Assessment Process Research Articles

Advancements in technology-enhanced assessment in tertiary education

The field of assessment in tertiary education is currently experiencing a significant shift mostly due to the technological advancements. Technology is, in fact, both threatening assessment practices and empowering them when integrated in the assessment process. This special issue discusses the evolution of technology-enhanced assessment in tertiary education focusing on the use of digital resources such as gamification and artificial intelligence to improve learning and evaluation methods. Research findings showcased in this issue delve on the ability of technology-enhanced assessments to promote effective personalised learning experiences while also addressing the ethical dilemmas these innovations bring. The blend of conversations about assessment with AI driven evaluations, immersive simulations and gamified platforms explores the benefits and challenges for teachers and learners alike, not forgetting the importance of sound methodological approaches for both summative and formative assessment. This editorial summarises contributors’ perspectives on how these innovations are changing assessment methods and emphasises the importance of integrating them thoughtfully. At the same time, questions are raised about the evolving role of educators in a technology-driven educational landscape.

Exploring the role of machine learning models in risk assessment models for developed organizations’ management decision policies

The goal of this work was to create and assess machine-learning models for estimating the risk of budget overruns in developed projects. Finding the best model for risk forecasting required evaluating the performance of several models. Using a dataset of 177 projects took into account variables like environmental risks employee skill level safety incidents and project complexity. In our experiments, we analyzed the application of different machine learning models to analyze the risk for the management decision policies of developed organizations. The performance of the chosen model Neural Network (MLP) was improved after applying the tuning process which increased the Test R2 from −0.37686 before tuning to 0.195637 after tuning. The Support Vector Machine (SVM), Ridge Regression, Lasso Regression, and Random Forest (Tuned) models did not improve, as seen when Test R2 is compared to the experiments. No changes in Test R2’s were observed on GBM and XGBoost, which retained same Test R2 across different tuning attempts. Stacking Regressor was used only during the hyperparameter tuning phase and brought a Test R2 of 0. 022219.Decision Tree was again the worst model among all throughout the experiments, with no signs of improvement in its Test R2; it was −1.4669 for Decision Tree in all experiments arranged on the basis of Gender. These results indicate that although, models such as the Neural Network (MLP) sees improvements due to hyperparameter tuning, there are minimal improvements for most models. This works does highlight some of the weaknesses in specific types of models, as well as identifies areas where additional work can be expected to deliver incremental benefits to the structured applied process of risk assessment in organizational policies.

Impacts, Adaptations, and Preparedness Among SNAP-Ed Implementers During the COVID-19 Pandemic: A Multistate Study

Objective: The study examines: 1) impacts of COVID-19 on the work of Supplemental Nutrition Assistance Program – Education (SNAP-Ed) implementers, 2) facilitators and barriers experienced in making adaptations, and 3) factors that would have helped with preparedness to adapt. Design, Setting, and Participants: A purposive sample of 181 SNAP-Ed program implementers from across five states completed a survey or interview based on the study aims. Quantitative data was summarized with descriptive statistics and qualitative data was analyzed thematically. Results: Direct education activities were found to be most impacted during COVID-19 while working to change policies, systems, and environments and indirect education were less impacted. Both positive and negative impacts surfaced. Respondents were most likely to say they were moderately or slightly prepared to make adaptations. Elements of internal organizations, technology, and partners both helped and hindered adapting. Pre-established virtual capacity, formal preparation and planning protocols and procedures, and better and more frequent communication with partners and program leadership would have helped with preparedness. Conclusion: SNAP-Ed needs assessment and implementation processes should integrate and expand upon the lessons learned in the study to enhance future emergency preparedness among program implementers. Program policy should be updated to allow for implementation flexibility during future emergencies.

Environmental study delays in Nepal: A comparison with India and Bangladesh and policy recommendations

This research examines the environmental approval process in Nepal and compares it with those in India and Bangladesh, focusing on the time taken to complete the assessment of environmental study reports. The study conducts three levels of analysis. First, the policy review outlines all relevant environmental policies and laws, highlighting key provisions related to approval timelines. While not every step of the environmental study process has a mandated time frame, the legal time limits for report forwarding and approval are explicitly defined. Second, statistical analysis reveals a significant discrepancy between the statutory and actual approval times. While the legal mandate is just 35 days for Environmental Impact Assessment (EIA), assessing a report actually takes 339 days on average. The trend line reveals that the average time taken is decreasing. However, it is still much higher than India’s average EIA approval time of 64 days. Third, through a comparative analysis of eighteen different components of the environmental assessment process, this paper identifies the possible factors contributing to the delays, such as ambiguous jurisdiction, lack of nodal agency, lack of integrated guidelines, high centralization, and no use of e-governance. This paper also compares the thresholds that trigger the environmental study across eight sectors for three countries and finds that Nepal’s thresholds are narrower than India’s and comparable to Bangladesh’s. Recommendations made include clarifying jurisdictional roles, developing integrated guidelines, establishing specialized nodal agencies, and implementing digital systems. Overall, this research attempts to address flaws in Nepal’s environmental assessment system, offering solutions for efficient environmental regulation and improving the business environment in Nepal.

C.E. Credit Practice Quiz for “Completing the Kindergarten Oral Health Assessment (KOHA) Using an AI-Supported Remote Assessment Process”

C.E. Credit Practice Quiz for “Completing the Kindergarten Oral Health Assessment (KOHA) Using an AI-Supported Remote Assessment Process”

Psychometric properties of an instrument to assess teachers’ practice of differentiated instruction (IATPDI): a confirmatory factor analysis on Bhutanese teachers’ data

IntroductionWe developed the IATPDI questionnaire, comprising seven scales for assessing teachers’ implementation of assessment methods, content, process, and product differentiation, familiarity with and use of various differentiated instruction (DI) strategies, factors influencing DI implementation, and resources used to enhance DI efficacy. This study examined the psychometric properties of the initial four scales with 35 items.MethodsThe questionnaire was administered to a sample of 237 Bhutanese teachers (66.2% male, 33.8% female), and confirmatory factor analysis (CFA) was used for psychometric evaluation.ResultsCFA supported the hypothesized four scales (CFI = 0.911, TLI = 0.903, SRMR = 0.052, RMSEA = 0.059, χ2/df = 1.58). Cronbach’s alpha coefficients ranged from 0.83 to 0.92, and Composite reliability (CR) ranged from 0.86 to 0.92, indicating high internal consistency reliability. Inter-factor correlations supported discriminant validity for most factor pairs, but correlations exceeding 0.85 between some pairs suggested potential overlap, prompting further investigation. Average Variance Extracted (AVE) values for assessment, content, process, and product factors were 0.50, 0.41, 0.53, and 0.51, respectively. While AVE for process and product factors surpassed the commonly accepted threshold (0.50) for convergent validity, the assessment factor approached the threshold and the content factor fell below it, indicating the need for further refinement of its indicators. However, all standardized factor loadings were significant (p < 0.05), confirming convergent validity.DiscussionThese results indicate that the proposed four scales of the IATPDI questionnaire are reliable and valid in measuring the intended constructs. Nevertheless, they also highlight the need for further refinement in identified areas to enhance the tool.

HPB SO55 - Utilisation of Telephone Assessment Clinic for booking Laparoscopic Cholecystectomy: Analysis of outcomes and cancellations

Abstract Background Despite extensive efforts to increase productivity after the Covid pandemic, there still remain areas of limited capacity. Face-to-face outpatient clinic provision is yet to return to normal levels, despite significant increases in referrals. Telephone assessment clinics are attractive solutions as they can be performed outside 'standard' operating hours of the hospital and require fewer support staff, giving increased flexibility to consultant and trainee surgeons who perform them. However, it is unclear whether telephone assessment alone is sufficient to book patients onto operating lists. This study aimed to assess the effectiveness of telephone assessment alone when booking patients for laparoscopic cholecystectomy. Method A standardised telephone assessment process was developed which included mandatory information about symptoms, past medical and surgical history and body weight. All patients referred for consideration of cholecystectomy from January 2023 to January 2024 received either a telephone or face-to-face clinic appointment; those who received a telephone appointment were not routinely offered face-to-face assessment unless clinically indicated. Patients were listed for laparoscopic cholecystectomy from either clinic and all operations took place on pooled operating lists. Operative and clinical outcomes were recorded, including cancellations on the day and unexpected anatomy or procedures. Outcomes between telephone and face-to-face appointments were compared. Results Over the study period, 189 patients had a laparoscopic cholecystectomy from an elective clinic source. Other sources included those patients who had presented acutely and were discharged and those on semi-acute lists. 106 patients had a telephone assessment as their only prior engagement with surgical services prior to surgery. Of these patients, there was one cancellation on the day (due to absence of symptoms between clinic assessment and date of surgery). Nine patients required cholangiography which had not been initially booked. In contrast, three patients who had had face-to-face assessment were cancelled on the day. Conclusion Telephone assessment alone is sufficient to book patients onto an elective theatre list for laparoscopic cholecystectomy, with no adverse impact on cancellations and theatre utilisation. To ensure low cancellation rates and safe surgery, senior decision making is required for telephone assessment clinics (in this study, all appointments were carried out by consultant surgeons) and a standardised proforma to ensure all relevant information is recorded. The low rate of cancellations was maintained even though a 'pooled' operating list is utilised in our hospital. Telephone assessment clinics for first appointments are safe and effective and could help in reducing overall waiting times.

Early Identification of Cognitive Impairment in Community Environments Through Modeling Subtle Inconsistencies in Questionnaire Responses: Machine Learning Model Development and Validation.

The underdiagnosis of cognitive impairment hinders timely intervention of dementia. Health professionals working in the community play a critical role in the early detection of cognitive impairment, yet still face several challenges such as a lack of suitable tools, necessary training, and potential stigmatization. This study explored a novel application integrating psychometric methods with data science techniques to model subtle inconsistencies in questionnaire response data for early identification of cognitive impairment in community environments. This study analyzed questionnaire response data from participants aged 50 years and older in the Health and Retirement Study (waves 8-9, n=12,942). Predictors included low-quality response indices generated using the graded response model from four brief questionnaires (optimism, hopelessness, purpose in life, and life satisfaction) assessing aspects of overall well-being, a focus of health professionals in communities. The primary and supplemental predicted outcomes were current cognitive impairment derived from a validated criterion and dementia or mortality in the next ten years. Seven predictive models were trained, and the performance of these models was evaluated and compared. The multilayer perceptron exhibited the best performance in predicting current cognitive impairment. In the selected four questionnaires, the area under curve values for identifying current cognitive impairment ranged from 0.63 to 0.66 and was improved to 0.71 to 0.74 when combining the low-quality response indices with age and gender for prediction. We set the threshold for assessing cognitive impairment risk in the tool based on the ratio of underdiagnosis costs to overdiagnosis costs, and a ratio of 4 was used as the default choice. Furthermore, the tool outperformed the efficiency of age or health-based screening strategies for identifying individuals at high risk for cognitive impairment, particularly in the 50- to 59-year and 60- to 69-year age groups. The tool is available on a portal website for the public to access freely. We developed a novel prediction tool that integrates psychometric methods with data science to facilitate "passive or backend" cognitive impairment assessments in community settings, aiming to promote early cognitive impairment detection. This tool simplifies the cognitive impairment assessment process, making it more adaptable and reducing burdens. Our approach also presents a new perspective for using questionnaire data: leveraging, rather than dismissing, low-quality data.

The impact of vocabulary assessments on quality of life: Insights from professionals on their application with students with disabilities.

Assessing vocabulary skills is a crucial aspect of educational interventions for students with disabilities, as it directly influences their academic progress, overall communication abilities, and quality of life. This study aims to explore professionals' perceptions regarding the vocabulary assessments used with students with disabilities. By gaining insights into their perspectives and experiences, we can improve the assessment process and enhance instructional practices, ultimately contributing to a better Quality of Life (QoL) for these students. Data were collected through a survey questionnaire completed by 375 professionals working in the field of special education. The findings indicate that professionals in the private sector express higher satisfaction levels and demonstrate better proficiency in applying vocabulary assessment tools compared to those in the public sector. Furthermore, the results reveal that professionals specializing in speech and language disorders report higher satisfaction levels compared to professionals in other specialized areas.

Self-administered sexual health testing in an open prison setting: a pilot health impact assessment and social return on investment analysis.

The sexual health of the male prison population is often among the poorest in a country. This paper aims to identify the wider health impacts and social value of a sexual health self-sampling programme offered to male prisoners in an open prison setting in Wales. This study applied a unique pilot approach of using Health Impact Assessment and Social Return on Investment Frameworks in tandem. Key stakeholder groups affected by the intervention were identified, and engaged with through workshops, interviews and questionnaires to identify and quantify the health impacts and wider outcomes. Outcomes were then valued using proxy financial values to present the overall estimated social value of the self-sampling service. Based on a small sample, results indicate that for every £1 spent on the self-sampling service in the prison, a potential value of £4.14 was created. This resulted in a ratio of £4.14:£1. Approximately one-third of the value created (£1,517.95) was categorised as monetarily returnable, whereas the remaining value (£3,260.40) was purely illustrative social value, for example improved mental well-being. This unique pilot study demonstrates the health impacts and wider social value of providing a self-sampling sexual health service to prisoners within an open prison setting. By innovatively testing the feasibility of using a Health Impact Assessment process alongside Social Return on Investment analyses, this paper has outlined how the frameworks can be used in synergy to illustrate not just direct return on investment but also the social value of providing such a service.

Unravelling the knowledge matrix: exploring knowledge-sharing behaviours on market-based platforms using regression tree analysis

PurposeThis study aims to enhance the effectiveness of knowledge markets and overall knowledge management (KM) practices within organisations. By addressing the challenge of internal knowledge stickiness, it seeks to demonstrate how machine learning and AI approaches, specifically a text-based AI method for personality assessment and regression trees for behavioural analysis, can automate and personalise knowledge market incentivisation mechanisms.Design/methodology/approachThe research employs a novel approach by integrating machine learning methodologies to overcome the limitations of traditional statistical methods. A natural language processing (NLP)-based AI tool is used to assess employees’ personalities, and regression tree analysis is applied to predict and categorise behavioural patterns in knowledge-sharing contexts. This approach is designed to capture the complex interplay between individual personality traits and environmental factors, which traditional methods often fail to adequately address.FindingsCognitive style was confirmed as a key predictor of knowledge-sharing, with extrinsic motivators outweighing intrinsic ones in market-based platforms. These findings underscore the significance of diverse combinations of environmental and individual factors in promoting knowledge sharing, offering key insights that can inform the automatic design of personalised interventions for community managers of such platforms.Originality/valueThis research stands out as it is the first to empirically explore the interaction between the individual and the environment in shaping actual knowledge-sharing behaviours, using advanced methodologies. The increased automation in the process extends the practical contribution of this study, enabling a more efficient, automated assessment process, and thus making critical theoretical and practical advancements in understanding and enhancing knowledge-sharing behaviours.

Green Synthesis of New Spiropyrrole Derivatives by Employing Activated Carbonyl Compounds and Graphen Oxide Decorated on Ag@CuO/ZnO NPs or CuO/ZnO NPs: Theoretical and Biological Study

ABSTRACTThis study focused on investigating the synthesis of new spiropyrrole derivatives with high yields. The two nanocatalyst CuO/ZnO@Graphen Oxide nanocomposites (CuO/ZnO@GO NCs) and Ag/CuO/ZnO@Graphen Oxide (Ag@CuO/ZnO@GO NCs) were employed in a multicomponent reaction at room temperature to synthesize new compounds. The reaction involved isatins, N‐alkyl isatins, and ninhydrin as activated carbonyl compounds, ammonium acetate, ethyl 2‐arylamino‐4‐dioxo‐4‐arylbutanoates, hydrazonoyl chloride, or ethylbromopyruvate in water. Also, the catalytic activity of the green synthesized CuO/ZnO@GO and Ag@CuO/ZnO@GO was evaluated in the reduction of organic pollutants such as 4‐nitrophenol (4‐NP) in water at mild conditions. The results indicated that the biosynthesized MNCs have very high and effective catalytic activity for organic pollutants within a few seconds. The newly synthesized spiro compounds demonstrate antioxidant activity as a result of two assessment processes conducted by its NH group. In addition, the antibacterial activity of newly created spiro compounds was assessed using a disk distribution method, involving two types of Gram‐negative bacteria. These compounds were found to limit the growth of Gram‐positive bacteria as well. Also, to better understand the reaction mechanism, density functional theory (DFT)–based quantum chemical methods have been applied. The advantages of this technology encompass rapid response times, high product yields, and uncomplicated catalyst and product separation via simple procedures.

Mixed Reality-Based Concrete Crack Detection and Skeleton Extraction Using Deep Learning and Image Processing

Advancements in image processing and deep learning offer considerable opportunities for automated defect assessment in civil structures. However, these systems cannot work interactively with human inspectors. Mixed reality (MR) can be adopted to address this by involving inspectors in various stages of the assessment process. This paper integrates You Only Look Once (YOLO) v5n and YOLO v5m with the Canny algorithm for real-time concrete crack detection and skeleton extraction with a Microsoft HoloLens 2 MR device. The YOLO v5n demonstrates a superior mean average precision (mAP) 0.5 and speed, while YOLO v5m achieves the highest mAP 0.5 0.95 among the other YOLO v5 structures. The Canny algorithm also outperforms the Sobel and Prewitt edge detectors with the highest F1 score. The developed MR-based system could not only be employed for real-time defect assessment but also be utilized for the automatic recording of the location and other specifications of the cracks for further analysis and future re-inspections.

Responding to AI

The influence of artificial intelligence “has enormous implications for higher education” (Bearman, Ryan & Ajjawi, 2023, p. 369). Those of us teaching in the higher education sector have felt this profoundly: after the rapid uplift in digital teaching and learning practices in response to COVID 19 we are required, yet again, to respond to a new set of challenges brought about by the impact of AI. As an example, chatbot technology is calling into question the reliability of the traditional essay as a valid assessment tool (Stokel-Walker, 2022), requiring significant critical reflection and reevaluation of assessments from teaching academics to ensure course learning outcomes are met. This presentation outlines how a teaching specialist academic working in an initial teacher education (ITE) programme at a South Australian university carried out one such redesign. The focus of the re-design was a ‘unit/year plan’ assessment task for a final year curriculum specialization topic. The redesign aligned with the two guiding principles from TEQSA’s Assessment reform for the age of artificial intelligence (Lodge et al., 2023). The first of these guiding principles: ‘Assessment and learning experiences equip students to participate in a world where AI is ubiquitous’ (p. 2) points to the importance of educating students around AI use relevant to their learning. The second guiding principle: ‘Forming trustworthy judgements about student learning in a time of AI requires multiple, inclusive and contextualised approaches to assessment’ (p. 3) underscores the ongoing necessity for universities to have effective means by which to measure student learning outcomes, especially in accredited courses. The redesign of the assessment task both acknowledged the potential of AI for use in the professional space, and simultaneously sought to ensure assessment validity through the inclusion of an interview with the pre-service teachers. Overall, the redesign of the assessment task incorporated three components. Firstly, a brief introductory module about discipline-specific uses of AI was incorporated into the online course content. Secondly, pre-service teachers were ‘permitted’ to utilise available AI platforms in the creation of their planning task. Thirdly, the assessment process was adapted to include an oral presentation (Gulikers, Bastiaens & Kirschner, 2004) conducted as a panel interview in which each pre-service teacher could provide evidence of the pedagogical perspectives underpinning their plan and subsequently demonstrate the successful achievement of topic learning outcomes. The purpose of the panel interview was to assist assessors in forming a judgement about pre-service teachers’ pedagogical understanding and application, in line with relevant criteria from Matheis and John’s Comprehensive Authentic Assessment Model (CAAM) (Matheis & John, 2024). Autoethnographic reflections on the first delivery of the redesigned assessment are provided, including alternative approaches for future iterations. The learnings from the teaching initiative outlined in this presentation contribute to the growing discussion about how we can develop the capacity of our pre-service teachers as skilled and critical practitioners who can utilize AI as a valuable tool for their ongoing professional practice.

Emergency hospital admissions for stress-related presentations among secondary school-aged minoritised young people in England.

Minoritised young people face a double burden of discrimination through increased risk of stress and differential treatment access. However, acute care pathways for minoritised young people with urgent mental health needs are poorly understood. To explore variation in stress-related presentations (SRPs) to acute hospitals across racial-ethnic groups in England. We examined rates, distribution, duration and types of SRPs across racial-ethnic groups in a retrospective cohort of 11- to 15-year-olds with one or more emergency hospital admissions between April 2014 and March 2020. SRPs were defined as emergency admissions for potentially psychosomatic symptoms, self-harm and internalising, externalising and thought disorders. White British (8-38 per 1000 births) and Mixed White-Black (9-42 per 1000 births) young people had highest rates of SRPs, whereas Black African (5-14 per 1000 births), Indian (6-19 per 1000 births) and White other (4-19 per 1000 births) young people had the lowest rates of SRPs. The proportion of readmissions were highest for Pakistani (47.7%), White British (41.4%) and Mixed White-Black (41.3%) groups. Black Other (36.4%) and White Other (35.8%) groups had the lowest proportions of readmissions. The proportion of admission durations ≥3 days was higher for Black Other (16.6%), Bangladeshi (16.3%), Asian Other (15.9%) and Black Caribbean (15.8%) groups than their White British (11.9%) and Indian (11.8%) peers. The type of SRPs varied across racial-ethnic groups. Patterns of SRP admissions systematically differed across racial-ethnic groups, indicative of inequitable triage, assessment and treatment processes. These findings highlight the need for implementation of race equality frameworks to address structural racism in healthcare pathways.

Leveraging TikTok and Technology-Enhanced Peer Assessment

Technology-enhanced learning (TEL) is a crucial element of contemporary higher education. This Pecha Kucha explores innovative TEL strategies to enhance student engagement and self-directed learning. This abstract outlines a technologically innovative education design in an Australian university’s commerce course. Within this course, students created TikTok videos to promote awareness of the United Nations Sustainable Development Goals (UN SDGs). The selection of TikTok as the social media tool of choice was informed by its demographic alignment, with 36.7% of its users aged 18 to 24 (Statista Search Department, 2024). The efficacy of short-form video content in fostering engagement, particularly among higher education students, is well-documented (Wong, 2023; Perez et al., 2023). Moreover, Khechine et al.’s (2020) research shows that the adoption of social media tools in education promotes the value of enjoyment and interest in performing an activity, thereby fostering intrinsic motivation - a critical determinant of effective learning (Ryan & Deci, 2000). Students also engaged in peer assessment of their TikTok videos, which centred on the creation of TikTok content tailored for first-year students. Given that the assessors themselves belonged to the first-year cohort, they were deemed to possess an inherent understanding of the target audience. Thus, they were best positioned to evaluate whether TikTok videos communicate the information clearly and stimulate audience’s interest and engagement. Peer assessment is reflexive and allows students to apply learning by assessing in the current context (Bloxham & West, 2004). Li et al. (2020) suggest peer review of assessment leads to significantly improved assessment performance. It also promotes ownership and accountability, encouraging self-directed learning (Ryan & Deci, 2020). Qualtrics, a technology platform, was employed to collect and collate evaluation scores to facilitate the peer assessment process. The peer assessment used Qualtrics with rubric sliders, thereby emulating the evaluative framework typically employed by instructors. This approach adhered to best practices in TEL (Chernikova et al., 2020). Qualtrics also provides additional advantages, including streamlining score collection from large student groups and efficiently generate descriptive statistics to examine whether students give higher scores to their own groups compared to others, and whether there's more variability in scores for their own group versus peer groups (Bloxham & West, 2020). Promoting UN SDGs using TikTok successfully supported TEL and enhanced self-directed learning. The course engagement score has increased by 9 basis points from the previous teaching period. Some examples of student feedback included: “Rehearsing for the video and interviewing students was fun and insightful.” “I was genuinely curious about the university’s sustainability approaches.” “TikTok makes learning about real-life UN SDG efforts approachable.” “Watching other students' TikTok videos was interesting.” Feedback from the teaching team also suggests the quality of the videos were well-reflected in the peer assessment scores. This Pecha Kucha presents an innovative education design that leverages TikTok and peer assessment to boost engagement. It provides a valuable model for academics seeking to integrate TEL strategies within their disciplines, like performing arts and medicine (Nicolaou et al., 2019; Cheston et al., 2013).

CROSS Safety Report: The potential impact of scaffolding on fire safety

This month's report discusses the potential fire safety risks of scaffolding when it is present around in-use buildings and the current risk assessment process.

Approach to ESG Risk Assessment of Construction Companies Considering Financial Data

In modern conditions, environmental, social and corporate responsibility (ESG) factors are becoming increasingly important in assessing the sustainability and financial performance of companies, especially in resource-intensive industries such as construction. The purpose of this study is to develop and apply a comprehensive methodology for assessing ESG risks of construction companies taking into account financial indicators. Due to the limited availability of ESG data of Russian construction firms, the methodology was tested on hypothetical companies X, Y and Z, which allowed to demonstrate the assessment process and identify the potential of the methodology for practical application. The study collected and normalized data on environmental, social and governance factors, as well as financial indicators, using weighting factors to reflect the relative importance of each indicator. The results showed significant differences between companies, identifying areas for improvement and providing customized recommendations to improve their ESG performance and financial sustainability. The methodology proved to be effective even with limited data availability, offering a valuable tool for companies and investors to assess and improve ESG practices in the construction sector

Systematic review and meta-analysis of workload among medical records coders in China.

The homepage of medical records holds significant importance for national performance assessments, DIP settlement lists, and DRG payments. Coders, as auditors of the codes, wield a crucial influence on the quality of the medical records' homepage. To analyze the general situation of the allocation of medical record full-time coders in China. CNKI, Wanfang, VIP, PubMed and other databases were searched from database inception to November 31, 2023. The primary outcome was the allocation of medical records to full-time coders, with the workload of the coders being the primary focus. Secondary outcomes encompassed the professional background of the coders, including their academic qualifications, professional titles, possession of medical coding certificates, and years of experience in coding. Eleven studies, comprising data from 1783 hospitals and 4448 coders, were analyzed. Among the coders, 61% had a medical-related professional background, 62% held a bachelor's degree or higher, 54% possessed an intermediate title or higher, 61% had coding certificates, and 51% had less than 5 years of work experience. The summary findings regarding the number of coders and coded medical records in secondary and tertiary hospitals indicated an average discharge rate of 22,704.0 per hospital in China. The number of coded cases averaged around 11,300. Specifically, coders in tertiary hospitals coded approximately 12,049 medical records on average, while those in secondary hospitals coded around 7,399 medical records. Our study highlights the shortage of medical record coding personnel in the majority of hospitals, coupled with a significant coding workload, low educational qualifications among staff, short working hours, and an imbalanced title structure. Given these findings, hospitals and relevant management authorities should prioritize the recruitment of highly educated professionals, streamline the assessment process for professional titles, alleviate the coding workload, and enhance coding quality.

Evaluating the Impact of an AI Critical Friend on Student Research Proposals, Critical AI Literacy, and Transparent Collaborative Assessment Practices

This paper presents a work-in-progress report on an innovative approach to integrating generative AI capabilities into the assessment process, focusing on research proposal development. Specifically, the study explores the impact of incorporating an AI-powered "critical friend" that provides targeted feedback and guidance to students as they craft their research proposals. The aim is to evaluate how this AI-powered support system affects students' critical AI literacy, the quality and transparency of the collaborative assessment process, and, ultimately, the strength of the final research proposals. Through a mixed-methods approach combining surveys, focus groups, and analysis of student work and interactions with generative AI, the researchers seek to uncover key insights on the benefits, challenges, and ethical considerations of leveraging AI to enhance the research proposal development experience. The findings from this study will contribute to the growing body of literature on the role of AI in academic assessments and provide practical recommendations for educators and institutions looking to responsibly integrate these emerging technologies.