Feedback Tool Research Articles

Pneumonia diagnosis performance in the emergency department: a mixed-methods study about clinicians' experiences and exploration of individual differences and response to diagnostic performance feedback.

We sought to (1) characterize the process of diagnosing pneumonia in an emergency department (ED) and (2) examine clinician reactions to a clinician-facing diagnostic discordance feedback tool. We designed a diagnostic feedback tool, using electronic health record data from ED clinicians' patients to establish concordance or discordance between ED diagnosis, radiology reports, and hospital discharge diagnosis for pneumonia. We conducted semistructured interviews with 11 ED clinicians about pneumonia diagnosis and reactions to the feedback tool. We administered surveys measuring individual differences in mindset beliefs, comfort with feedback, and feedback tool usability. We qualitatively analyzed interview transcripts and descriptively analyzed survey data. Thematic results revealed: (1) the diagnostic process for pneumonia in the ED is characterized by diagnostic uncertainty and may be secondary to goals to treat and dispose the patient; (2) clinician diagnostic self-evaluation is a fragmented, inconsistent process of case review and follow-up that a feedback tool could fill; (3) the feedback tool was described favorably, with task and normative feedback harnessing clinician values of high-quality patient care and personal excellence; and (4) strong reactions to diagnostic feedback varied from implicit trust to profound skepticism about the validity of the concordance metric. Survey results suggested a relationship between clinicians' individual differences in learning and failure beliefs, feedback experience, and usability ratings. Clinicians value feedback on pneumonia diagnoses. Our results highlight the importance of feedback about diagnostic performance and suggest directions for considering individual differences in feedback tool design and implementation.

Incorporating a deep‐learning client outcome prediction tool as feedback in supported internet‐delivered cognitive behavioural therapy for depression and anxiety: A randomised controlled trial within routine clinical practice

AbstractIntroductionMachine learning techniques have been leveraged to predict client psychological treatment outcomes. Few studies, however, have tested whether providing such model predictions as feedback to therapists improves client outcomes. This randomised controlled trial examined (1) the effects of implementing therapist feedback via a deep‐learning model (DLM) tool that predicts client treatment response (i.e., reliable improvement on the Patient Health Questionnaire‐9 [PHQ‐9] or Generalized Anxiety Disorder‐7 [GAD‐7]) to internet‐delivered cognitive behavioural therapy (iCBT) in routine clinical care and (2) therapist acceptability of this prediction tool.MethodsFifty‐one therapists were randomly assigned to access the DLM tool (vs. treatment as usual [TAU]) and oversaw the care of 2394 clients who completed repeated PHQ‐9 and GAD‐7 assessments.ResultsMultilevel growth curve models revealed no overall differences between the DLM tool vs. TAU conditions in client clinical outcomes. However, clients of therapists with the DLM tool used more tools, completed more activities and visited more platform pages. In subgroup analyses, clients predicted to be ‘not‐on‐track’ were statistically significantly more likely to have reliable improvement on the PHQ‐9 in the DLM vs. TAU group. Therapists with access to the DLM tool reported that it was acceptable for use, they had positive attitudes towards it, and reported it prompted greater examination and discussion of clients, particularly those predicted not to improve.ConclusionAltogether, the DLM tool was acceptable for therapists, and clients engaged more with the platform, with clinical benefits specific to reliable improvement on the PHQ‐9 for not‐on‐track clients. Future applications and considerations for implementing machine learning predictions as feedback tools within iCBT are discussed.

Designing Communication Feedback Systems To Reduce Healthcare Providers' Implicit Biases In Patient Encounters.

Healthcare providers' implicit bias, based on patients' physical characteristics and perceived identities, negatively impacts healthcare access, care quality, and outcomes. Feedback tools are needed to help providers identify and learn from their biases. To incorporate providers' perspectives on the most effective ways to present such feedback, we conducted semi-structured design critique sessions with 24 primary care providers. We found that providers seek feedback designed with transparent metrics indicating the quality of their communication with a patient and trends in communication patterns across visits. Based on these metrics and trends, providers want this feedback presented in a dashboard paired with actionable, personalized tips about how to improve their communication behaviors. Our study provides new insights for interactive systems to help mitigate the impact of implicit biases in patient-provider communication. New systems that build upon these insights could support providers in making healthcare more equitable, particularly for patients from marginalized communities.

COVID Recovery Laparoscopic Simulation Program for Gynecological Registrars—Trainee Perceptions of Regional Model

Study ObjectiveThe acquisition of gynecological operating skills can be challenging for trainees given the conflicting demands of clinical work. Alternative models of surgical skill training such as laparoscopic simulation is, therefore, required. This study demonstrates the development of a regional gynecological surgery laparoscopic simulation program and trainee perceptions of such an approach. DesignAn intervention-based cohort study. SettingA regional model based in West Midlands training region. Patients/ParticipantsResponses from 64 trainees in the training region who participated in this regional program were included. InterventionsA 3-stream curriculum was developed to deliver key training outcomes as required by the Royal College of Obstetricians and Gynaecologists (RCOG) core curriculum as a component of a COVID Recovery Program. Courses were held in 7 teaching hospitals. Courses consisted of both theory and practical teaching. MeasurementsA structured feedback tool was used to collect trainee perceptions of the program. Trainee satisfaction was measured on the Likert scale of 1 to 3. A qualitative thematic analysis was conducted with rank-order analysis of coded free-text responses. Main ResultsOverall, the majority of trainees 92% (n = 58/64) were very satisfied with the course. Rank-order analysis demonstrated hands-on-practice to be the key perceived benefit of laparoscopic simulation among basic and intermediate trainees, while feedback on procedural skills was felt most useful among advanced trainees. ConclusionA regional approach to laparoscopic simulation training is both achievable and acceptable. Trainee perceptions of usefulness are altered by seniority and experience. This should be accounted for in the development of laparoscopic simulation programs.

Effects of short foot exercises with ultrasound bio-feedback on motor learning and foot alignment: A double blinded randomized control trial.

Short foot exercises (SFE) take a long time to master and require a feedback tool to improve motor learning. This study aimed to investigate the effect of bio-feedback of talonavicular joint movements in learning SFE with ultrasound (US) imaging. This study included thirty-one healthy volunteers and was designed as a double-blind randomized control trial. Subjects were randomly assigned to one of two groups: the control group, which performed SFE under verbal instruction, and the US bio-feedback (USBF) group, which performed SFE with real-time bio-feedback of the talonavicular joint alignment. All subjects underwent two sessions of 5 minutes each, and SFE was performed as a self-exercise, between sessions, for one week. The difference in foot length and navicular height were assessed at baseline, after Session1, before Session2, and one week after Session2. These differences were compared between the two groups using the Mann-Whitney U test. In terms of navicular height change, the USBF group (7.5 ± 4.3 mm) was significantly higher than the control group (4.2 ± 3.3 mm) one week after session 2 (p= 0.04, effect size = 0.86). SFE with USBF is an effective intervention for performing SFE.

Social Infrastructure Provision Assessment as a Tool for Implementing Provincial Socio-Economic Policy

This article outlines the concept of a tool for the implementation of socio-economic policy and considers the assessment of social infrastructure provision of the population as one of them, namely a systemic feedback tool. The paper discusses the importance of social infrastructure for the socio-economic development of the province and the methods of assessing the provision of the population with it, identifies the main stages of this procedure and its relationship with other key tools, which is presented in the form of a graphical scheme; discusses the main tasks and features of rationing of indicators as a procedure for developing a reference measure of assessment; discusses and systematizes the main approaches to the calculation of socio-infrastructural provision. At the same time, not infrastructure objects, but more fractional units — elements — are taken as calculation units. It is proposed to systematize various calculation methods in the form of a matrix depending on the arguments used.

LEGAL ASSISTANCE FOR MARGINALIZED COMMUNITIES

To address the major obstacles that rural residents and members of minority communities have in obtaining essential legal information. By giving underprivileged populations access to a user-friendly, multilingual, and culturally aware chatbot platform, we hope to empower them. Through the application of sophisticated Natural Language Processing (NLP) technology and an extensive legal library, the chatbot is engineered to provide customized legal advice that is specific to the rights, responsibilities, and legal procedures that are pertinent to the distinct communities. We have integrated community feedback tools into our platform to continuously enhance relevance and accuracy.

Enhancing Study Experience using Handwritten Character and Digit Recognition and Text Summarization

The integration of Handwritten characters and, Digit Recognition and Deep Learning in education heralds a transformative era in learning methodologies. This abstract delves into the multifaceted benefits derived from the amalgamation of these technologies, redefining the educational landscape. Handwritten characters and Digit Recognition technology facilitates the seamless digitization of handwritten content, transcending the limitations of manual note-taking. Its introduction into educational frameworks enhances accessibility, promotes organization, and augments the searchability of diverse educational materials. Deep Learning, acting in tandem with Handwriting Recognition, amplifies these advantages manifold. Deep learning powered study assistants offer personalized learning experiences tailored to individual needs, adapting to varied learning styles. Additionally, these assistants facilitate collaborative opportunities, providing real time feedback and evaluation tools that revolutionize the learning process. Key Words: Image recognition, CNN, RNN, LSTM, Neural Networks, SVM, Deep Learning, Convolutional layer, PreLU, ReLU, Text Summarization, Extractive text summarization, Tokenization.

Use of eye-gaze technology feedback by assistive technology professionals: findings from a thematic analysis

Purpose: Eye-gaze technology offers professionals a range of feedback tools, but it is not well understood how these are used to support decision-making or how professionals understand their purpose and function. This paper explores how professionals use a variety of feedback tools and provides commentary on their current use and ideas for future tool development.Methods and Materials: The study adopted a focus group methodology with two groups of professional participants: those involved in the assessment and provision of eye-gaze technology (n = 6) and those who interact with individuals using eye-gaze technology on an ongoing basis (n = 5). Template analysis was used to provide qualitative insight into the research questions.Results: Professionals highlighted several issues with existing tools and gave suggestions on how these could be made better. It is generally felt that existing tools highlight the existence of problems but offer little in the way of solutions or suggestions. Some differences of opinion related to professional perspective were highlighted. Questions about automating certain processes were raised by both groups.Conclusions: Discussion highlighted the need for different levels of feedback for users and professionals. Professionals agreed that current tools are useful to identify problems but do not offer insight into potential solutions. Some tools are being used to draw inferences about vision and cognition which are not supported by existing literature. New tools may be needed to better meet the needs of professionals and an increased understanding of how existing tools function may support such development.

Development of a Mobile application for Effective Mental Health Intervention

The global surge in depression and anxiety, intensified by challenges such as cost and stigma, emphasizes the pressing need for accessible, evidence-based digital solutions. The research centers on the creation of a mobile application specifically designed to address mental health challenges. By integrating cognitive behavioral therapy techniques and features like appointment bookings and mindfulness feedback tools, the app is positioned to improve user outcomes. Utilizing platforms like React Native and React, combined with NestJS for enhanced back-end security, the application adheres to the rigorous standards required for mental health interventions. Collaborative efforts with experts, notably the counseling unit of IIUM, ensure the app's alignment with contemporary best practices and research. Preliminary findings indicate a promising tool with the potential to address the global mental health treatment disparity.

Practical aspects of the application of the remote physical rehabilitation support system under audiovisual self-control

The problem of developing a system for monitoring the health state of people with chronic non-communicable disease (CNCD) is considered. To improve the effectiveness of medical care for such patients, it is proposed to use long-term remote monitoring of their health outside the medical institution and, based on the analysis of personalized patterns of dynamics of diagnostically significant indicators for the state of remission and exacerbation of the disease, to ensure the possibility of detecting its exacerbation at an early stage of development. Diseases of the musculoskeletal system remain a serious problem for modern man. A long-term physical rehabilitation is required for the treatment of such condition. At the outpatient stage, it is carried out by the patient himself. The patient doesn’t have the possibility of qualified control over the correctness of performing rehabilitation exercises at home. The remote monitoring system will allow to assess the patient's condition during the outpatient period objectively. However, existing technical solutions are difficult to use for an ordinary user. Therefore, a convenient system is needed that could increase the availability of medical services without the use of additional equipment. The presence of a motion sensor system in a smartphone increases the possibility of using this technical tool to monitor motor activity in everyday life. Purpose of the present paper: to assess the effectiveness of a remote physical rehabilitation sup-port system through its implementation in a medical institution involving patients who have under-gone a rehabilitation course during their inpatient period, including the performance of a specific set of rehabilitation exercises, and to explore the participants' experiences using a mobile application and remote physical rehabilitation support system. During the development on the NEFITNESS project, the team developed and successfully implemented in clinical practice a system for remote physical rehabilitation support under audio-visual self-control. The support system, based on an inertial sensor, a mobile application, and an online platform, led to a 11% increase in muscle endurance over six months of regular sessions compared to standard rehabilitation techniques. The following objectives were achieved: a) experience with the mobile application and physical rehabilitation support system was studied; b) quantitative characteristics of rehabilitation trajectory success were obtained through exercise performance feedback; c) participant engagement in the topic of remote rehabilitation support increased; d) accessibility of rehabilitation technologies for a wide range of users improved. The practical possibility of using a remote rehabilitation and motor activity recovery system under audiovisual self- control is presented, where a smartphone served as a feed-back tool based on an integrated motion sensor to ensure the correct execution of rehabilitation exercises. Within the framework of joint scientific research carried out at the Research Institute of Emergency Pediatric Surgery and Traumatology under the project "Development of a complex of remote physical rehabilitation of children", the remote physical rehabilitation system and mobile application "NEFITNESS" were tested. The developed system resulted in increased muscle endurance over six months of regular sessions can be scaled and applied to address various tasks related to the digital representation of restorative exercises for other segments of the musculoskeletal system.

Patient-centered perspectives on perioperative care

IntroductionThe collection and evaluation of patient-reported outcomes is essential to the development of patient and family centered care. Current patient surveying techniques are limited by delayed response times and restriction to specific health systems. The use of random-domain intercept technology (RDIT), by Real-Time Interactive World-Wide Intelligence (RIWI, Toronto, ON, Canada) mitigates current barriers by creating a dynamic real-time feedback environment and providing a mass sampling technique.MethodsRDIT was employed to survey a wide sample of respondents across the United States (US). Respondents who self-identified as having had a surgical procedure or cared for someone having a surgical procedure were included in the analysis.Results1,004 participants completed the survey and answered questions regarding demographics, perioperative details, sentiments on postoperative recovery, postoperative clinical endpoints, sentiments on healthcare professionals, and opinions on future surgical care.DiscussionThe results of this cross-sectional study identified areas with potential for improvement in the patient perioperative experience that could improve the patient experience. This novel use of RDIT provided a valuable tool for real-time feedback and mass sampling allowing the creation of a dynamic healthcare environment that fosters timely and targeted improvements to patient experiences and outcomes.

Electronic feedback alone vs. electronic feedback plus in-person debriefing, for a serious game designed to teach novice anesthesiology residents to perform general anesthesia for cesarean delivery: a randomized controlled trial.

EmergenCSimTM, is a novel researcher-developed serious game (SG) with an embedded scoring and feedback tool that reproduces an obstetric operating room environment. The learner must perform general anesthesia (GA) for emergent cesarean delivery (CD) for umbilical cord prolapse. The game was developed as an alternative teaching tool because of diminishing real-world exposure of anesthesiology trainees to this clinical scenario. Traditional debriefing (facilitator-guided reflection) is considered to be integral to experiential learning but requires the participation of an instructor. The optimal debriefing methods for SGs have not been well-studied. Electronic feedback is commonly provided at the conclusion of SGs, so we aimed to compare the effectiveness of learning when an in-person debrief is added to electronic feedback compared to using electronic feedback alone. We hypothesized that an in-person debriefing in addition to the SG-embedded electronic feedback will provide superior learning than electronic feedback alone. Novice 1st year anesthesiology residents (CA-1) (n=51) (i) watched a recorded lecture on GA for emergent CD, (ii) took a 26-item multiple-choice question (MCQ) pre-test, and (iii) played EmergenCSimTM (maximum score 196.5). They were randomized to either the control group which experienced the electronic feedback alone (Group EF, n=26) or the intervention group, which experienced the SG-embedded electronic feedback and an in-person debriefing (Group IPD+EF, n=25). All subjects played the SG a 2nd time, with instructions to try to increase their score, then they took a 26-item MCQ post-test. Pre-and post-tests (maximum score of 26 points each), were validated parallel forms. For Groups EF and IPD+EF respectively, mean pre-test scores were18.6 (SD 2.5) and 19.4 (SD 2.3), and mean post-test scores were 22.6 (SD 2.2) and 22.1 (SD 1.6); F=1.8, P =.19. SG scores for Groups EF and IPD+EF respectively were - mean 1st play SG scores of 135 (SE 4.4) and 141 (SE 4.5), and mean 2nd play SG scores were 163.1 (SE 2.9) and 173.3 (SE 2.9); F= 137.7, P < .001. Adding an in-person debriefing experience led to greater improvement in SG scores, emphasizing the learning benefits of this practice. Improved SG performance in both groups suggests that SGs have a role as independent, less resource-intensive educational tools. None.

DEVELOPMENT OF A WEB APPLICATION FOR INTELLIGENT ANALYSIS OF CUSTOMER REVIEWS USING A MODIFIED SEQ2SEQ MODEL WITH AN ATTENTION MECHANISM

Machine learning, and neural networks in particular, are having a huge impact on business and marketing by providing convenient tools for analytics and customer feedback. The article proposes an intelligent analysis of customer feedback based on the use of a modified seq2seq deep learning model. Since the basic seq2seq model has a significant disadvantage - the inability to concentrate on the main parts of the input sequence, the results of machine learning may give an inadequate assessment of customer feedback. This disadvantage is eliminated by means of a model proposed in the work called the “attention mechanism”. The model formed the basis for the development of a web application that solves the problem of flexible interaction with customers by parsing new reviews, analyzing them and generating a response to a review using a neural network.

Exploring the Opportunities of Implementing Artificial Intelligence (AI) Technology for Teaching Arabic to Non-Native Speakers: A Theoretical Approach

Language is a tool through which humans exchange their ideas and thoughts among themselves. It helps humans express their feelings and needs. Because of the global pervasion of Islam, the Arabic language unites Arabs and non-Arab Muslims under one religion. In Islamic perception, the Arabic language holds vital importance in Muslims' lives. Thus, Muslims consider learning and teaching Arabic in their day-to-day lives. Learning Arabic presents several challenges due to its complex grammar, diverse dialects, unfamiliar script, and significant differences from many other languages in terms of structure and vocabulary. These difficulties can make learning the language a demanding task for learners. This research aims to explore the importance of learning and teaching the Arabic language in the contemporary world, examine theoretical frameworks for incorporating AI in teaching Arabic to non-native speakers, and evaluate existing AI tools for it. The methodology adopted is qualitative. Primary and secondary data were collected. This research concludes that Arabic, as a language, is deeply intertwined with Islamic religious and cultural heritage, while also serving as a vital means of global communication. Teaching Arabic to non-native speakers poses unique challenges, but the implementing of AI technology offers promising solutions. AI enables personalized learning experiences, instant feedback, and diverse learning tools, ranging from text generation to virtual reality, enhancing language acquisition. By combining AI with traditional teaching methods, educators can address these challenges and maximize learning outcomes, ultimately shaping the future of Arabic language teaching to be more accessible, effective, and engaging worldwide.

Enhancing ESP Learning: Investigating Students' Perspectives on Real-Time Written Corrective Feedback Delivery via Google Docs

The present research aims to investigate the practice of real-time or synchronous written corrective feedback (SWCF) delivery via Google Docs as a platform to deliver feedback, known as a linguistic feedback tool (LiFT) in ESP (English for Specific Purposes) classes, students’ preferences and viewpoints of SWCF. The research employed a qualitative case study involving 15 students from an advanced English correspondence course in a non-English department of a private university in Depok, West Java, Indonesia. Open-ended questionnaires, document analysis, and semi-structured interviews were used to gather the data. The findings revealed that auto-generated and human feedback are used to deliver SWCF via Google Docs, inferring that those features can build students' awareness of language aspects, content, and context in composing professional documents. Direct feedback was delivered more frequently, regarding the learning time efficiency and the students’ ability to identify errors. Further, the students preferred frequent SWCF, direct and indirect feedback, and correcting delivery methods. Finally, they considered that SWCF delivered via Google Docs assists them in improving letter accuracy. Their preferences and viewpoints might allow the lecturer to choose appropriate feedback delivery strategies and material reinforcement. The present research implies a balanced use of auto-generated and human feedback from LiFT in SWCF delivery. Evaluating the effectiveness of SWCF delivery via Google Docs for different levels of achievers in ESP classes might be a potential area of future research. Other LiFTs’ infusion into ESP classes is also worth researching to provide language instructors with alternative applications for SWCF delivery.

Research on Implementation and Management Evaluation System for Protection and Utilization of Traditional Villages:A Case Study of Traditional Chinese Villages in Lingnan Waterfront

The protection and utilization of traditional villages have seen an increase around China since the country issued the catalogue of tra-ditional village protection. However, the effect of the protection and utilization of traditional villages has not been remarkable, as physical decline and constructive failure of traditional villages occur frequently. Consequently, traditional villages are faced with challenges through a lack of evalua-tion and poor construction management. As an important feedback tool, conducting an evaluation can shed light on achievements in implementation of protection and utilization of traditional villages and effectively reveal problems impeding such implementation. Establishing a scientific evalua-tion system for traditional village protection and utilization is of great significance to the sustainable protection and utilization of traditional villages. Currently, China has not introduced an evaluation system for the implementation of traditional village protection and utilization, and only limited re-search has been conducted in this area. Existing studies have emphasized the evaluation of the implementation of protection and utilization but pay only limited attention to the implementation process. Based on a comprehensive analysis of the characteristics of implementation planning and man-agement, and of relevant studies on this topic, the current study examined the methods used to evaluate the effect of protection and utilization, the protection measures and supervision structures from the perspective of achievements and processes around implementation. Moreover, a case study based on the traditional villages in Lingnan Waterfront was used in the current study, with a focus on the local characteristics of the implementation and management of traditional village protection and utilization. On this basis, an evaluation index system is established, which comprises settlement protection and planning implementation, an active utilization degree, new-old settlement collaboration, protection measures, implementation supervi-sion and management. In Lingnan Waterfront, 44 national-level traditional villages are chosen as samples to examine the implementation and man-agement of traditional village protection and utilization. The results shows that the implementation of traditional village protection and utilization in Lingnan Waterfront poses obvious non-equilibrium challenges. Concerning the implementation of traditional village protection and utilization, there are three levels: Good, moderate and poor. Government policy support, urbanization and industrialization represent important factors that influence the implementation and management of traditional village protection and utilization. For traditional villages that are protected, utilized and managed well, the implementation and management of their protection and utilization are aligned with the early attention paid by the Chinese Government to the situation, as well as the impacts of both industrialization and urbanization. Traditional villages which exhibit moderate implementation and man-agement of protection and utilization are often influenced by industrialization and urbanization in rural areas in the Pearl River Delta. Driven by demolition of old houses and the construction of new housing sites for villagers facing a land shortage, the rural landscape and appearance of villages have undergone different degrees of damage. For traditional villages with poor implementation and management of protection and utilization, the locations of such villages are significantly influenced by urbanization or industrialization in the Pearl River Delta. As traditional villages are attrac-ted by the economic benefits, protection and utilization encounter severe impacts caused by urbanization and the construction of industrial parks in rural areas, and are stuck in a dilemma. Ultimately, the implementation and management evaluation system of traditional village protection and utili-zation is established in the current study based on the case study of Lingnan Waterfront. The results of the study not only deepen the existing body of knowledge on the implementation evaluation of traditional villages, but also provide a reference point for action to address the situation.

Study on the Impact of Utilizing ChatGPT and Other AI Tools for Feedback in EAP Writing Classrooms on the Discursive Writing Performance of English Major Students

This study aims to delve into the impact of utilizing ChatGPT and other AI tools on the discourse writing performance of English major students, as well as to explore their potential value and future prospects in the field of English education. With the continuous development and application of artificial intelligence technology, AI tools have gradually demonstrated their unique advantages and potential in language learning and writing instruction. However, for English major students, how to effectively utilize these technological tools to enhance their discourse writing abilities remains a subject of considerable interest. In the field of language learning, AI technology has made significant strides in vocabulary acquisition, grammar correction, and discourse generation. ChatGPT, as a natural language generation model, possesses strong language comprehension and generation capabilities and is widely used in dialogue generation, text generation, and other areas. In English education, ChatGPT can provide personalized writing guidance and feedback to students, helping them clarify their thoughts and improve their expression skills. Additionally, other AI tools such as speech recognition technology and text-to-speech technology offer more possibilities for students' English learning and writing. However, despite the numerous advantages demonstrated by AI tools in discourse writing instruction for English major students, their application also faces challenges and limitations. For instance, AI tools still have limitations in understanding academic language and specialized terminology, and they cannot fully replace human professional judgment and language application abilities. Furthermore, the widespread adoption and use of AI tools require addressing technical challenges and adaptation issues in teaching practice to ensure their effective application in education. In conclusion, this study will comprehensively explore the actual effects and potential value of AI tools in discourse writing instruction for English major students through a combination of literature review and empirical research methods, providing new insights and guidance for teaching practice and future research endeavors.

Improving inpatient hyperglycaemia in non-critically ill adults in resident wards through audit and feedback

Inpatient hyperglycaemia is associated with an increase in morbidity and mortality, number of rehospitalisations and length of hospitalisation. Although the advantages of proper glycaemic control in hospitalised patients with diabetes...

Automated Grading and Feedback Tools for Programming Education: A Systematic Review

We conducted a systematic literature review on automated grading and feedback tools for programming education. We analysed 121 research papers from 2017 to 2021 inclusive and categorised them based on skills assessed, approach, language paradigm, degree of automation, and evaluation techniques. Most papers assess the correctness of assignments in object-oriented languages. Typically, these tools use a dynamic technique, primarily unit testing, to provide grades and feedback to the students or static analysis techniques to compare a submission with a reference solution or with a set of correct student submissions. However, these techniques’ feedback is often limited to whether the unit tests have passed or failed, the expected and actual output, or how they differ from the reference solution. Furthermore, few tools assess the maintainability, readability, or documentation of the source code, with most using static analysis techniques, such as code quality metrics, in conjunction with grading correctness. Additionally, we found that most tools offered fully automated assessment to allow for near-instantaneous feedback and multiple resubmissions, which can increase student satisfaction and provide them with more opportunities to succeed. In terms of techniques used to evaluate the tools’ performance, most papers primarily use student surveys or compare the automatic assessment tools to grades or feedback provided by human graders. However, because the evaluation dataset is frequently unavailable, it is more difficult to reproduce results and compare tools to a collection of common assignments.