Online-Journals.org (International Association of Online Engineering)
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Enhancing Mobile Learning Platforms through Meaningful Gamification: Heatmaps, Quizzes, and Time-Based Rewards
Get PDFDeveloping long-term study habits through gamification in educational platforms presents a significant challenge. The approaches taken by modern gamified platforms and researchers to gamify the educational platform often rely on extrinsic rewards akin to video games, which engage students but fail to cultivate the intrinsic motivation necessary for habit development. Educational platforms commonly implement badges, leaderboards, achievements, and points (BLAP) that predominantly reward students for their results rather than their effort, thereby lacking meaningful gamification. To address these issues, this paper proposes alternative strategies such as competitive player-vs-player quizzes, visual time statistics, and a habit-tracking heatmap to foster intrinsic motivation through a competitive environment that enhances self-awareness and cultivates a growth mindset. This research utilized an initial survey and literature review to identify the shortcomings of existing gamification approaches, developed a prototype based on the hypotheses, and engaged participants in interactive sessions to gather qualitative feedback. Subsequently, a quantitative survey measured the effectiveness of the proposed strategies. As an initial exploratory study, this paper aims to lay the groundwork for future empirical validation of these strategies. The findings from this phase provide initial validation that these new features may serve as effective alternatives to traditional gamification elements. While the study suggests these methods have potential, a longitudinal evaluation is necessary to assess their long-term impact on study habits
Detection of Cognitive Distortions in Students’ Thoughts Using Topic Modeling and Fuzzy Clustering
Get PDFCognitive distortion (CD) refers to an irrational thinking pattern that causes individuals to misinterpret information and convince themselves of incorrect information. This study investigates patterns of CDs in students’ thoughts after academic exams. Machine learning models are utilized to detect and categorize these distortions. The methodology of this study utilizes topic modeling with two approaches: latent Dirichlet allocation (LDA) with Tf–Idf and non-negative matrix factorization (NMF). The NMF approach is applied with two different pre-trained embeddings (AraBERT and AraGPT). Fuzzy clustering is combined with these topic modeling approaches, and the results are compared. Experiments are conducted using two datasets: a collection of students’ thoughts and a generated dataset that is based on cognitive behavioral therapy (CBT) principles. When analyzing the students’ thoughts dataset, NMF with AraBERT demonstrated superior performance by producing the most meaningful topics with a coherence score of 0.78. However, in the generated dataset, NMF with AraGPT achieved a better balance between coherence and separation, along with clearer topic boundaries. Although NMF with AraBERT achieves the highest coherence score (0.86), it shows significant topic overlap inferred from the inter-clustering score (0.81). Fuzzy clustering, topic modeling, and NMF-AraGPT together provide the highest overall performance when applied to the students’ dataset. This combination provides distinct and well-separated topics inferred from the inter-clustering score (0.53). NMF topic modeling with AraGPT is the most effective model when integrated with fuzzy clustering based on the comprehensive analysis
Enhancing Student Engagement and Classroom Interaction through Mobile Interactive Technologies
Get PDFUnder the framework of educational informatization, mobile interactive technology has emerged as a promising approach to address inefficiencies in traditional classroom interaction and the problem of low student engagement. However, current practices continue to face critical challenges, including the lack of personalized strategy design and insufficient recognition of implicit low-engagement patterns. Using the building information modeling (BIM) courses as a case study, it has been observed that while mobile collaboration tools improve the efficiency of complex knowledge interaction, issues such as lecture-dominated discourse and an overabundance of low-level interactions remain prevalent. Existing research has largely focused on optimizing application functionalities or evaluating explicit participation indicators, often neglecting deeper structural characteristics such as students’ peripheral positions within interaction networks, sparse connectivity, and anomalies in interaction quality. Traditional graph models have been limited to direct connections and have failed to capture the potential for indirect collaboration through higher-order paths. Additionally, current deep learning approaches lack sufficient temporal modeling of dynamic interaction structures, resulting in strategy evaluation processes that remain empirically driven rather than data-driven. To address these limitations, a dynamic strategy evaluation method based on low-engagement graph link prediction was proposed. A heterogeneous graph model incorporating student, teacher, and resource nodes was constructed, supported by a subgraph extraction and line graph transformation algorithm to analyze multi-order indirect interaction paths. A tree-long short-term memory (LSTM) model was employed to aggregate edge features and generate embedded representations of interaction potential. Ranking loss training was utilized to address sample imbalance. This study transcends the limitations of explicit indicators by quantifying the structural characteristics of low-engagement students within the network. The proposed method provides data-driven support for the precise design of group collaboration suggestions and feedback optimization mechanisms on mobile platforms. A shift from “coarse-grained interaction” to “precision intervention” is thereby facilitated. These findings are expected to enhance the effectiveness of classroom interaction and contribute to greater equity in education
An Integrated Model for Online-Offline Classroom for Optimizing Academic Management and Enhancing Student Learning Outcomes
Get PDFThis study aims to develop an integrated online-offline classroom model based on academic management to enhance student learning outcomes at NanHang JinCheng College, Nanjing, China. Data were collected through a qualitative methodology from 392 students and 220 teachers using structured questionnaires. Descriptive statistical analysis focused on key dimensions such as student engagement, pedagogical flexibility, and academic management’s role in optimizing learning performance. The findings indicate high student satisfaction in collaborative learning, knowledge application, and group discussions. However, moderate satisfaction was observed regarding diverse learning resources and proactive feedback-seeking behaviors. Teachers exhibited strong confidence in integrating hybrid learning models yet showed limited adaptation of lessons based on real-time student feedback. Academic management emerged as a crucial factor in shaping learning outcomes, highlighting the need for enhanced assessment tools and adaptive learning strategies. The study concludes that while the proposed hybrid model significantly improves student achievement, its long-term effectiveness depends on continuous professional development, optimized resource utilization, and dynamic adjustments through decision-making approaches. Integrating uncertainty modeling and intelligent learning analytics can refine hybrid learning frameworks for sustainable educational innovation
A Cognitive Load Theory-Based Approach to Integrating Mobile Fragmented Learning Resources
Get PDFWith the rapid development of mobile internet technology, mobile fragmented learning has become a mainstream learning mode due to its convenience and flexibility. However, the vast quantity of learning resources available online often varies in quality and lacks coherent organization, leading to excessive cognitive load and reduced learning efficiency. Existing research primarily focuses on resource integration based on content similarity clustering or user behavior data, while largely overlooking learners’ cognitive characteristics and the dynamic regulation of cognitive load. As a result, current integration methods fail to meet the cognitive needs of fragmented learning. To address this issue, this study proposes a resource integration framework for mobile fragmented learning grounded in cognitive load theory. On one hand, it constructs a cognitively aligned resource analysis model using design structure matrix (DSM) to rank and categorize cognitive load elements in learning materials. On the other hand, it applies DSM-based decoupling and clustering strategies to decompose resources into manageable cognitive units and support personalized aggregation. This dual approach aims to reduce intrinsic cognitive load and enhance the efficiency of resource organization. The findings offer a cognition-informed pathway for mobile learning resource design and hold significant implications for optimizing fragmented learning experiences and advancing mobile learning theory and practice
A Real-Time Monitoring and Analysis Model for Regional Economic Activities Based on Mobile Computing
Get PDFWith the rapid advancement of the digital economy, the dynamic and complex nature of regional economic activities has posed significant challenges to traditional low-frequency statistical monitoring methods. Mobile computing, with its capability to capture high-frequency and multi-source data, offers a promising new approach for real-time monitoring. However, current research still faces several limitations: low integration efficiency of heterogeneous data sources, insufficient fusion of spatiotemporal features, and significant interference from noise. Traditional statistical models, reliant on low-frequency sampled data, often suffer from lag and sampling bias. Although machine learning methods have improved predictive accuracy, models such as long short-term memory (LSTM) lack the ability to capture spatial heterogeneity; conventional denoising algorithms struggle to handle complex noise patterns; and many studies fail to fully explore the spatiotemporal coupling of economic activity. To address these issues, this study proposes a real-time monitoring and analysis model for regional economic activities based on mobile computing. The model consists of five core modules: (1) a data acquisition and preprocessing module for real-time integration and outlier detection across multiple data sources; (2) a denoising module based on the rime ice optimization (RIME) algorithm, which enhances robustness against noise through soft frost search and hard frost penetration mechanisms; (3) a spatial feature extraction module using graph attention networks (GAT) to model inter-regional economic relationships and capture spatial spillover effects; (4) a temporal feature extraction module based on LSTM to uncover long-term temporal dependencies; and (5) a prediction output module that fuses spatial and temporal features for accurate forecasting of economic activities. The innovation of this model lies in its optimized denoising process through the RIME algorithm and the deep integration of spatiotemporal features via GAT-LSTM. It supports real-time data input and dynamic prediction, providing an intelligent tool that transforms regional economic governance from “post-event analysis” to “real-time perception.” This contributes to more precise policymaking and more efficient resource allocation
Privacy-Preserving Federated Learning for Prognostic Modeling in Rare Diseases: A Scalable Case Study on Kawasaki Disease
Get PDFPredictive modeling in rare diseases faces major challenges, including data scarcity, class imbalance, and strict privacy regulations that limit cross-border collaboration. These challenges are particularly critical in Kawasaki disease (KD) — a rare vasculitis in children — where 10% to 20% of patients are resistant to intravenous immunoglobulin (IVIG), the standard first-line treatment. This significantly increases the risk of coronary artery abnormalities, making early and accurate prediction of resistance to IVIG essential for improving patient outcomes. Our work proposes a Federated Learning approach to address the constraints imposed by security and privacy concerns. We investigate Convolutional Neural Networks as the shared model, collaboratively trained across clients. Coupled with strategies to address class imbalance resulting from the rarity of the condition, the federated approach yielded promising results when evaluated against conventional machine learning models. The proposed approach demonstrated strong performance, achieving 94% accuracy, 93% precision, 89% recall, and 91% F1 score. To ensure robustness and generalizability, an independent dataset was also used, where the proposed model excelled similarly. These results highlight the potential of federated learning to overcome data privacy barriers and provide a scalable, secure solution for predictive modeling in rare diseases, supporting its integration into medical prediction workflows
Disturbance Observer-Based Sliding Mode Control for Ventilation Blower-Based Systems: Controller Design and Simulation
Get PDFThis paper presents a disturbance observer-based sliding mode control (DO-SMC) for a ventilation blower-based system (VBS) to enhance control performance. Due to the complexity of physical modeling and the lack of blower specifications, the VBS model is approximated as a second-order transfer function using a black-box system identification approach. Additionally, the VBS model was evaluated using the Nash-Sutcliffe model efficiency coefficient, achieving a fit of 92.77%. To validate the performance of DO-SMC, various simulation scenarios were conducted both in the absence and presence of disturbances. In the disturbance-free scenario, the VBS controller effectively tracked the desired air volume during the inspiratory cycle. However, this effect is less evident at the start of the cycle due to rotor inertia and electrical driver characteristics. Specifically, the simulation data showed a maximum deviation of approximately 47 ml under these conditions. In contrast, under ramp and square disturbances combined with random noise, the proposed controller significantly reduced the steady-state error and improved response time, even in the presence of system uncertainties. Additionally, slight chattering was observed in the control signal, attributed to the controller’s attempts to compensate for abrupt system behavior changes. As a result, accurate estimation of the ramp and square disturbances contributed to enhanced overall control performance by mitigating their effects, even though some residual errors remained in the higher-order tracking dynamics due to system limitations
Big Data Analytics Reveals Pyrethrins’ Breast Cancer Risks: A Deep Learning-Enhanced Study Combining Mendelian Randomization and Molecular Dynamics
Get PDFPyrethrins, a class of broad-spectrum insecticides, have garnered extensive utilization in agricultural, public health, and environmental sectors. However, emerging concerns have arisen regarding their potential chronic carcinogenic risks. This study employed an integrative big data approach combining network toxicology, deep learning, mendelian randomization, molecular docking, and dynamics simulations to systematically evaluate pyrethrin's breast cancer-related targets. Computational screening identified 16 high-affinity targets with binding energies < -7.5 kcal/mol, indicative of stable interactions. Molecular dynamics simulations further validated the structural stability of pyrethrin-target complexes. MM/PBSA analyses revealed that both pyrethrin I and II exhibit thermodynamically spontaneous interactions with diverse targets, demonstrating binding free energies ranging from -9.53 to -27.37 kcal/mol. Complex interactions among targets were constructed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses revealed significant associations between these targets and breast cancer pathways. Our multi-omics big data evidence positions pyrethrins as a risky toxicant with carcinogenic potential. These findings provide insights for regulatory reevaluation of pyrethrin's safety profile and underscore the need for longitudinal biomonitoring studies to assess its population-level health impacts
Review of Introducing Augmented Reality and Internet of Things at Austrian HTL – Results from 2019 to 2024
Get PDFThis paper gives an overview of the introduction of Augmented Reality (AR) and Internet of Things (IoT) technology at the Austrian higher technical vocational colleges (HTL). All areas, such as software implementation and organizational management for 42 locations, as well as server operation and training, are described. The necessary work and adjustments in the organizational area are also described. At the time of the introduction of the two completely new technologies, an educational concept for training and use in the HTL was also jointly developed and introduced in a working group. This paper describes an example of the introduction of one or more new technologies in engineering education, which can possibly be used as a best practice example for other areas