Continuous Improvement Model Research Articles

An Extended Learning Analytics Framework Integrating Machine Learning and Pedagogical Approaches for Student Performance Prediction and Intervention

AbstractThe use of educational data mining and machine learning to analyse large data sets collected by educational institutions has the potential to discover valuable insights for decision-making. One such area that has gained attention is to predict student performance by analysing large educational data sets. In the relevant literature, many studies have focused on developing prediction models on student performance but comparatively less work exists on actions taken based on predicted at-risk students and evaluating their impact. Learning Analytics Intervention (LAI) studies have emerged as an approach that aims to address this gap. In LAI studies, student risk levels are predicted and disseminated to relevant stakeholders (academics, administrators and students) using learning analytics (LA) tools for targeted interventions. The interventions themselves are mainly left under the discretion of the academics and/or administrators, who are aware of the learning context and have the authority to make decisions, with LA tools facilitating this process. LAI studies have shown success in improving outcomes (e.g. improve pass rates, retention, grades), but their uptake has been slow. The main impediment to piloting LAIs by academics has been the lack of access to LAI infrastructure, which requires institutional investments to develop predictive models collecting data from diverse IT systems. Another challenge in LAIs is the development of effective interventions. This paper presents an extended LAI framework, termed Student Performance Prediction and Action (SPPA), which provides access to LAI infrastructure for academics to pilot LAIs in their courses without the need for institution-wide efforts. SPPA and its features are seamlessly accessed via a web browser and academics can develop course-specific predictive models based on historical course assessment data. Furthermore, SPPA integrates sound pedagogical approaches and provides relevant information (such as students’ knowledge gaps, personalised study plans) to assist academics in providing effective interventions. SPPA was evaluated by a number of academics piloting LAIs in their courses. Quantitative and qualitative data was collected and analysed. Academics were able to provide effective interventions using SPPA and also had a positive outlook on using SPPA and its features. SPPA is also provided as an open-source project for further development and can be a catalyst for widescale uptake in LAIs. Furthermore, a model for continuous improvement in LAIs is outlined along with a number of areas for future research and development.

Enhancing productivity through comprehensive evaluation and contextual analysis: A model for continuous improvement

The main objective of the case study is to measure the productivity of a laboratory dedicated to the development of dermatological products from coconut oil and other natural ingredients, as well as to analyze the influence of the external context on it in order to propose an improvement model that will allow it to improve its competitiveness in the market, strengthen its organizational structure and increase its profitability. This will be done through the application of the PESTEL analysis and the Integral Productivity Evaluation Technique in order to identify areas for improvement. It should be noted that the study is descriptive, with a mixed approach (qualitative and quantitative).

Digital maturity model for continuous improvement

The present research work presents the development and implementation of a Digital Maturation Model (MMD) designed and adapted to the Institute of Higher Education, Establishing the degree of Digital Maturity present in the Institute of Higher Education located in Orizaba, through the development and application of a Digital Maturity Model. Outlining strategic planning oriented to the analysis necessary to generate improvement proposals through the use of digital technologies, increasing efficiency and competitiveness, improving the service offered. The present research will take a quantitative methodology through a longitudinal study with the intention of obtaining a before and after, through a quantifiable approach, obtaining the evaluation of the Digital Maturity Index and the detection of areas of opportunity for the proposal of improvements.

School Climate Assessment and Continuous Improvement: What superintendent Beliefs Tell Us About Accountability Policy

Educational leaders are increasingly expected to use school climate data to improve outcomes and promote equity for all learners, including in California where school climate is included in state accountability policy. In this study, beliefs towards school climate assessment were explored in a sample (n=298) of California superintendents using a 37-item Likert-style instrument. Data were analyzed using an item response approach and latent class analysis. Findings showed variation in beliefs including three subgroups labeled “true believers” “still questioning” and “remains skeptical.” Findings suggest these groupings influence policy adoption and implementation. Education leaders are in a unique position to interpret and use school climate assessment data to facilitate change if they believe it is important, have the capacity, and trust the data. Implications for school and district improvement under the California continuous improvement model relies on stakeholders and local educational leaders believing in the value of school climate data and trusting its uses to effect change.

Promoting mental wellbeing among youth Australian Rules footballers through a model of continuous improvement.

The developmental period of adolescence is a known transitional life phase with unique risk and protective factors that can affect mental wellbeing outcomes. This, in combination with the pressures and demand of elite sport, make young elite athletes an important population in which positive mental wellbeing can be explored. This study aimed to examine the state of wellbeing, informed by positive psychology and the Positive emotions, Engagement, Relationships, Meaning, Accomplishment (PERMA) model of wellbeing, in a cohort of young athletes aged 16-19 years in the Australian setting as part of a model of continuous improvement. The objectives of these aims were to inform the development of a wellbeing curriculum for implementation in the Australian Rules Football talent pathway and to assess whether wellbeing presents differently in young athletes relative to the general school-attending population of that same age. Participants were 608 young Australian footballers participating in the developmental talent pathway during 2020 and 2021. There were 299 young male footballers, and 309 young women footballers who completed data collection. Wellbeing was assessed using the Engagement, Perseverance, Optimism, Connectedness, Happiness Measure of Adolescent Wellbeing (EPOCH) which corresponds to the PERMA model of wellbeing, and total mean scores were reported. Engagement and connectedness were similar for the young men and women athletes. Young men reported higher perseverance, optimism, happiness, and overall wellbeing relative to young women. Wellbeing among young athletes appears similar to the general population, however perseverance may be higher among young athletes. These findings form an important component of the continuous improvement model adopted in the football program in that the results informed the development of a tailored wellbeing curriculum program that is reflective of the wellbeing needs of the young athletes.

Digital Maturity Model for Continuous Improvement

The present research work presents the development and implementation of a Digital Maturation Model (MMD) designed and adapted to the Institute of Higher Education, Establishing the degree of Digital Maturity present in the Institute of Higher Education located in Orizaba, through the development and application of a Digital Maturity Model. Outlining strategic planning oriented to the analysis necessary to generate improvement proposals through the use of digital technologies, increasing efficiency and competitiveness, improving the service offered. The present research will take a quantitative methodology through a longitudinal study with the intention of obtaining a before and after, through a quantifiable approach, obtaining the evaluation of the Digital Maturity Index and the detection of areas of opportunity for the proposal of improvements.

A Virtual Pilot of a Multispecialty Group Mentoring Program for Underrepresented in Medicine (URiM) Trainees and Faculty

Background: Underrepresented in medicine (URiM) trainees and faculty often face limited mentorship and an increased sense of isolation in academic medicine. The objective of this pilot study was to determine if transitioning the Mentoring in Inclusion and Diversity program, a group mentoring model for URiM physicians spanning career levels and specialties, from an in-person to a virtual format during the COVID-19 pandemic was feasible and acceptable. Methods: MIND involves mentoring within multispecialty “families” composed of URiM medical students, residents, fellows, and faculty. A series of workshops comprises the formal program, which in 2020, transitioned to a virtual format. We applied the Context, Input, Process and Product (CIPP) evaluation model for continuous improvement of workshops. Through surveys, we elicited participants’ perceptions of engagement and helpfulness for each workshop, general feedback, and suggestions for improvement. Survey responses were analyzed descriptively, and open-ended comments were content-analyzed. Results: For the 2020-2021 academic year, the authors grouped 111 participants across 10 families and organized 6 virtual workshops on the following topics: How to be a Mentor, Meeting your MIND Family, Imposter Syndrome, Microaggressions, two Career Mentor Spotlights, and a book club. The CIPP model facilitated iterative improvements. Participants viewed workshops, on average, as being helpful (87%) and engaging (88%). Participants reported enjoyment of small groups, connecting with other URiM physicians, and desire for increased time for family discussions. Conclusions: The MIND group mentoring model was feasible and acceptable in a virtual format.

Design and Implementation of an Innovative, Rapid Data-Monitoring Strategy for Public Health Emergencies: Pilot of the United States School COVID-19 Mitigation Strategies Project.

During the COVID-19 pandemic, an urgent need existed for near-real-time data collection to better understand how individual beliefs and behaviors, state and local policies, and organizational practices influenced health outcomes. We describe the processes, methods, and lessons learned during the development and pilot testing of an innovative rapid data collection process we developed to inform decision-making during the COVID-19 public health emergency. We used a fully integrated mixed-methods approach to develop a structured process for triangulating quantitative and qualitative data from traditional (cross-sectional surveys, focus groups) and nontraditional (social media listening) sources. Respondents included students, parents, teachers, and key school personnel (eg, nurses, administrators, mental health providers). During the pilot phase (February-June 2021), data from 12 cross-sectional and sector-based surveys (n = 20 302 participants), 28 crowdsourced surveys (n = 26 820 participants), 10 focus groups (n = 64 participants), and 11 social media platforms (n = 432 754 503 responses) were triangulated with other data to support COVID-19 mitigation in schools. We disseminated findings through internal dashboards, triangulation reports, and policy briefs. This pilot demonstrated that triangulating traditional and nontraditional data sources can provide rapid data about barriers and facilitators to mitigation implementation during an evolving public health emergency. Such a rapid feedback and continuous improvement model can be tailored to strengthen response efforts. This approach emphasizes the value of nimble data modernization efforts to respond in real time to public health emergencies.

Workshop (Clinical/Best Practice Implementation) ID 2001167

Background Hamilton’s Spinal Cord Injury (SCI) Regional Rehabilitation Program in collaboration with SCI Consortium is implementing tissue integrity monitoring for pressure injury (PI) prevention. An increased prevalence of PI was found in 2021 - 2022 compared to 2020 on Hamilton’s SCI Rehabilitation Unit. To address this finding, the clinical team introduced a staff and patient education program focused on PI prevention. Upon completion of this workshop, the attendees will be aware of the CQI methodology used to implement the best practices related to tissue integrity and appreciate the education pathway for both staff and patients. Methods Using a Continuous Quality Improvement model, a systematic approach was used to enhance the education model for staff and patients on skin assessment and appropriate interventions. A process map was created outlining steps for each discipline involved in skin assessment, intervention, and patient education. Education plan included Skin & Wound Management workshop, individual and group patient education, daily skin check calendar for patient/staff use. The workshop will be delivered in a lecture format, ending with discussion time. Results Preliminary data post partial implementation of the education program showed a decrease of 12% in the prevalence of PIs in the patients discharged from the program between July 2022 and March 2023 compared to January 2021 – June 2022. Conclusion Preliminary results are encouraging in demonstrating that implementation of a comprehensive education plan for staff and patients using a structured framework is effective in reducing the prevalence of pressure injuries in the inpatient SCI rehab unit.

Evidence-based inquiry cycle: A model for meeting the council for the accreditation of educator preparation standard 5

This article outlines the need for support and best practices for educator preparation providers (EPPs) to meet the Council for the Accreditation of Educator Preparation (CAEP) standards, specifically, CAEP’s Standard Component 5.1 Quality Assurance System. The purpose of this article was to identify a continuous improvement model that EPPs can be utilized to support quality assurance processes in meeting CAEP Standard Component 5. The article used a secondary analysis methodology to examine past theories, models and research to outline best practices for EPPs. The findings provided a strong case for teacher education programs to utilize an evidence-based inquiry cycle for the foundation of their quality assurance systems and positive change in general. Additionally, best practices were identified to aid in maintaining such a system including support for providers, collaboration with stakeholders, an emphasis on positive culture, and a focus on quality outcomes and data. This article has implications not only for EPPs but for other providers focusing on quality assurance measures that should be supported through inquiry, strong evidence, and continuous improvement efforts. A practical recommendation of this study is that EPPs should use an evidence-based inquiry cycle to assist in developing, implementing, and modifying each provider’s quality assurance system to better serve their candidates.

Six-Sigma Reference Model for Industry 4.0 Implementations in Textile SMEs

The textile and apparel industry is a major contributor to Latin American economies. However, in these economies the industry is characterized by limited technological infrastructure, which has led to inefficient performance and the significant generation of water, material, and product waste through its supply chain (SC). Currently, Industry 4.0 has led to important benefits in manufacturing industries, but its application in the textile field has been limited to few case studies. The present work expands on this aspect with a critical review of Industry 4.0 concepts and principles, and our main contribution consists of an implementation guideline model. The model was based on Six Sigma and the Rothwell and Zegveld model for continuous improvement and innovation projects in manufacturing and business enterprises. In each step, key aspects such as the specific skills of the interdisciplinary team, assessment tools, and implementation/prototyping tools are highlighted. A discussion of a case study is presented to support the applicability of the model for Industry 4.0 pilot projects.

Upaya Peningkatan Kualitas dengan Menggunakan Analisis Siklus PDCA pada Perusahaan Otomotif

In this era of globalization, the competition in the automotive industry is getting more challenging, and consumer demands are also continually increasing. This is what encourages the automotive industry to make products as perfect as possible, one of which is minimizing the level of defects in their production lines. By reducing defects, the process will flow faster without going through the repair process. But in actuality, there are still defects found in the poses of its production. This research was conducted in the welding process in the automotive industry. This paper aims to identify defects in the welding production process in the automotive industry. Then analyze the most dominant defect, find the root cause, and improve. This study uses the PDCA cycle method. The PDCA cycle is a continuous improvement model consisting of four sequential components, Plan, Do, Check, and Action. The results of this study identified five major defects in the welding process: hole nut not centered, dents, NG stud bolts, NG parts, and non-stick spots. The average defect per month is 110 defects, and the dominant defect is the non-centered nut hole of 63.8 defects (58%). The root cause is when the part setting stopper position is unstable. The improvement made is adding a locator pin cylinder hole nut to stabilize the position. The results of this improvement can reduce welding defects to 58.5 defects per month, and the average defect hole nut is not centered by 3.5 defects (6%). So the PDCA cycle is very effective in efforts to improve product quality.

The Effect of Applying the DMAIC Model for Continuous Improvement in Reducing Supply chain risks : an analytical study of the opinions of a sample of managers in the General Company for the Pharmaceutical Industry and Medical Appliances / Samarra

The current research seeks to determine the extent of the impact of the of the DMAIC model for continuous improvement in reducing supply chain risks in the General Company for the Pharmaceutical Industry and Medical Appliances in Samarra, As a model that seeks to identify the root causes of the problem and work to find solutions that caused defects to occur through already defined stages based on the research problem represented by the main question that states (Does the application of the DMAIC model have a direct impact on reducing supply chain risks? ?) In order to answer this question, the questionnaire was formulated as a main tool for data collection, which included the research sample of the senior management in the company as well as managers of departments, units, and production and administrative divisions, which amounted to (225) individuals, and the data was analyzed using the SPSS V.26 program to test the research hypotheses emanating from his model, and the research reached a number of conclusions, the most prominent of which is the significant effect of applying the DMAIC model in reducing supply chain risks. Based on these conclusions, a set of proposals were presented that would serve the company under study.

Deming Cycle Utilization In Construction Project For Modular Building Construction For Oil And Gas Industry

Deming Cycle method, which is a standard method that contains a continuous improvement model developed by W. Edward Deming. This method consists of four main components arranged sequentially, namely the PDSA (Plan-Do-Study-Action) cycle. The workflow of the production department will be fast and efficient, while the Quality Control will be in a position to ensure that all work is completed to the exact specifications and terms according to the standards suggested in the project. The data collection method used to obtain data in writing this final project uses several data collection techniques of Observation, Interview and Documentation. The research model uses the Deming Cycle or PDCA Cycles. To re-check whether the corrective action is working as intended or not with FMEA analysis (Failure Mode and Effect Analysis). FMEA analysis is carried out with FMEA spreadsheet. The Deming Cycle Method is a methodology that will be used as a method to ensure that Total Quality Management will be able to reduce defects and repetitive work during production process for modular building construction in the oil and gas sector. This PDCA found that Quality Issues are the main factor of failures or delays which will impact to the cost.

Plan-Do-Check-Act Model (PDCA) as a Possible Methodological Basis for Antimonopoly Compliance

The article is devoted to the questions of functioning of the system of antimonopoly compliance, its implementation in Russia. It is proposed to use the model of continuous process improvement — PDCA — as a possible methodological basis for the functioning of the antitrust compliance system. The authors conclude that the continuous process improvement model (PDCA) can be applied in practice, which will also be used as a methodological basis for antimonopoly compliance. The PDCA methodology can contribute to the organization of a compliance program with elements such as a sales policy aimed at defining a transparent pricing mechanism and ensuring non-discriminatory conditions with counterparties.

Reducing length of stay in patients following liver transplantation using the model for continuous improvement

Length of stay (LOS) is a significant contributor to overall patient outcomes for patients undergoing liver transplantation. This study documents a quality improvement project aiming to reduce the median post-transplant...

Teaching reform and practice of 'Production Internship' course for biotechnology specialty from the perspective of training application-oriented talents

Production internship is an important teaching tache for undergraduate students to carry out engineering training by using professional skills, and it is a key starting point for fostering application-oriented talents in biotechnology. The Course Group of 'production internship of biotechnology majors' of Binzhou University is investigating application-oriented transformation for local regular colleges and universities, as well as fostering high-level application-oriented talents. By taking green fluorescent protein (GFP) polyclonal antibody as an example, the reform and practice on teaching content, teaching mode, assessment method, continuous improvement of curriculum were carried out. Moreover, the characteristics of the Yellow River Delta-Binzhou Biotechnology & Pharmaceutical Industrial Cluster were taken into account to intensify academic-enterprise cooperation. On one hand, this Course Group designed and rearranged the course contents, carried out essential training through online resources and platforms such as virtual simulation, and recorded, tracked and monitored the progress of production internship through practical testing and software platforms like 'Alumni State'. On the other hand, this Course Group established a practice-and application-oriented assessment method in the process of production internship and a dual evaluation model for continuous improvement. These reform and practices have promoted the training of application-oriented talents in biotechnology, and may serve as a reference for similar courses.

Blue Circle Health: A Novel Patient-Centered Model of Health Care Delivery for Low-Income Patients With Type 1 Diabetes.

Analog insulins, insulin pumps, and continuous glucose monitors (CGM) have revolutionized type 1 diabetes (T1D) treatment over the last 50 years. Nevertheless, less than 20% of patients in the United States reach guideline-based HbA1c targets. The dysfunctional delivery of U.S. health care has further worsened glycemic outcomes among structurally disadvantaged groups such as non-Hispanic Black and low-income populations. Administrative complexities resulting from mixed insurance coverage and delivery systems, incongruity between effective policies and reimbursement, structural racism, and implicit biases have led to high diabetes care-related costs, provider scarcity and burnout, and patient diabetes distress. The Extension for Community Healthcare Outcomes (ECHO) Diabetes tele-education outreach model was created to increase self-efficacy among primary care providers through a combination of weekly didactic sessions led by a team of diabetes experts and access to community-based peer coaches. As an evolution of ECHO Diabetes, Blue Circle Health has been established as a philanthropically funded health care delivery system, using a whole-person, individualized approach to T1D care for adults living in underserved communities. The program will provide direct-to-patient telehealth services, including diabetes education, management, and related psychological care regardless of ability to pay. Community-based diabetes support coaches will serve as the primary point of contact, or guide on the "Blue Circle Health Member Journey." Access to needed insulins, supplies, and CGMs will be provided at no cost to the individual. Through a continuous learning and improvement model, a person-centered, equitable, accessible, and effective health care delivery model will be built for people living with T1D.

Embedding data science innovations in organizations: a new workflow approach

Abstract There have been consistent calls for more research on managing teams and embedding processes in data science innovations. Widely used frameworks (e.g., the cross-industry standard process for data mining) provide a standardized approach to data science but are limited in features such as role clarity, skills, and cross-team collaboration that are essential for developing organizational capabilities in data science. In this study, we introduce a data workflow method (DWM) as a new approach to break organizational silos and create a multi-disciplinary team to develop, implement and embed data science. Different from current data science process workflows, the DWM is managed at the system level that shapes business operating model for continuous improvement, rather than as a function of a particular project, one single business unit, or isolated individuals. To further operationalize the DWM approach, we investigated an embedded data workflow at a mining operation that has been using geological data in a machine-learning model to stabilize daily mill production for the last 2 years. Based on the findings in this study, we propose that the DWM approach derives its capability from three aspects: (a) a systemic data workflow; (b) multi-disciplinary networks of collaboration and responsibility; and (c) clearly identified data roles and the associated skills and expertise. This study suggests a whole-of-organization approach and pathway to develop data science capability.

Retracted: Application of a Nursing Data-Driven Model for Continuous Improvement of PICC Care Quality.

[This retracts the article DOI: 10.1155/2022/7982261.].