General Learning Research Articles

Leveling up Learning: Enhancing Self-Directed Learning in Computer Applications Technology with Classcraft

This study explores how gamification can be incorporated when facilitating theoretical content in Computer Applications Technology (CAT) to promote self-directed learning of Generation Z learners. The CAT curriculum consists of practical and theoretical content, and Generation Z learners tend to perceive theoretical content as less engaging and less stimulating. The dynamic nature of CAT, however, requires CAT learners to be self-directed learners who take ownership of their learning and are motivated and engaged in learning both the practical and theoretical components. A basic interpretive study, within an interpretivist paradigm, was applied as the research design. Purposive sampling was utilized, and 106 CAT learners at a South African high school participated in the research. A traditional teacher-centered approach was first followed to facilitate theoretical content. Thereafter, gamification with Classcraft was applied as an intervention. Data collection was performed by implementing post-open-ended questionnaires and focus group interviews. Results indicate that Generation Z learners crave instant gratification and lack skills such as self-motivation and critical thinking. A teacher-centered approach was found unsuitable for facilitating theoretical content to Generation Z learners. Gamification has the potential to entice Generation Z learners from different socioeconomical and cultural backgrounds to experience theoretical content in a more engaging and enjoyable manner and foster self-directed learning.

Literacy assessment of English teachers: Need analysis for higher education contexts

This paper is aimed to describe what knowledge and skills an English teacher should have to be assessment literate. The paper focuses on the following four basic knowledge areas of assessment literacy. First, English teachers should know what assessment is in general and what classroom assessment is. Second, the teachers should know the purposes of assessment. This knowledge will guide the teachers to create general learning goals, construct clear learning targets, and formulate clear rubrics. Third, the teachers should be aware of various assessment methods in ELT. Last, the teachers should comprehend assessment's positive influences in teaching and learning. To have a positive washback of assessment, the teachers should know how to create effective classroom assessments, how to make constructive feedback, how to support the role of students in assessment, and how to modify instruction based on assessments' results. Assessment literacy is an individual's understanding of the basic concepts and procedures of assessment which will be used as a basis for educational decisions (Popham, 2009). To be assessment literate, a teacher should have the knowledge and skills in relation to basic principles of quality assessment. This essay will discuss four basic knowledge areas of assessment literacy: definition of assessment, purposes of assessment, methods of assessment and keys for creating positive washback of assessment that can improve instruction and students' learning.

Memorizing plans with an app: Large individual differences in the effectiveness of retrieval-based and generative learning activities in a naturalistic context

Memorizing plans with an app: Large individual differences in the effectiveness of retrieval-based and generative learning activities in a naturalistic context

Generative learning of the solution of parametric Partial Differential Equations using guided diffusion models and virtual observations

Generative learning of the solution of parametric Partial Differential Equations using guided diffusion models and virtual observations

Mobile Microlearning in Continuing Professional Development for Nursing: A Scoping Review.

Mobile microlearning (MML) provides concise and engaging educational activities that correspond with various learning preferences and styles. Microlearning is defined as bite-sized instruction, with modules ranging from approximately 90 seconds to 5 minutes. To consider MML as a form of continuing professional development it is essential first to identify the learning preferences of a new generation of nurses entering the professional field of health care. This scoping review addresses generational learning preferences using current technological approaches to identify nurses' interest in using MML as a form of continuing professional development. A total of 34 articles were identified for this scoping review. Little information was available on microlearning and nursing addressing both formal education and continuing professional development. Among the studies evaluated on MML for this project, a thread of discrepancy included (a) inconsistent definitions of time limitations, (b) the significance of MML for continuing professional development, and (c) application to knowledge translation and research dissemination. [J Contin Educ Nurs. 2025;56(2):53-62.].

Denoising Multi-Level Cross-Attention and Contrastive Learning for Chest Radiology Report Generation.

Chest radiology report generation plays a vital role in supporting diagnosis, alleviating physician workload, and reducing the risk of misdiagnosis. However, significant challenges persist: (1) Data bias and background noise in chest images often obscure subtle lesion details, leading models to generate similar reports; (2) the distinct modal spaces of radiology images and reports weaken the semantic correlation between detailed visual lesion features and report sentences; (3) generated reports often lack crucial patient background and disease extent details, impeding report quality and accuracy. To address these challenges, this paper proposes a novel approach for generating chest radiology reports, utilizing denoising multi-level cross-attention and multi-level contrastive. The proposed method first involves sequential encoding of frontal and lateral radiology images into a visual extractor to enhance semantic coherence across image patches and improve visual feature representation. The enhanced visual features are processed through denoising multi-level cross-attention, which effectively suppresses noise and highlights the details of subtle lesions. Secondly, a multi-level contrastive learning module leverages contrastive among images, text, and disease labels to distinguish positive samples from negative ones, thereby strengthening the semantic correlation between detailed visual lesion features and report sentences. Finally, relevant knowledge is incorporated into the report generator to enhance the description of patient lesion details. Comparative experiments were performed against other state-of-the-art methods on the IU-Xray and MIMIC-CXR datasets, demonstrating that the proposed method significantly improves model performance. Additionally, ablation studies confirm that each module contributes to enhancing the quality of generated reports.

Understanding Machine Learning Principles: Learning, Inference, Generalization, and Computational Learning Theory

Machine learning has become indispensable across various domains, yet understanding its theoretical underpinnings remains challenging for many practitioners and researchers. Despite the availability of numerous resources, there is a need for a cohesive tutorial that integrates foundational principles with state-of-the-art theories. This paper addresses the fundamental concepts and theories of machine learning, with an emphasis on neural networks, serving as both a foundational exploration and a tutorial. It begins by introducing essential concepts in machine learning, including various learning and inference methods, followed by criterion functions, robust learning, discussions on learning and generalization, model selection, bias–variance trade-off, and the role of neural networks as universal approximators. Subsequently, the paper delves into computational learning theory, with probably approximately correct (PAC) learning theory forming its cornerstone. Key concepts such as the VC-dimension, Rademacher complexity, and empirical risk minimization principle are introduced as tools for establishing generalization error bounds in trained models. The fundamental theorem of learning theory establishes the relationship between PAC learnability, Vapnik–Chervonenkis (VC)-dimension, and the empirical risk minimization principle. Additionally, the paper discusses the no-free-lunch theorem, another pivotal result in computational learning theory. By laying a rigorous theoretical foundation, this paper provides a comprehensive tutorial for understanding the principles underpinning machine learning.

DPRA: a dual pooling attention network for point cloud classification and segmentation

Abstract Point cloud data, widely used in fields such as autonomous driving and robotic navigation, involves classification and segmentation tasks. The extraction of local and global features has become a major research focus. The paper proposes two modules: the Dual Pooling Attention model (DP-Attention) and the Residual Attention Module model (RA-MLP). DP-Attention utilizes max pooling and average pooling to compute attention, extracting information between points as well as between features to enhance local feature extraction. RA-MLP integrates self-attention and residual connections to improve global feature extraction. These modules are combined to construct the DPRA network, which is tailored for point cloud classification and segmentation tasks. The DPRA network architecture is based on the encoder-decoder structure of U-Net, using DP-Attention and RA-MLP in the encoder, and only RA-MLP in the decoder. Experimental results on three datasets demonstrate that the DPRA network achieves outstanding performance in the synthetic ModelNet40 classification experiment, with a mean class accuracy of 91.6%. It also achieves the highest mean class accuracy (85.3%) and overall accuracy (86.1%) in the real-world ScanObjectNN classification experiment. Additionally, it attains the highest mean Intersection over Union (mIoU) of 85.2% in the synthetic ShapeNet segmentation experiment. These results indicate that DPRA is applicable to various tasks, demonstrating strong generalization, robustness, and multi-task learning capabilities.

Feature Enhancement-Based Few-Shot Bearing Surface Defect Image Classification Method

The application of intelligent bearing surface defect classification based on deep neural networks remains challenging in real factories, due to the scarcity of defect samples. Under real-working conditions, with less training samples, the paper proposes a few-shot bearing defect image classification network which can recognize different bearing surface defects image, including notch, reddish rust, scratching, incising, conformity, pitting and mill scale. Based on general metric learning neural network framework, a local feature extraction layer is designed, which calculates the auto-correlation vector of global feature in a sliding region to enhance detail features. Additionally, a similar feature attention module emphasizes the the regions of similarity between the query set and the class prototype center to overcome the influence of background noise on classification. To validate the effectiveness of the proposed network, comparative experiments were conducted using the benchmark dataset miniImageNet, achieving classification accuracies of 59% in the 5-way 1-shot setting and 76% in the 5-way 5-shot setting respectively. Furthermore, to assess its performance in a real-factory condition, a self-made dataset of bearing defects from a factory was employed. The proposed network achieved a remarkable classification accuracy of 88% in the 5-way 5-shot setting. These experimental results confirm the practical application value of our few-shot bearing surface defect image classification network, demonstrating its ability to accurately recognize various bearing defects with limited training samples.

Unpaired learning for digital holographic reconstruction and generation.

Traditional numerical reconstruction methods in digital holography (DH) are faced with problems such as inaccurate and time-consuming unwrapping or the need to capture multiple holograms with different diffraction distances. In recent years, deep learning, believed to be a new and effective optimization tool, has been widely used in digital holography. However, most supervised deep learning methods require large-scale paired data, and their preparation is time-consuming and laborious. Here, we propose what we believe to be a new deep learning approach that can use less unpaired data to train neural networks, thereby reducing the need for labeled data. By using CycleGAN, the calculation process of the loss function does not need paired data, and the network can learn the mapping knowledge between two domains, instead of the data and its corresponding label. As such, the proposed method can reconstruct complex amplitudes for holographic reconstruction. And more importantly, the trained network learns the knowledge of the imaging system, leading to the capability to generate experimentally synthetic holograms at the same time. To the best of our knowledge, this is the first demonstration of such high-quality physics-based experimental hologram simulator in DH. The reconstructed complex amplitudes have higher image quality, while the generated holograms can represent realistic system status and reconstruct the input complex amplitudes.

Effects of described demonstrator ability on brain and behavior when learning from others

Observational learning enables us to make decisions by watching others’ behaviors. The quality of such learning depends on the abilities of those we observe, but also on our beliefs about those abilities. We have previously demonstrated that observers learned better from demonstrators described as high vs. low in ability, regardless of their actual performance. The current study aimed to conceptually replicate these findings, and explore the neural mechanisms involved. Forty-five participants performed an observational learning task while undergoing functional magnetic resonance imaging (fMRI). We hypothesized that participants would perform better when demonstrators were described as having high vs. low ability. Unexpectedly, participants performed equally well regardless of described demonstrator ability. The behavioral effects of biased observational learning seem to be driven by mentalizing processes together with general learning and decision-making processes.

Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review

Background: Electronic health records (EHR) are now widely available in healthcare institutions to document the medical history of patients as they interact with healthcare services. In particular, routine care EHR data are collected for a large number of patients.These data span multiple heterogeneous elements (i.e., demographics, diagnosis, medications, clinical notes, vital signs, and laboratory results) which contain semantic, concept, and temporal information. Recent advances in generative learning techniques were able to leverage the fusion of multiple routine care EHR data elements to enhance clinical decision support. Objective: A scoping review of the proposed techniques including fusion architectures, input data elements, and application areas is needed to synthesize variances and identify research gaps that can promote re-use of these techniques for new clinical outcomes. Design: A comprehensive literature search was conducted using Google Scholar to identify high impact fusion architectures over multi-modal routine care EHR data during the period 2018 to 2023. The guidelines from the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) extension for scoping review were followed. The findings were derived from the selected studies using a thematic and comparative analysis. Results: The scoping review revealed the lack of standard definition for EHR data elements as they are transformed into input modalities. These definitions ignore one or more key characteristics of the data including source, encoding scheme, and concept level. Moreover, in order to adapt to emergent generative learning techniques, the classification of fusion architectures should distinguish fusion from learning and take into consideration that learning can concurrently happen in all three layers of new fusion architectures (i.e., encoding, representation, and decision). These aspects constitute the first step towards a streamlined approach to the design of multi-modal fusion architectures for routine care EHR data. In addition, current pretrained encoding models are inconsistent in their handling of temporal and semantic information thereby hindering their re-use for different applications and clinical settings. Conclusions: Current routine care EHR fusion architectures mostly follow a design-by-example methodology. Guidelines are needed for the design of efficient multi-modal models for a broad range of healthcare applications. In addition to promoting re-use, these guidelines need to outline best practices for combining multiple modalities while leveraging transfer learning and co-learning as well as semantic and temporal encoding.

Anomaly Detection Framework With Contrastive Learning and Multiview Augmentation for Time-Series Domain Generalization

Anomaly Detection Framework With Contrastive Learning and Multiview Augmentation for Time-Series Domain Generalization

Use of transfer learning for affordable in-context fake review generation

Use of transfer learning for affordable in-context fake review generation

Wireless Sensing in Artificial Intelligence of Things: A General Quantum Machine Learning Framework

Wireless Sensing in Artificial Intelligence of Things: A General Quantum Machine Learning Framework

Advancements in Clinical Evaluation and Regulatory Frameworks for AI-Driven Software as a Medical Device (SaMD).

Owing to the rapid progress in artificial intelligence (AI) and the widespread use of generative learning, the problem of sparse data has been solved effectively in various research fields. The application of AI technologies has resulted in important transformations in healthcare, particularly in radiology. To ensure the high quality, safety, and effectiveness of AI and machine learning (ML) medical devices, the US Food and Drug Administration (FDA) has established regulatory guidelines to support the performance evaluation of medical devices. Furthermore, the FDA has proposed continuous surveillance requirements for AI/ML medical devices. This paper presents a summary of SaMD products that have passed the FDA 510 (k) AI/ML pathway, the challenges associated with the current AI/ML software-as-a-medical-device, and solutions for promoting the development of AI technologies in medicine. We hope to provide valuable information pertaining to medical-device design, development, and monitoring to ultimately achieve safer and more effective personalized medical services.

MADeGen: Multi-Agent based Deep Reinforcement Learning for Sequential Keyphrase Generation

MADeGen: Multi-Agent based Deep Reinforcement Learning for Sequential Keyphrase Generation

Study on the Role of AI in Transforming HR Practices in Pune's IT Industry

This research study examines the evolving role of AI (Artificial Intelligence) in human resources, aiming on how IT employees perceive and engage with AI Automation at their workplace. It highlights the impact of AI technologies on employee identity, job security, and job satisfaction, while noting significant research gaps in understanding AI's influence on workforce dynamics. The literature classifies AI into categories like general AI tools, machine learning, and robotics, each affecting workplace interactions differently. A major concern is the "dark side" of AI, which may reduce employee autonomy and transparency, impacting their identity and sense of purpose. The research emphasizes AI's transformative potential in HR functions such as recruitment, training & development, performance evaluation and compliance management but also highlights challenges like resistance to change and ethical concerns about AI tools. It advocates for longitudinal studies to better understand AI's long-term effects and suggests that demographic factors may shape employee responses to AI. In conclusion, while AI could enhance HR operations, many questions remain about effective implementation of AI tools in HR functions. Future research should address these gaps to enable organizations to leverage AI for a more innovative and adaptable workforce.

Effect of two teaching methods on nursing students' acquisition of patient-centered communication competence in older people care: a cluster randomized trial.

Patient-centered communication is an essential skill in nursing, particularly in the care of older adult patients. However, generation Z nursing students, who primarily communicate through digital platforms, face unique challenges in adapting to traditional face-to-face communication with older adults. As a result, there is a need for teaching methods that align with this generation's learning style to enhance their communication skills. This study aimed to compare the effectiveness of two teaching methods-standardized patient simulation and role-play-on nursing students' acquisition of patient-centered communication competence in older people care. A controlled cluster-randomized trial was conducted with 124 nursing students, divided into eight teaching groups. Students participated in either a standardized patient simulation or a role-play workshop, each consisting of a 1.5-h online module and a 1.5-h face-to-face session. The three components of patient-centered communication competence-knowledge, skills, and self-efficacy-were assessed using simulated scenarios at pre-test, post-test, and 6-week follow-up. Between-group and within-group differences were measured based on the number of students who achieved competence. Both interventions significantly improved students' knowledge, skills, and self-efficacy in patient-centered communication between pre- and post-tests, with improvements maintained at follow-up. No significant differences were found between the two methods. Both standardized patient simulation and role-play are effective in enhancing patient-centered communication competence in older people care. However, neither method was found to be superior in teaching knowledge, skills, or self-efficacy.

Photonic quantum generative adversarial networks for classical data

In generative learning, models are trained to produce new samples that follow the distribution of the target data. These models were historically difficult to train, until proposals such as generative adversarial networks (GANs) emerged, where a generative and a discriminative model compete against each other in a minimax game. Quantum versions of the algorithm have since been designed for the generation of both classical and quantum data. While most work so far has focused on qubit-based architectures, in this article we present a quantum GAN based on linear optical circuits and Fock-space encoding, which makes it compatible with near-term photonic quantum computing. We demonstrate that the model can learn to generate images by training the model end-to-end experimentally on a single-photon quantum processor.