Beyond passivity: Depressive symptoms predict persistent active avoidance under ambiguity.

AI-driven digital twin and delay-aware surrogate MPC framework for biogas production

Timing is a key feedback-design feature that may shape how second language (L2 learners notice, interpret, and apply written corrective feedback (WCF). With collaborative digital tools such as Google Docs, teachers can provide synchronous, anchored metalinguistic comments while learners compose, making “immediacy” a practical classroom option. Yet most timing research has been conducted in laboratory contexts, typically focusing on a single ­structure in one-to-one interaction, limiting generalizability to teacher-to-many classroom ecologies. This classroom quasi-experiment compared immediate versus delayed metalinguistic WCF in three intact L2-French classes (N = 75; CEFR B1). Learners wrote in self-selected dyads in shared Google Docs. The immediate group (n = 26) received metalinguistic comments during drafting; the delayed group (n = 24) received comparable comments on the same drafts one week later and revised in a brief, dedicated revision episode; a comparison group (n = 25) completed the same tasks without WCF during the study. Feedback targeted multiple curricular features within the same instructional cycle: noun-phrase agreement, verb-phrase agreement, and the passé composé–imparfait contrast. Development was assessed using individual writing tests administered at pretest, immediate posttest, and delayed posttest, with accuracy indexed as errors per 100 words per target category. Overall, delayed feedback showed the most consistent short-term advantage across targets at the immediate posttest, whereas longer-term effects were smaller and target dependent. Results suggest that timing effects in technology-mediated collaborative writing are shaped by classroom workflow constraints and by the processing demands of specific linguistic targets rather than reflecting a uniform “immediate is better” principle.

Much research has investigated learners’ engagement with automated written corrective feedback (AWCF). However, few have employed eye-tracking technology, which captures real-time cognitive processes during reading, and even fewer have targeted postgraduates. This case study investigated how two English postgraduates used Pigai’s AWCF to revise their academic writing during the final draft of their doctoral research proposal. A mixed approach based on the established framework was used to collect and analyze data in behavioral, cognitive, and affective dimensions. Eye-tracking technology was utilized to triangulate cognitive engagement. The findings show that each student’s engagement with AWCF differed across three dimensions. Specifically, eye movement data provided objective evidence of cognitive engagement differences: whether their visual attention was guided by color-coded feedback severity. The difference was also reflected in their behavioral and affective engagement, with the student who read top-down but selectively, rather than by color-coded severity, demonstrating higher revision effort and trust. These differences, as well as the observed deviations between the tool’s processing mechanism and the conventions of academic writing, show several key implications: develop more genre adaptive automated writing evaluation (AWE) tools, critically evaluate the consistency of the tool and genre before adoption, distinguish writing guidance, cultivate balanced trust in AWCF, and integrate eye tracking technology for personalized learning.

ABSTRACT Additive Manufacturing (AM) offers significant design flexibility but suffers from defects such as porosity, lack of fusion and warping due to complex thermo-physical interactions during fabrication. Conventional controllers, including PID-based approaches, struggle with these challenges because observability between surface measurements and subsurface thermal states is inherently limited. This paper presents a Twin-in-the-Loop system combining high-fidelity Digital Twins (DTs) with Reinforcement Learning (RL) for autonomous, closed-loop AM process control enabling real-time defect mitigation. We introduce a Cyber-Physical-Digital (CPD) architecture with three layers: (i) a multi-modal sensing layer using infrared thermography, pyrometry, acoustic emission (AE) and high-speed vision; (ii) a Digital Twin layer driven by Fourier Neural Operators (FNOs) predicting 3D thermal fields with 12.4 ms inference and 3.8% MAPE; (iii) a Soft Actor-Critic (SAC) RL controller adjusting laser power, scan speed and feed rate at 50 Hz. Acoustic-emission integration enables detection of crack-initiation events invisible to thermal and optical sensors. Ti-6Al-4V Laser Powder Bed Fusion experiments validated by X-ray Computed Tomography achieved 99.79% relative density, substantially surpassing open-loop baselines. The system deploys on edge devices such as NVIDIA Jetson AGX Orin, enabling real-time, autonomous defect-free AM production without cloud infrastructure.

Over the past fifteen years (2010–2026), research on written corrective feedback (WCF) and dynamic written corrective feedback (DWCF) in ESL/EFL contexts has expanded rapidly. Following Petticrew and Roberts’ s (2008) seven-stage framework, this review synthesizes 54 primary studies through content analysis, identifying 41 word-level concepts grouped into six themes: types of feedback, research design, writing process, participants and educational contexts, types of errors, writing performance. Results show that direct WCF remains most frequently examined, while indirect and metalinguistic forms are less common and often combined. More recent studies highlight growing attention to computer-mediated feedback and DWCF. Research designs were dominated by pre-post and post-test quasi-experiments, with fewer mixed-methods and qualitative studies. In addition, both WCF and DWCF consistently improved accuracy and psychological outcomes, while effects on complexity and fluency were mixed. Based on the systematic literature review, this study is expected to contribute more meaningful references and give some implications for future research.

Neural correlates of retrieval practice with feedback in foreign vocabulary learning: An fNIRS study.

Observing others' actions can blur the boundary between self and other, leading to false memories of self-performance. Although previous research using the observation-inflation paradigm has examined the mechanisms underlying this phenomenon, little is known about its persistence and the cognitive processes that sustain it. In Experiment 1, participants performed or read action phrases and later observed videos of others performing actions. During the five days before the memory test, they received corrective feedback distinguishing actions they had performed from those merely observed. Corrective feedback reduced false self-performance reports, yet participants still showed longer response times when judging previously observed action phrases than unobserved ones, suggesting that prior observation continued to influence retrieval processing even after correction. Experiment 2 employed event-related potentials to differentiate familiarity- and recollection-based processes. Larger LPC amplitudes for read-and-observed phrases, together with the absence of a reliable FN400 difference, suggested a greater contribution of recollection-related than familiarity-related processes. Together, the findings show that even when false beliefs are corrected, observed actions may continue to exert a lasting influence on subsequent memory judgments.

According to the "universal" theory of self-teaching, phonological decoding acts as a self-teaching mechanism, allowing word-specific orthographic knowledge to be acquired incidentally during reading. Empirical evidence suggests that word-specific context and prior phonological knowledge support self-teaching across languages, especially for inconsistent words and in less advanced readers. The computational models of self-teaching developed so far are dual-route models. They predict a major role for decoding in orthographic learning and postulate the involvement of context for phonologically known, partially decoded words. In this work, we describe BRAID-Acq, a single-route computational model of self-teaching. In three simulations, we tested the model's ability to generate phonological forms of novel words (consistent or inconsistent) using only lexical knowledge. We further assessed learning outcomes across four conditions defined by the presence or absence of prior phonological knowledge and/or contextual information. Simulation results show that the BRAID-Acq model successfully acquires new orthographic and phonological representations through reading, autonomously detecting whether words were novel or familiar, even without context. Learning benefited from two corrective mechanisms: phonological lexical feedback and contextual pronunciation correction, which allowed the correction of transient mispronunciations for orally known words without generating errors for unknown words. Simulations showed that context and phonological knowledge had the greatest effect for inconsistent words and for intermediate-sized lexicons. A further simulation showed that context was robust, supporting learning across a wide range of sizes and strengths without causing context-induced errors. The overall findings provide a proof-of-concept that self-teaching can be successfully implemented in a single-route framework.

Observed outcomes of our movements sometimes differ from our expectations. These sensory prediction errors recalibrate the brain's internal models for motor control, reflected in alterations to subsequent movements that counteract these errors (motor adaptation). While leading theories suggest that all forms of motor adaptation are driven by learning from sensory prediction errors, dominant models of speech adaptation argue that adaptation results from integrating time-advanced copies of corrective feedback commands into feedforward motor programs. Here, we tested these competing theories of speech adaptation by inducing planned, but not executed, speech. Human speakers were prompted to speak a word and, on a subset of trials, were rapidly cued to withhold the prompted speech. On standard trials, speakers were exposed to real-time playback of their own speech with an auditory perturbation of the first formant to induce single-trial speech adaptation. Speakers experienced a similar sensory error on movement cancellation trials, hearing a perturbation applied to a recording of their speech from a previous trial at the time they would have spoken. Speakers adapted to auditory prediction errors in both contexts, altering the spectral content of spoken vowels to counteract formant perturbations even when no actual produced speech coincided with the perturbed feedback. Such adaptation was not observed when participants passively listened to perturbed feedback without the intention to speak, ruling out observational learning as the cause of adaptation in movement cancellation trials. These results suggest that prediction errors, rather than corrective motor commands, drive audiomotor adaptation in speech, building on recent findings in reaching.

Teacher corrective feedback (TCF) plays a vital role in second language (L2) learning. Recent studies have examined feedback provided by both human teachers and large language models (LLMs). However, little is known about how students' trust differs toward scaffolded corrective feedback (SCF)—that is, feedback that incrementally progresses from indirect to direct during interaction—when it is provided by an LLM such as ChatGPT versus a language teacher. To address this gap, this study compared the effects of SCF, delivered by language teachers and ChatGPT, on L2 learning outcomes and student trust. Using a mixed-methods design, 40 lower-intermediate Iranian learners of English as a foreign language were randomly assigned to two conditions to receive scaffolded CF on English article usage from either a teacher or ChatGPT across four sessions. Learning gains obtained from immediate and delayed post-tests were analyzed using ANOVA and paired-sample t -tests, while semi-structured interviews and feedback interaction logs were examined using thematic analysis. Results showed that students in the teacher-delivered feedback group significantly outperformed those in the ChatGPT-delivered feedback group on both post- and delayed post-tests. Qualitative analyses suggested that this advantage stemmed from higher trust in the teacher, driven by the teacher's personalized emotional and technical support. The findings highlight that while ChatGPT can serve as a feedback tool in L2 instruction, its effectiveness depends on teacher mediation that attends to learners' individual differences and affective needs.

Industrial Internet of Things (IIoT) anomaly detection imposes concurrent requirements for temporal consistency, computational latency control, and representation of high-dimensional heterogeneous sensor data. Addressing these constraints requires models capable of processing non-stationary streams while maintaining bounded inference time and preserving inter-sensor dependencies. The proposed Quantum-Enhanced Spiking Neural Network (QESNN) integrates parameterized quantum circuits with event-driven spiking computation within a unified inference structure. The architecture is comprised of five computation modules. The Quantum Neurons for Sensor Data Fusion module maps multimodal sensor observations onto amplitude-phase quantum states and then uses nonlinear unitary transformations to generate joint feature representations through multiple channels. The Entanglement-Based Device Synchronization module aligns phases between nodes by using entangled states, coherence-control operators, and correlations for synchronization. The Probabilistic Quantum Spiking for Anomaly Detection module uses spike probability based on tunneling and incorporates fidelity and entropy metrics to estimate the likelihood of anomalies under uncertainty. The Adaptive Quantum Learning for Dynamic Optimization module tunes model parameters via time-dependent Hamiltonian dynamics with annealing strategies and feedback correction schemes to ensure robust convergence in dynamic environments. Finally, the Quantum Decoherence Management for Reliable Processing module models the impact of noise, utilizes coherence-control operations, and conducts reliability checks in order to maintain stable quantum-state computation amidst external disturbances. When tested on the MVTec Anomaly Detection dataset, the model achieves classification accuracy of 98.7% with 512 logical quantum-spiking units and an inference time of 3.29s. The effect of each component is estimated via ablation study - eliminating the Quantum Neurons for Sensor Data Fusion module leads to classification accuracy of 92.5%, whereas elimination of the Quantum Decoherence Management for Reliable Processing module produces the lowest classification accuracy of 88.9%.

This study aims to analyze linguistic errors in Indonesian expository essays written by students of the Guidance and Counselling Study Program, Faculty of Teacher Training and Education, Pattimura University, following the implementation of the KWL (Know–Want–Learned) guided writing model. The study was motivated by students’ limited ability to produce expository texts that conform to Indonesian linguistic and academic writing conventions. A qualitative descriptive approach was employed through textual analysis of expository essays written by 20 university students participating in KWL-based writing instruction. Data were collected through writing tests, classroom observations, worksheets, interviews, and field notes. The data were analyzed using Corder’s error analysis framework, which includes error identification, classification, explanation, and interpretation. The findings revealed four major categories of language errors: syntactic errors (46.4%), morphological errors (31.1%), lexical errors (14.7%), and phonological errors (7.1%). Syntactic errors emerged as the most dominant category, indicating that students still experienced substantial difficulties in constructing grammatically accurate and coherent sentences. The contributing factors included limited mastery of Indonesian grammatical rules, inadequate academic writing competence, low reading and writing habits, restricted vocabulary, and writing anxiety. The study also found that the KWL model contributed positively to students’ ability to organize ideas, structure expository content systematically, and engage actively in the writing process through planning and reflective learning stages. However, the model did not significantly reduce grammatical inaccuracies, particularly in syntax and morphology. The study concludes that guided writing instruction using the KWL model should be integrated with explicit grammar instruction and corrective feedback to improve students’ linguistic accuracy and overall academic writing quality. These findings provide pedagogical implications for Indonesian language instruction in higher education, particularly in strengthening students’ expository writing competence through the integration of cognitive writing strategies and language-focused instruction.

Dance motion recognition and correction present unique challenges due to the subtle distinctions in movement execution that differentiate correct performance from flawed attempts. This paper proposes an integrated system combining 3D motion capture technology with a novel spatiotemporal attention-based graph convolutional network for accurate dance action recognition and intelligent feedback generation. The proposed architecture features a dual-stream design incorporating adaptive graph topology learning that discovers task-relevant relationships between non-adjacent joints, alongside multi-scale temporal modeling to capture movement dynamics across varying time scales. A multi-dimensional correction feedback algorithm translates recognition outputs into prioritized, actionable guidance by comparing performer movements against professional reference templates through dynamic time warping alignment and joint-level deviation analysis. We constructed DanceMotion-86, a comprehensive dataset comprising 10,836 clips across 86 action categories spanning five dance genres. Experimental results demonstrate that the proposed method achieves 92.3% recognition accuracy, outperforming state-of-the-art baseline methods. User studies with 36 participants confirmed 87.4% error detection rate and showed significantly accelerated skill acquisition among feedback-enabled learners compared to control conditions. The system offers practical applications for intelligent dance instruction, cultural heritage preservation, and remote training platforms.

This study adopts the long-established SLA concept, focus on form, in investigating the degree and characteristics of attention to language during teacher-whole class interaction embedded in content communication in English medium instruction (EMI) science classes. With the global spread of EMI programs, there is an urgent need to explore whether these meaning-oriented classes, often carrying a large amount of subject knowledge, are realizing the integration of language into content. Data was obtained from 30 video recorded EMI science lessons from seven high schools in China, taught by 15 native speakers of English. The teachers’ high English proficiency eliminates an often-limiting factor in conducting classroom interaction in EMI classrooms. Findings show that compared to previous studies there was fairly regular focus on form, although still limited, and a balanced use between preemptive and reactive focus on form. Negotiation of meaning was rare. While negative evidence was lacking, high quality elaborated input was often found in teacher feedback moves in both preemptive and reactive focus on form. A shift in EMI pedagogy is called for, particularly in increasing the quantity and diversity of corrective feedback, and a more fundamental change of the mode of interaction in EMI classes to include more learner-learner interaction.

In the modern era of working, Musculoskeletal Disorders (MSDs) are increasing drastically. One of the leading causes of MSD is Low Back Pain (LBP). Patient health monitoring technology is paramount to the investigators, enabling remote recovery services via cutting-edge technologies that lower the barrier between clinicians and patients. This work provides a low-cost, efficient, and user-friendly visual capture recovery system for the administration of Low Back Pain (LBP). This study proposes a unique computer vision and deep learning method for remotely monitoring patients' joint angles during physiotherapy rehabilitation. A single long-short term memory layer with 64-unit lightweight model with dense neurons was used to identify the correct postures for LBP recovery exercises in real-time video. The proposed system exploits a 3D human skeleton representation for calculating angles on three landmarks to recognize the angle deviations and classify the nine LBP recuperation exercise poses with high cross-validation accuracy, low computational cost, real-time exercise correction feedback, and minimal latency to process frames. The suggested approach successfully predicts and provides feedback on LBP exercise postures from real-time video feeds captured by common RGB cameras, without additional hardware or specialist cameras, thereby improving the quality of life for people around the globe.

Grounded in the sociocultural theory of Lev Vygotsky, this study investigates the role of TikTok influencers as potential More Capable Others within the framework of the Zone of Proximal Development (ZPD) in English language learning. The research explores how digital mediation provided by influencers contributes to learners’ linguistic development, particularly in grammar comprehension, speaking confidence, and argumentative construction. Using qualitative methods, including participant responses and thematic analysis, the study examines whether influencer-generated content facilitates movement from assisted performance to independent competence. The findings indicate that influencers function as episodic and micro-level mediators who provide accessible explanations, corrective feedback, and affective support. This mediation enables learners to perform tasks previously beyond their independent ability, reflecting the mechanism of internalization central to ZPD theory. However, the mediation is learner-initiated, fragmented, and digitally situated, differing structurally from traditional classroom scaffolding. The study concludes that TikTok influencers can extend the ZPD into socio-digital spaces, repositioning epistemic authority while complementing, rather than replacing, formal English language instruction.

The purpose of this study was to investigate the impact of GenAI-Automatic Corrective Feedback (ACF) integration in collaborative writing based on metacognitive instruction on argumentative essay writing skills. The method used in this study was a quasi-experimental study involving 250 students divided into two groups: an experimental group and a control group. The experimental group received the GenAI-Automatic Corrective Feedback (ACF) integration intervention in collaborative writing based on metacognitive instruction, while the control group used a genre-based writing approach. The instruments used included writing assessments covering lexical, accuracy, and fluency. Data analysis used ANOVA and regression analysis to investigate the impact of the intervention on writing skills. The research findings showed that the GenAI-Automatic Corrective Feedback (ACF) intervention in collaborative writing based on metacognitive instruction significantly improved argumentative essay writing skills compared to the genre-based writing intervention. Improvements in argumentative essay writing skills in the experimental group were evident in several aspects, including lexical complexity (lexical density, lexical sophistication, and lexical variety), accuracy, and fluency. Increased lexical complexity was evident in the use of complex vocabulary, complex sentence combinations, and more varied lexical use. Improved argumentative essay accuracy was evident in the accuracy of sentences and paragraphs and supporting ideas relevant to the problem of the argumentative essay. Improved argumentative essay fluency was evident in the use of sentences and grammar with minimal errors and no discordant sentences. The components of metacognitive instruction that significantly contributed to argumentative essay writing skills were metacognitive knowledge, including declarative, procedural, and conditional knowledge, and metacognitive regulation, including planning, monitoring, and evaluation, as well as information management and debugging strategies. Received: 09 December 2025 / Accepted: 13 April 2026 / Published: May 2026

Gamified virtual patients (VPs) can enhance motivation and learning in military trauma management. However, there is a need to better understand design preferences and expectations regarding VP features and game elements. This study explores how such elements are experienced and interpreted by military trauma care professionals. This qualitative study applied systematic text condensation to analyze the shared experiences of 17 participants, consisting of military medics and instructors, who interacted with a gamified VP system. Five main themes were identified: Feeling Challenged; Supporting Reflection and Learning; Realism Matters; Developing Confidence; and Balancing Learning and Playing. Participants expressed mixed views on game rewards, competition, and time-pressure, with instructors warning that such features could detract from learning objectives. Instructors emphasized the value of feedback that explains consequences, while both instructors and medics highlighted the importance of immediate corrective feedback. Gamified VPs can support military trauma training by enhancing engagement, building confidence, and supporting reflection and learning. However, the inclusion of game elements requires careful consideration. Elements that contribute to realism and immersion, such as narrative, multimedia, and tactical challenges, were viewed as valuable for maintaining authenticity and contextual relevance. Hints and progressive difficulty levels were also perceived as beneficial for supporting gradual skill development. Features such as scoring, competition, rewards and time-pressure elicited mixed responses. While some participants found these elements engaging, others perceived them as distracting or misaligned with the goal of acquiring life-saving skills. Instructors were critical of mechanisms that induced artificial stress or rewarded speed over reasoning, warning that such features could shift focus from learning to performance. Therefore, rather than adopting gamification features uncritically, designers and educators should carefully evaluate which elements enhance learning in high-stakes environments and which risk undermining it.

Digital development is changing the organization of music education, but there are still widespread problems in existing teaching such as resource dispersion, feedback lag and insufficient process support. Around this practical demand, this paper, supported by computer technology, studies the construction path of music education intelligent teaching system under the background of digital, and designs the system from two aspects of music education network course design and teaching resources comprehensive management. On the course side, an intelligent teaching process covering goal decomposition, task arrangement, audio collection, process evaluation and feedback correction was constructed. At the management end, an integrated management system is designed, which integrates resource storage, label modeling, authority control and intelligent matching. The experimental results show that the average evaluation of network course design experts reaches 4.71, the accuracy of resource recognition is 95.4%, and the average response time of the system is 8.7 s under the scale of 5000 resources. Research shows that the system can improve the organizational efficiency, resource invocation ability and personalized support level of music teaching, and provide technical reference for the continuous optimization of digital music education.