Dynamic Learning Research Articles

Teaching Ain’t Easy, but Somebody Has Gotta Do It: Education in an Age of Anti-Diversity and Civil Rights Rollbacks

In the aftermath of Supreme Court decisions and the consistent assault on public education, educators must focus on discovering the best methods and approaches for addressing diversity within the classroom and the importance of learning different perspectives as part of a solid education. Diversity, Equity, Inclusion, and Belonging (DEIB), while criticized by political operatives and demonized in the court of public opinion, the tenants of inclusion, creating space for marginalized voices, and assisting students in seeing the gift in the differences each person brings to the classroom and society at large is still valid and essential as a methodology in education while navigating difficult conversations in an anti-diversity climate. Civil and human rights rollbacks are becoming more prevalent, it is crucial to reimagine how we discuss, teach, and embody DEIB or human-focused methodology as an educational tool. Educators must explore how to develop, question, learn, and reimage relationships, community, and educational institutions as incubators for people of diverse cultural backgrounds to share dynamic learning and teaching together.

Neural dynamics of reversal learning in the prefrontal cortex and recurrent neural networks.

In probabilistic reversal learning, the choice option yielding reward at higher probability switches at a random trial. To perform optimally in this task, one has to accumulate evidence across trials to infer the probability that a reversal has occurred. In this study, we investigated how this reversal probability is represented in cortical neurons by analyzing the neural activity in prefrontal cortex of monkeys and recurrent neural networks trained on the task. We found that neural trajectories encoding reversal probability had substantial dynamics associated with intervening behaviors necessary to perform the task. Furthermore, the neural trajectories were translated systematically in response to whether outcomes were rewarded, and their position in the neural subspace captured information about reward outcomes. These findings suggested that separable dynamic trajectories, instead of fixed points on a line attractor, provided a better description of neural representation of reversal probability. Near the behavioral reversal, in particular, the trajectories shifted monotonically across trials with stable ordering, representing varying estimates of reversal probability around the reversal point. Perturbing the neural trajectory of trained networks biased when the reversal trial occurred, showing the role of reversal probability activity in decision-making. In sum, our study shows that cortical neurons encode reversal probability in a family of dynamic neural trajectories that accommodate flexible behavior while maintaining separability to represent distinct probabilistic values.

Investigating the Benefits of Imaginative Teaching Practices in Enhancing Educational Quality

The study examines the benefits of imaginative teaching practices in enhancing educational quality by focusing on innovative pedagogical approaches. It aims to evaluate the influence of these performances on key variables, including Student Engagement (SE), Academic Performance (AP), Educational Satisfaction (ES), Creativity Development (CD), and Critical Thinking Skills (CTS). Using SPSS software, various statistical methods, such as x2 tests, ANOVA (Analysis of Variance), T-tests, and descriptive statistics, were employed to analyze data collected from educators. The findings reveal significant improvements across all measured variables, indicating that imaginative teaching practices effectively enhance student engagement, academic performance, critical thinking skills, creativity, and overall satisfaction with the educational experience. These results suggest that incorporating creative methods in teaching can create a more dynamic, engaging, and effective learning environment, ultimately leading to improved educational outcomes.

Tracing the dynamics of motivational self-constructs across the lifespan: Tales from senior learners of English

In the context of an ageing and increasingly more independent learning population, a narrative inquiry into the life stories of motivated and still active senior language users remains a promising yet unexplored area. This case study forms part of a PhD research (Darnault, 2023) on the motivational dynamics of lifelong foreign language learning (FLL) individuals. We recorded the retrospective stories of 3 exceptionally motivated French senior learners of English, aged 65 to 80, from childhood to their current learning experience. Our triangulated and multimodal approach elicited written, oral and visual data. An inductive thematic analysis first highlighted the emergence of motivational peaks, subsequently followed by the examination of clusters of self-constructs within participants’ individual time frames in light of the L2 Motivational Self System (L2MSS) framework. Results showed that upon retirement all three learners integrated language learning into their daily routine, incorporating it as an integral part of their identity and broader sense of self outside L2 domain-specific motivational constructs. A particularly unyielding hybrid L2 self emerged in later years, drawing from the combination of ought-to, ideal and anti-ought-to selves that prevailed with different degrees of intensity and interaction according to life periods.

Towards emotion-aware intelligent agents by utilizing knowledge graphs of experiences

Because of the increasing presence of intelligent agents in various aspects of human social life, social skills play a vital role in ensuring these systems exhibit acceptable and realistic behavior in social communication. The importance of emotional intelligence in social capabilities is noteworthy, so incorporating emotions into the behaviors of intelligent agents is essential. Therefore, some computational models of emotions have been presented to develop intelligent agents that exhibit emotional human-like behaviors. However, most current computational models of emotions neglect the dynamic learning of the affective meaning of events based on agents’ experiences. Such models evaluate the events in the environment according to emotional aspects without considering the context of the situations. Also, these models capture the emotional states of agents by using predefined rules determined according to psychological theories. Therefore, they disregard the data-driven methods that can obtain the relationships between appraisal variables and emotions based on natural human data with fewer assumptions on the nature of such relationships. To address these issues, we proposed a novel and unified affective-cognitive framework (EIAEC) to facilitate the development of emotion-aware intelligent agents. EIAEC uses appraisal theories to acquire the emotional states of the agent in various situations. This paper contains four main contributions: 1- We have designed an efficient episodic memory that uses events and their conditional contexts to store and retrieve knowledge and experiences. This memory facilitates emotional expressions and decision-making adapted to the situations of the agent. 2- A novel method has been proposed that learns context-dependent affective values associated with events by using the agent’s experiences in various contexts. Subsequently, we acquired appraisal variables using the elements and related meta-data in episodic memory. 3- We have proposed a new data-driven method that maps appraisal variables to emotional states. 4- Moreover, a method has been developed to update the activation values regarding actions by using the emotional states of the agent. This method models the influence of emotions on the agent’s decision-making. Finally, we simulate a driving scenarios in our proposed framework to manifest the generated emotions in different situations and conditions. Moreover, we show how the proposed method learns the affective meaning of events and actions used in appraisal computing.

Learning Dynamics and Adaptations of Learners with Special Educational Needs (LSENs) in English Language Teaching in Inclusive Classrooms

Learning Dynamics and Adaptations of Learners with Special Educational Needs (LSENs) in English Language Teaching in Inclusive Classrooms

Behavioral biometric optical tactile sensor for instantaneous decoupling of dynamic touch signals in real time

Decoupling dynamic touch signals in the optical tactile sensors is highly desired for behavioral tactile applications yet challenging because typical optical sensors mostly measure only static normal force and use imprecise multi-image averaging for dynamic force sensing. Here, we report a highly sensitive upconversion nanocrystals-based behavioral biometric optical tactile sensor that instantaneously and quantitatively decomposes dynamic touch signals into individual components of vertical normal and lateral shear force from a single image in real-time. By mimicking the sensory architecture of human skin, the unique luminescence signal obtained is axisymmetric for static normal forces and non-axisymmetric for dynamic shear forces. Our sensor demonstrates high spatio-temporal screening of small objects and recognizes fingerprints for authentication with high spatial-temporal resolution. Using a dynamic force discrimination machine learning framework, we realized a Braille-to-Speech translation system and a next-generation dynamic biometric recognition system for handwriting.

Atypical dynamic trust learning in individuals with high autistic traits in a multi-round trust game with multiple trustworthiness cues

BackgroundUnderstanding the trust-building process, especially in contexts requiring the integration of multiple trustworthiness cues, is a fundamental aspect of social interactions among individuals with high autistic traits (ATs). It can offer valuable insights into the social functioning of these individuals. MethodParticipants with high and low ATs (N = 120; 30 in each group for two experiments) completed a 10-round trust game that embedded various trustworthiness cues, including reputation, advice (Experiment 1 = direct advice; Experiment 2 = social-emotional advice), and reciprocity. Linear mixed-effects models with investment rates as the dependent variable were used to analyze the data. ResultsMethodologically, the experimental results elucidated the fundamental characteristics of dynamic trust formation within the context of multidimensional trust information. Furthermore, High-AT individuals showed assigned less weight to reciprocity but relied heavily on reputation, and exhibited maladaptive advice-taking. Unlike low-AT individuals, high-AT individuals did not adaptively reduce reputational effect over rounds. ConclusionsOur findings highlight the unique challenges faced by individuals with high ATs when navigating complex social interactions.

Combining realist evaluation and appreciative inquiry: A participatory, learning-focused methodology for tourism interventions

Useful lessons can be drawn from realist evaluation and appreciative inquiry for the evaluation of tourism interventions in complex contexts. We combine the strengths of these two approaches to design a participatory learning-focused evaluation with an emphasis on co-created intervention improvement. We apply this proposed ‘Realist Appreciative Evaluation’ on a government-led intervention aimed at supporting tourism firms to improve their sustainability practices. While realist evaluation introduces causal thinking within a framework of contexts-mechanisms-outcomes, appreciative inquiry enables participants to be solution-oriented. The study shows how evaluation can constitute a constructive, dynamic learning process for participants, rather than something to fear. Our methodology promotes collaboration and real-time change, within the rigorous theory-based realist evaluation approach, to improve the evaluation of tourism interventions.

Solution-Processed Polymer Memcapacitors with Stimulus-Controlled and Evolvable Synaptic Functionalities: From Short-Term Plasticity to Long-Term Plasticity to Metaplasticity.

In the vanguard of neuromorphic engineering, we develop a paradigm of biocompatible polymer memcapacitors using a seamless solution process, unleashing comprehensive synaptic capabilities depending on both the stimulation form and history. Like the human brain to learn and adapt, the memcapacitors exhibit analogue-type and evolvable capacitance shifts that mirror the complex flexibility of synaptic strengthening and weakening. With increasing frequency and intensity of the stimulation, the memcapacitors demonstrate an evolution from short-term plasticity (STP) to long-term plasticity (LTP), and even to metaplasticity (MP) at a higher level. A physical picture, featuring the stimulus-controlled spatiotemporal ion redistribution in the polymer, elaborates the origin of the memcapacitive prowess and resultant versatile synaptic plasticity. The distinctive MP behavior endows the memcapacitors with a dynamic learning rate (LR), which is utilized in an artificial neural network. The superiority of implementing a dynamic LR compared with conventional practices of using constant LR shines light on the potential of the memcapacitors to exploit organic neuromorphic computing hardware.

Prospective on applying machine learning in computational fluid dynamics (CFD) simulation of metallurgical reactors

Metallurgical reactors, especially in ironmaking/steelmaking process, characterise with high-temperature turbulence, multiphase flow, mass/heat transfer and reactions. Computational fluid dynamics (CFD) simulation-based design and optimisation are of significance for efficient metallurgical performance. However, the difficulty and cost to numerically solve the nonlinear controlling equations combined with data pre/post-processing make the whole CFD simulation process time-consuming, which makes it challenging to provide in-time feedback for industrial practices. The popularisation and prosperous development of machine learning bring new opportunities for promoting CFD performance. Discussion has been made on the current research progress of applying machine learning in the whole CFD workflow including pre-processing, solving, and post-processing. Among them, the time consumed by manual pre-processing exceeds 50% of CFD tasks in general. The machine learning or parametric modelling methods can reduce pre-processing time by three orders in the estimate. The solving step is expected to be accelerated by 5 to 1000 times using machine learning. A brief review of machine learning coupled CFD is provided, as is a prospective on its development. Discussion is presented on the main functions, challenges, typical techniques and future directions of applying machine learning in CFD simulation of metallurgical reactors, for the purpose of making CFD faster, more accurate, and better visualised based on the metallurgical practices.

Dynamic and concordance-assisted learning for risk stratification with application to Alzheimer's disease.

Dynamic prediction models capable of retaining accuracy by evolving over time could play a significant role for monitoring disease progression in clinical practice. In biomedical studies with long-term follow up, participants are often monitored through periodic clinical visits with repeat measurements until an occurrence of the event of interest (e.g. disease onset) or the study end. Acknowledging the dynamic nature of disease risk and clinical information contained in the longitudinal markers, we propose an innovative concordance-assisted learning algorithm to derive a real-time risk stratification score. The proposed approach bypasses the need to fit regression models, such as joint models of the longitudinal markers and time-to-event outcome, and hence enjoys the desirable property of model robustness. Simulation studies confirmed that the proposed method has satisfactory performance in dynamically monitoring the risk of developing disease and differentiating high-risk and low-risk population over time. We apply the proposed method to the Alzheimer's Disease Neuroimaging Initiative data and develop a dynamic risk score of Alzheimer's Disease for patients with mild cognitive impairment using multiple longitudinal markers and baseline prognostic factors.

Peran Program KKN Mengajar dalam Meningkatkan Motivasi Belajar Siswa di SDN Rowobranten, Kendal

This research aims to analyze the role of the KKN Teaching Program in increasing student learning motivation at SDN Rowobranten, Kendal. The KKN Mengajar program, which involves students in service and teaching activities, was implemented as an effort to overcome low learning motivation among elementary school students. Using a mixed-methods approach, data was collected through surveys, interviews, and classroom observations over a three-month period. The research results showed a significant increase in student learning motivation, which was demonstrated by increased attendance, active participation in class, and enthusiasm for the learning process. Additionally, qualitative feedback from teachers and students shows that the KKN Mengajar Program creates a more dynamic and interactive learning environment, and encourages positive attitudes towards education. This research concludes that the KKN Mengajar Program has an important role in increasing student learning motivation and recommends the continuation of this program in similar educational settings to achieve a broader impact on student achievement and engagement.

Transformative Islamic Education from the Perspective KH Abdullah Munjul (Study of Manuscript Layang Siti Hasanah)

The "Layangan Siti Hasanah" manuscript, a significant Islamic text, provides valuable insights into historical educational practices. This research explores how its teachings can be applied to contemporary Islamic education to foster meaningful change and personal growth. The study involves a detailed analysis of the manuscript’s content, focusing on its educational methodologies and philosophies. It examines how these principles can be integrated into modern educational frameworks to enhance critical thinking, moral development, and holistic learning. Key findings reveal that the manuscript contains educational approaches that align with transformative educational goals. These approaches include methods for developing critical thinking and ethical understanding, which are relevant to current educational practices. The research suggests that incorporating these traditional principles into contemporary Islamic education can create a more dynamic and effective learning environment. By merging historical insights with modern needs, educators can promote both academic and personal development in students. Overall, this study highlights the importance of integrating traditional Islamic teachings with modern educational methods to achieve a transformative educational experience that benefits students in various aspects of their lives.The method used in this research is qualitative, while the approach used is the historical and anthropological approach to Islamic education, then the data collection techniques used are documentation and interviews and data analysis in the researcher's research using philological and symbiotic analysis. The results of research on Transformative Islamic education in the Layang Siti Hasanah manuscript according to KH Abdullah Munjul are divided into 3 domains of Transformation, namely: 1. Syariat teachings, 2. Tarekat teachings. 3. Marifat teaching.

Stimulus repetition induces a two-stage learning process in primary visual cortex.

Repeated stimulus exposure alters the brain's response to the stimulus. We investigated the underlying neural mechanisms by recording functional MRI data from human observers passively viewing 120 presentations of two Gabor patches (each Gabor repeating 60 times). We evaluated support for two prominent models of stimulus repetition, the fatigue model and the sharpening model. Our results uncovered a two-stage learning process in the primary visual cortex. In Stage 1, univariate BOLD activation in V1 decreased over the first twelve repetitions of the stimuli, replicating the well-known effect of repetition suppression. Applying MVPA decoding along with a moving window approach, we found that (1) the decoding accuracy between the two Gabors decreased from above-chance level (∼60% to ∼70%) at the beginning of the stage to chance level at the end of the stage (∼50%). This result, together with the accompanying weight map analysis, suggested that the learning dynamics in Stage 1 were consistent with the predictions of the fatigue model. In Stage 2, univariate BOLD activation for the remaining 48 repetitions of the two stimuli exhibited significant fluctuations but no systematic trend. The MVPA decoding accuracy between the two Gabor patches was at chance level initially and became progressively higher as stimulus repetition continued, rising above and staying above chance level starting at the ∼35th repetition. Thus, results from the second stage supported the notion that sustained and prolonged stimulus repetition prompts sharpened representations. Additional analyses addressed (1) whether the neural patterns within each learning stage remained stable and (2) whether new neural patterns were evoked in Stage 2 relative to Stage 1.

Optimizing Recurrent Neural Network with Bayesian Algorithm for Behavioural Authentication System

Current studies on behavioural biometrics authentication have been focused on the use of deep learning and keystroke dynamics but the aspect of conscious optimization of the algorithm in order to obtain best outcome has not been considered. This study employed and incorporated Bayesian algorithm into Recurrent Neural Network to build a Keystroke Behavioural Biometric (KBB) authentication model used against social engineering attacks. The model begins with importing the keylogging dataset for data pre-processing, feature extraction, and RNN algorithm was used to build the KBB model. Hyperparameter tuning was done to achieve optimal results. A traditional optimizer called Adaptive Momentum Estimation (Adam) was used and evaluated so as to estimate the impact of optimization in model inferencing. RNN model result with Bayesian optimization technique shows a better performance than the result of RNN model with ADAM optimization. The essence of incorporating and evaluating the best optimization technique is to come up with an effective and accurate model for behavioural biometric authentication, that could mitigate effectively against social engineering attacks.

Control knowledge tracing: Modeling students' learning dynamics from a control-theory perspective

A student's learning system is a system that guides the student's knowledge acquisition process using available learning resources to produce certain learning outcomes that can be evaluated based on the scores of questions in an assessment. Such a learning system is analogous to a control system, which regulates the process of a plant through a controller in order to generate a desired response that can be inferred from sensor measurements. Inspired by this analogy, this study proposes to model the monitoring of students' knowledge acquisition process from a control-theory viewpoint, which is referred to as control knowledge tracing (CtrKT). The proposed CtrKT comprises a dynamic equation that characterizes the temporal variation of students' knowledge states in response to the effects of learning resources and an observation equation that maps their knowledge states to question scores. With this formulation, CtrKT enables tracking students' knowledge states, predicting their assessment performance, and teaching planning. The insights and accuracy of CtrKT in postulating the knowledge acquisition process are analyzed and validated using experimental data from psychology literature and two naturalistic datasets collected from a civil engineering undergraduate course. Results verify the feasibility of using CtrKT to estimate the overall assessment performance of the participants in the psychology experiments and the students in the naturalistic datasets. Lastly, this study explores the use of CtrKT for teaching scheduling and optimization, discusses its modeling issues, and compares it with other knowledge-tracing approaches.

Creating Edinburgh: diffracting interdisciplinary learning and teaching in the contemporary city

This article follows an experimental interdisciplinary undergraduate course in the busy, unpredictable space of the contemporary city. It locates practice-based research of interdisciplinary higher education in a dynamic learning environment, which is comprised of unpredictable connections between disciplinary perspectives. Following Karen Barad, the aim is to diffract interdisciplinary higher education in order to recognise and work with a multiplicity of meanings and experiences. This article explores an alternative to the dominant model of challenge-based learning in the interdisciplinary classroom. Creating Edinburgh: The Interdisciplinary City is an undergraduate elective offered by Edinburgh Futures Institute at the University of Edinburgh. It provides students with opportunities to explore the city of Edinburgh in small groups of students from a wide range of degree programmes. Groups are invited to engage with a selection of themed fieldwork topics throughout the 11 weeks of the course, visiting specific sites and responding to a series of tasks and questions. These include themes such as Sustainability, Decolonisation and Wildness, which are presented as interdisciplinary field topics to explore rather than problems to solve. This article develops a research methodology that sets out to travel with students as they navigate their way through the city during their weekly field trips. Combining first-hand autoethnographic accounts with walking interviews, it offers an insight into interdisciplinary learning and teaching in the expanded field of the contemporary city. Conceiving urban space as an assemblage of digital and non-digital objects, events and activities, members of the research team accompany students during their fieldwork, equipped with audio recorders, cameras and notebooks. The documents of these research journeys are then diffracted within a new materialist framework. The article concludes with questions and prompts for working with the agency and the affordances of a field-based education practice.

Analysis of Teacher Needs in Developing Flipbook-Based Interactive Teaching Materials in Elementary Schools

This research analyzes teachers' needs in developing flipbook-based interactive teaching materials in elementary schools. This research method uses quantitative research with a survey research type and descriptive qualitative. The research locations were Sidosermo 1 Surabaya public Elementary School, Margorejo VI public Elementary School Surabaya, Margorejo 1 public Elementary School, Taquma Elementary School, Kyai Ibrahim Elementary School, and Jemursari I Surabaya public elementary school. Data collection techniques using questionnaires. The research instrument is a wide questionnaire that experts have validated. Data analysis techniques were carried out using descriptive statistics and descriptive qualitative using data reduction, data display, and verification. The research results show that 100% of teachers need flipbook-based interactive teaching materials for elementary school students. This teaching material is considered the most appropriate and effective solution for meeting the learning needs of Pancasila Education in class IV, semester 2. The implications of these findings are significant. The demand for such teaching materials underscores the necessity of providing resources that are not only aligned with the curriculum but also capable of captivating students' interest and facilitating a deeper understanding of the content. Modern, interactive materials like flipbooks can offer a more dynamic learning environment, helping students better grasp complex concepts through engaging and visually stimulating content.

Machine learning, network pharmacology, and molecular dynamics reveal potent cyclopeptide inhibitors against dengue virus proteins.

The dengue virus is a major global health hazard responsible for an estimated 390 million diseases yearly. This study focused on identifying cyclopeptide inhibitors for envelope structural proteins E, NS1, NS3, and NS5. Additionally, 5579 cyclopeptides were individually screened against the four target proteins using a machine learning-based quantitative structure-activity relationship model. Subsequently, the best 10 cyclopeptides from each protein were selected for molecular docking with their corresponding proteins. Moreover, the protein-peptide complexes with the highest affinity were subjected to a 100-ns molecular dynamics simulation. The protein-protein complexes exhibited superior structural stability and binding interactions. Based on the results of the MD simulation analyses, which included checking values for Root Mean Square Deviation, Root Mean Square Fluctuation, Principal Component Analysis (PCA), free energy landscapes, and energetic components, it was found that NS5-CP03714 complex is more stable and has stronger binding interactions than NS3-CP02054. PCA and free energy landscape plots have confirmed the higher conformational stability of NS5-CP03714. Analysis of the energetic components revealed that NS5-CP03714 (total binding energy = -47.19kcal/mol) exhibits more favorable interaction energies and overall binding energy compared to NS3-CP02054 (total binding energy = -27.36kcal/mol), suggesting a stronger and more stable formation of the complex. In addition, the drug-target network of two specific peptides (CP02950 and CP05582) and their associated target proteins were analyzed. This analysis revealed valuable information about their ability to target several proteins and their potential for broad-spectrum activity. Additional experimental investigations are necessary to validate these computational results and assess the efficacy of identified peptide inhibitors in biological systems.