Monitoring Feedback Research Articles

Threshold Modeling for Antibiotic Stewardship in Oman.

Antimicrobial stewardship supports rational antibiotic use. However, balancing access to antibiotic treatment while controlling resistance is challenging. This research used a threshold logistic modelling approach to identify targets for antibiotic usage associated with carbapenem-resistant Acinetobacter baumannii (CRAb), carbapenem-resistant Klebsiella pneumonia (CRKP), and extended-spectrum β-lactamases- producing Escherichia coli (ESBL-producing E. coli) incidence in hospitals. This study utilises an ecological population-level design. Monthly pathogen cases and antibiotic use were retrospectively determined for inpatients between January 2015 and December 2023. The hospital pharmacy and microbiology information systems were used to obtain this data. Thresholds were identified by applying nonlinear modelling and logistic regression. Incidence rates of 0.199, 0.175, and 0.146 cases/100 occupied bed-days (OBD) for CRAb, CRKP, and ESBL-producing E. coli, respectively, were determined as the cutoff values for high (critical) incidence rates. Thresholds for aminoglycosides (0.59 defined daily dose (DDD)/100 OBD), carbapenems (6.31 DDD/100 OBD), piperacillin-tazobactam (3.71 DDD/100 OBD), third-generation cephalosporins (3.71 DDD/100 OBD), and fluoroquinolones (1.91 DDD/100 OBD), were identified. Exceeding these thresholds would accelerate the gram-negative pathogens' incidence rate above the critical incidence levels. Employing the threshold logistic approach enabled near real-time performance monitoring feedback, risk scores and alert signals for when the incidence of the selected pathogens are entering critical levels. Threshold logistic models can help inform and implement effective antimicrobial stewardship interventions to manage resistance within hospital settings.

Contextual barriers to infection prevention and control program implementation in hospitals in Latin America: a mixed methods evaluation

BackgroundInfection prevention and control (IPC) programs are essential to prevent and control the spread of multidrug-resistant organisms in healthcare facilities (HCFs). The current implementation of these programs in Latin America remains largely unknown.MethodsWe conducted a mixed-methods evaluation of IPC program implementation in HCFs from Guatemala, Panama, Ecuador, and Argentina, March-July 2022. We used the World Health Organization (WHO) IPC Assessment Framework (IPCAF) survey, a previously validated structured questionnaire with an associated scoring system that evaluates the eight core components of IPC (IPC program; IPC guidelines; IPC education and training; healthcare-associated infection [HAI] surveillance; multimodal strategies; monitoring and audit of IPC practices and feedback; workload, staffing, and bed occupancy; and the built environment and materials and equipment for IPC). Each section generates a score 0–100. According to the final score, the HCF IPC program implementation is categorized into four levels: inadequate (0–200), basic (201–400), intermediate (401–600), or advanced (601–800). Additionally, we conducted semi-structured interviews among IPC personnel and microbiologists using the Systems Engineering Initiative for Patient Safety model to evaluate barriers and facilitators for IPC program implementation. We performed directed content analysis of interview transcripts to identify themes that focused on barriers and facilitators of IPC program implementation which are summarized descriptively.ResultsThirty-seven HCFs (15 for-profit and 22 non-profit) completed the IPCAF survey. The overall median score was 614 (IQR 569, 693) which corresponded to an “advanced” level of IPC implementation (32% [7/22] non-profit vs. 93% [14/15] for-profit HCFs in this category). The lowest scores were in workload, staffing and bed occupancy followed by IPC training and multimodal strategies. Forty individuals from 16 HCFs were interviewed. They perceived inadequate staffing and technical resources, limited leadership support, and cultural determinants as major barriers to effective IPC guideline implementation, while external accreditation and technical support from public health authorities were perceived as facilitators.ConclusionsEfforts to strengthen IPC activities in Latin American HCFs should focus on improving support from hospital leadership and public health authorities to ensure better resource allocation, promoting safety culture, and improving training in quality improvement.

Unveiling User Sentiment: Aspect-Based Analysis and Topic Modeling of Ride-Hailing and Google Play App Reviews

Background: Mobile app usage is increasing in the digital age, with Ride-Hailing app becoming the primary example of this trend. To obtain valuable understanding of how people perceive and interact with mobile app, user reviews on platforms such as Google Play are usually analyzed. This analysis can assist developers to identify areas for improvement in both Ride-hailing and Google Play App. A promising method that can be used to analyze user perception in this instance is Aspect-Based Sentiment Analysis (ABSA). Objective: This research aimed to apply ABSA to user reviews using Bidirectional Encoder Representations from Transformers (BERT) models. In this context, aspect identification and topic modeling were performed by using Latent Dirichlet Allocation (LDA). The model extracted topics from the reviews and used Generative Artificial Intelligence (GenAI) to define the aspects of the topics to further enhance the analysis. For consistency and accuracy, the method included sentiment annotation by a human annotator. Methods: A total of two datasets were used in this research, with the first collected by scraping user reviews of Ride-Hailing App while the second was obtained from Kaggle, and to identify relevant topics, modeling was performed using LDA. These topics were then categorized into aspects using GenAI, covering areas, such as customer experience, service, payment, app features, task management, and event management. Subsequently, sentiment labeling was conducted using human annotators to provide a reliable baseline. BERT model was then used to classify sentiment with aspect hints, and the evaluation included calculations of accuracy, precision, recall, and F1-score. Results: The results showed that BERT model achieved the highest accuracy of 97% in sentiment analysis across all datasets. Conclusion: This research provided valuable understanding of user experience and established a strong ABSA framework for analyzing user reviews using LDA, Aspect Annotation, GenAI, and BERT sentiment models. Future research could expand this method to other app categories and incorporate real-time ABSA for continuous monitoring and dynamic feedback. Keywords: User Reviews, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, Topic Modeling, Generative Artificial Intelligence (GenAI)

Impact of motivational feedback on levels of physical activity and quality of life by activity monitoring following knee arthroplasty surgery—protocol for a randomized controlled trial nested in a prospective cohort (Knee-Activity)

BackgroundEvidence on how to improve daily physical activity (PA) levels following total knee arthroplasty (TKA) or medial uni-compartmental knee arthroplasty (mUKA) by motivational feedback is lacking. Moreover, it is unknown whether a focus on increased PA after discharge from the hospital improves rehabilitation, physical function, and quality of life. The aim of this randomized controlled trial (RCT) nested in a prospective cohort is (a) to investigate whether PA, physical function, and quality of life following knee replacement can be increased using an activity monitoring device including motivational feedback via a patient app in comparison with activity monitoring without feedback (care-as-usual), and (b) to investigate the potential predictive value of PA level prior to knee replacement for the length of stay, return to work, and quality of life.MethodsThe study is designed as a multicenter, parallel-group, superiority RCT with balanced randomization (1:1) and blinded outcome assessments. One hundred and fifty patients scheduled for knee replacement (TKA or mUKA) will be recruited through Odense University Hospital, Denmark, Vejle Hospital, Denmark and Herlev/Gentofte Sygehus, Denmark. Patients will be randomized to either 12 weeks of activity monitoring and motivational feedback via a patient app by gamification or 'care-as-usual,' including activity monitoring without motivational feedback. The primary outcome is the between-group change score from baseline to 12-week follow-up of cumulative daily accelerometer counts, which is a valid proxy for average objectively assessed daily PA.DiscussionImproving PA through motivational feedback following knee replacement surgery might improve post-surgical function, health-related quality of life, and participation in everyday life.Trial registrationClinicalTrials.gov, ID: NCT06005623. Registered on 2023–08-22.Trial statusRecruiting.

Does pre-speech auditory modulation reflect processes related to feedback monitoring or speech movement planning?

Previous studies have revealed that auditory processing is modulated during the planning phase immediately prior to speech onset. To date, the functional relevance of this pre-speech auditory modulation (PSAM) remains unknown. Here, we investigated whether PSAM reflects neuronal processes that are associated with preparing auditory cortex for optimized feedback monitoring as reflected in online speech corrections. Combining electroencephalographic PSAM data from a previous data set with new acoustic measures of the same participants’ speech, we asked whether individual speakers’ extent of PSAM is correlated with the implementation of within-vowel articulatory adjustments during /b/-vowel-/d/ word productions. Online articulatory adjustments were quantified as the extent of change in inter-trial formant variability from vowel onset to vowel midpoint (a phenomenon known as centering). This approach allowed us to also consider inter-trial variability in formant production, and its possible relation to PSAM, at vowel onset and midpoint separately. Results showed that inter-trial formant variability was significantly smaller at vowel midpoint than at vowel onset. PSAM was not significantly correlated with this amount of change in variability as an index of within-vowel adjustments. Surprisingly, PSAM was negatively correlated with inter-trial formant variability not only in the middle but also at the very onset of the vowels. Thus, speakers with more PSAM produced formants that were already less variable at vowel onset. Findings suggest that PSAM may reflect processes that influence speech acoustics as early as vowel onset and, thus, that are directly involved in motor command preparation (feedforward control) rather than output monitoring (feedback control).

ELECTRONIC ASSESSMENT ANXIETY SCALE: DEVELOPMENT, VALIDITY AND RELIABILITY

The widespread availability of internet access in daily life has resulted in a greater acceptance of online assessment methods. E-assessment platforms offer various features such as randomizing questions and answers, utilizing extensive question banks, setting time limits, and managing access during online exams. Electronic assessment enables real-time monitoring, customization, and scalability of feedback, benefiting students, academic staff, and administrative personnel. However, students encounter specific challenges in the electronic assessment environments. These challenges include limited control over test settings and the isolated nature of taking exams without peers. Furthermore, the technological proficiency of both instructors and students, along with resource constraints (computers, mobile devices, internet), can impede the effective utilization of these assessment tools. Technical issues like slow internet connection or disconnections can have significant consequences, especially in online exams, posing difficulties for corrections. The main goal of this study is to develop a Likert-type scale capable of measuring anxiety related to technical issues, social isolation, and the test interface experienced in e-assessment contexts. The study consists of two separate groups: the first group comprising 359 participants and the second group consisting of 356 participants. Both groups include undergraduate and pedagogical formation certificate program students from a university in the Eastern Anatolia Region of Turkiye. Construct validity was assessed through exploratory and confirmatory factor analyses. Item parameters were examined using item analysis based on classical test theory. As a result of the study, a two-factor scale structure comprising 21 items measuring social and technical anxiety was developed. These two dimensions account for 59.89% of the total variance. The Cronbach’s alpha coefficient for the entire scale was 0.93, the McDonald’s omega coefficient was 0.93, and the construct reliability was 0.99.

Mindfulness-based relapse prevention targeting psychological craving and trait mindfulness in young Chinese women with methamphetamine dependence: a randomized controlled trial.

We recruited 58 MA-dependent young adult females from a compulsory isolation drug rehabilitation center in Sichuan Province and randomly divided them into an MBRP group (n = 29) and a control group (n = 29) according to their degree of psychological craving. The MBRP group received 2 hours of MBRP training twice a week for 4 weeks, alongside routine treatment at the drug rehabilitation center. Meanwhile, the control group solely received routine treatment at the drug rehabilitation center without any additional interventions. The assessment was conducted before and immediately after the intervention, with the Compulsive Drug Use Scale (OCDUS) used to assess craving and the Five-Factor Mindfulness Scale (FFMQ) used to assess trait mindfulness. Also, a "mental feedback monitoring balance" instrument was used to assess concentration and relaxation during some training sessions. This randomized trial was conducted to evaluate the effectiveness of decreasing psychological craving and increasing trait mindfulness. At baseline, there were no significant differences in total or dimension scores for FFMQ or OCDUS between the two groups (all P > 0.05). After the intervention, the repeated measures ANOVA showed a significant time main effect on changes in observing, non-judging, and non-reacting scores (all P < 0.05), and a significant interaction effect between time and group on both FFMQ total score and OCDUS score (P < 0.01 or P < 0.05). Mental feedback monitoring indicated significant improvement in concentration and relaxation after breath meditation exercises (P < 0.05 or P < 0.001). Additionally, the MBRP group showed improved relaxation during the body scan exercise (P < 0.01). MBRP training can improve the trait mindfulness of MA addicts and reduce psychological cravings effectively.

A Remote Patient Monitoring System with Feedback Mechanisms using a Smartwatch: Concept, Implementation and Evaluation based on the activeDCM Randomized Controlled Trial.

Technological advances allow recording and sharing of health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable a dense management of individual health trajectories, monitoring of risk factors and asynchronous feedback. However, most Remote Patient Monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack the patient-centric design. The objective was to conceptualize and implement a user-friendly, reusable, interoperable and secure RPM system incorporating asynchronous feedback mechanisms, using a broadly available consumer wearable (Apple Watch). Additionally, the study sought to evaluate factors influencing patient acceptance of such systems. The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach, ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was employed as part of the activeDCM prospective, randomized, interventional study focusing on Dilated Cardiomyopathy (DCM). Each patient used the system for at least 12 months. The System Usability Scale (SUS) was employed to measure usability from a subjective patient perspective. Additionally, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data, classified into Sensor-based Health Data (SHD) and Patient Reported Outcome Measures (PROM). Descriptive statistics using boxplots, along bootstrapped multiple linear regression with a 95% confidence interval (CI) were utilized for evaluation, analyzing the influence of age, sex, device experience and intervention group membership. The RPM system consists of four interoperable components: patient-devices, data-server, data-viewer and notification-service. The evaluation of the system was conducted with 95 consecutive DCM patients (female: 28 of 95 (29%), age: 50±12 years) completing the activeDCM study protocol. The wearable/ smartphone application of the system achieved a mean SUS score of 78±17, which was most influenced by device experience. 83 of 95 patients (87%) could integrate the wearable application (very) well into their daily routine and 67 of 95 (70%) saw a benefit of the RPM system for management of their health condition. Patients interacted on average with the wearable on 61%±26% of days enrolled in the study, corresponding to 239±99 of 396±39 days. SHD was available on average for 78%±23% of days and PROM data 64%±27% of weeks enrolled in the study, corresponding to 307±87 of 396±39 days and 35±15 of 56±5 weeks, respectively. Wearable interaction frequency, SHD and PROM completeness were most influenced by intervention group membership. Our results mark a first step towards integrating RPM systems, based on a consumer wearable device for primary patient input, into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable and secure RPM system, that can be integrated into patients' daily routines. ClinicalTrials.gov-Identifier: NCT04359238.

Effects of delay and error in the feedback structure of ecological management

Ecosystems face various emergent uncertainties owing to factors such as climate change and accelerating anthropogenic impacts. Uncertainty is a major challenge and a barrier that ecosystem management faces, because it is difficult to precisely predict a priori risks that can have significant impacts on ecosystems. Hence, management with adaptive capacity is recommended to deal with such uncertainties, and feedback structures are central mechanisms for such flexible management. This study used mathematical models to clarify the specific impacts of feedback structures on ecosystem management, such as resource and wildlife management. In particular, the impact of errors in estimating ecosystem status when providing feedback and the impact of the time lag before feedback effects were implemented into management were examined. Overestimation of ecosystem status or a large time lag led to undesirable temporal oscillations in ecosystem status. However, these scenarios can be avoided when combined with management practices that limit the impact of management on the ecosystem, such as input control. Ecosystem management tends to have a large spatiotemporal scale, and implementing highly accurate monitoring and sophisticated feedback structures is difficult. However, the results suggest that effective ecosystem management with a simple feedback structure can be achieved through such complementary institutional design.

Effects of digital monitoring and immediate feedback on physical activity and fitness in undergraduates

Effects of digital monitoring and immediate feedback on physical activity and fitness in undergraduates

Does pre-speech auditory modulation reflect processes related to feedback monitoring or speech movement planning?

Previous studies have revealed that auditory processing is modulated during the planning phase immediately prior to speech onset. To date, the functional relevance of this pre-speech auditory modulation (PSAM) remains unknown. Here, we investigated whether PSAM reflects neuronal processes that are associated with preparing auditory cortex for optimized feedback monitoring as reflected in online speech corrections. Combining electroencephalographic PSAM data from a previous data set with new acoustic measures of the same participants' speech, we asked whether individual speakers' extent of PSAM is correlated with the implementation of within-vowel articulatory adjustments during /b/-vowel-/d/ word productions. Online articulatory adjustments were quantified as the extent of change in inter-trial formant variability from vowel onset to vowel midpoint (a phenomenon known as centering). This approach allowed us to also consider inter-trial variability in formant production and its possible relation to PSAM at vowel onset and midpoint separately. Results showed that inter-trial formant variability was significantly smaller at vowel midpoint than at vowel onset. PSAM was not significantly correlated with this amount of change in variability as an index of within-vowel adjustments. Surprisingly, PSAM was negatively correlated with inter-trial formant variability not only in the middle but also at the very onset of the vowels. Thus, speakers with more PSAM produced formants that were already less variable at vowel onset. Findings suggest that PSAM may reflect processes that influence speech acoustics as early as vowel onset and, thus, that are directly involved in motor command preparation (feedforward control) rather than output monitoring (feedback control).

KANO+QFD 모델을 기반으로 IoT 반려묘 음수기의 디지털 터미널 디자인 방향 제안

The pet supplies market is witnessing a gradual expansion, with a particularly notable increase in demand for intelligent IoT-based pet management products. In this context, consumer demands are shifting from mere interest to desires for experiences and emotional engagements. This study aims to address the prevalent issue in the current market where the majority of pet water dispensers focus solely on basic functionalities, neglecting deep user experience design. The research begins by constructing a user journey map to analyze the behavioral paths of users interacting with mobile and IoT water dispensers, thereby deriving user needs. Subsequently, a qualitative analysis based on the Kano model is conducted to determine the priorities of user satisfaction and requirements, and a Quality Function Deployment(QFD) chart is developed to analyze and quantify the importance of design requirements. The findings indicate that the importance weights of water intake statistics design requirements and water quality monitoring feedback design requirements are the highest, becoming key design considerations in the digital end-to-end system design process. Therefore, this study proposes a design direction for the IoT smart drinking system platform through the combined application of the Kano and QFD models, offering guidance on the remote user end system design for IoT pet water dispensers from a user needs perspective.

CHAT MARKETING'S IMPACT ON CUSTOMER SUPPORT SATISFACTION IN FINANCIAL STARTUPS

The subject of this paper is the influence of chat marketing on customer support satisfaction. The purpose of the study is to determine the relationship between the use of chat marketing and customer support satisfaction, as well as to identify the essential factors that contribute to this relationship. Furthermore, this paper aims to provide theoretical insights into the influence of chat marketing on customer support satisfaction in financial startups. Many companies struggle to achieve the most efficient chat session and are uncertain about the best strategies, factors and types of chat to implement. Therefore, these findings will help financial startups to theoretically understand the aspects of chat marketing and customer support satisfaction. The object of this article is the impact of chat marketing on customer satisfaction, and the purpose is to reveal theoretical knowledge that will help identify the factors that contribute to chat marketing and customer satisfaction in financial startups. This paper discusses the importance of customer support for financial startups and the different approaches to providing customer support through emotional means. In addition, this paper highlights the importance of customer satisfaction through trust and commitment to build a positive relationship with the customer. Furthermore, this paper demonstrated the framework behind chat marketing, which includes the use of different tools such as live chat platforms, chatbots and live agents or human representatives that help to achieve customer satisfaction through customer support. Moreover, using methods such as: observation, survey and regression analysis, this paper has demonstrated the importance of customer centricity in the financial sector to provide key essentials for employees as incentives that maintain customer satisfaction, while ensuring the flexibility of real-time communication with chatbots or live agents, with the need to have the ease of use and flexibility and various other factors to contribute to customer support satisfaction. As a result, the paper emphasises that in order to increase customer satisfaction with financial start-ups, it is crucial to maintain a low response rate by providing fast and reliable customer assistance. Developing and regularly updating chatbots to facilitate smooth transitions to live agents in complex situations is essential. Implementing an effective ticketing system to automate and monitor issue resolution is essential, with a focus on resolving issues during chat interactions whenever possible. Adopting a hybrid strategy that combines live agents and chatbots helps to meet diverse customer needs in a timely manner. Investing in comprehensive training for live agents is necessary to ensure professional and timely service. In addition, continuous monitoring and analysis of customer feedback plays a key role in improving overall business performance.

Higher education and graduates' success in the labor market

This study reflects one of the aspects of social stratification; the relationship between education and economic remuneration is traced. The purpose of the study is to reveal the correlation of skills and competencies acquired during education and the graduates' employment. Over time, universities have begun to pay more attention to social and cognitive skills that are considered important for the modern labor market: critical thinking, teamwork, communication, problem solving without micromanagement. The data show the current state of affairs in higher education and in the labor market, where universities, to a greater extent, focus on competencies related to research and scientific activities, without relying on the opinions of employers. On the other hand, employers want to see graduates with a set of professional competencies that will allow them to perform their job duties without a long period of internal training. The work uses general scientific approaches to the study of the transformation of higher education, in particular, the method of institutional analysis was applied. The analyzed data allow us to take a look at the tasks that higher education faces at this stage. It was found that the link between diplomas and real skills, as well as economic rewards, is optional. Over time, universities have begun to pay more attention to social and cognitive skills that are considered important for the modern labor market: critical thinking, teamwork, communication, problem solving without micromanagement. At the same time, the problem of the slow response of higher education to the changing reality remains, in this regard, recommendations are given on monitoring feedback from graduates and employers (using quantitative and qualitative methods), so that universities will be able to adapt the courses taught more effectively. Especially important is the interaction of universities with public and private organizations that will immerse students in real work processes corresponding to the specialty.

Smart EV Charging Station Monitoring and User Feedback System

The efficiency and reliability of electric vehicle (EV) charging stations are pivotal for user satisfaction and broader EV adoption. This research introduces a system that monitors essential parameters of EV charging stations, such as temperature, voltage, and current, using advanced sensors and the ESP32 microcontroller. The collected data is transmitted through the Bylnk platform, for real-time monitoring and analysis. To enhance the user experience, a mobile application has been developed, allowing users to review these critical details and receive notifications. The app provides a detailed overview of each charging station's performance, helping users identify the best stations based on historical data and user reviews. By offering insights into the operational status and efficiency of charging stations, the system aids users in making informed decisions about where to charge their vehicles. This integration of real-time monitoring with user-friendly mobile access improves user convenience and optimizes the EV charging infrastructure management.

Improving Cardiopulmonary Resuscitation (CPR): Integrating Internet of Medical Things (IoMT) and Machine Learning (ML) - A Review

This review explores the pivotal role of cardiopulmonary resuscitation (CPR) in the chain of survival during cardiac events and delves into the challenges and advancements in CPR techniques and technologies. While manual interventions and automated devices have improved survival rates, they present limitations such as rescuer fatigue and lack of real-time feedback. The emergence of the Internet of Medical Things (IoMT) and machine learning (ML) algorithms offers transformative opportunities to enhance CPR rescue efforts by facilitating real-time data acquisition, remote monitoring, and adaptive feedback. However, challenges including interoperability and data security must be addressed for effective integration. The study discusses major findings from related literature, gaps in research, and future directions, highlighting the potential of integrating IoMT and ML to improve CPR outcomes and revolutionize healthcare delivery. Finally, it concludes with recommendations for optimizing CPR strategies and advancing technology for better patient outcomes.

Modeling rehabilitation dataset to implement effective AI assistive systems

The demand for automated systems monitoring and supporting patients in their home-based recovery programs is substantial. While emerging technologies have been proposed as potential solutions to enhance at-home patient care, limited systems are in place due to their challenges in offering real-time monitoring and corrective feedback. Most proposed methodologies provide an overall measure or score for the executed movement. The proposed work involves the adaptation of an existing published dataset to cater to the needs of a system capable of remotely assisting patients, effectively acting as a virtual physical therapist able to provide corrective feedback. A dataset containing a set of three simple exercises for shoulder rehabilitation was processed. Each movement was meticulously annotated for temporal and categorical motion domains to monitor exercise execution in terms of the range of motion completeness and to evaluate compensatory movement patterns. This work carries substantial significance by offering a standardized and easily accessible model for human movement data, thus fostering the advancement of digital assistive systems designed to support home-based rehabilitation programs.

The importance of seeking feedback for benefiting from feedback: A case of second language writing

AbstractThis study explored the role of feedback‐seeking behaviors (FSB) in how English‐as‐a‐second language (ESL) learners benefit from written corrective feedback (WCF). Seventy‐six learners enrolled in an ESL writing course at a major university in the United States completed an FSB questionnaire, wrote a narrative essay, received WCF on their essays, and were given the opportunity to seek further feedback while revising their essays. Five writing measures were used to assess the quality of the revised essays and code the WCF provided. Paired‐samples t‐tests showed that the students made statistically significant improvements in all but one (content) of the target measures. Multiple regression analyses showed that WCF predicted improvements in only one measure (language use), whereas the learners’ feedback monitoring (an implicit feedback‐seeking strategy involving attending to, processing, and using feedback) predicted the organization, vocabulary, language use, mechanics, as well as the overall quality of the students’ revisions. The results suggest that students benefit from WCF only if they seek, process, and use it. These findings confirmed the importance of feedback monitoring in how students benefit from WCF and support a learner‐centered perspective that views students as proactive agents in the feedback process. Theoretical and pedagogical implications are discussed.

A unified framework for digital twin development in manufacturing

The concept of digital twin (DT) is undergoing rapid transformation and attracting increased attention across industries. It is recognised as an innovative technology offering real-time monitoring, simulation, optimisation, accurate forecasting and bi-directional feedback between physical and digital objects. Despite extensive academic and industrial research, DT has not yet been properly understood and implemented by many industries, due to challenges identified during its development. Existing literature shows that there is a lack of a unified framework to build DT, a lack of standardisation in the development, and challenges related to coherent goals of DT in a multi-disciplinary team engaged in the design, development and implementation of DT to a larger scale system. To address these challenges, this study introduces a unified framework for DT development, emphasising reusability and scalability. The framework harmonises existing DT frameworks by unifying concepts and process development. It facilitates the integration of heterogeneous data types and ensures a continuous flow of information among data sources, simulation models and visualisation platforms. Scalability is achieved through ontology implementation, while employing an agent-based approach, it monitors physical asset performance, automatically detects faults, checks repair status and offers operators feedback on asset demand, availability and health conditions. The effectiveness of the proposed DT framework is validated through its application to a real-world case study involving five interconnected air compressors located at the Connected Facility at Devonport Royal Dockyard, UK. The DT automatically and remotely monitors the performance and health status of compressors, providing guidance to humans on fault repair. This guidance dynamically adapts based on feedback from the DT. Analyses of the results demonstrate that the proposed DT increases the facility’s operation availability and enhances decision-making by promptly and accurately detecting faults.

EXplainable AI for routine outcome monitoring and clinical feedback

AbstractArtificial intelligence (AI), specifically machine learning (ML), is adept at identifying patterns and insights from vast amounts of data from routine outcome monitoring (ROM) and clinical feedback during treatment. When applied to patient feedback data, AI/ML models can assist clinicians in predicting treatment outcomes. Common reasons for clinician resistance to integrating data‐driven decision‐support tools in clinical practice include concerns about the reliability, relevance and usefulness of the technology coupled with perceived conflicts between data‐driven recommendations and clinical judgement. While AI/ML‐based tools might be precise in guiding treatment decisions, it might not be possible to realise their potential at present, due to implementation, acceptability and ethical concerns. In this article, we will outline the concept of eXplainable AI (XAI), a potential solution to these concerns. XAI refers to a form of AI designed to articulate its purpose, rationale and decision‐making process in a manner that is comprehensible to humans. The key to this approach is that end‐users see a clear and understandable pathway from input data to recommendations. We use real Norse Feedback data to present an AI/ML example demonstrating one use case for XAI. Furthermore, we discuss key learning points that we will employ in future XAI implementations.