Improve Learning Efficiency Research Articles

The Impact Mechanism of Negative Academic Emotions on Academic Procrastination: The Mediating and Moderating Roles of Self-Efficacy and Goal Orientation

Academic procrastination is a widespread phenomenon among college students, significantly affecting their academic performance and mental health. Although previous studies have suggested a relationship between negative academic emotions and academic procrastination, the underlying mechanisms of this relationship remain insufficiently explored. Based on theoretical analysis and a review of the literature, this study utilizes structural equation modeling to examine the effects of negative academic emotions, self-efficacy, and goal orientation on academic procrastination. The results indicate a significant positive correlation between negative emotions and academic procrastination. Furthermore, self-efficacy fully mediates the relationship between negative academic emotions and academic procrastination, while goal orientation plays a moderating role in this relationship. This study reveals the intricate relationships between negative academic emotions and academic procrastination among Chinese college students, emphasizing the importance of enhancing self-efficacy and goal orientation to prevent procrastination. It provides theoretical and empirical support for improving learning efficiency and academic achievement, as well as for designing interventions to address academic procrastination.

Curiosity-Driven Multi-Level Intrinsic Reward DQN for Enhanced Exploration in Reinforcement Learning

Abstract. Reinforcement learning (RL) has shown great potential in solving complex decision-making tasks. However, efficient exploration remains a significant challenge, particularly in environments with sparse or deceptive rewards. This paper introduces DQN-Mult-Cur, a novel reinforcement learning algorithm that enhances exploration by integrating curiosity-driven and multi-level intrinsic rewards within the Deep Q-Network (DQN) framework. The proposed method addresses the limitations of conventional exploration strategies by incentivizing agents to explore novel and meaningful states, thereby improving learning efficiency and performance. Extensive experiments across three standard environmentsCartPole-v1, MountainCar-v0 and Acrobot-v1demonstrate that DQN-Mult-Cur outperforms traditional DQN variants, achieving faster convergence, higher rewards, and greater stability. An ablation study further highlights the importance of each intrinsic reward component, confirming the robustness of the proposed approach. The results suggest that DQN-Mult-Cur offers a comprehensive solution to the exploration-exploitation trade-off in reinforcement learning, making it applicable to a wide range of challenging environments.

Heuristic dense reward shaping for learning-based map-free navigation of industrial automatic mobile robots

This paper presents a map-free navigation approach for industrial automatic mobile robots (AMRs), designed to ensure computational efficiency, cost-effectiveness, and adaptability. Utilizing deep reinforcement learning (DRL), the system enables real-time decision-making without fixed markers or frequent map updates. The central contribution is the Heuristic Dense Reward Shaping (HDRS), inspired by potential field methods, which integrates domain knowledge to improve learning efficiency and minimize suboptimal actions. To address the simulation-to-reality gap, data augmentation with controlled sensor noise is applied during training, ensuring robustness and generalization for real-world deployment without fine-tuning. Training results underscore HDRS’s superior convergence speed, training stability, and policy learning efficiency compared to baselines. Simulation and real-world evaluations establish HDRS-DRL as a competitive alternative, outperforming traditional approaches, and offering practical applicability in industrial settings.

Application of 3D Motion Graphic Design Technology of Object Simulation Human Body in Sports Teaching

In the current era, the rapid development of computer technology has brought unprecedented opportunities for innovation in physical education teaching. The introduction of computer technology not only improves the efficiency of physical education teaching but also provides more accurate and scientific means for sports training. In response to this trend, this paper proposes an innovative human body model design method and explores in detail the key implementation techniques of computer simulation of 3D motion maps, aiming to further optimize and improve the quality of physical education teaching. The design of human body models plays a crucial role in physical education teaching and training, as it can help students and teachers better understand the mechanisms and principles of human movement. By introducing computer technology, this paper can construct a more precise, dynamic, and realistic human body model that conforms to the actual motion situation. This model can simulate various movement states of the human body, providing students with an intuitive and vivid learning experience. In the classroom, teachers can use projection or virtual reality devices to display 3D motion models, allowing students to intuitively see the details and changes of motion movements, deepening their understanding of the essentials of the movements. Teachers can demonstrate different movements in real time by operating models, and explain the technical points and precautions of each movement. Students can observe the motion trajectory and posture changes of the model from multiple perspectives to improve learning efficiency. The system generates personalized feedback reports based on students’ movement data, pointing out the problems and improvement directions that students have during the exercise process. Teachers can provide more accurate guidance to students based on these feedbacks, helping them correct incorrect movements and improve their skill levels.

Online adaptive critic designs with tensor product B-splines and incremental model techniques

As an effective optimal control scheme in the field of reinforcement learning, adaptive dynamic programming (ADP) has attracted extensive attention in recent decades. Neural networks (NNs) are commonly employed in ADP algorithms to realize nonlinear function approximation. However, the learning process with NN approximation typically requires a substantial amount of training data and places a heavy computational burden. To improve learning efficiency and alleviate computational load, this paper develops a novel model-free ADP approach based on multivariate splines and incremental control techniques, aimed at achieving online learning of nonlinear control. By utilizing input and output data, an incremental linear approximation model is identified online without any prior knowledge of system dynamics. To improve nonlinear approximation capabilities and lessen computational demands, tensor product B-splines, instead of NNs, are integrated into the critic module to approximate the value function, and the recursive least squares temporal difference (RLS-TD) algorithm is employed to update the weights of spline bases. Convergence analysis of the proposed control scheme is conducted based on the dual-timescale stochastic approximation theory. To illustrate the effectiveness and feasibility of the proposed control scheme, numerical simulations are performed in solving the online nonlinear control problem within the context of the inverted pendulum system.

Effects of Collaborative Support on Learning in Serious Games for Programming

This study examines the effectiveness of a collaborative support method in enhancing programming learning outcomes in a serious game. The game Code Hasp was developed to facilitate the comparison between two support methods, worked examples and a collaborative peer support approach. A total of 111 elementary school students participated, divided into two groups. The experimental group received collaborative support from teammates alongside worked examples, while the control group relied solely on worked examples. Pre- and post-test results and in-game data revealed that the collaborative support method improved learning efficiency and problem-solving skills. A notable effect of the process was the emergence of a leadership role in most collaborative groups. The game’s support design and shared goal structure mitigate potential problems associated with unbalanced participation. These findings suggest that collaborative support systems in serious games are a promising strategy for promoting engagement and improving learning outcomes in programming education.

Establishing effective learning bridge cross multi-scale feature maps for object detection and semantic segmentation

ABSTRACT In the field of image object detection and semantic segmentation, improving the accuracy of object identification and segmentation is a primary goal. To achieve this, leveraging the potential of multi-scale information through feature map refinement and fusion has been widely recognized. However, existing feature fusion methods either design more complex feature pyramid networks, replace existing detectors, or incrementally introduce feature fusion modules, overlooking the effective approach of enhancing spatial information in deep feature maps. We propose a novel pluggable feature fusion paradigm termed ‘Effective Learning Bridge’. Our research introduces an efficient and adaptive learning mechanism that builds learning bridges between feature maps at different scales within the feature pyramid, thereby enhancing the spatial information of objects in deep feature maps. This mechanism is specifically designed for multi-scale feature maps and can be seamlessly integrated into any network incorporating feature maps. By altering the model’s backpropagation path, we successfully improve learning efficiency, which in turn enhances the accuracy of object detection and segmentation. Our proposed paradigm and method were extensively evaluated through experiments on SIMD, HRSID, and WHDLD datasets and benchmark models. The results unequivocally demonstrate the effectiveness of our approach in significantly improving the accuracy of object detection and semantic segmentation, as well as the overall learning efficiency of the model.

Quantum classical hybrid convolutional neural networks for breast cancer diagnosis.

The World Health Organization states that early diagnosis is essential to increasing the cure rate for breast cancer, which poses a danger to women's health worldwide. However, the efficacy and cost limitations of conventional diagnostic techniques increase the possibility of misdiagnosis. In this work, we present a quantum hybrid classical convolutional neural network (QCCNN) based breast cancer diagnosis approach with the goal of utilizing quantum computing's high-dimensional data processing power and parallelism to increase diagnosis efficiency and accuracy. When working with large-scale and complicated datasets, classical convolutional neural network (CNN) and other machine learning techniques generally demand a large amount of computational resources and time. Their restricted capacity for generalization makes it challenging to maintain consistent performance across multiple data sets. To address these issues, this paper adds a quantum convolutional layer to the classical convolutional neural network to take advantage of quantum computing to improve learning efficiency and processing speed. Simulation experiments on three breast cancer datasets, GBSG, SEER and WDBC, validate the robustness and generalization of QCCNN and significantly outperform CNN and logistic regression models in classification accuracy. This study not only provides a novel method for breast cancer diagnosis but also achieves a breakthrough in breast cancer diagnosis and promotes the development of medical diagnostic technology.

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.

HAGCN: A hybrid-order brain network-based graph convolution learning framework with multi-head attention for brain disorder classification

Brain networks provide essential insights in diagnosing many brain disorders, such as autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD). However, current works often fail to fully capture the potential interaction characteristics of inter-regional functional connectivity within the brain network. To address this limitation, we propose a Hybrid-Attention Graph Convolution Network (HAGCN) for brain disorder classification. The learning framework designs a hybrid-order effective network (graph) for representing the lower-order and higher-order spatial interaction relations between brain regions, proposes a prior graph based on statistical tests to eliminate the noisy edges effectively, presents a dual-channel graph convolution module for simultaneously capturing information flow features of each brain region in out-degree and in-degree directions, and implement a multi-head attention module to focus and discern the significant features from the multidimensional interaction relations for improving the expressiveness of the model. Experiments on ADHD-200 and ABIDE-I datasets show the effectiveness and advantages of our proposed method, achieving the highest average accuracy values of 77.95 % and 78.17 %, respectively. The results demonstrate that integrating multidimensional and directional interaction characteristics can enhance the network’s capacity and improve learning efficiency. Additionally, our approach identifies and analyzes several key brain regions linked to brain disorders.

Skill acquisition framework in multi-robot precision assembly based on cooperative compliant control

Robotic assemblies are widely used in manufacturing processes. However, high-precision assembly remains challenging because of numerous uncertain disturbances. Current research mainly focuses on a single robot or weakly coupled multi-robot assembly. Nevertheless, more complex and uncertainty-filled tightly coupled multi-robot assemblies have been overlooked. This study proposes an efficient skill-acquisition framework to address this challenging task by improving learning efficiency. The framework integrates a dual-loop coupled force-position control (DLCFPC) algorithm, a parallel skill-learning algorithm, and collision detection. The DLCFPC was presented to address simultaneous motion and force control challenges. In addition, a parallel skill-learning algorithm was proposed to accelerate assembly skill acquisition. Simulations and experiments on a multi-robot cooperative peg-in-hole assembly confirm that the framework enables a multi-robot system to accomplish high-precision assembly tasks even without prior knowledge, demonstrating robustness against disturbances.

Computer-Assisted Online Learning of English Oral Pronunciation Based on DAE End-to-End Recurrent Neural Networks

With the development of globalization, learning a second language has received increasing attention from people. To improve English oral proficiency, a computer-aided online learning system for English oral pronunciation is studied. A denoising autoencoder is integrated into the system to create a simplified end-to-end recurrent neural network for pronunciation detection and diagnosis based on deep learning. The study first collected and preprocessed oral pronunciation data of English learners, including enhancing speech signals and reducing noise. Next, an RNN model with Long Short-Term Memory (LSTM) as the core was constructed to capture time series characteristics in pronunciation. And use DAE to extract features and reduce the influence of background noise to enhance the recognition of pronunciation features. At the same time, the study utilized web crawler technology to collect a large amount of oral pronunciation data from non-native English learners, and constructed an English oral corpus containing pronunciation errors. And in order to simulate real situations, white noise and pink noise were artificially added to the corpus in the study, and they were divided into training and testing sets in a ratio of 60% to 40%. The results showed that the classification accuracy of the system in the training and testing sets under white noise environment was 78.97% and 94.01%, respectively, and the classification accuracy in the pink noise environment was 76.19% and 94.03%, respectively. The system’s error detection accuracy in vowel and consonant pronunciation detection is 88.91% and 91.68%, respectively, and the error correction accuracy in vowel and consonant pronunciation detection is 90.67% and 91.96%, respectively. In summary, the research on computer-aided online learning of English oral pronunciation based on Denoising Auto Encoders end-to-end recurrent neural networks has effectively improved learning efficiency.

Approximate Geometric Structure Transfer for Cross-Domain Image Classification

The main purpose of domain adaptation (DA) is to conduct cross-domain related knowledge transfer. Considering the issue of unsupervised DA (UDA), learning a transformation that reduces the differences between domains is the primary goal. In addition to minimizing both the marginal and conditional distributions between the source and target domains, many methods explore potential factors that show the commonalities among domains to yield improved learning efficiency. However, geometric structure information is overlooked by most existing approaches, indicating that the shared information between domains has not been fully exploited. On account of this finding, by taking advantage of more potential shared factors to further enhance the results of DA, we propose an approximate geometric structure transfer (AGST) method for cross-domain image classification in this paper. By combining structural consistency and sample reweighting techniques, AGST encodes the geometric structure information taken from the samples in both domains, enabling it to easily obtain richer interdomain features and effectively facilitate knowledge transfer. Extensive experiments are conducted on several cross-domain data benchmarks. The experimental results indicate that our AGST method can outperform many state-of-the-art algorithms.

Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning

This study primarily investigates advanced object detection and time series prediction methods in image-based visual servoing systems, aiming to capture targets better and predict the motion trajectory of robotic arms in advance, thereby enhancing the system’s performance and reliability. The research first implements object detection on the VOC2007 dataset using the Detection Transformer (DETR) and achieves ideal detection scores. The particle swarm optimization algorithm and 3-5-3 polynomial interpolation methods were utilized for trajectory planning, creating a unique dataset through simulation. This dataset contains randomly generated trajectories within the workspace, fully simulating actual working conditions. Significantly, the Bidirectional Long Short-Term Memory (BILSTM) model was improved by substituting its traditional Multilayer Perceptron (MLP) components with Kolmogorov–Arnold Networks (KANs). KANs, inspired by the K-A theorem, improve the network representation ability by placing learnable activation functions on fixed node activation functions. By implementing KANs, the model enhances parameter efficiency and interpretability, thus addressing the typical challenges of MLPs, such as the high parameter count and lack of transparency. The experiments achieved favorable predictive results, indicating that the KAN not only reduces the complexity of the model but also improves learning efficiency and prediction accuracy in dynamic visual servoing environments. Finally, Gazebo software was used in ROS to model and simulate the robotic arm, verify the effectiveness of the algorithm, and achieve visual servoing.

AI Solutions for Accessible Education in Underserved Communities

This paper explores the application of artificial intelligence technology in the field of education, particularly how it can help bridge educational gaps in remote and underserved communities through scalable and accessible learning solutions. The aim of the study is to enhance educational equity and provide personalized learning experiences by utilizing AI technologies such as adaptive learning systems, language processing technologies, and data analytics. The paper analyzes these tools and discusses how they integrate with practical cases like mobile learning platforms, cloud infrastructure, open resources, and collaborative learning to massively distribute educational resources and address global educational inequalities. The research methods include case studies and data analysis, with results indicating that these technologies significantly improve learning efficiency and engagement among students in remote areas. Ultimately, the paper demonstrates the potential of artificial intelligence in promoting global educational equity and offers suggestions for the future development of educational technology. This work is of significant importance to the field of educational technology, providing innovative perspectives and practical solutions for addressing disparities in educational resources.

Unsupervised selective labeling for semi-supervised industrial defect detection

In industrial detection scenarios, achieving high accuracy typically relies on extensive labeled datasets, which are costly and time-consuming. This has motivated a shift towards semi-supervised learning (SSL), which leverages labeled and unlabeled data to improve learning efficiency and reduce annotation costs. This work proposes the unsupervised spectral clustering labeling (USCL) method to optimize SSL for industrial challenges like defect variability, rarity, and complex distributions. Integral to USCL, we employ the multi-task fusion self-supervised learning (MTSL) method to extract robust feature representations through multiple self-supervised tasks. Additionally, we introduce the Enhanced Spectral Clustering (ESC) method and a dynamic selecting function (DSF). ESC effectively integrates both local and global similarity matrices, improving clustering accuracy. The DSF maximally selects the most valuable instances for labeling, significantly enhancing the representativeness and diversity of the labeled data. USCL consistently improves various SSL methods compared to traditional instance selection methods. For example, it boosts Efficient Teacher by 5%, 6.6%, and 7.8% in mean Average Precision(mAP) on the Automotive Sealing Rings Defect Dataset, the Metallic Surface Defect Dataset, and the Printed Circuit Boards (PCB) Defect Dataset with 10% labeled data. Our work sets a new benchmark for SSL in industrial settings.

Machine Learning-Based Personalized Learning Path Decision-Making Method on Intelligent Education Platforms

With the rapid development of information technology, the application of intelligent education platforms has become increasingly widespread. Traditional teaching methods struggle to meet the demands for personalized learning. Personalized learning path decision-making methods, which analyze learners’ behavioral data and mastery of knowledge points, tailor learning paths for each individual. Current research indicates that these methods can significantly improve learning efficiency and effectiveness. However, existing personalized learning path decision-making methods still have shortcomings in predicting the difficulty of knowledge points and dynamically adjusting path recommendations. This paper proposes a machine learning-based personalized learning path decision-making method, focusing on two main aspects: predicting the difficulty of knowledge points for personalized learning and integrating knowledge point localization for personalized learning path decisions. Through accurate prediction of knowledge point difficulty and dynamically optimized learning path recommendations, this method provides more refined and personalized learning support on intelligent education platforms, aiming to enhance learners’ learning experience and outcomes.

Enhancing Temporal Knowledge Graph Representation with Curriculum Learning

Temporal knowledge graph representation approaches encounter significant challenges in handling the complex dynamic relations among entities, relations, and time. These challenges include the high difficulty of training and poor generalization performance, particularly with large datasets. To address these issues, this paper introduces curriculum learning strategies from machine learning, aiming to improve learning efficiency through effective curriculum planning. The proposed framework constructs a high-dimensional filtering model based on graph-based high-order receptive fields and employs a scoring model that uses a curriculum temperature strategy to evaluate the difficulty of temporal knowledge graph data quadruples at each stage. By progressively expanding the receptive field and dynamically adjusting the difficulty of learning samples, the model can better understand and capture multi-level information within the graph structure, thereby improving its generalization capabilities. Additionally, a temperature factor is introduced during model training to optimize parameter gradients, alongside a gradually increasing training strategy to reduce training difficulty. Experiments on the benchmark datasets ICEWS14 and ICEWS05-15 demonstrate that this framework not only significantly enhances model performance on these datasets but also substantially reduces training convergence time.

Residential demand response online optimization based on multi-agent deep reinforcement learning

Demand side management in the context of smart grids exploits the value stream of load flexibility, promoting stable and economic operation of the power grid. To address multi-uncertainties such as device characteristics, distributed renewable generation, and residential electricity demand, this paper proposes a model-free framework based on multi-agent deep reinforcement learning to achieve efficient online optimization of demand response strategies. Firstly, the demand side management is formulated as a partially observable Markov game. Then, a multi-agent soft actor-critic algorithm is proposed, combining a temporal electricity price feature extraction technology to improve learning efficiency. Reward correction mechanism is designed to avoid new charging and discharging peaks in the demand response. Finally, case studies demonstrate the effectiveness of the proposed method in reducing the electricity cost and aggregate peak demand of residents. The proposed method reduces the daily average cost by 21.40%, 8.09%, and 5.88% compared to MAPPO, MADDPG, and SUMADRL, and exhibits the highest training efficiency and scalability against the benchmarks.

Exploration of Personalized Learning for University Students in the Era of AI

This paper delves into the pivotal role of Artificial Intelligence (AI) in enhancing personalized learning for university students. It begins by highlighting the challenges faced by students in resource acquisition, learning method adaptability, and motivation, and then discusses how AI can address these issues through customized resources and adaptive learning environments. The paper further explores the application of AI in course design, intelligent tutoring, learning analytics, and collaborative learning platforms, showcasing the technology s potential to improve learning efficiency and outcomes. It also examines the future trends and challenges in the popularization of personalized learning, emphasizing the need for ethical considerations and data security. The paper concludes by suggesting that future research should focus on the long-term impact of AI in education and the importance of ensuring equitable access to its benefits for all students.