Learning Strategies Research Articles

Online Learning from the Learning Cycle Perspective: Discovering Patterns in Recent Research

We propose a method for automatically extracting new trends and best practices from the recent literature on online learning, aligned with the learning cycle perspective. Using titles and abstracts of research articles published in high ranked educational journals, we assign topic proportions to the articles, where the topics are aligned with the components of the learning cycle: engagement, exploration, explanation, elaboration, evaluation, and evolution. The topic analysis is conducted using keyword-based Latent Dirichlet allocation, and the topic keywords are chosen to reflect the nature of the learning cycle components. Our analysis reveals the time dynamics of research topics aligned on learning cycle components, component weights, and interconnections between them in the current research focus. Connections between the topics and user-defined learning elements are discovered. Concretely, we examine how effective learning elements such as virtual reality, multimedia, gamification, and problem-based learning are related to the learning cycle components in the literature. In this way, any innovative learning strategy or learning element can be placed in the landscape of the learning cycle topics. The analysis can be helpful to other researches when designing effective learning activities that address particular components of the learning cycle.

Blended Learning Approach in TVET Colleges: Lecturers and Students Perceptions of Teaching and Learning Practical Subjects Online

With the advent of artificial intelligence, institutions are now adopting the Blended learning approach, which infuses face-to-face strategies and online teaching methodologies. A blended learning approach is a way to enhance the quality of teaching and learning in higher education institutions, but how feasible is that in TVET Colleges? This study thus investigated the global perceptions of lecturers and students in Technical Vocational Education (TVET) colleges regarding Blended learning for teaching practical subjects online. It examined the challenges and opportunities of moving practical learning online, considering the unique requirements of first-hand subjects like Electrical Engineering, Tourism, Hospitality, Art, and Design. The study utilised articles from Google Scholar and Science Direct databases to comprehensively understand the effectiveness of Blended learning in the TVET context. Using Blended learning as a theoretical framework, a new methodological approach, and a promising blueprint for transforming higher education, the study adopted an integrative literature review methodology to synthesise identified cases and draw themes from the survey. The study brings new knowledge regarding the significant shift in online learning usage during and after the COVID-19 pandemic, particularly in vocational education, providing valuable insights for designing effective online learning strategies. Keywords: Artificial Intelligence, Blended Learning, Integrative Literature Review, Technical Vocational Education (TVET) Colleges

THE FORMATION OF POSITIVE EXPECTATIONS AS A MOTIVATIONAL RESOURCE FOR PERSONNEL TRAINING

The purpose of the article: based on the analysis of the most popular theories of motivation to study, to show the role of the formation of positive expectations in adult study participants on motivation to study and its results. Motivation to study is understood as a hypothetical construction that consists of a general set of processes, methods, and means of motivating individuals to productive cognitive activity, and active assimilation of knowledge, which depends on the level of formation of their motivation to study, as well as the desire and desire to learn. One of the factors of motivation is expectations, which are interpreted as the result or its probability, what a person would like to get from his efforts and, accordingly, exactly what efforts he is ready to spend for this. The article examines the theories of activity motivation and learning motivation with a focus on the formation of positive learning expectations as a factor in motivating the learning activities of employees of enterprises. It analyzes how expectations affect the motivation of learning in adults, how they can be used in the formation of learning strategies, and which needs are the most critical for the formation of internal learning motivation. It was determined that according to the latest research, the most relevant expectations in the formation of educational motivation are the interaction of the need for autonomy, competence, social context, the practicality of acquired knowledge, the formation of a positive educational experience, and incorrectly formed expectations can be a powerful demotivating factor. Keywords: learning motivation, internal and external learning motives, learning motivation in crisis conditions

Interdisciplinary Brain-based Learning Strategies in Addressing Liver Fluke Disease and Cultural Dietary Practices among Early Childhood Students

Liver fluke disease remains a critical public health issue in Southeast Asia, driven by cultural dietary practices involving raw or undercooked freshwater fish. This study evaluates the impact of an interdisciplinary brain-based learning intervention aimed at increasing awareness and modifying dietary behaviors among early childhood students in Central Northeast Thailand. A quasi-experimental design was employed, involving 122 students from four provinces. The intervention incorporated brain-based learning activities such as storytelling, songs, and visual aids to enhance engagement. Pre- and post-tests, behavioral observations, and attitudinal surveys were conducted to assess the program’s effectiveness. Post-intervention results indicated significant improvements in knowledge about liver fluke transmission, symptoms, and prevention, alongside positive changes in behavior, such as increased avoidance of raw fish consumption and improved hygiene practices. Attitudinal surveys showed a shift in preferences toward safer eating habits. These findings suggest integrating brain-based learning with culturally relevant health education can foster sustainable behavior change in early childhood populations. Further research is recommended to explore this approach’s long-term impacts and scalability.

The paradox of explaining: When feeling unknowledgeable prevents learners from engaging in effective learning strategies.

People often need to learn complex information as part of their daily lives. One of the most effective strategies for understanding information is to explain it, for instance to a hypothetical other (Pilots 1 and 2). Yet, we find that learners prefer equally effortful but less effective learning strategies, even when incentivized to perform well (Study 1). Critically, we propose and find that learners' reluctance to explain is tied to their subjective knowledge of the material; learners who feel less knowledgeable about what they learned are most reluctant to explain it, despite the strategy being as effective for them (Study 2). An intervention that increased subjective knowledge (by having learners answer a few easy questions) increased learners' choice to explain, which was mediated by learners believing that explaining would be more pleasant and effective (Study 3). Directly manipulating beliefs about how fun and effective explaining is also boosted learners' willingness to explain (Study 4). Finally, because Studies 1-4 incentivized performance financially, we replicated key results in the classroom with students, finding improved scores on a class quiz (Study 5). The paradoxical implication of these findings is that those who need effective learning strategies the most are the ones least likely to use them. Put together, we find that subjective knowledge plays a key role in learning decisions and that boosting subjective knowledge is a simple intervention that can improve learning-related choices. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm.

The UAV path planning algorithm has many applications in urban environments, where an effective algorithm can enhance the efficiency of UAV tasks. The main concept of UAV path planning is to find the optimal flight path while avoiding collisions. This paper transforms the path planning problem into a multi-constraint optimization problem by considering three costs: path length, turning angle, and collision avoidance. A multi-strategy improved POA algorithm (IPOA) is proposed to address this. Specifically, by incorporating the iterative chaotic mapping method with refracted reverse learning strategy, nonlinear inertia weight factors, the Levy flight mechanism, and adaptive t-distribution variation, the convergence accuracy and speed of the POA algorithm are enhanced. In the CEC2022 test functions, IPOA outperformed other algorithms in 69.4% of cases. In the real map simulation experiment, compared to POA, the path length, turning angle, distance to obstacles, and flight time improved by 8.44%, 5.82%, 4.07%, and 9.36%, respectively. Similarly, compared to MPOA, the improvements were 4.09%, 0.76%, 1.85%, and 4.21%, respectively.

The impact of Brain-Based Learning on critical and creative thinking in English Language Teaching

This paper investigates the impact of Brain-Based Learning (BBL) strategies on fostering critical and creative thinking skills in English language teaching. In the context of 21st-century education, where the demands for innovation, critical thinking, and problem-solving are paramount, BBL emerges as a pedagogical approach that aligns teaching methods with cognitive science principles. Through a qualitative exploration of teacher perspectives and a quantitative analysis of classroom outcomes, this study highlights how BBL encourages the development of both hemispheres of the brain, promoting not only logical reasoning but also imaginative problem-solving. The findings underscore the potential of BBL to transform traditional English language teaching (ELT) practices by fostering a more holistic, engaging, and dynamic learning environment. Practical recommendations for integrating BBL into English classrooms are also provided..

Perturbation defense ultra high-speed weak target recognition

Ultra high-speed target recognition in complex electromagnetic environments is a critical and fundamental machine perception issue. It is difficult to ensure privacy protection and intelligent confrontation with centralized training in many practical situations. In this paper, an efficient ultra high-speed target recognition approach, named InVision, for robot systems is proposed using deep trustworthy federated learning (DTFL). InVision is accurate, safe, fast, and robust. Particularly, geometric component transformer (GCT) is presented to significantly promote neural element's complex representation description ability. And an ambiguity-aware cooperative learning (AACL) scheme is developed to relieve the noisy label problem. Moreover, decentralized federated training (DFT) is designed to mitigate the intractable privacy protection problem, to efficiently search for similarities and reduce the representation redundancy. Furthermore, to promote the running speed of the system in real-world environments, a lightweight deep architecture, called Mobile-XB, is developed. Extensive quantitative and qualitative experiments are carried out, and the results demonstrate that InVision greatly outperforms the outstanding comparison methods, establishing efficient connections and extraction, and providing security guarantees.

Smart Grid Forecasting with MIMO Models: A Comparative Study of Machine Learning Techniques for Day-Ahead Residual Load Prediction

With the fast expansion of intermittent renewable energy sources in the upcoming smart grids, simple and accurate day-ahead systems for residual load forecasts are urgently needed. Machine learning strategies can facilitate towards drastic cost minimizations in terms of operating-reserves avoidance to compensate the mismatches between the actual and forecasted values. In this study, a multi-input/multi-output model is developed based on artificial neural networks to map the relationship between different predictor inputs, including time indices, weather variables, human activity parameters, and energy price indicators, and target outputs such as wind and photovoltaic generation. While the information flows in only one direction (from the predictor nodes through the hidden layers to the target node), benchmark training algorithms are employed and assessed under different case studies. The model is evaluated under both parametric and non-parametric formulations, namely neural networks and Gaussian process regression. Essential improvements are achieved by enhancing the number of embedded predictors, while superior performance is observed by using Bayesian regularization mechanisms. In terms of mean-error indices and determination coefficient, this opens the pathway towards minimization via Bayesian inference-based approaches in the presence of increased and highly stochastic renewable inputs.

The Effect of Project Based Learning in Realizing Performing Arts on Entrepreneurial Motivation in Art Learning in Vocational School Students

The competence that must be possessed by Vocational School (VS) students is to be able to find business opportunities independently. Related to the subjects of art and culture, it is hoped that VS students have the motivation that can be carried out by the presence of performing arts in the form of performances that can be used as opportunities for entrepreneurship. Thus, VS students must have entrepreneurial motivation to have business opportunities in the arts and culture sector. One of the learning strategies that can be used is to use PBL. PBL can increase students' learning motivation and also facilitate students' problem-solving abilities. This study aimed to determine how the implementation of PBL in realizing performances and the influence of PBL on entrepreneurial motivation in art learning for VS students. The research method uses an experimental research method with a one-group pretest-posttest design. Participants in this study were 37 students in one of the VS in the Indramayu district, and the study was conducted using a systematic sampling technique. The research instrument used an entrepreneurial motivation questionnaire. Data analysis techniques using statistical descriptions and Paired-Sample T-Test. This study concludes that PBL affects VS students' entrepreneurial motivation in art learning. PBL can motivate students to reflect on what they learn in an actual project and provide opportunities for students to think and be creative when realizing a project in the arts and culture. Students can understand that in the performance, various job descriptions can be used for business opportunities or jobs in arts and culture to conduct further research with more participants so that the results of this study can be more generalized.

Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy

Experimental Evaluation of Remote Sensing–Based Climate Change Prediction Using Enhanced Deep Learning Strategy

Learning Terrain Traversability for a Mobile Robot based on Information Fusion

In this work, we propose an approach to determine terrain traversability for a car-like robot. Our approach has two main modules: a neural network classifier that makes use of sensors' readings to assign traversability levels to control inputs of the robot, and a second neural network that, based on the outputs of the first network, mimics the control selection performed by a human driver. The approach incorporates sensor fusion from a variety of sources to enhance the traversability estimation, and it is trained employing a semi-supervised learning scheme with examples resulting from the interaction of the car with the environment. This semi-supervised scheme avoids exhausting manual labeling and is built on the premise that there is a correlation between the terrain traversability and the required and observed behaviors of the vehicle. The method is validated with data obtained from a physical electric car.

Methods and Strategies for Effective Teaching of Mathematics in Secondary Schools in Nigeria

If the mathematics is to be interesting rather than a dull experience for the students, the first steps must be to make it a positive rather than negative emotional experience for the teachers. The importance placed on the subject is derived from the fact that it would help the nation meet its quest for science and technological advancement. A lot required from the mathematics teacher in terms of methods and strategies for effective teaching and learning of mathematics in secondary schools to meet the learning expectation of the students and other stake-holders. You need satisfied, competent and confident teacher to produce satisfied and confident students. The teacher is a major learning resource in mathematics at any level. If he is effective, he is catalyst, a stimulator, an inspirer and facilitator. He has to be able to facilitate learning by creating opportunities which enable learner to investigate situations. He has to have internalized techniques which enable him to go beyond ordinary routine questions ending at the knowledge level, comprehension, application, analysis, synthesis and evaluation which may together be called higher intellectual skills and abilities have to be viewed by the mathematics teacher as important attributes to be acquired by his students. Teaching process cannot be said to be complete if assessment of the students, teachers and the, methods and strategies employed are not thoroughly carried out, The teacher must know the best techniques and methods of presenting his ideas to the students putting into consideration students interest, needs, individual differences, difficulties and abilities. Thus, for effective teaching and learning of mathematics, a teacher must embrace active learning as against the traditional method of teaching mathematics.

Fine-art recognition using convolutional transformers

Digital image processing is a constantly evolving field encompassing a wide range of techniques and applications. Researchers worldwide are continually developing various algorithms across multiple fields to achieve accurate image classification. Advanced computer vision algorithms are crucial for architectural and artistic analysis. The digitalization of art has significantly enhanced the accessibility and conservation of fine-art paintings, yet the risk of art theft remains a significant challenge. Improving art security necessitates the precise identification of fine-art paintings. Although current recognition systems have shown potential, there is significant scope for enhancing their efficiency. We developed an improved recognition system for categorizing fine-art paintings using convolutional transformers, specified by an attention mechanism to enhance focused learning on the data. As part of the most advanced architectures in the deep learning family, transformers are empowered by a multi-head attention mechanism, thus improving learning efficiency. To assess the performance of our model, we compared it with those developed using four pre-trained networks: ResNet50, VGG16, AlexNet, and ViT. Each pre-trained network was integrated into a corresponding state-of-the-art model as the first processing blocks. These four state-of-the-art models were constructed under the transfer learning strategy, one of the most commonly used approaches in this field. The experimental results showed that our proposed system outperformed the other models. Our study also highlighted the effectiveness of using convolutional transformers for learning image features.

Empowering Mental Health Monitoring Using a Macro-Micro Personalization Framework for Multimodal-Multitask Learning: Descriptive Study.

The field of mental health technology presently has significant gaps that need addressing, particularly in the domain of daily monitoring and personalized assessments. Current noninvasive devices such as wristbands and smartphones are capable of collecting a wide range of data, which has not yet been fully used for mental health monitoring. This study aims to introduce a novel dataset for personalized daily mental health monitoring and a new macro-micro framework. This framework is designed to use multimodal and multitask learning strategies for improved personalization and prediction of emotional states in individuals. Data were collected from 298 individuals using wristbands and smartphones, capturing physiological signals, speech data, and self-annotated emotional states. The proposed framework combines macro-level emotion transformer embeddings with micro-level personalization layers specific to each user. It also introduces a Dynamic Restrained Uncertainty Weighting method to effectively integrate various data types for a balanced representation of emotional states. Several fusion techniques, personalization strategies, and multitask learning approaches were explored. The proposed framework was evaluated using the concordance correlation coefficient, resulting in a score of 0.503. This result demonstrates the framework's efficacy in predicting emotional states. The study concludes that the proposed multimodal and multitask learning framework, which leverages transformer-based techniques and dynamic task weighting strategies, is superior for the personalized monitoring of mental health. The study indicates the potential of transforming daily mental health monitoring into a more personalized app, opening up new avenues for technology-based mental health interventions.

Telemedicine data secure sharing scheme based on heterogeneous federated learning

The forward triage characteristic of telemedicine highlights its importance again in the COVID-19 pandemic. Telemedicine can provide timely emergency response in the case of environmental or biological hazards, and the patient’s medical privacy data generated in this process can also accelerate the establishment of models for preventing and treating infectious diseases. However, the reuse process of telemedicine user privacy data based on federated learning also faces significant challenges. Differences in regions, economic levels, and grades lead to heterogeneous data and resource-constrained environments, seriously damaging the federated learning process. Besides, the weak password authentication of medical terminals and eavesdropping attacks on transmission channels may cause illegal access to terminals and platforms and leakage of sensitive data. This paper proposed a telemedicine data secure-sharing scheme based on heterogeneous federated learning. Specifically, we proposed a heterogeneous federated learning scheme with model alignment to guide telemedicine practice through the reuse of telemedicine data; in addition, we designed an SM9 threshold identity authentication scheme to guarantee that the patient’s medical privacy data is protected from leakage during the federated learning process. We evaluated our scheme using two third-party medical datasets. The evaluation results indicate that this scheme can still assist the federated learning process in resisting data heterogeneity and resource constraints with almost no performance cost.

Comparing generating predictions with retrieval practice as learning strategies for primary school children.

This eye tracking study run in 2019 examines the learning benefits of two common active learning approaches-generating predictions and retrieval practice-for young children. Both generating predictions and retrieval practice are active learning approaches that involve generating responses and then being provided with the correct information or retrieving previously provided correct information. Participants included 90 children (Mage = 7 years; female = 46, male = 42). Parents reported children's race and ethnicity as follows: 2% Asian/Pacific Islander, 5% African American, 74% Caucasian, 3% other, and 6% identified as two or more categories; demographics largely reflective of the county where the data were collected, but nevertheless the generalizability of these findings to more diverse populations may be limited. In this study, young children learned facts about insects (e.g., "Insects are hard on the outside.") while we measured their attention to the lesson using eye tracking technology. Then their knowledge was assessed on an immediate test. All children were presented with the same materials, but the presentation order was modified based on condition assignment. In the generating predictions condition, children saw examples of animals and were asked if an animal was an insect or they saw animals and were asked to identify which one was the insect, followed by the correct response. In the retrieve condition, the presentation order was reversed such that children first saw the correct response and then were asked if the animal was an insect, or which of two examples was an insect. Results suggest that although retrieval practice results in overall better learning outcomes, generating predictions increased children's attention to the materials (d = 1.92), and among children who were able to maintain attention, learning outcomes were equal among the two conditions in an immediate test. This work highlights the importance of considering student-level factors when deciding the best learning strategies to implement. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Low-complexity end-to-end deep learning framework for 100G-PON

End-to-end learning allows communication systems to achieve optimal performance compared with conventional blockwise structure design. By modeling the channel with neural networks and training the transmitter and receiver on this differentiable channel, the whole system can be jointly optimized. However, in existing schemes, channel modeling methods, such as the generative adversarial network and long short-term memory network, have complex architectures and cannot track channel changes, leading to less effective end-to-end learning. Meanwhile, the complexity of neural networks deployed at the transmitter and receiver is too high for practical applications. In this work, we propose an efficient and low-complexity end-to-end deep learning framework and experimentally validate it on a 100G passive optical network. It uses a noise adaptation network to model channel response and noise distribution and employs offline pretraining and online tracking training to improve the efficiency and accuracy of channel modeling. For the transmitter, it consists of a pattern-dependent look-up table (PDLUT) based on a neural network (NN-PDLUT) with a single convolutional layer. Further, the receiver is also an NN with a single convolutional layer; thus, the end-to-end signal processing is extremely simple. The experimental results show that end-to-end learning improves the receiver sensitivity by 0.85 and 1.59 dB compared with receiver-only equalization based on Volterra nonlinear equalization (VNLE) and joint equalization based on a PDLUT and a feed-forward equalizer, respectively. Moreover, the number of multiply–accumulate operations consumed by the transmitter and receiver in the end-to-end learning scheme is reduced by 75.7% compared with VNLE-based receiver-only equalization.

Camera-Based Respiratory Imaging System for Monitoring Infant Thoracoabdominal Patterns of Respiration.

Existing respiratory monitoring techniques primarily focus on respiratory rate measurement, neglecting the potential of using thoracoabdominal patterns of respiration for infant lung health assessment. To bridge this gap, we exploit the unique advantage of spatial redundancy of a camera sensor to analyze the infant thoracoabdominal respiratory motion. Specifically, we propose a camera-based respiratory imaging (CRI) system that utilizes optical flow to construct a spatio-temporal respiratory imager for comparing the infant chest and abdominal respiratory motion, and employs deep learning algorithms to identify infant abdominal, thoracoabdominal synchronous, and thoracoabdominal asynchronous patterns of respiration. To alleviate the challenges posed by limited clinical training data and subject variability, we introduce a novel multiple-expert contrastive learning (MECL) strategy to CRI. It enriches training samples by reversing and pairing different-class data, and promotes the representation consistency of same-class data through multi-expert collaborative optimization. Clinical validation involving 44 infants shows that MECL achieves 70% in sensitivity and 80.21% in specificity, which validates the feasibility of CRI for respiratory pattern recognition. This work investigates a novel video-based approach for assessing the infant thoracoabdominal patterns of respiration, revealing a new value stream of video health monitoring in neonatal care.

Active Learning Guided Discovery of High Entropy Oxides Featuring High H2-production.

High entropy oxides (HEOs) represent a class of solid solutions comprising multiple elements, offering significant scientific potential. Due to the enormous combination types of elements, the design of HEOs with desirable properties within high-dimensional composition spaces has traditionally relied heavily on knowledge and intuition. In this study, we present an active learning (AL) strategy tailored to efficiently explore the vast compositional space of HEOs. Our approach operates as a closed-loop system, iteratively cycling through "Training, Prediction, and Experiment" stages. Across multiple AL iterations, we have successfully identified four novel HEOs from a vast array of potential compositions. These newly discovered materials exhibit exceptional stability and demonstrate outstanding performance in H2 evolution rate (251 μmol gcat-1 min-1) during the water-gas shift reaction, surpassing benchmarks set by established catalysts such as Pt/γ-Al2O3 (135 μmol gcat-1 min-1) and Cu/ZnO/Al2O3 (81 μmol gcat-1 min-1). X-ray photoelectron spectroscopy and density functional theory calculations revealed a loss of elemental identity in the selected HEOs. This catalyst discovery process underscores the efficacy of Machine Learning in accelerating the identification of HEOs with unique characteristics by effectively leveraging insights from limited experimental data.