knowledge and self-efficacy in the context of learning disabilities (LD) in mathematics. Despite the increasing use of both interactive online learning and face-to-face training methods in professional development, limited research has compared their relative effectiveness in this specific field. Furthermore, existing studies have not adequately addressed whether improvements in teachers’ knowledge and self-efficacy are sustained over time. To address this gap, the present study employed a quasi-experimental design with two experimental groups. The sample consists of 80 classroom teachers, with 40 participants in the interactive online learning education group and 40 in the face-to-face education group. The training program consists of 16 h of instruction over four weeks. Data were collected using a demographic questionnaire and the Mathematics Learning Difficulty Area Teacher Self-Efficacy Scale, and statistical analyses were conducted. The findings indicate that, prior to the intervention, teachers in the interactive online learning education group exhibited significantly higher levels of knowledge and self-efficacy. However, the post-intervention results revealed no statistically significant differences between the two groups. Cohen’s d analysis indicated a moderate effect size for interactive online learning education before the intervention, which diminished to a small effect size afterward. This study validates the efficiency of interactive online learning from smart learning environments for in-service training programs aimed at enhancing teachers’ knowledge and self-efficacy about learning disabilities in mathematics. These results suggest that both training modalities effectively improve teachers’ knowledge and self-efficacy, but neither demonstrate a clear long-term advantage. This study underscores the need for further research to determine optimal strategies for sustaining professional development in this domain. Full article

Biological research traditionally relies on experimental methods, which can be inefficient and hinder knowledge transfer due to redundant trial-and-error processes and difficulties in standardizing results. The complexity of biological systems, combined with large volumes of data, necessitates precise mathematical models like ordinary differential equations (ODEs) to describe interactions within these systems. However, the practical use of ODE-based models is limited by the need for curated data, making them less accessible for routine research. To overcome these challenges, we introduce LazyNet, a novel machine learning model that integrates logarithmic and exponential functions within a Residual Network (ResNet) to approximate ODEs. LazyNet reduces the complexity of mathematical operations, enabling faster model training with fewer data and lower computational costs. We evaluate LazyNet across several biological applications, including HIV dynamics, gene regulatory networks, and mass spectrometry analysis of small molecules. Our findings show that LazyNet effectively predicts complex biological phenomena, accelerating model development while reducing the need for extensive experimental data. This approach offers a promising advancement in computational biology, enhancing the efficiency and accuracy of biological research. Full article

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study. Full article

Computational thinking skills among high school students have become a global concern, especially in the context of the ever-evolving digital education era. However, the attention given by teachers to this skill during mathematics instruction has not been a priority. This study aims to evaluate and explore the impact of project-based learning (PBL) integrated with flipped classroom on high school students’ computational thinking skills in mathematics. The research design employed a mixed-method approach with a quasi-experimental, nonequivalent pre-test post-test control group design. The experimental group (46 students) and control group (45 students) were selected through simple random sampling from 12th-grade science students. Data were collected through tests, questionnaires, and in-depth interviews, using instruments such as computational thinking skills assessment questions, questionnaires, and interview protocols. Quantitative data analysis was performed using SPSS Version 26 for t-tests and ANOVA, while qualitative analysis was conducted using ATLAS.ti with an abductive-inductive and thematic approach. The findings indicate that PBL integrated with flipped classrooms significantly improved students’ decomposition, pattern recognition, and abstraction skills. The implementation of PBL, integrated with a flipped classroom, created an interactive learning environment, fostering active engagement and enhancing students’ understanding and skills in solving mathematical concepts. Although there was an improvement in algorithmic thinking skills, some students still faced difficulties in developing systematic solutions. The results of this study suggest that further research could explore other methodologies, such as grounded theory and case studies integrated with e-learning, and emphasize visual analysis methods, such as using photo elicitation to explore thinking skills. Full article