Dynamic Learning Research Articles

Relationship between attitudes toward mathematics and perceptions of virtual teaching in the COVID-19 context

BackgroundThe shift to virtual education from 2019 to 2021 in response to COVID-19 necessitated both teachers and students to adapt to new technological skills, thereby impacting the teaching and learning dynamics. With limited existing research on this evolving domain, our study aimed to gauge attitudes toward mathematics, and perceptions of virtual education during the COVID-19 era and explore potential correlations between these variables.MethodsIn this cross-sectional quantitative correlational primary study, secondary school students at a public school were asked to rate their attitudes toward mathematics and their perceptions of virtual teaching in the context of COVID-19 using two validated questionnaires. We used linear regression analysis to respond to our objectives.ResultsThe findings show that perceptions of virtual teaching (34.65%) and attitudes toward mathematics (40.16%) are both low. The dimensions of attitudes toward mathematics and perceptions of virtual education have a positive association and influence on one another (B = 0.464, p < 0.001).ConclusionBoth study variables show a low level and found a significant relationship between the dimensions of virtual learning perception and attitudes toward mathematics. More research is required to grasp how cognitive processing influences attitudes toward mathematics. This understanding can improve the proficiency of math educators, benefiting students at all educational stages.

You Can Make it Too Easy: Simulating Easy-To-Hard Effects in Auditory Perceptual Learning with an Unsupervised Neural Network Model of Plasticity

ABSTRACT The easy-to-hard effect is a phenomenon where training on initially easy perceptual discrimination contrasts before introducing hard contrasts yields greater perceptual learning than training with hard contrasts for the same amount of time. However, easy-to-hard effects can be erased if initial easy trials are made too easy. Here, we show that this seemingly paradoxical result naturally emerges out of an artificial neural network model of perceptual learning based on the incremental differentiation of stimulus representations: the self-organizing map (SOM). Like human listeners, networks show a sweet spot for easy-to-hard effects at intermediate levels of easy contrast somewhere between too easy and too hard. This trend is apparent across a wide range of free parameters. Analysis of network learning dynamics shows that easy-to-hard effects manifest when competition for network representational space is reduced (relative to constant hard training), but the learned stimuli are still similar enough to hard contrast stimuli to allow generalization. The data show that an incremental perceptual learning model can account for subtle characteristics of easy-to-hard effects where learning theories grounded in attentional discovery of relevant features cannot. We suggest several avenues for further development of the model in order to account for a wider range of auditory learning phenomena, its use in the optimization of auditory training regimens, and its role in elucidating perceptual learning mechanisms.

Assessing internship learning performance and its predictors: moderation of learning climate

PurposeThis research aims to elucidate the complex relationships among internship learning performance, problem-solving efficacy, and their determinants by simultaneously examining a key variable that may moderate these relationships.Design/methodology/approachThis study carried out its empirical examination by surveying graduate students at a prestigious university in Taiwan. The investigation selected two programs from the College of Management and another two from the College of Engineering within the university through a random sampling approach. The selection of research participants from the domains of management and engineering is well-suited to this study’s objectives, given the pronounced prevalence of internships in these fields. Of the 280 questionnaires, 234 usable questionnaires were finally collected for a response rate of 83.57%.FindingsThe findings of this study show that learning goal orientation and communication skill learning both represent critical motivations that directly dominate the growth of problem-solving efficacy to ultimately boost learning performance. At the same time, the positive moderation of learning climate suggests the learning climate as an accelerator for learning autonomy that boosts interns’ problem-solving efficacy more strongly.Originality/valueThis study presents an expansion of the social learning theory’s conventional focus on general self-efficacy beliefs by delving into the realm of internships as a unique research context to explore problem-solving efficacy as a distinct facet of self-efficacy. Within this context, the study integrates the mediating role of problem-solving efficacy into the learning dynamics of internships, where interns engage in both learning goal orientation and communication skill learning. By concentrating on the internship setting within the workplace, this study effectively bridges the domains of management education and vocational psychology, extending insights beyond the classroom to comprehensively grasp the impact of learning goal orientation and communication skill learning.

Innovation Ecosystem Dynamics, Value and Learning I: What Can Hamilton Tell Us?

AbstractHeld in Dublin, Ireland, IS2024 invites us to refresh understanding of contributions to systems engineering by Ireland's greatest mathematician–Sir William Rowan Hamilton (1805 ‐ 1865), Professor of Astronomy at Trinity College Dublin and Royal Astronomer of Ireland. His profound contributions to STEM deserve greater systems community attention. Supporting theory and practice, they intersect Foundations and Applications streams of INCOSE's Future of Systems Engineering (FuSE) program. Strikingly, key aspects apply to systems of all types, including socio‐technical and information systems. Hamilton abstracted the energy‐like generator of dynamics for all systems, while also generalizing momentum. Applied to the INCOSE Innovation Ecosystem Pattern as dynamics of learning, development, and life cycle management, this suggests an architecture for integration of the digital thread and machine learning in innovation enterprises, along with foundations of systems engineering as a dynamical system.

Estrategias metodológicas en el módulo cultivos de ciclo corto y su influencia en el desarrollo de habilidades laborales en estudiantes agropecuarios

Short-cycle crops contribute to food security and sustainability. The research focuses on proposing didactic strategies for the short cycle crops module in order to train high school graduates who contribute to the economic and social development of the community. The methodology used was a field investigation, of a descriptive nature, and a mixed quali-quantitative approach with empirical methods and theoretical methods such as analytical -synthetic, historical-logical, deductive-inductive. The results reflect that the traditional teaching paradigm was used and the strategies proposed by the ministry were not applied. The students perceived the module as monotonous and boring, uninteresting and not at all transcendental, which is why problem-based learning strategies are proposed, based on research, gamification, Design and Visual Thinking, showing through a correlation matrix that the strategies create learning environments. dynamic learning that enhances collaboration, creativity and strengthens the professional skills of high school graduates to adapt to diverse and changing work situations in the agricultural sector.

A living lab for Structural Health Monitoring at the Nibelungen Bridge Worms for Transfer Learning of Structural Dynamics

The Nibelungen Bridge in Worms, Germany has been selected as a national demonstration structure for advanced non-destructive testing (NDT) and structural health monitoring concepts to extend the lifetime of civil structures and to optimize O&M actions. Parts of the research that involves this bridge as a demonstrator belong to the focus area program SPP100+. In this program, the bridges SHM System has been extended and combined with an additional setup of vibration sensors. The used digital smart sensor with pre-processing functions, the arrangement of the sensors at the structure and additional edge computing capability allows the investigation of transfer learning and other methods directly into the real structure. The living lab with seven triaxial accelerometers can be reconfigured in real-time and adjusted to the needs of AI models for classification. The comparison with the existing conventional SHM sensors has been made possible by hardware synchronization to the existing SHM System and collocating sensors at similar positions, so that a hardware exchange can be an investigated use-case for the transfer learning. During idle times, the system collects vibration data like a conventional SHM system. Keywords: Structural Health Monitoring (SHM), Data acquisition, Sensor technology.

Model-based machine learning of critical brain dynamics

Criticality can be exactly demonstrated in certain models of brain activity, yet it remains challenging to identify in empirical data. We trained a fully connected deep neural network to learn the phases of an excitable model unfolding on the anatomical connectome of human brain. This network was then applied to brain-wide fMRI data acquired during the descent from wakefulness to deep sleep. We report high correlation between the predicted proximity to the critical point and the exponents of cluster size distributions, indicative of subcritical dynamics. This result demonstrates that conceptual models can be leveraged to identify the dynamical regime of real neural systems.

Bridging distribution gaps: invariant pattern discovery for dynamic graph learning

Bridging distribution gaps: invariant pattern discovery for dynamic graph learning

The Effects of Digital Storytelling on the Retention and Transferability of Student Knowledge

This study aimed to investigate the effects of Storytelling Narrated Videos (SNV) on students’ knowledge retention and transferability. A total of 56 students from a university in Indonesia were randomly assigned to a quasi-experimental research design exposed to SNV and to Lecture Narrated Videos (LNV). Two videos were created to deliver content on Bloom’s Taxonomy, one using a lecture-style format and the other adopting a storytelling approach. Data were collected through tests, questionnaires, and essays. The findings revealed that participants exposed to SNV had higher retention memory scores, indicating a positive impact on knowledge retention compared to those who watched LNV. Moreover, the storytelling videos facilitated cognitive skill progression, enhanced understanding through engaging visuals, and fostered a strong connection with a familiar narrator, resulting in a more dynamic and memorable learning experience. The study also examined knowledge transfer and found that participants who watched the storytelling videos performed better in applying Bloom’s Taxonomy concepts to planning teaching objectives in the essay test. This suggests that the incorporation of storytelling narration and promoting transfer knowledge activities can enhance students’ understanding, retention, and practical application of the learned material. Overall, the findings highlight the potential of incorporating storytelling in narrated videos to improve students’ knowledge retention, transferability, and engagement in educational settings.

Advancing the Knowledge of Spaniards’ Attitudes Towards Immigration

This study aims to assist Spanish authorities in enhancing the prevention of negative attitudes towards immigrants by addressing two key questions derived from prior research by the authors: (1) Which variables consistently influence attitudes towards migration over time? (2) What are the distinct attitudinal differences among various groups? Thus, the study delves into the evolution of attitudes towards migration in Spain from 2015 to 2017. It employs a dynamic perspective and advanced machine learning (ML) techniques recognized for their superior predictive capacity in social sciences. It identifies key variables influencing attitudes within diverse groups, including Multicultural and Non-Multicultural, and traces their changes over time. The research addresses the persistence of specific variables tied to subtle biases and distinctions among attitudinal segments. Despite an overall positive trend towards welcoming differences, the study unveils enduring ambivalence, elements of aversive racism, and subtle prejudice, highlighting the intricate factors contributing to the non-acceptance of immigration. Notably, self-perception emerges as a determinant, with optimism and professional attributes correlating with heightened tolerance. In conclusion, the study provides nuanced insights into the multifaceted nature of attitudes towards migration, offering valuable perspectives for more informed policy development.

Adaptive chaotic dynamic learning-based gazelle optimization algorithm for feature selection problems

Feature Selection (FS) is considered a crucial procedure for eliminating unnecessary features from datasets. FS is considered a challenging problem that is difficult to solve efficiently due to its combinatorial nature. As the problem size increases, so does the computation time. Recently, researchers have been focusing on metaheuristic FS algorithms. Therefore, this paper introduces an adaptive chaotic dynamic gazelle optimization algorithm (ACD-GOA) with enhanced elite Strategy for FS problems. The ACD-GOA is a novel enhanced version of the recently published GOA, incorporating multiple improved strategies to enhance its search capabilities and convergence speed. The initialization phase and iteration initialization adopt the dynamic opposition learning strategy to avoid premature convergence. Additionally, several enhancement strategies are employed to improve the efficiency of the standard GOA. Adaptive inertia weight and sigmoid function are used to enhance search efficiency. Furthermore, enhanced elite and exchanging information strategies are implemented to maintain population diversity and avoid local solutions, respectively. Experimental evaluations are conducted using various functions, including the twelve CEC2022 standard functions and fourteen FS benchmark datasets. The performance of ACD-GOA is compared with several other metaheuristic algorithms. Statistical tests such as Friedman and Wilcoxon signed-rank tests are used to analyze the experimental data. The experimental results highlight the proposed algorithm’s exceptional capacity to overcome problems associated with local minima and expedite the convergence process. The suggested algorithm has been extensively compared to state-of-the-art algorithms, demonstrating significant breakthroughs. The reported accuracy of the proposed algorithm ranges from 0.78 to 1.00 with K-NN classifier.

Peran Guru Pendidikan Agama Kristen (PAK) Dalam Implementasi Pembelajaran Interaktif Berbasis Teknologi Untuk Meningkatkan Motivasi Belajar Siswa

Technology-enhanced interactive learning in Christian religious education holds great potential for enhancing students' learning motivation and understanding of Christian values. This article discusses the crucial role of Christian Religious Education (CRE) teachers in designing and implementing this approach, as well as the challenges encountered in the process. Interactive learning leverages tools and platforms such as Classpoint, Slido, and other applications to create a dynamic and contemporary learning environment. Teachers are tasked with selecting and adapting Christian religious content that meets students' needs while utilizing interactive features to enhance student engagement. Challenges including limited technology access, inadequate technological skills, and resistance to change are also addressed within the context of technology integration in religious education. By combining technological innovation with appropriate pedagogical approaches, teachers can maximize the potential of interactive learning to create meaningful and effective learning experiences that deepen students' understanding and application of Christian values in everyday life.

Optimization of Pin Fins Using Computational Fluid Dynamics and Machine Learning

This paper presents a two-part study focusing on the optimization of pin-fin arrays for gas turbine blade cooling. The first part of the study examines the thermal performance of various pin-fin shapes and sizes using computational fluid dynamics. The study investigates circular, elliptical, hexagonal, and rectangular cross sections, with emphasis on the hydraulic diameter and Reynolds number. Two-dimensional simulations mimicked a confined, staggered array of uniform-sized pins under different inlet conditions. For a hydraulic diameter of 0.053 m and a Reynolds number of 5500, the rectangular pins showed the highest Nusselt number 8.98, 16.01, and 20.62% larger than circular, elliptical, and hexagonal pins, respectively, but also a pressure increase 68.30% higher on average. For a hydraulic diameter of 0.046 m and a Reynolds number of 1000–15,000, it was shown that all shapes have an exponential increase in pressure and a logarithmic decay in the Nusselt number. Standing out was the rectangular shape, which, at a maximum Reynolds number of 15,000, had a pressure increase of 108% larger than the next in line, the hexagonal pin. In the same range of inlet conditions, the Gee–Webb coefficient was the highest for the circular pin, which was selected as the main shape for the following segment. In the second part of the study, a machine learning framework based on a neural network architecture is presented. The neural network predicts the thermal performance of different pin-fin array configurations. At the end, this framework is used in an optimization process that explores the advantages of nonuniform arrays consisting of differently sized circular pins as opposed to the standard uniform structures of the first part. The model proves to be capable of accurately predicting the characteristic thermal performance coefficients across a wide range of input parameters. The nonuniform array managed a 23% reduction in pressure rise at a 0.2% exit temperature increase compared to a reference standard array of the same hydraulic diameter.

Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing.

This article presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion-processing task. Our findings reveal the pivotal role of the subcortical-cerebellum in emotion processing.

Kiai's Transformative Leadership in Developing an Organizational Culture of Islamic Boarding Schools: Multicase Study

This research aims to analyze the transformative leadership of kiai in developing Islamic boarding school organizational culture. The research method used is qualitative with a case study approach. Data collection uses interviews, observation and documentation. Meanwhile, data analysis uses data reduction, data display, and conclusion/verification. The research results show that the leadership role of the Kiai in the Leadership Transformation is related to changes in roles and responsibilities within the Islamic boarding school and the ever-changing society while maintaining the organizational culture of the Islamic boarding school refers to efforts to maintain and develop existing traditions and distinctive identities. In the Islamic boarding school environment Transformative Kiai Leadership at that the Pondok Pesantren Al-Mashduqiah and the Pondok Pesantren Nurul Wafa Besuki, both Islamic boarding schools may stand out by having a clear and strong vision of the desired direction for the Islamic boarding school. This vision may not only be limited to the religious aspect, but also includes personal development, education, and social contribution. These kiai may convey collaborative leadership, and open communication, and be role models for the students in terms of integrity and Exemplary. This cultural development has had a positive impact on cultural development in the two Islamic boarding schools. Fostering a progressive educational culture, Islamic boarding schools become dynamic and relevant learning centers, preparing students to become skilled, knowledgeable individuals and contribute positively to society.

ENHANCING STUDENTS’ ENGAGEMENT IN FLSP CLASS: THE IMPACT OF NEARPOD

This quasi-experimental study investigates the effectiveness of Nearpod, a student-response-system, on elevating student engagement in Foreign Language for Specific Purposes (FLSP) classes. Utilizing Nearpod's interactive features to deliver the materials and activities through a shareable link, the study aimed to create a dynamic and immersive learning experience. The study involved 74 students from an FLSP course, divided into control and experimental groups. The experimental group were taught using Nearpod while the control group received regular instructions. Engagement levels were measured using a self-report questionnaire based on Wang et al. (2016), encompassing four domains of student engagement. The Mann-Whitney U-Test revealed that Nearpod significantly increased overall student engagement (U = 263.500, p < .05). Notably, behavioral, emotional, and social engagement showed significant improvements, while cognitive engagement did not (U = 450.500, p > .05). This empirical results show that Nearpod significantly enhances overall student engagement and positively impacts behavioral, emotional, and social engagement in FLSP integrated classes. However, cognitive engagement remains unaffected, suggesting a need for further exploration of strategies to address this aspect.This research provides valuable insights for language instructors, highlighting the potential of technology to foster engaging and interactive learning environments.

The Future of Education: Trends and Innovations in Teaching and Learning

The landscape of education is rapidly evolving, driven by technological advancements, changing student needs, and shifts in pedagogical approaches. This paper explores the future of education by examining emerging trends and innovations in teaching and learning. It discusses the impact of digital technologies, personalized learning, interdisciplinary approaches, and global collaboration on the educational landscape. Additionally, the paper explores the role of educators as facilitators of learning, the importance of fostering 21st-century skills, and the potential of inclusive and equitable education. By embracing these trends and innovations, educators can create dynamic, engaging, and transformative learning experiences that prepare students for success in an increasingly complex and interconnected world.

Balancing Feedback for Optimal Learning: Evaluative and Descriptive Methods in English Language Classrooms

This study explores the impact of evaluative and descriptive feedback on student learning in English language classrooms. It distinguishes between the two feedback types—evaluative, which is further divided into rewarding and punishing, and descriptive, which focuses on specific performance aspects. The study is based on observations of classroom interactions and interviews with an English teacher who employs both feedback types to enhance student engagement and educational outcomes. Evaluative feedback in the study is shown to affect students' emotional responses and behavioral adjustments, with rewarding feedback boosting morale and motivation, while punishing feedback serves to correct behavior and ensure classroom discipline. Conversely, descriptive feedback, as used by the teacher, involves detailed explanations and discussions with students about their performances, encouraging deeper cognitive processing and active participation in the learning process. The findings suggest that a balanced approach, utilizing both evaluative and descriptive feedback, can significantly improve students’understanding and retention of the material. This paper argues for the strategic use of feedback to foster a more dynamic and effective learning environment, highlighting the nuances of feedback application in educational practices.

The Implementation of the Dilemma-STEAM Model in Fluid Dynamics Subjects through the Archimedes Screw Project

Physics is one of the sciences that is closely related to daily life. One of them is fluid material. However, understanding of physics, especially fluid material, is still relatively low. These problems can be overcome by applying a learning model that is integrated with teaching materials that have developed over time. The dilemma STEAM learning model is a learning model that can help students to understand physics based on students' experiences. The dilemma STEAM learning model has five learning stages that can make learners recognize the problem, find solutions to then solve the problem. This study aims to develop teaching materials based on the dynamic dilemma STEAM learning model based on the dilemma STEAM learning model. The development of the Archimedes Screw Project equipped with E-Modules as teaching materials was developed using the ADDIE development model. The ADDIE development model has several stages, namely Analysis, Design, Development, Implementation, and Evaluation. E-Modules are made using the Canva application which can be accessed anywhere and anytime. After the E-Module was developed, the next stage was the feasibility test of the E-Module by experts. The results of the feasibility test by media experts obtained a result of 88.3% with a very feasible category. Meanwhile, the results of the feasibility test by material experts obtained a result of 84% with a very feasible category. And the results of the feasibility test by learning experts obtained a result of 83.3%. And the readability of the E-Modul by the student after using it is 72.4% with a good read, which is demonstrated by the success of the student assembling Archimedes Screw by following five stages of the dilemma STEAM learning model.

MEMPELAJARI BENCANA ALAM MELALUI PEMBELAJARAN IPS DI SD KELAS 1 TEMA 8 SUBTEMA 4 KURIKULUM 2013

The Ring of Fire that traverses Indonesia makes it a region vulnerable to earthquakes. Not only are Indonesia’s characteristic earthquakes a concern, but being an archipelagic nation also renders it susceptible to tsunamis. This article explores how Science Learning can provide a deeper understanding of natural disasters. Additionally, the research method employed—qualitative descriptive approach—is discussed, along with an analysis of the student textbook for Grade 1, Theme 8, titled “Natural Disasters.” The challenges faced by teachers in explaining this material are also addressed, including how they can create a dynamic learning environment, provide additional learning resources, and emphasize the importance of understanding natural disasters.