The development of domain-specific and domain-general metacognitive monitoring

Metacognitive monitoring may be a critical element in self-regulated learning. Two types of metacognitive monitoring have been identified: domain-specific and domain-general. Domain-specific metacognitive monitoring occurs when an individual is monitoring content-specific knowledge. Domain-general metacognitive monitoring occurs in situations when content-specific knowledge is not available. Currently no research is available that examines the developmental differences between domain-specific and domain-general metacognitive monitoring in children. This study attempted to address this issue by asking children in first, fourth, and seventh grade to make item-by-item confidence judgments while providing answers in two domain-specific tasks and two domain-general tasks. Two working memory spans tasks were also employed to control for maturational processes. Domain-specific metacognitive monitoring appeared earlier than domain-general metacognitive monitoring. Both domain-specific and domain-general metacognitive monitoring appear to benefit from experience because older students were more accurate metacognitive monitors and less overconfident than younger students. Maturational processes likely play a less significant role than experience in student improvement at metacognitive monitoring than previously thought

"Our Care through our eyes": impact of a co-produced digital education programme on nurses' knowledge, confidence and attitudes in providing care for children and young people who have self-harmed: a mixed-methods study in the UK

Objectives: 1. To determine the impact of a digital educational intervention on the knowledge, attitudes, confidence and behavioural intention of registered children’s nurses working with Children and Young People (CYP) admitted with self-harm 2. To explore the perceived impact, suitability and usefulness of the intervention. Intervention: A digital educational intervention that had been co-produced with CYP service users, registered children’s nurses, and academics. Setting: A prospective, uncontrolled, intervention study with pre and post-intervention measurement, conducted at a large acute NHS Trust in the UK. Participants: From a pool of 251 registered children’s nurses, 98 participants were recruited to complete the intervention (response rate = 39%). At follow-up, 52% of participants completed the post-intervention questionnaire, with 65% (n=33) of those reporting to have completed the digital educational intervention. Primary Outcome measures: Attitudes towards self-harm in CYP was measured using a 13 item questionnaire; knowledge of self-harm in CYP was measured through an adapted 12 item questionnaire; confidence in different areas of practice was measured through Likert scale responses; Self-efficacy for working with CYP who have self-harmed was measured through an adapted version of the Self-efficacy Towards Helping (SETH) scale; Clinical behavioural intention was measured by the Continuing Professional Development Reaction Questionnaire. Semi-structured interviews were undertaken with a purposive sample of participants. Results: For those who completed the intervention (n=33), improvements were observed in knowledge (Effect size, ES: 0.69), confidence, and in some domains relating to attitudes (Effectiveness domain- ES: 0.49), and clinical behavioural intention (Belief about consequences-ES:0.49; Moral Norm-ES: 0.43; Beliefs about capability-ES: 0.42). Qualitative findings suggest participants experienced skill development, feelings of empowerment, and reflection on own practice. Conclusions: The effect of the intervention is promising and demonstrates the potential it has in improving registered children’s nurse’s knowledge, confidence and attitudes. However, further testing is required to confirm this

An innovative quality improvement curriculum for third-year medical students

Background: Competence in quality improvement (QI) is a priority for medical students. We describe a self-directed QI skills curriculum for medical students in a 1-year longitudinal integrated third-year clerkship: an ideal context to learn and practice QI. Methods: Two groups of four students identified a quality gap, described existing efforts to address the gap, made quantifying measures, and proposed a QI intervention. The program was assessed with knowledge and attitude surveys and a validated tool for rating trainee QI proposals. Reaction to the curriculum was assessed by survey and focus group. Results: Knowledge of QI concepts did not improve (mean knowledge score±SD): pre: 5.9±1.5 vs. post: 6.6±1.3, p=0.20. There were significant improvements in attitudes (mean topic attitude score±SD) toward the value of QI (pre: 9.9±1.8 vs. post: 12.6±1.9, p=0.03) and confidence in QI skills (pre: 13.4±2.8 vs. post: 16.1±3.0, p=0.05). Proposals lacked sufficient analysis of interventions and evaluation plans. Reaction was mixed, including appreciation for the experience and frustration with finding appropriate mentorship. Conclusion: Clinical-year students were able to conduct a self-directed QI project. Lack of improvement in QI knowledge suggests that self-directed learning in this domain may be insufficient without targeted didactics. Higher order skills such as developing measurement plans would benefit from explicit instruction and mentorship. Lessons from this experience will allow educators to better target QI curricula to medical students in the clinical years

JENTIL: responsive clothing that promotes an ‘holistic approach to fashion as a new vehicle to treat psychological conditions’

This paper explores an ongoing interdisciplinary research project at the cutting edge of sensory, aroma and medical work, which seeks to change the experience of fragrance to a more intimate communication of identity, by employing emerging technologies with the ancient art of perfumery. The project illustrates .holistic' clothing called the JENTIL® Collection, following on from the Author’s SmartSecondSkin' PhD research, which describes a new movement in functional, emotional clothing that incorporates scent. The project investigates the emergent interface between the arts and biomedical sciences, around new emerging technologies and science platforms, and their applications in the domain of health and well-being. The JENTIL® Collection focuses on the development of .gentle., responsive clothing that changes with emotion, since the garments are designed for psychological end benefit to reduce stress. This is achieved by studying the mind and advancing knowledge and understanding of how known well-being fragrances embedded in holistic Fashion, could impact on mental health. This paper aims to combine applied theories about human well-being, with multisensory design, in order to create experimental strategies to improve self and social confidence for individuals suffering from depressive illnesses. The range of methodologies employed extends beyond the realm of fashion and textile techniques, to areas such as neuroscience, psychiatry, human sensory systems and affective states, and the increase in popularity of complementary therapies. In this paper the known affective potential of the sense of smell is discussed, by introducing Aroma-Chology as a tool that is worn as an emotional support system to create a personal scent bubble. around the body, with the capacity to regulate mood, physiological and psychological state and improve self-confidence in social situations. The clothing formulates a healing platform around the wearer, by creating novel olfactory experiences in textiles that are not as passive as current microencapsulated capsule systems generally are

Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation

Continuous Video Domain Adaptation (CVDA) is a scenario where a source model is required to adapt to a series of individually available changing target domains continuously without source data or target supervision. It has wide applications, such as robotic vision and autonomous driving. The main underlying challenge of CVDA is to learn helpful information only from the unsupervised target data while avoiding forgetting previously learned knowledge catastrophically, which is out of the capability of previous Video-based Unsupervised Domain Adaptation methods. Therefore, we propose a Confidence-Attentive network with geneRalization enhanced self-knowledge disTillation (CART) to address the challenge in CVDA. Firstly, to learn from unsupervised domains, we propose to learn from pseudo labels. However, in continuous adaptation, prediction errors can accumulate rapidly in pseudo labels, and CART effectively tackles this problem with two key modules. Specifically, The first module generates refined pseudo labels using model predictions and deploys a novel attentive learning strategy. The second module compares the outputs of augmented data from the current model to the outputs of weakly augmented data from the source model, forming a novel consistency regularization on the model to alleviate the accumulation of prediction errors. Extensive experiments suggest that the CVDA performance of CART outperforms existing methods by a considerable margin.Comment: 16 pages, 9 tables, 10 figure

MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation

Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowledge transfer. Existing UniDA methods may suffer from the problems of overlooking intra-domain variations in the target domain and difficulty in separating between the similar known and unknown class. To address these issues, we propose a novel Mutual Learning Network (MLNet) with neighborhood invariance for UniDA. In our method, confidence-guided invariant feature learning with self-adaptive neighbor selection is designed to reduce the intra-domain variations for more generalizable feature representation. By using the cross-domain mixup scheme for better unknown-class identification, the proposed method compensates for the misidentified known-class errors by mutual learning between the closed-set and open-set classifiers. Extensive experiments on three publicly available benchmarks demonstrate that our method achieves the best results compared to the state-of-the-arts in most cases and significantly outperforms the baseline across all the four settings in UniDA. Code is available at https://github.com/YanzuoLu/MLNet.Comment: Accepted by AAAI 2024 (Poster

How special are teachers of specialized schools? Assessing self-confidence levels in the technology domain

This study investigates whether specialized high school mathematics teachers, chosen to educate selected students, are mentally ready to integrate Fatih project technologies into their teaching. Forty mathematics teachers from randomly selected specialized and general high schools in Ankara responded to a survey comprising 31 items grouped under four measures of self-confidence in the technology domain. An independent t-test revealed no statistically significant difference between specialized and general high school teachers' self-confidence levels. We conclude that technological pedagogical content knowledge should be an essential criterion for selecting specialized school teachers, who educate the country's future innovators. © 2016 by iSER, International Society of Educational Research

Calibrated Adaptive Teacher for Domain Adaptive Intelligent Fault Diagnosis

Intelligent Fault Diagnosis (IFD) based on deep learning has proven to be an effective and flexible solution, attracting extensive research. Deep neural networks can learn rich representations from vast amounts of representative labeled data for various applications. In IFD, they achieve high classification performance from signals in an end-to-end manner, without requiring extensive domain knowledge. However, deep learning models usually only perform well on the data distribution they have been trained on. When applied to a different distribution, they may experience performance drops. This is also observed in IFD, where assets are often operated in working conditions different from those in which labeled data have been collected. Unsupervised domain adaptation (UDA) deals with the scenario where labeled data are available in a source domain, and only unlabeled data are available in a target domain, where domains may correspond to operating conditions. Recent methods rely on training with confident pseudo-labels for target samples. However, the confidence-based selection of pseudo-labels is hindered by poorly calibrated confidence estimates in the target domain, primarily due to over-confident predictions, which limits the quality of pseudo-labels and leads to error accumulation. In this paper, we propose a novel UDA method called Calibrated Adaptive Teacher (CAT), where we propose to calibrate the predictions of the teacher network throughout the self-training process, leveraging post-hoc calibration techniques. We evaluate CAT on domain-adaptive IFD and perform extensive experiments on the Paderborn benchmark for bearing fault diagnosis under varying operating conditions. Our proposed method achieves state-of-the-art performance on most transfer tasks.Comment: 23 pages. Under revie

Knowledge Gaps, Barriers, and Facilitators to Fertility Preservation Counseling Among Oncology Nurses Managing the Care of Newly Diagnosed Cancer Patients

Newly diagnosed cancer patients are inconsistently counseled about the infertility risks associated with oncologic treatments and the fertility preservation options currently available. Oncology nurses are placed in a unique position to introduce fertility topics with oncology patients; however, several barriers prevent counseling on this subject. The purpose of this paper is to determine the knowledge gaps, barriers, and facilitators of counseling newly diagnosed reproductive-aged cancer patients about fertility issues before cancer treatments among oncology nurses. An anonymous web-based, cross-sectional survey was accessed from August 2018-November 2018 and completed by oncology nurses employed in the medical oncology and infusion centers of a large multicenter cancer institution. The survey consisted of five elements: study consent, demographic information and general fertility questions, the American Society of Clinical Oncology (ASCO) 2013 clinical practice guideline questions, a validated knowledge tool to assess general fertility knowledge, and a validated oncology fertility preservation survey to determine barriers and facilitators to counseling patients about fertility issues. Thirty-eight participants completed the survey in its entirety, and the collected data were reviewed and analyzed. The majority of participants were full-time, Caucasian oncology nurses with an oncology experience of 1-5 years or 6-10 years. All of the participants were female. The majority of oncology nurses reported that they were unfamiliar with the clinical guidelines related to fertility preservation and oncology patients. The average baseline knowledge score using the validated knowledge tool was 7.1 (out of 13 questions). The higher domain scores in self-awareness, confidence, and external barriers from the fertility preservation survey indicated that self-perceived barriers and self-related preparedness hindered oncology nurse counseling on fertility topics. The findings suggest that oncology nurses would benefit from comprehensive training about fertility issues that impact oncology patients to adequately and confidently counsel these patients on this topic. Presenting these topics to patients who are interested in future fertility and those that are physiologically stable enough to pursue fertility preservation options will allow them the opportunity to make informed decisions about their future fertility and quality of life before possible sterilizing treatments