Dynamic Learning Research Articles

REVOLUTIONIZING HEALTHCARE THROUGH AUGMENTED REALITY INTEGRATION IN MEDICAL TRAINING

Through the transformation of conventional anatomy education techniques, the incorporation of augmented reality (AR) into medical training is revolutionising healthcare education. This creative method uses augmented reality (AR) tools, such as Microsoft® HoloLens II, to improve anatomy lab courses and give students a more engaging and dynamic learning environment. Medical schools can simplify anatomy instruction by using holograms to replicate human dissection. This allows students to study intricate anatomical structures in a step-by-step three-dimensional presentation. In addition to optimising the amount of time spent on dissection, this move towards AR-based learning creates new opportunities for remote and independent learning that meet the needs and preferences of a wide range of learners. Furthermore, the challenges that traditional cadaver-based anatomy instruction faces are addressed by integrating AR into medical education. AR offers a thorough and captivating learning environment because it can present anatomical material in different sequences, from superficial to deep structures, or by creating functional body units. Clinical correlations can be easily incorporated into lessons to improve students' comprehension and memorization of anatomy. The future of augmented reality (AR) in medical education appears bright, with potential breakthroughs in pathology, histology, and surgical applications, despite obstacles like realism constraints and technological complexity. Key Words: Augmented Reality (AR), Medical Training, Healthcare Education, Microsoft HoloLens, Anatomy Education, Validation Process

Investigating the multifaceted role of warm experts in enhancing and hindering older adults’ digital skills in Finland

ABSTRACT Despite widespread digitalization, certain marginal and societal groups can still encounter challenges in the digital world. Promoting digital inclusion and digital support aims to reduce these disparities and enable equal participation. In this article, we examine the quality and dynamics of informal learning and digital support provided by warm experts from the perspective of older adults’ subjective experiences. Specifically, we ask (1) what are the key elements of informal learning in the context of older adults’ acquisition of digital skills? Based on the first question, we further examine (2) how do the identified key elements of informal learning influence older adults’ subjective learning experiences and independent use of digital technologies? Our inductive thematic analysis is based on participant-induced elicitation (PIE) interviews (n = 21), conducted with older adults (aged 65+) in Finland in 2018. Our findings suggest that older adults are a heterogeneous group with different support needs and diverse prior experiences with digital technologies. Even when digital support from friends and family is available, it does not always facilitate independent use or meaningful learning experiences. The results show how informal digital support provided by warm experts can not only enhance, but also hinder digital inclusion and independent aging.

Exploring Education as a Complex System: Computational Educational Research with Multi-Level Agent-Based Modeling

Our study employs multi-level agent-based modeling and computational techniques to explore education as a complex system. With an underlying focus that education should be underpinned by a scientific understanding of student learning, we created computational models that simulated learning dynamics in classrooms, integrating both quantitative and qualitative insights. Through these models, we conducted experiments aligned with real classroom data to address key questions, such as “How can we effectively support the academic progress of underperforming students, who are disproportionately from low socio-economic status (SES) backgrounds, to close their multi-year achievement gap in mathematics?” Our study analyzes various instructional approaches for mathematical learning, and our findings highlight the potential effectiveness of Productive Failure as an instructional approach. Considerations of the broader applicability of computational methods in advancing educational research are also provided.

Short-Term Electrical Load Forecasting Using an Enhanced Extreme Learning Machine Based on the Improved Dwarf Mongoose Optimization Algorithm

Accurate short-term electrical load forecasting is crucial for the stable operation of power systems. Given the nonlinear, periodic, and rapidly changing characteristics of short-term power load forecasts, this paper introduces a novel forecasting method employing an Extreme Learning Machine (ELM) enhanced by an improved Dwarf Mongoose Optimization Algorithm (Local escape Dwarf Mongoose Optimization Algorithm, LDMOA). This method addresses the significant prediction errors of conventional ELM models and enhances prediction accuracy. The enhancements to the Dwarf Mongoose Optimization Algorithm include three key modifications: initially, a dynamic backward learning strategy is integrated at the early stages of the algorithm to augment its global search capabilities. Subsequently, a cosine algorithm is employed to locate new food sources, thereby expanding the search scope and avoiding local optima. Lastly, a “madness factor” is added when identifying new sleeping burrows to further widen the search area and effectively circumvent local optima. Comparative analyses using benchmark functions demonstrate the improved algorithm’s superior convergence and stability. In this study, the LDMOA algorithm optimizes the weights and thresholds of the ELM to establish the LDMOA-ELM prediction model. Experimental forecasts utilizing data from China’s 2016 “The Electrician Mathematical Contest in Modeling” demonstrate that the LDMOA-ELM model significantly outperforms the original ELM model in terms of prediction error and accuracy.

A novel framework using 3D-CNN and BiLSTM model with dynamic learning rate scheduler for visual speech recognition

A novel framework using 3D-CNN and BiLSTM model with dynamic learning rate scheduler for visual speech recognition

Crafting An AI-Powered Adaptive E-Learning Framework: Based on Kolb's Learning Style

In the contemporary educational realm, the convergence of pedagogical advancements with machine learning presents a profound opportunity for transformation. This in-depth exploration delves into the complexities of constructing an adaptive e-learning platform, seamlessly integrating Kolb's learning preferences with state-of-the-art machine learning techniques. At the core of this initiative lies the certification module, meticulously scrutinized to bolster its effectiveness in acknowledging student accomplishments. With a focus on personalized education driven by machine learning for error analysis, learner profiling, and content adaptation, this amalgamation reshapes the educational landscape, offering bespoke and dynamic learning journeys. By proposing an intelligent and dynamic adaptive learning system, this study addresses the constraints of passive and one-size-fits-all platforms, aiming to discern and furnish personalized learning environments tailored to the unique needs of individual learners within the hybrid teaching paradigm.

Exploring Pakistani University Lecturers’ Professional Identity Construction and Negotiation in ESL Classroom

Background: In the context of English as a second language (ESL) education in Pakistani universities, understanding the professional identity construction and negotiation of university lecturers is crucial. Objective: The current study aims to explore the complex dimensions of professional identity among non-native English speaking (NNES) multilingual Pakistani university lecturers. Theoretical Framework: This research aims to address the research gap by providing a nuance exploration of the experiences of Pakistani university lecturers by relating sociocultural theory proposed by Lev Vygotsky (1978). As this theory describes how individual’s cognition is associated with linguistic, cultural, institutional and social contexts, therefore, the focus of the sociocultural perception is on involvement in social interactions Method: Employing the qualitative research approach, the study seeks to investigate the multifaceted aspects that influence the professional identity of the lecturers. Six lecturers were purposively selected on the basis of their linguistic backgrounds and work experiences for classroom observations and in-depth interviews. Results and Discussion: The findings shed light on the challenges and opportunities faced by Pakistani university lecturers in shaping their roles within the ESL teaching domain. Research Implications: The study offers valuable insights into the evolving landscape of ESL education in Pakistan, providing various perspectives of how professional identity is negotiated and constructed within this specific educational context. Originality/Value: The relevance of this study lies in its potential to profoundly influence both academic discourse and educational practices. By delving into the subtle processes of professional identity construction, the study offers valuable insights into the dynamics of teaching and learning in diverse linguistic and cultural settings. It also highlights the critical need for continuous professional development for lecturers to enhance the quality of education.

Dynamical stability and chaos in artificial neural network trajectories along training

The process of training an artificial neural network involves iteratively adapting its parameters so as to minimize the error of the network’s prediction, when confronted with a learning task. This iterative change can be naturally interpreted as a trajectory in network space–a time series of networks–and thus the training algorithm (e.g., gradient descent optimization of a suitable loss function) can be interpreted as a dynamical system in graph space. In order to illustrate this interpretation, here we study the dynamical properties of this process by analyzing through this lens the network trajectories of a shallow neural network, and its evolution through learning a simple classification task. We systematically consider different ranges of the learning rate and explore both the dynamical and orbital stability of the resulting network trajectories, finding hints of regular and chaotic behavior depending on the learning rate regime. Our findings are put in contrast to common wisdom on convergence properties of neural networks and dynamical systems theory. This work also contributes to the cross-fertilization of ideas between dynamical systems theory, network theory and machine learning.

Assessing The Impact of Organizational Context on Learning Barrier Formation in Lima’s Private Universities

This research focuses on exploring the relationship between organizational learning barriers and organizational context in Private Universities in Lima, with the primary aim of elucidating how these elements interact and affect the learning dynamics within these institutions. Adopting a correlational cross-sectional non-experimental approach, a specific survey was designed and validated for application in the Peruvian context, gathering responses from 384 collaborators. The findings reveal that the most frequent barriers related to thought-action coherence highlight concerns about authenticity and sincerity in discussions, as well as a significant discrepancy between public and private opinions of workers. On the other hand, in the realm of organizational context, the critical factors identified include employees' perceptions of replaceability, fear of evaluations, deficiencies in communication channels, and the predominance of opinions from those with greater power. The conclusions point to the need to address these barriers to foster a more conducive environment for organizational learning, suggesting that effective knowledge and organizational environment management are fundamental for institutional development and adaptability.

Enhancing active learning through collaboration between human teachers and generative AI

To address the increasing demand for AI literacy, we introduced a novel active learning approach that leverages both teaching assistants (TAs) and generative AI to provide feedback during in-class exercises. This method was evaluated through two studies in separate Computer Science courses, focusing on the roles and impacts of TAs in this learning environment, as well as their collaboration with ChatGPT in enhancing student feedback. The studies revealed that TAs were effective in accurately determining students’ progress and struggles, particularly in areas such as “backtracking”, where students faced significant challenges. This intervention’s success was evident from high student engagement and satisfaction levels, as reported in an end-of-semester survey. Further findings highlighted that while TAs provided detailed technical assessments and identified conceptual gaps effectively, ChatGPT excelled in presenting clarifying examples and offering motivational support. Despite some TAs’ resistance to fully embracing the feedback guidelines-specifically their reluctance to provide encouragement-the collaborative feedback process between TAs and ChatGPT improved the quality of feedback in several aspects, including technical accuracy and clarity in explaining conceptual issues. These results suggest that integrating human and artificial intelligence in educational settings can significantly enhance traditional teaching methods, creating a more dynamic and responsive learning environment. Future research will aim to improve both the quality and efficiency of feedback, capitalizing on unique strengths of both human and AI to further advance educational practices in the field of computing.

RobLoc: Robust Wireless Localization With Dynamic Self-Adaptive Learning

RobLoc: Robust Wireless Localization With Dynamic Self-Adaptive Learning

Identifying 124 new anti-HIV drug candidates in a 37 billion-compound database: An integrated approach of machine learning (QSAR), molecular docking, and molecular dynamics simulation

Recent data from the World Health Organization reveals that in 2023, 38.8 million people were living with HIV. Within this population, there were 1.5 million new cases and 650 thousand deaths attributed to the disease. This study employs an integrated approach involving QSAR-based machine learning models, molecular docking, and molecular dynamics simulations to identify potential compounds for inhibiting the bioactivity of the CC chemokine receptor type 5 (CCR5) protein, a key entry point for the HIV virus. Using non-redundant experimental data from the CHEMBL database, 40 different machine learning algorithms were trained and the top four models (XGBoost, Histogram based gradient Boosting, Light Gradient Boosted Machine, and Extra Trees Regression) were utilized to predict anti-HIV bioactivity for 37 billion compounds in the ZINC-22 database. The screening resulted in the identification of 124 new anti-HIV drug candidates, confirmed through molecular docking and dynamics simulations. The study underscores the therapeutic potential of these compounds, paving the way for further in vitro and in vivo investigations. The convergence of machine learning and experimental findings presents a promising avenue for significant advancements in pharmaceutical research, particularly in the treatment of viral diseases such as HIV. To guarantee the reproducibility of our study, we have made the Python code (google colab) and the associated database available on GitHub. You can access them through the following link: GitHub Link: https://github.com/AlexandreCOBRE/code.

Learning with filopodia and spines: Complementary strong and weak competition lead to specialized, graded, and protected receptive fields.

Filopodia are thin synaptic protrusions that have been long known to play an important role in early development. It has recently been found that they are more abundant in the adult cortex than previously thought, and more plastic than spines (button-shaped mature synapses). Inspired by these findings, we introduce a new model of synaptic plasticity that jointly describes learning of filopodia and spines. The model assumes that filopodia exhibit strongly competitive learning dynamics -similarly to additive spike-timing-dependent plasticity (STDP). At the same time it proposes that, if filopodia undergo sufficient potentiation, they consolidate into spines. Spines follow weakly competitive learning, classically associated with multiplicative, soft-bounded models of STDP. This makes spines more stable and sensitive to the fine structure of input correlations. We show that our learning rule has a selectivity comparable to additive STDP and represents input correlations as well as multiplicative models of STDP. We also show how it can protect previously formed memories and act as a synaptic consolidation mechanism. Overall, our results can be seen as a phenomenological description of how filopodia and spines could cooperate to overcome the difficulties that strong and weak competition each have.

EfficientTrain++: Generalized Curriculum Learning for Efficient Visual Backbone Training.

The superior performance of modern computer vision backbones (e.g., vision Transformers learned on ImageNet-1K/22K) usually comes with a costly training procedure. This study contributes to this issue by generalizing the idea of curriculum learning beyond its original formulation, i.e., training models using easier-to-harder data. Specifically, we reformulate the training curriculum as a soft-selection function, which uncovers progressively more difficult patterns within each example during training, instead of performing easier-to-harder sample selection. Our work is inspired by an intriguing observation on the learning dynamics of visual backbones: during the earlier stages of training, the model predominantly learns to recognize some 'easier-to-learn' discriminative patterns in the data. These patterns, when observed through frequency and spatial domains, incorporate lower-frequency components, and the natural image contents without distortion or data augmentation. Motivated by these findings, we propose a curriculum where the model always leverages all the training data at every learning stage, yet the exposure to the 'easier-to-learn' patterns of each example is initiated first, with harder patterns gradually introduced as training progresses. To implement this idea in a computationally efficient way, we introduce a cropping operation in the Fourier spectrum of the inputs, enabling the model to learn from only the lower-frequency components. Then we show that exposing the contents of natural images can be readily achieved by modulating the intensity of data augmentation. Finally, we integrate these two aspects and design curriculum learning schedules by proposing tailored searching algorithms. Moreover, we present useful techniques for deploying our approach efficiently in challenging practical scenarios, such as large-scale parallel training, and limited input/output or data pre-processing speed. The resulting method, EfficientTrain++, is simple, general, yet surprisingly effective. As an off-the-shelf approach, it reduces the training time of various popular models (e.g., ResNet, ConvNeXt, DeiT, PVT, Swin, CSWin, and CAFormer) by [Formula: see text] on ImageNet-1K/22K without sacrificing accuracy. It also demonstrates efficacy in self-supervised learning (e.g., MAE). Code is available at: https://github.com/LeapLabTHU/EfficientTrain.

Improving First-Year English-Majored Students’ Speaking Skills through Using Digital Storytelling

This study aims to investigate the effectiveness of digital storytelling on Vietnamese first-year English-majored students' English-speaking learning at a university in Vietnam. In this study, the students were asked to create English-dubbed animated stories with different topics relating to their speaking lessons by using the Movie Adventure application. Methods for data collection were speaking pre-and post-tests, questionnaires, and interviews. The findings revealed that using digital storytelling as a pedagogical tool for practicing English speaking benefited students' speaking ability. The findings also showed that engaging in the content of stories not only encouraged students' motivation but instilled their confidence to speak in English. The results of the given questionnaires and interviews showed that students responded positively to the implementation of digital storytelling, which facilitated learning by allowing learners to interact and collaborate in English. From the results gained, it was concluded that digital storytelling can be used to create a dynamic speaking learning environment that helps students participate actively in their English-speaking lessons during their learning time.

Improving English Language Proficiency through Podcasts: A Multifaceted Educational Approach

This paper probes into the strength of podcasts in building the English language through varied dimensions of a learning approach. The paper sought to establish the applicability of podcasts in the learning of languages among EFL students, given that podcasts are increasingly becoming an informal learning platform. The study was carried out among learners of different age groups and language proficiency levels, and the collection of statistics regarding the use of podcasts within language learning was affected through both quantitative and qualitative data. Important findings indicate that regular use of podcasts can make the process of vocabulary acquisition, listening comprehension, and general proficiency stronger. Such findings support the use of podcasts in gaining better language competence in that it not only presents rich contextual content in an accessible manner but also puts into play learner skills. In effect, the inclusion of podcasts into the learning curriculum has implications that are far-reaching in that it could offer an effective improvement in the traditional methodologies of language learning by enhancing a dynamic and flexible learning environment that meets the needs of all learners. This research paper points out the imperative nature of podcasts in modern educational strategies and suggests their application in a wider context of language learning frameworks to bring about superior linguistic competence.

The synergistic and dynamic relationship between learning design and learning analytics

The synergistic relationship between learning design and learning analytics has the potential for improving learning and teaching in near real-time. The potential for integrating the newly available and dynamic information from ongoing analysis into learning design requires new perspectives on learning and teaching data processing and analysis as well as advanced theories, methods, and tools for supporting dynamic learning design processes. Three perspectives of learning analytics design provide summative, real-time, and predictive insights. In a case study with 3,550 users, the navigation sequence and network graph analysis demonstrate the potential of learning analytics design. The study aims to demonstrate how the analysis of navigation patterns and network graph analysis could inform the learning design of self-guided digital learning experiences. Even with open-ended freedom, only 608 sequences were evidenced by learners out of a potential number of hundreds of millions of sequences. Advancements of learning analytics design have the potential for mapping the cognitive, social and even physical states of the learner and optimise their learning environment on the fly.

As Teachers Not Tourists: Creating Knowledge During a Short-Term Study Abroad Program

Here, I address the call for teacher education programs to feature international elements to help pre-service teachers craft powerful classroom instruction and successfully work with students from culturally diverse backgrounds. I share a qualitative investigation of participants’ experiences during a short-term study abroad program in Japan that explored contemporary disaster preparedness as a result of the Great East Japan Earthquake and Tsunami. The program sponsors, the Japanese Ministry of Foreign Affairs, hoped participants would develop an appreciation for the people and culture of Japan while they learned about disaster preparedness. I wondered if collecting curriculum items during a short-term study abroad program could help social studies pre-service teachers create—not just receive—historical domain knowledge? In Japan, participants collected items to use as featured resources around which they designed classroom activities for secondary students. Their activities demonstrated three levels of second-order historical domain knowledge and two elements of civic competence. This study suggests that pre-service teachers with no previous international experience may require explicit modeling and scaffolding to translate their short-term study abroad participation into dynamic learning. Another implication is that participants need strong support to consider the photographs they capture as artifacts of contemporary material culture.

Implemented classification techniques for osteoporosis using deep learning from the perspective of healthcare analytics.

Osteoporosis is a medical disorder that causes bone tissue to deteriorate and lose density, increasing the risk of fractures. Applying Neural Networks (NN) to analyze medical imaging data and detect the presence or severity of osteoporosis in patients is known as osteoporosis classification using Deep Learning (DL) algorithms. DL algorithms can extract relevant information from bone images and discover intricate patterns that could indicate osteoporosis. DCNN biases must be initialized carefully, much like their weights. Biases that are initialized incorrectly might affect the network's learning dynamics and hinder the model's ability to converge to an ideal solution. In this research, Deep Convolutional Neural Networks (DCNNs) are used, which have several benefits over conventional ML techniques for image processing. One of the key benefits of DCNNs is the ability to automatically Feature Extraction (FE) from raw data. Feature learning is a time-consuming procedure in conventional ML algorithms. During the training phase of DCNNs, the network learns to recognize relevant characteristics straight from the data. The Squirrel Search Algorithm (SSA) makes use of a combination of Local Search (LS) and Random Search (RS) techniques that are inspired by the foraging habits of squirrels. The method made it possible to efficiently explore the search space to find prospective values while using promising areas to refine and improve the solutions. Effectively recognizing optimum or nearly optimal solutions depends on balancing exploration and exploitation. The weight in the DCNN is optimized with the help of SSA, which enhances the performance of the classification. The comparative analysis with state-of-the-art techniques shows that the proposed SSA-based DCNN is highly accurate, with 96.57% accuracy.

Academics’ practices and perceptions of career development learning in the curriculum

ABSTRACT Career development learning (CDL) equips students with the skills, knowledge and resources to navigate their careers amidst dynamic work and learning environments. While CDL is known to be facilitated by central careers services in higher education institutions, embedding CDL activities within the curriculum is purported to be an effective approach to generate meaningful reflections that prepare students for their future lives. This study examined academics’ perceptions and applications of CDL to understand the extent to which CDL is currently practiced in the curriculum. At one institution, interviews were conducted with 55 academics teaching across 101 subjects and in various disciplines. Findings demonstrate that most CDL is delivered informally or in isolation without a structured framework, highlighting the need to improve purposeful integration of CDL in the curriculum. Recommendations point to greater adoption of strategic institution-wide approaches to CDL, which ultimately impact students’ employability choices, career decision-making, and preparation for the workforce.