Assessment System Research Articles

Revolutionizing Primary Education Assessment: Opportunities and Challenges in Bandar Lampung's Technology-Driven Landscape

This study aims to analyze the opportunities and challenges associated with implementing a technology and information-based educational assessment system in elementary schools in Bandar Lampung. The study recruited ten respondents who were IT staff members, teachers, and principals from upper-grade primary levels (grades IV, V, and VI). It used a qualitative descriptive research design. The research focused on identifying the opportunities and challenges related to IT-based assessment systems. Data collection methods included observations, interviews, and documentation, while data analysis was conducted using the Miles and Huberman model, encompassing data reduction, data display, and data verification. The findings reveal significant opportunities in adopting a technology-based assessment system, such as using Google Forms for flexible assessment scheduling and enhanced data analysis capabilities afforded to teachers through IT tools. However, notable challenges were also identified, including insufficient IT proficiency among teachers and staff, concerns over data privacy and protection, and inadequate internet connectivity within school premises, which hampers the effective use of IT-based applications. The study recommends that schools provide intensive training for teachers and staff to enhance their IT skills. The conclusion is that while technology-based assessment systems offer considerable advantages in facilitating teachers' tasks, the identified challenges must be addressed to optimize the benefits and overcome the obstacles encountered.

Correlation between Matrix Metalloproteinase presence and caries surface appearance.

Cariogenic bacterial acids dissolve the inorganic elements in dentine, leaving the dentine matrix exposed. Host-derived matrix metalloproteinases (MMPs) play an essential role in caries progression as they are significant regulators of extracellular matrix turnover and can degrade exposed collagen. This paper investigates the expression of MMP2 and MMP9 across various stages of caries in primary human teeth and relate this with a diagnosis recorded by The International Caries Detection and Assessment System (ICDAS). Twenty-four sections (150um in thickness) from extracted teeth, clinically diagnosed using ICDAS, were immunohistochemically treated with monoclonal anti-MMP2 and anti-MMP9 antibodies. Positive staining was visualised by immunofluorescence using a VectorFluor Duet Double Labeling Kit. Images from triplicate samples for each ICDAS score were analysed using ImageJ software. Collagen degradation in caries lesions was detected using a hydroxyproline assay. MMPs were weakly detected in caries with ICDAS 1-2 scores, and an insignificant increase was detected in ICDAS 3. However, a significant increase in MMP expression was seen in caries with an ICDAS score of 4-6. There was a strong positive correlation between the ICDAS score and MMP2, [r(6) = .86, p = .002] and between ICDAS and MMP9, [r(6) = .82, p = .004]. Data were analysed using two-way ANOVA followed by Tukey multiple comparison test (*p < 0.05). The use of ICDAS to assess the severity of caries lesions and how this correlates with the presence of MMP in these lesions validates the modern approach to caries management with a minimally invasive concept.

Responsible Research Assessment and Research Information Management Systems

In the context of open science, universities, research-performing and funding organizations and authorities worldwide are moving towards more responsible research assessment (RRA). In 2022, the European Coalition for Advancing Research Assessment (CoARA) published an agreement with ten commitments, including the recognition of the “diversity of contributions to, and careers in, research”, the “focus on qualitative evaluation for which peer review is central, supported by responsible use of quantitative indicators”, and the “abandon (of) inappropriate uses in research assessment of journal- and publication-based metrics”. Research assessment (RA) is essential for research of the highest quality. The transformation of assessment indicators and procedures directly affects the underlying research information management infrastructures (also called current research information systems) which collect and store metadata on research activities and outputs. This entry investigates the impact of RRA on these systems, on their development and implementation, their data model and governance, including digital ethics.

Improvement of assessment system of investment attractiveness of regions

The article studies some foreign and local methods of assessing the investment attractiveness of regions, and based on these, the author proposed a system for assessing the investment attractiveness in regions. Formulas for calculating investment potential and investment risk indicators have been displayed in this assessment system and the region can be assessed based on the results.

Efficacy of F-ACP-Containing Dental Mousse in the Remineralization of White Spot Lesions after Fixed Orthodontic Therapy: A Randomized Clinical Trial.

Fixed appliance (FA) therapy predisposes patients to white spot lesions (WSLs). The F-ACP complex (amorphous calcium phosphate nanoparticles enriched with carbonate and fluorine and coated with citrate) has been effective for in vitro enamel remineralization. The aim of this study was to evaluate the efficacy of the F-ACP complex in remineralizing WSLs after FA therapy. One hundred and six adolescents (aged 12-20 years) were randomized into study and control groups after FA therapy. Patients in the study group were advised to use dental mousse containing F-ACP applied within Essix retainers for six months. The presence of WSLs was recorded at baseline (T0), 3 months (T1), and 6 months (T2) according to the International Caries Detection and Assessment System (ICDAS). Visual Plaque Index (VPI) and Gingival Bleeding Index (GBI) were recorded. Among 106 study participants, 91 (52 and 39 in study and control groups, respectively) completed the study. The results showed that the ICDAS score was significantly lower (p < 0.001) in the study group than in the control group between T0 and T2. The application of mousse containing the F-ACP complex inside Essix retainers for six months is effective in remineralizing white spot lesions in patients after FA therapy without side effects.

Microscopic Characterization and Fractal Analysis of Pore Systems for Unconventional Reservoirs

The complex pore structure of unconventional oil and gas reservoirs is one of the reasons for the difficulties in resource evaluation and development. Therefore, it is crucial to comprehensively characterize the pore structure, understand reservoir heterogeneity from multiple perspectives, and gain an in-depth understanding of fluid migration and accumulation mechanisms. This review outlines the methods and basic principles for characterizing microporous systems in unconventional reservoirs, summarizes the fractal analysis corresponding to the different methods, sorts out the relationship between the fractals and reservoir macroscopic physical properties (porosity, permeability, etc.) with the reservoir microscopic pore structures (pore structure parameters, pore connectivity, etc.). The research focuses on cutting-edge applications of characterization techniques, such as improved characterization accuracy, calibration of PSD ranges, and identification of different hydrogen compositions in pore systems for dynamic assessment of unconventional reservoirs. Fractal dimension analysis can effectively identify the quality level of the reservoir; complex pore-throat structures reduce permeability and destroy free fluid storage space, and the saturation of removable fluids is negatively correlated with Df. As for the mineral composition, the fractal dimension is positively correlated with quartz, negatively correlated with feldspar, and weakly correlated with clay mineral content. In future qualitative characterization studies, the application and combination of contrast agents, molecular dynamics simulations, artificial intelligence techniques, and 4D imaging techniques can effectively improve the spatial resolution of the images and explore the adsorption/desorption of gases within the pores, and also help to reduce the computational cost of these processes; these could also attempt to link reservoir characterization to research on supercritical carbon dioxide-enhanced integrated shale gas recovery, carbon geological sequestration, and advanced underground hydrogen storage.

Modelling the behaviour in socio-technical systems for policy assessment - A comparison of modelling approaches using the example of Sponge City concept implementation

How are policies affecting the social behaviour of actors and thus the dynamics of the corresponding socio-technical systems? This question is of central relevance to understand what policies might be effective for sustainable transitions and transformation where social changes are immanent. Exploratory modelling approaches such as agent-based modelling (ABM) are suitable and established to analyse how individual, especially heterogeneous behaviour emerges to affect social systems. In contrast to this micro-level perspective of understanding how social behaviour influences system behaviour, System Dynamics models (SDM) focus on the macro-level perspective by analysing how system interactions drive complex system behaviour. Qualitative modelling approaches such as Causal Loop Diagrams belonging to the Systems Thinking Modelling (STM) toolbox remain descriptive but need much less effort for model development. In this article, we compare how the different approaches ABM, SDM and STM can model social behaviour in socio-technical systems and what different kind of insights can be gained to assess the efficacy of policies. It discusses the advantages and limitations of the different approaches for their application in behavioural public policy modelling for sustainability transition. Therefore, three exemplary models (STM, SDM, ABM) are created for the same topic of implementation dynamics of adaptation measures to foster rainwater retention (Sponge City concept) in a multi-residential quarter. As a result, the risk of green gentrification is identified. Three hypothetical policies are implemented to illustrate the differences between the three modelling approaches for their use in policy assessment. Based on that, we discuss how the three modelling approaches conceptualise behaviour in general.

Quality Indicators for Residential Long-Term Care for the Elderly: A Scoping Review

The objective of this study was to review the existing literature on the evaluation of care in residential homes for elderly, considering its quality through quality indicators, using secondary databases. A systematic search was conducted, following the PRISMA guidelines for scoping reviews. One hundred and ten studies were included, in which 1,239 indicators were identified. More than half of the identified indicators (N=663, 53%) were linked to outcomes, with about 33% (N=413) related to structure, and 14% (N=175) to process indicators. The most frequently studied indicators as dependent variables were: "Pressure ulcer," "Staffing issues," and "Psychotropic Drug Use." As independent variables, indicators relating to structure were prominent: "Facility characteristics," "Resident characteristics," and "Staffing Issues”. There is a broad consensus around a care approach centered on clinical and structural aspects, in accordance with the profile of residents, and reflecting historical concerns about quality of care and patient safety that were the origin of quality assessment systems, and which remain the focus of concerns to this day.

Information technology-supported integrated health service for older adults in long-term care settings

BackgroundTo examine the effectiveness and safety of a data sharing and comprehensive management platform for institutionalized older patients.MethodsWe applied information technology-supported integrated health service platform to patients who live at long-term care hospitals (LTCHs) and nursing homes (NHs) with cluster randomized controlled study. We enrolled 555 patients aged 65 or older (461 from 7 LTCHs, 94 from 5 NHs). For the intervention group, a tablet-based platform comprising comprehensive geriatric assessment, disease management, potentially inappropriate medication (PIM) management, rehabilitation program, and screening for adverse events and warning alarms were provided for physicians or nurses. The control group was managed with usual care. Co-primary outcomes were (1) control rate of hypertension and diabetes, (2) medication adjustment (PIM prescription rate, proportion of polypharmacy), and (3) combination of potential quality-of-care problems (composite quality indicator) from the interRAI assessment system which assessed after 3-month of intervention.ResultsWe screened 1119 patients and included 555 patients (control; 289, intervention; 266) for analysis. Patients allocated to the intervention group had better cognitive function and took less medications and PIMs at baseline. The diabetes control rate (OR = 2.61, 95% CI 1.37–4.99, p = 0.0035), discontinuation of PIM (OR = 4.65, 95% CI 2.41–8.97, p < 0.0001), reduction of medication in patients with polypharmacy (OR = 1.98, 95% CI 1.24–3.16, p = 0.0042), and number of PIMs use (ꞵ = − 0.27, p < 0.0001) improved significantly in the intervention group. There was no significant difference in hypertension control rate (OR = 0.54, 95% CI 0.20–1.43, p = 0.2129), proportion of polypharmacy (OR = 1.40, 95% CI 0.75–2.60, p = 0.2863), and improvement of composite quality indicators (ꞵ = 0.03, p = 0.2094). For secondary outcomes, cognitive and motor function, quality of life, and unplanned hospitalization were not different significantly between groups.ConclusionsThe information technology-supported integrated health service effectively reduced PIM use and controlled diabetes among older patients in LTCH or NH without functional decline or increase of healthcare utilization.Trial registrationClinical Research Information Service, KCT0004360. Registered on 21 October 2019.

Occupant-centric cabin thermal sensation assessment system based on low-cost thermal imaging

Assessing cabin occupants' thermal sensation in real-time enables automatic control of car air conditioning, improving driving safety and energy efficiency. Traditional methods, limited by their need for numerous physiological parameters, have restricted practicality. For this purpose, this study employs a low-cost thermal imaging sensor as the hardware core to establish a cost-effective and non-contact real-time assessment system for the thermal comfort of occupants within a cabin. The system comprises a thermal sensation assessment model developed based on subject experiments, along with facial recognition and segmentation algorithms optimized for thermal images. The thermal sensation assessment model developed, which employs cheek temperature, solar radiation intensity, and cabin air temperature as its features, exhibited an R2 of 0.617 on the test set. Furthermore, the facial recognition algorithm established for thermographic imaging achieved a mean accuracy of 96.5% and a recall rate of 99.0%. The system underwent validation in real-world vehicle environments, proving its ability to accurately detect and measure the cheek temperatures of occupants in the cabin and execute thermal sensation assessments. With a mean absolute error of 0.5 thermal sensation units in its output, its accuracy in practical applications was affirmed. This research provides an effective solution for automatically adjusting cabin air conditioning.

Insurance Premium Prediction Using Machine Learning

RiskInsight represents an innovative company poised to transform the world of insurance by applying state-of-the-art machine learning techniques. The focus of this project is the Insurance Risk Assessment System, a high-level framework carefully designed using the Python programming language and the sklearn library. Anchored by unprecedented accuracy and operational efficiency, RiskInsight enters the field of risk assessment with unwavering determination. Using the power of advanced algorithms inspired by artificial neural networks, the system navigates huge historical data and evaluates real-time risk dynamics. The cornerstone of RiskInsight is its in-depth analysis of various risk factors carefully curated in a robust database. Combining machine learning capabilities and domain expertise, RiskInsight seeks to redefine the boundaries of risk assessment by transcending traditional paradigms. The goal is to bring about a paradigm shift in super forecasting and provide stakeholders with insights that promote fairer payment structures and informed decision-making. With its innovative design and flexible functionality, RiskInsight promises to catalyze major changes in the insurance ecosystem. By providing accurate payment forecasts and advanced risk management strategies, the platform aims to enhance the efficiency of insurance operations. A testament to its transformative potential, RiskInsight is poised to usher in a new era of efficiency and equity in the insurance industry and sets a precedent for technological innovation in risk assessment and predictive systems. Keywords: Machine Learning, Fairer Payment Structure, Efficiency, Transformative Potential Acronyms: sklearn - Scikit-learn

A WebGIS-Based System for Supporting Saline-Alkali Soil Ecological Monitoring: A Case Study in Yellow River Delta, China

The monitoring and evaluation of soil ecological environment is very important to ensure the saline-alkali soil health and the safety of agricultural products. It is of foremost importance to, within a regional ecological risk-reduction strategy, develop a useful online system for soil ecological assessment and prediction to prevent people from suffering the threat of sudden disasters. However, the traditional manual or empirical parameter adjustment causes the mismatch of the hyperparameters of the model, which cannot meet the urgent needs of high-performance prediction of soil properties using multi-dimensional data in WebGIS system. For the end, this study aims to develop a saline-alkali soil ecological monitoring system for real-time monitoring of soil ecology in the Yellow River Delta, China. The system applied advanced web-based GIS, including front-end and back-end technology stack, cross-platform deployment of machine learning models, and a database embedded in multi-source environmental variables. The system adopts a five-layer architecture and integrates functions such as data statistical analysis, soil health assessment, soil salt prediction and data management. The system visually displays the statistical results of air quality, vegetation index and soil properties in the study area. It provides users with ecological risk assessment functions to analyze soil heavy metal pollution. Specially, the system introduces a tree structured Parzan estimator (TPE) optimized machine learning model to achieve accurate prediction of soil salinity. The TPE-RF model had the highest prediction accuracy (R2 = 94.48%) in the testing set in comparison with the TPE-GBDT model, which exhibited the strong ability for the nonlinear relationship between environmental variables and soil salinity. The system developed in this study can provide accurate saline-alkali soil information and health assessment results for government agencies and farmers, which is of great significance for agricultural production and saline-alkali soil ecological protection.

Advancing maintenance, monitoring, and control techniques for the optimization of power electronic systems

This paper explores the innovative methodologies and technologies in predictive maintenance, real-time monitoring, fault detection, and advanced control strategies for power electronic devices. Initially, we delve into data acquisition and preprocessing techniques crucial for ensuring the quality and reliability of data used in predictive models. These models, leveraging machine learning and time-series analysis, predict the remaining useful life of devices, guiding proactive maintenance strategies. Furthermore, we discuss the significance of risk assessment and decision support systems in prioritizing maintenance tasks and allocating resources efficiently. The narrative then shifts to real-time monitoring and fault detection, emphasizing the role of sensor integration, data fusion, anomaly detection, and diagnostics in maintaining system integrity. Condition-based maintenance strategies, underscored by real-time data analytics, are presented as a means to optimize maintenance activities and enhance operational performance. The paper concludes with a detailed examination of advanced control strategies, including model predictive control, reinforcement learning, and distributed control and optimization techniques, highlighting their potential to improve system efficiency, reliability, and adaptability. Through comprehensive research and analysis, this work aims to provide valuable insights into the development of sophisticated maintenance and control mechanisms for power electronic systems, ultimately contributing to their longevity and operational efficacy.

ProgEdu4Web: An automated assessment tool for motivating the learning of web programming course

AbstractThe expanding software industry has increased the demand for proficient programming professionals. However, more than technical expertise is needed for successful software development; code quality assurance and effective teamwork are also indispensable. Unfortunately, recent graduates often exhibit deficiencies in programming, code quality assurance, and teamwork skills, highlighting the need for improvements in these areas. In response to these challenges, this study presents ProgEdu4Web, an innovative Automated Programming Assessment System designed to evaluate code quality and teamwork proficiency in web programming. The system integrates automatic source code evaluation tools for static code analysis, ensuring timely feedback on coding quality issues. To assess the effectiveness of ProgEdu4Web, a questionnaire and hypothesis model was employed, surveying a cohort of 111 students enrolled in web programming classes. The study's findings demonstrate that the system significantly alleviated instructor workload while motivating students to enhance their web programming with good code quality and teamwork abilities.

A deep introspection into the role of ChatGPT for transforming hospitality, leisure, sport, and tourism education

This perspective article explores the emerging role of artificial intelligence (AI), specifically the OpenAI language model ChatGPT, in the field of Hospitality, Leisure, Sport, and Tourism (HLST) education. With the capacity to personalize education, streamline administrative tasks, and provide data-driven insights, AI holds the potential to revolutionize teaching, learning, and research in these sectors. However, the integration of AI in education also presents challenges, including data privacy, quality control, and the risk of widened digital divide. We delve into these challenges and discuss future directions for the harmonious integration of AI into the HLST educational landscape. Future directions include improved AI-educator collaboration, adaptive and personalized learning, intelligent assessment systems, and the promotion of lifelong learning. This exploration of the transformative potential of AI in education offers valuable insights for educators, policymakers, and researchers in the HLST sectors, highlighting both the challenges to navigate and the opportunities to seize in the journey towards an AI-enhanced educational future.

Artificial intelligence model system for bone age assessment of preschool children.

Our study aimed to assess the impact of inter- and intra-observer variations when utilizing an artificial intelligence (AI) system for bone age assessment (BAA) of preschool children. A retrospective study was conducted involving a total sample of 53 female individuals and 41 male individuals aged 3-6 years in China. Radiographs were assessed by four mid-level radiology reviewers using the TW3 and RUS-CHN methods. Bone age (BA) was analyzed in two separate situations, with/without the assistance of AI. Following a 4-week wash-out period, radiographs were reevaluated in the same manner. Accuracy metrics, the correlation coefficient (ICC)and Bland-Altman plots were employed. The accuracy of BAA by the reviewers was significantly improved with AI. The results of RMSE and MAE decreased in both methods (p < 0.001). When comparing inter-observer agreement in both methods and intra-observer reproducibility in two interpretations, the ICC results were improved with AI. The ICC values increased in both two interpretations for both methods and exceeded 0.99 with AI. In the assessment of BA for preschool children, AI was found to be capable of reducing inter-observer variability and enhancing intra-observer reproducibility, which can be considered an important tool for clinical work by radiologists. The RUS-CHN method is a special bone age method devised to be suitable for Chinese children. The preschool stage is a critical phase for children, marked by a high degree of variability that renders BA prediction challenging. The accuracy of BAA by the reviewers can be significantly improved with the aid of an AI model system. This study is the first to assess the impact of inter- and intra-observer variations when utilizing an AI model system for BAA of preschool children using both the TW3 and RUS-CHN methods.

A deep learning-assisted skin-integrated pulse sensing system for reliable pulse monitoring and cardiac function assessment

Long-term pulse monitoring plays a vital role in the prevention and diagnosis of cardiovascular diseases since pulse signals can provide rich characteristics of cardiac conditions. Flexible sensors mounting on the wrist for pulse monitoring have drawn great attention due to the advantages of soft, skin-interfaced and non-invasive features. However, slight wrist movement and transposition of the sensor on skin would dramatically impact the signal quality because of motion artifacts and interfacial properties. Here, we report an intelligent skin-integrated system by combining a sensitive pressure sensor array and a signal enhancement algorithm to provide reliable pulse signal monitoring. The sensors have the advantages of low cost, simple fabrication, reusability and high sensitivity. The multiple sensor channels offer rich information of vital signals, which can facilitate deep learning-based algorithms to denoise and reconstruct pulses more accurately and thus enable precise heart rate monitoring and heart variability assessments. The reported technology suggests a novel approach for pulse waveform detection in static and dynamic conditions, allowing users to more easily achieve reliable pulse monitoring.

Does managerial myopia exacerbate firms excessive financialization? Evidence from Malaysia

Purpose This study aims to examine the influence of managerial myopia on the excessive financialization behavior of listed firms on Bursa Malaysia. Design/methodology/approach Through a sample of 313 firms from 2015 to 2021, the author examine whether managerial myopia promotes or inhibits corporate financialization. The author uses ordinary least squares and Logit as the baseline models and addresses potential endogeneity through the dynamic-panel generalized method of moments. The results are also robust to alternative measures of financialization and managerial myopia. Findings The results show a significant positive effect of managerial myopia on the excessive financialization of enterprises. Furthermore, the findings indicate that the impact of managerial myopia on the over-financialization of enterprises is more prominent in periods of low economic policy uncertainty. However, the relationship between excessive financialization and managerial myopia is weakened in the presence of female chief executive officers. Practical implications The empirical results have useful policy implications. First, firms should establish scientific managerial assessment and supervision systems to avoid excessive financial investment behavior by myopic managers caused by assessments that place too much emphasis on short-term performance. Second, regulators and policymakers should encourage firms to appoint women to top management positions, which may inhibit short-sighted financialization behavior. Finally, the regulatory authorities should undertake the necessary measures driving companies to disclose the investment direction of the funds so that shareholders and investors can understand the use direction of the funds in a timely manner, which can effectively prevent the economy “from the real to the virtual” and promote the development of the real economy. Originality/value This paper expands the existing research on corporate financialization behavior and provides a new theoretical basis for the underlying factors of excessive financialization. It studies the influence of corporate financialization from the perspective of short-run managerial actions and deepens the understanding of managerial myopia and companies’ financialization levels.

Assessing resilience through social networks: A case study of flood disaster management in China

The study of resilience has gained significant traction in the field of disaster risk reduction (DRR) due to the escalating frequency and intensity of extreme weather events such as floods. Social capital accumulated within social networks comprising stakeholders serves as a crucial resource for the establishment of disaster resilience. Therefore, this study examines and evaluates resilience to changes within DRR process through a social capital perspective. First, it embraces the conceptual framework of disaster resilience through social capital and elucidates six distinct forms of social capital relevant to enhancing disaster resilience. Subsequently, the governance structure of flood emergency management in China is thoroughly examined and a disaster resilience assessment system is established from the standpoint of social capital using a social network analysis methodology. Finally, using the case of the devastating flooding triggered by the 7–20 rainstorm in Henan, China, resilience levels are measured at various stages of the flooding employing 4R theory. The results of this study will aid governmental and non-governmental organizations in enhancing their resilience in the face of natural disasters.

Analysis of The Performance Appraisal &amp; Bonus Payments at Real Ispat

The goal of the research project "Analysis of The Performance Appraisal &amp; Bonus Payments at Real Ispat" is to examine the complex workings of performance assessment systems and how they affect worker satisfaction, productivity, and motivation. Acknowledging the pivotal function that incentive payments and performance reviews play in molding organizational results, this study attempts to offer a thorough examination of these mechanisms in the context of Real Ispat, a prominent participant in the steel production industry. This study's primary goal is to examine how performance reviews affect workers' motivation and output. Performance reviews are meant to provide workers input on how they perform at work, point out their areas of strength and weakness, and establish goals for the future. This study investigates the relationship between these evaluations and workers' incentive to improve performance and total output. Through gathering information from workers at different levels, the study seeks to measure how much performance reviews impact employee engagement and productivity, which in turn boosts the effectiveness and production of the company. Finding out how employees feel about and are satisfied with the performance evaluation process is the second goal. The success of the system depends heavily on how fair, transparent, and successful the assessment process is by the workforce. This survey looks at how Real Ispat employees feel about the assessment process, whether they think it's fair and impartial, and if they're happy with how often these appraisals are done. Since these impressions have a direct influence on employee morale, engagement, and retention, it is important to understand them. Evaluating the efficacy of Real Ispat's present performance evaluation system is the third goal. This entails determining whether the system of appraisals successfully identifies top performers and appropriately represents the work performance of employees. The study also looks at the impact that appraisals have on career development and how they help define reasonable and attainable performance goals. It also looks for areas where the assessment system needs to be improved in order to better fit with employee expectations and company goals. The study uses a mixed-method approach, integrating quantitative and qualitative data-gathering approaches, to accomplish these goals. Likert scale surveys are used to collect quantifiable information about worker productivity, satisfaction, motivation, and views. To obtain qualitative insights into employees' experiences and ideas for improvement, focus groups, and in-depth interviews are held. It is anticipated that the study's conclusions will give Real Ispat's management insightful information that will help them improve their processes for incentive payments and performance reviews. The organization may improve employee motivation, contentment, and productivity by implementing focused interventions after determining the present strengths and weaknesses of these systems. This study also adds to the larger body of knowledge on performance reviews by presenting best practices that other companies may use and providing actual data from the manufacturing industry. Finally, the "Analysis of The Performance Appraisal &amp; Bonus Payments at Real Ispat" hopes to establish a standard for other businesses looking to optimize their performance assessment procedures in addition to improving Real Ispat's internal operations. This research aims to promote a more driven, productive, and contented workforce—which will eventually drive corporate success and growth—through thorough analysis and practical recommendations. Submit Research Paper Submit Research Paper Publication Procedure Publication Ethics Call for Papers Publication Topics Guidelines Disclamiar Copyright Infragmentation FAQ Terms and Conditions Digital Object Identifier Editorial Board Health Care+ Download Manuscript Template Copyright Form Sample Certificates