Family stability is increasingly understood as a dynamic outcome shaped by cumulative life experiences rather than static structural conditions. This study examines the contribution of women’s learning trajectories to family stability, conceptualizing learning as a continuous and adaptive process unfolding across the life course. Using a quantitative explanatory design, survey data were collected from 312 adult women actively involved in family functioning. Women’s learning trajectories and family stability were measured as composite variables, and data were analyzed using linear regression and one-way analysis of variance. The results indicate that women’s learning trajectories significantly predict family stability (β = 0.61, p < 0.001), explaining 37% of the variance in the outcome. ANOVA results further reveal significant differences in family stability across low, moderate, and high learning trajectory groups, with the highest stability observed among women with sustained learning engagement over time. These findings provide empirical support for life-course perspectives on learning, demonstrating that cumulative learning experiences among women play a central role in sustaining family stability. The study contributes to research on gender, education, and family by offering a trajectory-oriented quantitative approach with implications for lifelong learning and family-centered policy interventions.

This paper presents an open-access telemetry dataset designed to support research and training in intelligent fixed-wing unmanned aerial systems. The dataset contains 240 fully annotated autonomous missions flown outdoors over repeatable, waypoint-based trajectories using two onboard architectures: a compact SpeedyBee F405 flight controller running INAV, and a Holybro Pixhawk 6X paired with a Jetson Orin NX companion computer running PX4. The missions cover key phases, including take-off, cruise, dynamic manoeuvres, and autonomous landing. Each log provides synchronised multi-sensor telemetry (IMU, GNSS, barometric altitude, actuator states, flight modes, and power metrics) at high temporal resolution, enabling realistic modelling of flight dynamics, estimator behaviour, and sensor noise. The dataset supports benchmarking for trajectory tracking under degraded GNSS, anomaly detection, wind-aware navigation, and energy-optimised mission planning. The paper documents hardware integration, communication architecture, mission procedures, and the dataset file structure, and includes representative analyses to illustrate reuse for contested, safety-critical, and complex operational environments in field. No neural network is trained or evaluated; deep learning is cited only as a motivating application domain.

Objective This study investigates the learning trajectories of primary caregivers of children with hearing loss in acquiring aural-oral teaching and behavioural skills through a structured parent training program. Design Study employed a longitudinal design to track the progression of caregiver skills over time. Data were collected at multiple time points to capture changes in caregivers’ abilities to recognise, interpret, and respond to their children’s communicative behaviours. Study Sample A longitudinal evaluation was conducted involving the primary caregivers of 271 children with severity range from unilateral to profound of bilateral hearing loss who participated in Auditory-Verbal Intervention between October 1, 2021, and February 18, 2025. Results Findings reveal a progressive pattern of skill development, beginning with the recognition and response to children’s communicative intent within the first month of training. By the fourth month, caregivers demonstrated increased awareness of child interests, followed by the ability to assess developmental capabilities and apply appropriate strategies by the sixth to eighth month. More advanced skills emerged between the twelfth and twenty-first months. Conclusions At least eight months are needed for effective integration of strategies, with full mastery taking up to two years. Sustained support is essential for family success.

Growing Problem Solvers provides four original, related, classroom-ready mathematical tasks, one for each grade band. Together, these tasks illustrate the trajectory of learners’ growth as problem solvers across their years of school mathematics.

TS-RIL: A two-stage robot imitation learning framework with motion trajectory learning and obstacle avoidance in real-world operating scenarios

ABSTRACT Dynamic process modeling is essential for simulating time‐evolving biochemical systems, particularly those with multistate interactions and combinatorial complexity. Traditional Ordinary Differential Equation (ODE) models offer mechanistic clarity but struggle with scalability and context‐sensitive encoding. Rule‐Based Modeling (RBM) frameworks address these limitations through modular rule abstraction, yet require manual specification and lack adaptive learning. This study introduces algorithmic innovations within the Neural Ordinary Differential Equation (Neural ODE) paradigm to bridge the gap between mechanistic interpretability and scalable expressivity. Neural ODEs can be considered as a revolutionary approach in the field of modeling dynamic biochemical interactions. They have made it possible to create models of such interactions that are flexible enough to adapt to different scenarios and do so without requiring any manual intervention in terms of rule encoding or predefined reaction schemes. This is achieved by employing differential solvers within the framework of neural networks, thus enabling a learning process that is in accordance with the behavior of the system. Using the DARPP‐32 signaling network—a benchmark system characterized by multivalent phosphorylation and dynamic perturbations—the proposed Neural ODE framework demonstrates the ability to replicate key dynamic behaviors observed in ODE and RBM models. Comparative simulations under baseline and perturbed conditions reveal that Neural ODEs maintain trajectory fidelity while offering enhanced modularity and computational efficiency. Feature importance analysis and latent space visualizations further validate the model's interpretability and robustness. Unlike ODEs and RBMs, Neural ODEs adapt to structural mutations and binding schemes through latent trajectory learning, enabling flexible simulation of biochemical variability without manual rule encoding. This work establishes Neural ODEs as a viable and scalable alternative for modeling complex biochemical systems, combining the strengths of data‐driven learning with the interpretability of differential equations.

With the rapid development of artificial intelligence and big data technologies, personalized learning systems have become a key focus in education, aiming to tailor educational content to individual students' needs. However, existing systems still face challenges in fully adapting to students' diverse learning styles and behaviors, often failing to provide optimal learning paths and recommendations based on comprehensive student data. To address these shortcomings, this study proposes a personalized mathematics education system based on adaptive learning algorithms, integrating Weighted Feature Clustering Adaptive Learning (WFCAL), Weighted Knowledge Point Similarity Recommendation, and Q-learning Path Optimization. This novel approach dynamically evaluates students' knowledge mastery and adjusts the learning content and path accordingly, ensuring that each student follows the most efficient and personalized learning trajectory. The system’s key advantages lie in its ability to leverage both cognitive and behavioral data to optimize learning outcomes and enhance student engagement. Experimental results demonstrate that the proposed model significantly outperforms traditional methods in terms of knowledge mastery prediction accuracy, task completion rate, learning progress rate, and knowledge retention. Specifically, our model achieves higher learning efficiency and effectiveness across two publicly available datasets, ASSISTments and MATH, confirming the superiority of the adaptive learning approach. In summary, this research contributes to the field of personalized education by providing a robust and adaptive learning system that not only enhances learning outcomes but also offers valuable insights for future advancements in intelligent educational systems.

Elementary students’ mathematical thinking is frequently constrained by persistent misconceptions and an overreliance on procedural instruction. International assessments of mathematical literacy consistently report lower levels of achievement among students in many developing countries, underscoring the need for instructional approaches that promote conceptual understanding rather than rote learning. Scaffolding, understood as temporary and adaptive instructional support, has been widely acknowledged as an effective means of facilitating students’ conceptual development. Nevertheless, its classroom enactment—particularly the processes through which support is responsively adjusted and gradually withdrawn—remains insufficiently documented and systematically analyzed in empirical research. This study aims to examine the forms of scaffolding employed by teachers, their responsive strategies in addressing student errors, and the observable indicators of scaffolding reduction (fading) in mathematics instruction grounded in visual pattern recognition and comparative reasoning. A descriptive qualitative methodology was adopted, using a case study design involving three upper elementary school students. Data were collected through analyses of students’ written work, classroom interaction observations, and semi-structured interviews. The data were analyzed thematically within the framework of contingent scaffolding. The findings indicate differentiated learning trajectories among the participants. Student MA demonstrated a shift from intuitive verbal descriptions to symbolic comparative reasoning following interactive scaffolding. Student RFM exhibited independent formal reasoning from the outset, requiring minimal instructional support. In contrast, student IAM experienced a substantial conceptual transition after receiving explicit instructional intervention. Notably, all three students were ultimately able to generalize that the number of blue triangles was consistently less than half of the total number of triangles. These results highlight the critical role of adaptive and contingent scaffolding in fostering conceptual understanding and learning autonomy in elementary mathematics. By documenting the forms, timing, and transitions of instructional support, this study contributes to the empirical literature on scaffolding practices in primary education. Importantly, the findings provide a novel account of how scaffolding is dynamically enacted and strategically faded in response to students’ errors, enabling a progression from descriptive to symbolic proportional reasoning. The identification of concrete indicators of scaffolding reduction aligned with students’ emerging autonomy offers theoretically grounded and practice-oriented implications for the design of adaptive instructional support in elementary mathematics classrooms. Keywords: instructional scaffolding; mathematical reasoning; mathematics education; proportional reasoning; gradual fading of support.

ABSTRACT Children from a low SES environment may exhibit reduced language performance as a function of family or environmental socioeconomic status (SES) and this can impact oral language assessment and further development, as well as learning trajectories in school. We aimed to establish whether children in a multicultural and low socio-economic environment would show this disadvantage on normed tasks for oral language development in early grades. An oral language screener (Phophlo), a verb production task, and a metalinguistic task were used to screen 48 children in schools from two highly multicultural French schools with varying levels of SES environments. Scores on the tasks were lower than expected, with failure rates between 15% and 65%, depending on the task. SES did not systematically impact results, and percentage of exposure to French was found to impact some task results significantly. Children in schools with lower SES may have oral language delays as compared to children in schools from middle SES districts. These delays can be identified using the Phophlo screener, verb production and meta-linguistic tasks. Recommendations are made for class-based interventions.

Trajectory similarity calculation, a cornerstone of trajectory data mining, is pivotal for diverse applications such as clustering, classification, and retrieval. While existing representation learning-based methods offer notable advantages in efficiency and accuracy, preserving the fidelity of similarity computation when processing large-scale trajectory data remains a significant challenge. To address this, this paper introduces a novel hybrid network architecture integrating Long Short-Term Memory (LSTM) and attention mechanisms to learn discriminative latent representations of trajectories. Moreover, we propose an Adaptive Contrastive Trajectory Learning (ACTL) module that dynamically refines the learning process through batch-adaptive temperature scaling and strategic hard negative mining, substantially improving boundary discrimination and robustness to data perturbations. Experimental validation on two real-world datasets, Porto and Chengdu, demonstrates the superiority of our model over state-of-the-art (SOTA) baselines in both similarity trajectory search and k-Nearest Neighbor (k-NN) query evaluations. The model exhibits exceptional performance, particularly under conditions of high noise and with large trajectory volumes, underscoring its practical applicability in demanding scenarios.

Abstract Parliaments are institutions of perpetual learning. Unlike other workplaces, their functions are malleable, and MPs lack predefined credentials or standardised job descriptions. While this fluidity is essential to their representative role, it produces persistent institutional, organisational, and informational learning gaps, especially in new democracies where norms and resources are limited. How, then, do parliaments learn beyond internal socialisation? And how does the choice of learning modality shape their development? This article reframes parliaments as learning institutions and maps the wider ecosystem of extra-parliamentary learning, identifying four primary modalities: legislative professionalisation, bilateral exchange, parliamentary networking, and legislative strengthening. Drawing on practitioner insights, a conceptual model of parliamentary learning, and organisational isomorphism, it examines how each modality functions and why parliaments take them up. The analysis suggests an over-reliance on cross-national learning, often shaped by the policy interests of external actors, at the expense of institutional self-definition and the symbolic dimension of representation. By reframing parliaments as institutions of learning rather than mere sites of political contestation, this study shifts the debate on legislative development. It offers scholars a new lens, practitioners a structured approach to capacity-building, and MPs a deeper understanding of their environment. In doing so, it encourages parliaments to shape their own learning trajectories with greater intentionality.

Mathematics is a subject that plays an important role in developing students' logical, critical, and systematic thinking skills. However, in reality, many junior high school students still have difficulty understanding abstract mathematical concepts, one of which is fractions. This difficulty often arises because students understand fractions procedurally without adequate conceptual understanding. The Realistic Mathematics Education (RME) approach is considered relevant because it links mathematics learning to real contexts that are close to students' lives. This study aims to describe the learning trajectory of junior high school students in understanding the concept of fractions through the application of the RME approach. The research used a descriptive qualitative method with a case study approach. The research subjects consisted of three seventh-grade students from SMP Negeri 2 Depok who were selected using purposive sampling. The learning was carried out in two meetings with real contexts, such as pizza sharing, juice consumption, and study schedules. Data were collected through direct observation and documentation of students' work, then analyzed using qualitative data analysis techniques, including data reduction, data presentation, and conclusion drawing. The results of the study indicate that the application of the RME approach can facilitate the gradual understanding of fractions, from concrete experiences to abstract understanding. There were variations in learning paths among students, where some students were able to abstract directly, while others needed visual models to overcome misconceptions. These findings indicate that the RME approach is effective in helping students build a more meaningful understanding of fractions.

Smart virtual reality (VR) systems are becoming central to media production education, where immersive practice, real-time feedback, and hands-on simulation are essential. This review synthesizes the integration of artificial intelligence (AI) into human-centered, interactive VR learning for television and media production. Searches in Scopus, Web of Science, IEEE Xplore, ACM Digital Library, and SpringerLink (2013-2024) identified 790 records; following PRISMA screening, 94 studies met the inclusion criteria and were synthesized using a systematic scoping review approach. Across this corpus, common AI components include learner modeling, adaptive task sequencing (e.g., RL-based orchestration), affect sensing (vision, speech, and biosignals), multimodal interaction (gesture, gaze, voice, haptics), and growing use of LLM/NLP assistants. Reported benefits span personalized learning trajectories, high-fidelity simulation of studio workflows, and more responsive feedback loops that support creative, technical, and cognitive competencies. Evaluation typically covers usability and presence, workload and affect, collaboration, and scenario-based learning outcomes, leveraging interaction logs, eye tracking, and biofeedback. Persistent challenges include latency and synchronization under multimodal sensing, data governance and privacy for biometric/affective signals, limited transparency/interpretability of AI feedback, and heterogeneous evaluation protocols that impede cross-system comparison. We highlight essential human-centered design principles-teacher-in-the-loop orchestration, timely and explainable feedback, and ethical data governance-and outline a research agenda to support standardized evaluation and scalable adoption of smart VR education in the creative industries.

Autism Spectrum Disorder (ASD) presents challenges in mastering Activities of Daily Living (ADLs), which are essential for independence. This study applies Bayesian Knowledge Tracing (BKT) to model the mastery of five ADL skills—eating, dressing, toothbrushing, combing, and bathing—using data from 27 learners (1,350 responses). BKT parameters, including initial mastery, learning transition, guessing, and slipping, were used to estimate individual learning trajectories. Results showed that eating was the easiest skill (predicted mastery = 0.78), while bathing and combing were the most difficult (<0.55). The model achieved an overall accuracy of 0.62, with strong detection of actual mastery (TP = 722) but a high false-positive rate (FP = 429), indicating sensitivity to the guessing parameter. Learning curves and heatmaps revealed substantial inter-student variability. A comparative evaluation with the Performance Factors Analysis (PFA) model showed that BKT achieved higher overall predictive accuracy (BKT = 0.6356; PFA = 0.5917), while PFA demonstrated a higher AUC (0.6747) but exhibited strong positive-class bias in classification. These findings demonstrate the usefulness of BKT in modeling ADL development and highlight its potential for adaptive learning systems that support personalized interventions for ASD learners.

Multi-object tracking (MOT) is crucial for applications such as autonomous driving and robotics, yet traditional image-based methods struggle in high-speed scenarios due to motion blur and temporal gaps caused by low frame rates. Spike cameras, with their ability to continuously record spatiotemporal signals, overcome these limitations. However, existing spike-based methods often rely on intermediate image reconstruction or discrete clustering, limiting real-time performance and temporal continuity. To address this, we propose SNNTracker, the first fully spiking neural network (SNN)-based MOT algorithm tailored for spike cameras. SNNTracker integrates a dynamic neural field (DNF)-based attention mechanism for target detection and a winner-take-all (WTA)-based tracking module with online spike-timing-dependent plasticity (STDP) for adaptive learning of object trajectories. By directly processing spike streams without reconstruction, SNNTracker reduces latency, computational overhead, and dependency on image quality, making it ideal for ultra-high-speed environments. It maintains robust, continuous tracking even under occlusions, severe lighting variations, or temporary object disappearance, by leveraging SNN-estimated motion predictions and long-term online clustering. We construct three types of spike-camera MOT datasets covering dense and sparse annotations across diverse real-world scenarios, including camera ego-motion, deformable and ultra-fast motion (up to 2600 RPM), occlusion, indoor/outdoor lighting changes, and low-visibility tracking. Extensive experiments demonstrate that SNNTracker consistently outperforms state-of-the-art MOT methods-both ANN- and SNN-based-achieving MOTA scores above 96% and up to 100% in many sequences. Our results highlight the advantages of spike-driven SNNs for low-latency, high-speed, and label-free multi-object tracking, advancing neuromorphic vision for real-time perception.

Learning from history for personalized federated learning.

Mathematical problem-solving skills and self-regulated learning (SRL) are essential components of effective mathematics education in the 21st century. But recent studies indicate that prospective mathematics teachers’ mathematical problem-solving skills and self-regulated learning in calculus, particularly in the topic of definite integrals, still remain at a low level. To solve this problem, this study tried to design a Local Instructional Theory (LIT) based on Realistic Mathematics Education (RME) on Definite Integrals learning. The LIT consist of Hypothetical Learning Trajectory (HLT), Lecturers’ book and prospective mathematics teachers’ book. This study is design research using Plomp model on the third phase (field test) using quasi-experimental approach (non-equivalent post-test only groups design). The participants of this study are 32 prospective mathematics teachers that divided into experimental and control group. The post-test consists of mathematical problem-solving skills test and self-regulated learning questionnaire. Based on the post-test, the data was analyzed using the compare means test with prerequisite test done first, namely normality and homogeneity test. The result shows that the LIT based on RME is effective to improve students’ mathematical problem-solving skills and all of the aspect of mathematical problem-solving skills. For the selfregulated learning, there is no significant effect of using LIT based on RME, only in goal setting and task strategies aspect.

BackgroundThis study aimed to evaluate the learning curve of robotic intracorporeal single-stapling anastomosis (RiSSA) using risk-adjusted cumulative sum (RA-CUSUM) analysis based on standardized procedural intervals.MethodsWe retrospectively analyzed 36 consecutive patients who underwent robotic left-sided colorectal resection with RiSSA by a single surgeon. Eight intraoperative timepoints were annotated from surgical videos to define two composite metrics: pure RiSSA interval and total purse-string suture time. RA-CUSUM analysis was applied to assess technical proficiency over time.ResultsThe RA-CUSUM curve showed an inflection point at case 17 for the pure RiSSA interval and at case 11 for purse-string suture time, indicating earlier acquisition of suture skills compared to overall procedural fluency. Two Clavien–Dindo grade ≥ III complications occurred in the late phase, including one anastomotic leak (5.3%, 1/19), whereas no major complications were observed during the early phase, although the limited sample size precludes any definitive safety interpretation. Technical metrics, including console time, pure RiSSA interval, and total purse-string suture duration, significantly improved after the inflection point.ConclusionsIn this single-surgeon cohort, RiSSA demonstrated a definable learning trajectory, with technical efficiency stabilizing after approximately 17 cases. The occurrence of major complications after the learning phase highlights that technical proficiency does not eliminate procedural risks. The pure RiSSA interval offers a reproducible metric to evaluate anastomotic proficiency and could support skill assessment frameworks in robotic colorectal procedures. Studies involving multiple surgeons and institutions are warranted to determine the generalizability of these findings.Supplementary informationThe online version contains supplementary material available at 10.1007/s00384-025-05078-3.

This study explores the enduring relevance of René Descartes’ philosophy of autodidacticism within the context of postdigital education. In this era, artificial intelligence (AI) enables hyper-personalized and autonomous learning experiences by processing information and adapting to individual needs. By tracing the evolution of self-directed learning from Descartes’ philosophical framework to modern AI-driven platforms, this study highlights the importance of autodidacticism as a key philosophical principle for postdigital education. Rooted in intellectual autonomy, autodidacticism empowers individuals to control their learning trajectories. AI further enhances learners’ capacity to shape their intellectual pathways, facilitating knowledge acquisition that embodies the spirit of independent inquiry. This study argues that integrating Descartes’ vision of autodidacticism with contemporary technological capabilities provides a robust framework for reimagining education as a self-driven, personalized journey in the postdigital world.

This study explores the intricate mechanisms under which companies integrate sustainability principles into the fabric of their business strategies and the implications on organizational performance that follow. Identifying a wide gap between the common practice of sustainability reporting and the use of ESG platforms and the reality of integrating sustainability into operational and strategic DNA, this research pursues an in-depth qualitative case study approach. It is particularly concerned with identifying the routes and processes by which sustainability gets actually embedded in organizational practices, going beyond tokenism. Through in-depth, multi-dimensional case studies of multinational enterprises from a range of industries, supplemented by thorough analysis of internal documents (e.g., strategy memos, minutes from meetings) and public statements (reports, websites, press releases), the study reveals the complex mechanisms, crucial obstacles, and decisive facilitators that are part of this integration process. The research finds that integration is a non-linear task but an organizational learning process that varies according to situation and it is deeply affected by profound leadership commitment that means vision as executable priorities. Key facilitators are creating effective cross-functional alignment to dismantle silos and integrating significant stakeholder participation to match outside demands with inside abilities. Conversely, significant barriers consistently emerge, most notably deeply seated cultural resistance to altering deeply ingrained norms and priorities, as well as actual resource limitations (financial, human, technological) that cap implementation capacity. This study is valuable to the literature as it generates an empirically rich, process-focused, and detailed image of sustainability integration. It throws particular illumination on the precise, adaptive organizational skills required, emphasizing that successful sustainability practices are critically contingent on particular organizational contexts and learning trajectories. Accordingly, the study offers concrete managerial advice and actionable leanings for business executives, practitioners, and policymakers in actual pursuit of navigating and accelerating the required transition to genuinely sustainable and resilient business models, emphasizing the necessity of certain approaches that consider both structural and cultural factors.