Learning Perspective Research Articles

Analysis of the Causes and Countermeasures of Classroom Silence Phenomenon among Students in Contemporary Chinese Colleges and Universities

The college classroom is an important field for talent cultivation and student development in colleges and universities. However, in actual teaching, only a relatively small number of Chinese college students are able to actively participate in classroom activities and speak positively. Most students often choose to remain silent in response to teachers' questions. Classroom silence is generally categorized into negative silence and positive silence. Negative silence has become a major obstacle to improving the efficiency of classroom teaching, which to some extent affects the effectiveness and function of classroom teaching. By analyzing classroom silence, a common phenomenon in Chinese university classrooms, and analyzing the phenomenon of classroom silence and its causes from the perspectives of teachers' teaching and students' learning, we can better understand the characteristics of college students' classroom participation, reveal the deficiencies of teachers' classroom teaching, further propose strategies to improve classroom teaching and reduce the phenomenon of negative silence in college classrooms, and improve the effectiveness of college classroom teaching.

Impact and learner perspectives of a spaced-education platform to assess hand hygiene auditor competency.

To ensure whether spaced education, which increases long-term knowledge retention, could be integrated into auditor competency assessment. Quality improvement project. Academic, freestanding children's hospital. Hand hygiene (HH) auditors. We enrolled trained HH auditors in an online spaced-education platform to assess mastery of knowledge, delivering 46 unique questions at spaced intervals followed by rationale; we retired questions after 3 correct answers. An e-mailed 10-item survey gauged participant satisfaction with the program. The Wilcoxon signed-rank test was used to compare change in median knowledge score from first to final attempt. A total of 12,120 questions were attempted by 126 auditors, and 49 (39%) completed the entire course. Median knowledge score increased significantly by 10.5 percentage points (IQR 4-15) between first and final attempts (P < 0.001). Thirty auditors (27%) responded to the survey. The majority agreed the number and complexity of questions were appropriate (57% and 67%, respectively). Eighty-seven percent reported the platform easy to navigate, and 77% agreed adequate time was provided for completion. Free-text suggestions included delivering fewer questions at a narrower spacing interval over a shorter time frame because of competing work demands. Auditor knowledge of HH indications and technique is critical to ensuring data validity. A spaced-education competency program improved HH auditor knowledge in the short term. Completion rate was low, and some participants expressed a desire for fewer questions over a shorter time frame. This study offers insight into ways to optimize spaced education as a potential tool for HH competency assessment.

Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes.

Diabetes mellitus (DM) presents a critical global health challenge, characterized by persistent hyperglycemia and associated with substantial economic and health-related burdens. This study employs advanced machine-learning techniques to improve the prediction and classification of antidiabetic peptides, with a particular focus on differentiating those effective against T1DM from those targeting T2DM. We integrate feature selection with analysis methods, including logistic regression, support vector machines (SVM), and adaptive boosting (AdaBoost), to classify antidiabetic peptides based on key features. Feature selection through the Lasso-penalized method identifies critical peptide characteristics that significantly influence antidiabetic activity, thereby establishing a robust foundation for future peptide design. A comprehensive evaluation of logistic regression, SVM, and AdaBoost shows that AdaBoost consistently outperforms the other methods, making it the most effective approach for classifying antidiabetic peptides. This research underscores the potential of machine learning in the systematic evaluation of bioactive peptides, contributing to the advancement of peptide-based therapies for diabetes management.

Evaluating AI's impact on self-regulated language learning: A systematic review

AI technology is reshaping language classrooms, prompting students to adopt flexible roles exhibiting linguistic competence and self-regulated learning (SRL) skills. Considerable studies explore the necessary integrated learning perspectives, emphasizing AI's adaptive role as a mind tool. In AI-mediated language learning, the technology's metacognitive importance enables students to learn with AI as a partner, encouraging independent critical thinking. Within Zimmerman's SRL model, AI as a mind tool is integrated for improving language students' strategic employment in a cyclical process. A systematic review, following PRISMA protocols, examines the intersection of AI and self-regulated language learning (SRLL) over 2000–2022. Findings highlight AI's evolving role, predominantly through algorithms and systems, aiming for micro and macro integration. Interactive AI has not fully engaged in two-way directions, despite a familiar process approach in reviewed studies. In the favored ESL/EFL research context, task-specific AI is utilized to encourage cyclical improvement with learner autonomy enhancement mainly among higher education students at intermediate level or above. Pedagogical values are possible when major SRL phases are fully practiced, even without highly autonomous AI. Future research is directed toward adaptive personalized technology by exploring the dynamic interplay between AI technologies and SRLL as educational practices under Education 4.0 principles.

Innovative Learning Strategies to Enhance Student Motivation and Academic Achievement: A Constructivist Learning Perspective in Islamic Religious Education

This study aims to evaluate the effectiveness of innovative, constructivist-based learning strategies in enhancing student motivation and academic achievement. The constructivist approach, which emphasizes active, project-based, and collaborative learning, was implemented in a higher education setting to assess its impact on student motivation and academic outcomes. A quasi-experimental research design with treatment and control groups was employed, involving students from various academic programs. Data were collected through questionnaires measuring learning motivation and academic performance before and after the implementation of innovative strategies. Data analysis was conducted using descriptive and inferential statistics, including t-tests to compare differences between the treatment and control groups. The findings indicate that the application of constructivist-based innovative learning strategies significantly improves student motivation and academic achievement. Active and project-based learning methods increased student engagement and facilitated better understanding of the material, as reflected in improved academic outcomes. The study also underscores the importance of a supportive learning environment, including the use of digital technology, in fostering the learning process. Limitations of the study include a limited sample size and contextual variables that may influence the results. The study provides recommendations for educational institutions to adopt innovative learning strategies and encourages further research to explore various learning approaches in greater depth

Heterogeneity in the development of differential reinforcement: examining predictive power of definitions and differential association from a social learning perspective

Social learning theory is a prominent framework for understanding the relevance of socialization processes for predicting behavior. While recent research has focused on placing these processes within developmental contexts, there remain gaps in understanding how differential reinforcement for involvement in antisocial behavior may develop across adolescence and emerging adulthood and how definitions and differential association predict development. All 11 waves of the Pathways to Desistance dataset were analyzed, comprising the responses of 1354 justice-involved youth across seven years post-adjudication. Group-based trajectory modeling was used to identify developmental patterns of differential reinforcement and regression modeling was used to examine predictors of development. Results indicated that a two-group model of development best fit the reinforcement data (High, Low). Greater endorsement of antisocial definitions and greater differential association with deviant peers and a significant and positive interaction between these constructs predicted increased odds of assignment to the High group.

Enhancing Support for Slow Learners Evaluating Inclusive Education Models in Contemporary Classrooms

Present study assesses the effectiveness of inclusive education frameworks in supporting slow learners, a student needs extra time and resources to achieve academic success. The objective is to include these students in regular classes to promote fairness and social unity. Qualitative research study supported by six teachers working in primary, elementary, and secondary institutions in Karachi. The data collected provided thematic analysis identifying key challenges, strategies, and impacts in the implementation of inclusive education toward children with learning difficulties. Findings also show that though the models of inclusive education may uplift the slow learners both in academic performance and self-esteem, their effectiveness depends on adequate resources, flexible curriculum, and comprehensive training for teachers. Individual education plans and differentiated instruction are also identified as important. Future research has to be based on longitudinal studies and comparative studies across different cultural contexts of inclusive education models with specific attention to teacher preparation and the perspectives of slow learners.

Equitable and Inclusive Education of NEP 2020: It’s Importance for Learning Perspective.

Equitable and Inclusive Education of NEP 2020: It’s Importance for Learning Perspective.

Analysis of Activities in Senior English Textbook from the Perspective of Deep Learning: Taking Required Course Book One by FLTR Press (2019 Edition) as an Example

Deep learning is an effective way to cultivate students’ core competencies. As an organic part and crucial resource of deep learning in English class, the textbook presents activities at diverse cognitive levels. In this study, the cognitive levels of activities are analyzed in a senior English textbook by adopting a qualitative method, which intends to reveal the characteristics of deep learning activities. Then in the study an even distribution is revealed between surface and deep learning activities, the latter featuring integration, contextualization, and participatory nature. Finally, three suggestions are put forward to promote students’ deep learning.

Pengukuran Kinerja Industri Kelapa Sawit Menggunakan Metode Balanced Scorecard dan Proses Hirarki Analitis

&lt;p&gt;&lt;strong&gt;Palm oil plantations are one of the agricultural commodities in Indonesia that play a crucial role in food availability and the economy. This research employs the Balanced Scorecard (BSC) model as an integrated performance measurement system. The BSC measures company performance from both financial and non-financial perspectives. The study aims to measure the performance of the palm oil industry at PT. KMP Masamba, and covers four perspectives&lt;/strong&gt;&lt;strong&gt; &lt;/strong&gt;&lt;strong&gt;namely&lt;/strong&gt;&lt;strong&gt; financial, customer, internal business, and learning and growth. The weighting of performance measurement elements is done using the analytical hierarchy process with the assistance of Expert Choice software. The results are expected to assist PT. KMP Masamba in determining necessary improvement priorities. The performance measurements from each perspective, ranked from highest to lowest, are as follows: customer perspective (0.148), internal business perspective (0.048), learning and growth perspective (0.047), and financial perspective (0.030). The overall company performance score is 0.543, indicating satisfactory performance and approaching high targets. Based on these conclusions, recommendations for this research include periodic performance measurement by the company to assess its comprehensive condition and improve functional activities with low performance, to improve their performance indicators.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt; - &lt;em&gt;Analytical Hierarchy Process, Balance Scorecard,&lt;/em&gt; &lt;em&gt;The Performance Measurements. &lt;/em&gt;&lt;em&gt;&lt;/em&gt;&lt;/p&gt;

The Effect of Linear Discriminant Analysis and Quantum Feature Maps on QSVM Performance for Obesity Diagnosis

Obesity, characterized by an excessive increase in body fat, is not only a significant disease but also a condition that serves as the basis for many other illnesses. Therefore, early intervention and necessary precautions for diagnosing and treating obesity are of paramount importance. Classical machine learning algorithms are actively utilized in medical fields to expedite prediction processes. However, the increasing volume of data renders even effective classification algorithms inadequate for experts to diagnose diseases. Quantum computing-based algorithms come into play at this point, offering a new perspective in machine learning by utilizing quantum physics, which is contrary to the rules of classical physics. Dimensionality reduction techniques required for the use of quantum-based algorithms play an essential role in both classical and quantum applications. In this study, Linear Discriminant Analysis (LDA) and Principal Component Analysis (PCA), classical dimensionality reduction methods, were applied to the obesity dataset and analyzed with Quantum Support Vector Machine (QSVM) and Support Vector Machine (SVM) algorithms. To conduct QSVM studies, the comparison of three different quantum feature maps providing the qubit transformation of classical bit data is also included in this study. As a result of the analysis, it was determined that the proposed method as LDA-QSVM achieved 100% success when used with Z and Pauli X feature maps. This success, which is rare in literature studies on obesity data, emphasizes the future potential of quantum-based algorithms in obesity diagnosis and treatment.

Effectiveness of Play-Based Learning Method in Promotion of Early Literacy Skills Among Early Childhood Development Education Children

Literacy skills for children in their early years are critical to their cognitive development and future success in primary, secondary and higher education levels. Children acquire literacy skills early through listening and interacting through play-based activities both at home and school with their teachers, colleagues and parents. This paper looks at the effectiveness of play-based learning in promotion of early literacy skills among children in early childhood development education level. Specifically, the paper analyses the conceptual and theoretical perspectives of play-based learning and literacy skills acquisition by children through the theories of Maria Montessori, Vygotsky social constructivism theory and Jean Piaget cognitive theories. The study uses secondary sources of information for analysis based on the following objectives; types of play-based approaches used by teachers in ECD setting, teachers’ role in the implementation of play-based learning and effect of play-based learning approaches towards promotion of early literacy skills by ECD pupils in schools. The paper has found out that acquisition of early literacy skills by learners was dependent on the degree to which teachers were utilising play-based learning methods in classroom. The review of studies has shown that majority of teachers are not using play-based teaching methods and hence promotion of early literacy skills for children was significantly affected. Further, time allocated for play-based learning processes was inadequate and majority of public ECD centres were found not to have adequate infrastructure and materials to support play-based learning. The paper recommends that teachers need to be supported in implementation of play-based learning through attendance of in-service training programmes and benchmarking sessions in other schools, schools to upgrade the infrastructure and instructional materials required to support play-based learning and education officials to conduct supervision of implementation of play-based learning in schools

Recent advancements toward the incremsent of drug solubility using environmentally-friendly supercritical CO2: a machine learning perspective.

Inadequate bioavailability of therapeutic drugs, which is often the consequence of their unacceptable solubility and dissolution rates, is an indisputable operational challenge of pharmaceutical companies due to its detrimental effect on the therapeutic efficacy. Over the recent decades, application of supercritical fluids (SCFs) (mainly SCCO2) has attracted the attentions of many scientists as promising alternative of toxic and environmentally-hazardous organic solvents due to possessing positive advantages like low flammability, availability, high performance, eco-friendliness and safety/simplicity of operation. Nowadays, application of different machine learning (ML) as a versatile, robust and accurate approach for the prediction of different momentous parameters like solubility and bioavailability has been of great attentions due to the non-affordability and time-wasting nature of experimental investigations. The prominent goal of this article is to review the role of different ML-based tools for the prediction of solubility/bioavailability of drugs using SCCO2. Moreover, the importance of solubility factor in the pharmaceutical industry and different possible techniques for increasing the amount of this parameter in poorly-soluble drugs are comprehensively discussed. At the end, the efficiency of SCCO2 for improving the manufacturing process of drug nanocrystals is aimed to be discussed.

Buffering the Effects of Students’ Perceived Task Difficulty on Cognitive Engagement in EFL Writing Classrooms: The Role of Foreign Language Enjoyment and Growth Mindset

Submitted Currently, research on second language acquisition (SLA) and psychology of language learning has conceived foreign language learning as a complex system of beliefs, thoughts, emotions, and actions that contribute immensely to shape students’ learning experiences. The significance of such research lies in the fact that they provide a holistic perspective of FL learning by considering the multiple and interrelated interactions of beliefs, emotional and cognitive factors, rather than examining any single variable in isolation. Therefore, this study seeks to provide such insights by examining the interaction between perception of task difficulty and cognitive engagement in an EFL writing classroom, taking into consideration the possible effects of foreign language enjoyment (FLE) and mindset on such relationship. Data were collected from 218 Saudi undergraduate students using a questionnaire. Results revealed that: a) students’ perception of task difficulty is linked to their cognitive engagement; b) FLE acts as a buffer against the negative effects of difficult EFL writing tasks on students’ cognitive engagement; c) growth mindset has no moderating role in the relationship between perceived difficulty and cognitive engagement.

A Review and a Perspective of Deep Active Learning for Remote Sensing Image Analysis: Enhanced adaptation to user conjecture

A Review and a Perspective of Deep Active Learning for Remote Sensing Image Analysis: Enhanced adaptation to user conjecture

3D Multimodal Fusion Network with Disease-induced Joint Learning for Early Alzheimer's Disease Diagnosis.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer's disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Prediction of material stability of two-dimensional semiconductors: An interpretable machine learning perspective

Despite significant advancements in leveraging artificial intelligence (AI) for drug design, materials science, and other fields, the question of how each dataset feature influences a target metric—essential for constructing better predictive models and targeted materials design—remains largely unaddressed. In this study, we explored the application of interpretable machine learning (ML) techniques to the inverse design of two-dimensional (2D) semiconductor materials, a critical yet underexplored area within the AI4Science domain. Our approach utilized a dataset from the C2DB database, incorporating advanced feature engineering and data imputation strategies to predict material stability, a key determinant of a materials industrial and academic value. Through the calculation of Shapley additive explanation scores and counterfactual analysis, we provided a nuanced understanding of feature contributions toward material stability, enabling the targeted design of 2D semiconductors with optimized properties. This work not only fills the gap in the current literature by emphasizing the role of interpretability in materials design but also demonstrates the potential of interpretable ML in guiding the development of novel materials with enhanced performance characteristics.

Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts.

Accurately basecalling sequence backbones in the presence of nucleotide modifications remains a substantial challenge in nanopore sequencing bioinformatics. It has been extensively demonstrated that state-of-the-art basecallers are less compatible with modification-induced sequencing signals. A precise basecalling, on the other hand, serves as the prerequisite for virtually all the downstream analyses. Here, we report that basecallers exposed to diverse training modifications gain the generalizability to analyze novel modifications. With synthesized oligos as the model system, we precisely basecall various out-of-sample RNA modifications. From the representation learning perspective, we attribute this generalizability to basecaller representation space expanded by diverse training modifications. Taken together, we conclude increasing the training data diversity as a novel paradigm for building modification-tolerant nanopore sequencing basecallers.

Assessing Languages Across the Curriculum: Critical reflection and student learning perspectives

Assessing Languages Across the Curriculum: Critical reflection and student learning perspectives

Motivation and self-regulated learning profiles: A person-centered perspective of English learning and achievement in an Asia context

Motivation and self-regulated learning profiles: A person-centered perspective of English learning and achievement in an Asia context