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Background: Mathematics is not just a science for its own sake; it is a science that proves to be useful for most other sciences. Mathematical connections play a crucial role in helping students develop a deeper understanding and enhance their thinking about mathematics. These connections represent relationships between different mathematical ideas, between mathematics and other subjects, and between mathematics and everyday life. Aim: This study aims to classify students' mathematical connection abilities when solving non-routine mathematical problems. Method: This research employs a qualitative approach using descriptive and exploratory methods. The study involved 23 students who exhibited mathematical connection skills in problem-solving. Data collection methods included tests, observations, and interviews. The data analysis process consisted of two main stages: data reduction and data presentation. To ensure data validation, the triangulation method was used by comparing the results from the subject tests (answer sheets), observations, and interviews. Results: The findings of this study revealed three distinct classifications of students’ mathematical connections when solving non-routine problems: 1) Patterns; 2) Variables; and 3) Arguments Conclusion: The implications of these findings suggest that teachers need to be aware of the different classifications of students' mathematical connections. This awareness will enable them to implement mathematics learning strategies that cater to the diverse mathematical connection classifications found in their classrooms. For instance, teachers can utilize different mathematics learning media tailored to students’ learning styles, such as mind, incubation, and visual learning. By doing so, students can better optimize their mathematical connections and achieve improved outcomes in their mathematics learning.

Abstract In real-life situations, we have to analyze the data that contains the atypical observations, and the presence of outliers has adverse effects on the performance of ordinary least square estimates. In this situation, redescedning M-estimators, proposed by Huber (1964), are used to tackle the effects of outliers to increase the efficiency of least square estimates. In this study, we introduce a redescending M-estimator designed to generate robust estimates by mitigating the influence of outlier observations, even when the tuning constant is set to low values. This innovative estimator exhibits enhanced linearity at its core and maintains continuity throughout its range. Our proposed estimator stands out for its novelty, simplicity, differentiability, and practical applicability across real-world scenarios. The results of the proposed redescedning M-estimators are compared with existing robust estimators using an extensive simulation study. Two examples based on real-life data are also added to validate the performance of the suggested function. The formulated redescedning M-estimator produced efficient results as compared to all the considered redescedning M-estimators.

The integration of learning models and software is a trend in mathematics courses. However, no existing learning model for geometry courses involves the students in the making of a tool or media project. The researchers noticed the potential of the project-based learning (PjBL) model and GeoGebra in analytical geometry courses. This study revealed differences in the influence of the Geo-PjBL and PjBL models on students' achievement. The subjects consisted of 137 prospective mathematics teachers. The Basic Geometry Instrument (BGI) was used to measure the subjects' initial ability in basic geometry, and the Geometry Analytic Instrument (GAI) was used to evaluate the model and prospective teachers' performance. The Geo-PjBL and PjBL classroom activities lasted for 15 weeks. Both classrooms received the same content; the difference between the Geo-PjBL and PjBL classrooms was the tools used to present the problems and the project results. An analysis of covariance (ANCOVA) was conducted to analyze the data ([alpha] = 0.01). The Geo-PjBL model is more effective in applying analytical geometry subjects that require precision and accurate visual illustrations. Meanwhile, in the range of algebraic operations, the Geo-PjBL model is as effective as the PjBL model.

Abstract The Max-Mixed EWMA Exponentially Weighted Moving Average (MM EWMA) control chart is a statistical process control technique used for joint monitoring of the mean and variance of a process. This control chart is designed to detect small and moderate shifts in the mean and variance of a process by comparing the maximum of two statistics, one based on the mean and the other on the variance. In this paper, we propose a new MM EWMA control chart. The proposed chart is compared with existing control charts using simulation studies, and the results show that the chart performs better in detecting small and moderate shifts in both the mean and variance. The proposed chart can be helpful in quality control applications, where joint monitoring of mean and variance is necessary to ensure a product’s or process’s quality. The real-life application of the proposed control chart on yogurt packing in a cup data set shows the outperformance of the MM EWMA control chart. Both simulations & real-life application results demonstrate the better performance of the proposed chart in detecting smaller shifts during the production process.

Students need to have the ability to think in order to solve a problem from the problem at hand. This study aims to describe the reflective thinking ability of seventh grade students of SMP Batik Surakarta when they solve problems involving statistics in terms of Self-Regulated Learning. This research used qualitative research method with descriptive research type. The subjects in this study were students of class VII C SMP Batik Surakarta. Data collection begins with filling out a self-regulated learning questionnaire by students and reflective thinking ability test questions then from the results of the categorisation based on the results of the questionnaire conducted interviews. The results of this study are students who have self-regulated learning with high category have high reflective thinking ability can fulfil the indicators of reflective thinking reacting, comparing, and contemplating. Students who have moderate self-regulated learning have moderate reflective thinking ability can fulfil two indicators of reacting, and comparing. Students who have low self-regulated learning category have less reflective thinking ability that only fulfils one reflective thinking indicator which is reacting

Abstract The increased global warming has increased the likelihood of recurrent drought hazards. Potential links between the frequency of extreme weather events and global warming have been suggested by earlier research. The spatial variability of meteorological factors over short distances can cause distortions in conclusions or limit the scope of drought analysis in a particular region when extreme values predominate. Therefore, it is challenging to make trustworthy judgments regarding the spatiotemporal characteristics of regional drought. This study aims to improve the quality and accuracy of regional drought characterization and the process of continuous monitoring. The new drought indicator presented in this study is called the Support Vector Machine based drought index (SVM-DI). It is created by adding different weights to an SVM-based X-bar chart that is displayed with regional precipitation aggregate data. The SVM-DI application site is located in Pakistan's northern area. Using the Pearson correlation coefficient for pairwise comparison, the study compares the SVM-DI and the Regional Standard Precipitation Index (RSPI). Interestingly, compared to RSPI, SVM-DI shows more pronounced regional characteristics in its correlations with other meteorological stations, with a significantly lower Coefficient of Variation. These results confirm that SVM-DI is a useful tool for regional drought analysis. The SVM-DI methodology offers a unique way to reduce the impact of extreme values and outliers when aggregating regional precipitation data.

Abstract This article introduces an adaptive approach within the Bayesian Max-EWMA control chart framework. Various Bayesian loss functions were used to jointly monitor process deviations from the mean and variance of normally distributed processes. Our study proposes the mechanism of using a function-based adaptive method that picks self-adjusting weights incorporated in Bayesian Max-EWMA for the estimation of mean and variance. This adaptive mechanism significantly enhances the effectiveness and sensitivity of the Max-EWMA chart in detecting process shifts in both the mean and dispersion. The Monte Carlo simulation technique was used to calculate the run-length profiles of different combinations. A comparative performance analysis with an existing chart demonstrates its effectiveness. A practical example from the hard-bake process in semiconductor manufacturing is presented for practical context and illustration of the chart settings and performance. The empirical results showcase the superior performance of the Adaptive Bayesian Max-EWMA control chart in identifying out-of-control signals. The chart’s ability to jointly monitor the mean and variance of a process, its adaptive nature, and its Bayesian framework make it a useful and effective control chart.

Abstract In the current study, we demonstrate the use of a quality framework to review the process for improving the quality and safety of the patient in the health care department. The researchers paid attention to assessing the performance of the health care service, where the data is usually heterogeneous to patient’s health conditions. In our study, the support vector machine (SVM) regression model is used to handle the challenge of adjusting the risk factors attached to the patients. Further, the design of exponentially weighted moving average (EWMA) control charts is proposed based on the residuals obtained through SVM regression model. Analyzing real cardiac surgery patient data, we employed the SVM method to gauge patient condition. The resulting SVM-EWMA chart, fashioned via SVM modeling, revealed superior shift detection capabilities and demonstrated enhanced efficacy compared to the risk-adjusted EWMA control chart.

Abstract The OLS model is built on the assumption of normality in the distribution of error terms. However, this assumption can be easily violated, especially when there are outliers in the data. A single outlier can disrupt the normality assumption of error terms, making the OLS model less effective. In such situations, M-estimators (MEs) come into play to obtain reliable estimates. We introduce a redescending M-estimators (RME) for robust regression to handle datasets with outliers. The proposed RME produces more robust estimates by effectively managing the influence of outliers, even at lower values of the tuning constant. We compared the performance of this estimator with existing RMEs using real-life data examples and an extensive simulation study. The results show that our suggested RME is more efficient than the compared ME in various situations.

Corrosion in maritime infrastructure, particularly in reinforced concrete, has emerged as a significant cause for concern due to the presence of chloride ions in seawater. To address this challenge, geopolymer concrete has been proposed as a viable solution for retrofitting and restoring marine structures. This review paper explores the potential application of fly ash geopolymer concrete in marine infrastructure restoration. Fly ash's properties make it ideal for marine infrastructure restoration. Its high levels of amorphous silica and alumina enable geopolymerization, forming a strong binder resistant to chloride corrosion. Its fine, spherical particles enhance concrete workability and density, improving mechanical strength and impermeability. This geopolymer binder offers excellent resistance to corrosion from chloride ions commonly found in seawater, making fly ash geopolymer concrete highly suitable for marine applications. Overall, fly ash's chemical composition and physical traits offer resilience and sustainability in restoring marine infrastructure, ensuring long-term durability against corrosion. This review paper explores the potential application of fly ash geopolymer concrete in marine infrastructure restoration. By examining the primary forms of damage and mechanisms underlying concrete degradation in marine settings, this study highlights the durability and sustainability of geopolymer concrete compared to traditional concrete. Additionally, it discusses current solutions for repairing and retrofitting concrete in marine environments, emphasizing the promising characteristics of geopolymer concrete for integration into such structures. Through this analysis, innovative and environmentally conscious approaches are introduced for addressing corrosion-related challenges in the maritime industry, offering a resilient solution for the construction of enduring marine structures. Finally, recommendations for further research on the application of fly ash geopolymer concrete in marine infrastructure restoration are presented.

Despite technological advances, clinical skills are the foundation of medical practice. Taking a comprehensive clinical history, performing physical examinations, and communicating effectively are crucial for high-quality patient care. Medical students need to focus on developing strong clinical skills to ensure they are well-prepared for their careers and adapt to the evolving field of medicine through continual practice and refinement.1 The medical community is worried about declining essential clinical skills among undergraduate medical students.2,3 This editorial aims to analyze the reasons for the decline and suggest evidence-based methods to address the situation. 05885.0258063. The declining standards of clinical skills among medical students can be attributed to the widespread adoption of technology-based learning, which has the potential to diminish the importance of traditional hands-on bedside teaching.4 Technology should enhance, not replace, practical clinical experiences. Practical training improves the clinical abilities, expertise, and self-assurance of medical students.5 Moreover, the growing magnitude and expansion of medical curricula provide difficulties in the development of educational programs without ample time for bedside practical teaching.6,7 There is frequently an excessive focus on remembering facts rather than on cultivating clinical reasoning abilities.8 The reduced patient interaction during medical school, due to increased reliance on simulations and less time in clinical rotations and bedside teaching, hampers the development of clinical skills.9 External factors such as limited time and extensive paperwork may affect the quality of patient encounters. Efficient communication is crucial for building good relationships with patients, obtaining accurate medical histories, and ensuring patient understanding and adherence to treatment.9 Insufficient training in communication skills can result in less-than-ideal interactions between patients and healthcare providers and can also contribute to errors in diagnosis and medical treatment.10 The provision of trained faculty for teaching medical students is an issue worldwide. Insufficient supervision and feedback during clinical rotations hinder the development of essential clinical skills.11 Larger student cohorts reduce the opportunities for individual students to practice and receive personalized instruction in clinical skills.12 Medical education often prioritizes theoretical testing over practical examinations and undervalues communication skills training. Variability in clinical experiences at different medical schools can lead to inconsistencies in skill acquisition. The scarcity of skilled clinicians as role models impacts students’ learning. Economic constraints and modern healthcare pressures limit opportunities for thorough patient interactions and clinical training.13 THE WAY FORWARD Educational institutions should prioritize hands-on clinical experiences throughout the curriculum to improve students’ diagnostic and communication abilities. These experiences should include simulation-based learning, standardized patients, and longitudinal clerkships. Faculty members need support and resources for effective clinical mentorship and development. Reflective practice should be integrated into the curriculum to enhance self-awareness and critical thinking. Open communication and mentorship programs between students and faculty can strengthen medical training and promote continual learning. To address declining clinical proficiency among medical students, institutions can adopt evidence-based approaches including curriculum changes, faculty training, promoting reflective thinking, collaboration among healthcare professionals, and fostering a culture of ongoing learning. Regulatory bodies should ensure adequate time for bedside teaching and appropriate assessment weightage.

Knowing the level of SARS-CoV-2 transmission is crucial for decision-making on Coronavirus disease (COVID-19) handling. Guidelines for measuring SARS-CoV-2 antibodies to estimate seroprevalence are conceptually straightforward and internationally available. However, challenges exist for developing countries with limited laboratory capacity, diverse geographical topography, fragmented health information systems and limited resources such as Indonesia. One year after the first case was confirmed in Indonesia, approximately ten infections were undiagnosed or underreported for every reported case. Under those circumstances, we described the method and lessons learned in conducting nationwide cohort COVID-19 serology surveys in a nation with limited resources, such as Indonesia. • Strong cross-sectoral collaboration between ministries and levels of governance (central and subnational) enables strategic use of civil registration database for efficient sampling. • All agglomeration districts (highly dense population and urban area) were selected as study sites, and additional non-agglomeration districts were selected using probability-proportional-to-size (PPS). • Involving the experienced phlebotomist in community health service provider and district laboratory cut down preparation time.

Mentoring by the mentor teachers to the trainee teacher is an important element in the component of professional practice, namely practicum. Mentoring of mentor teachers in this study refers to the quality of guidance given by mentor teachers to trainee teachers. This aspect is seen to have an impact on the success of trainee teachers during the practicum session. However, there are past studies that state that mentor teachers do not provide guidance as required by the trainee teachers. Therefore, this study aimed to examine the quality of mentoring of mentor teachers from the perspective of trainee teachers. This study used a mixed-method approach. Quantitative study was using a questionnaire namely mentor teacher's guidance while utilizing random sampling method was used on a sample of 217 trainee teachers from the Institute of Teacher Education in the northern zone of Malaysia who have undergone the practicum session. The qualitative approach, on the hand involve semi-structured interview with two trainee teachers as the participants. This study found that the quality of guidance of mentor teachers is at a very good level. Quantitative findings are supported by qualitative findings. Four themes were identified from the interview analysis namely; excellent guidance, informative, cooperation and the needs of mentor teachers.

Palm oil shows significant potential for development as a lubricant due to its long molecular fatty acid chain, which is capable of minimizing wear and friction. However, in certain conditions, the formation of a thin layer of fluid film may no longer sustain to protect the contact surfaces. Therefore, a relevant additive is needed to address this issue. In this study, trimethylolpropane (TMP) was used as a lubricant, and Graphene Oxide (GO) was employed as the nano-based additive. A Four-ball tribotester was utilized to determine the friction coefficient, wear scar, and surface roughness of the contact surfaces under various operating conditions. The results were compared to mineral and synthetic-based oils. It was found that the combination of TMP and GO exhibited a lower friction coefficient compared to mineral and synthetic-based oils, but slightly higher compared to pure TMP. However, TMP+GO demonstrated significant improvement in terms of wear scar diameter and surface roughness. The physical appearance of the wear was also discussed in this study.

Abstract The simultaneous monitoring of both process mean and dispersion, particularly in normal processes, has garnered significant attention within the field. In this article, we present a new Bayesian Max-EWMA control chart that is intended to track a non-normal process mean and dispersion simultaneously. This is accomplished through the utilization of the inverse response function, especially in cases where the procedure follows a Weibull distribution. We used the average run length (ARL) and the standard deviation of run length (SDRL) to assess the efficacy of our suggested control chart. Next, we contrast our suggested control chart's performance with an already-existing Max-EWMA control chart. Our results show that compared to the control chart under consideration, the proposed control chart exhibits a higher degree of sensitivity. Finally, we present a useful case study centered around the hard-bake process in the semiconductor manufacturing sector to demonstrate the performance of our Bayesian Max-EWMA control chart under different Loss Functions (LFs) for a Weibull process. The case study highlights how flexible the chart is to various situations. Our results offer strong proof of the outstanding ability of the Bayesian Max-EWMA control chart to quickly identify out-of-control signals during the hard-bake procedure. This in turn significantly contributes to the enhancement of process monitoring and quality control.

The research area is an oil and gas field that is currently undergoing development, located in the South Sumatra Basin. Coal content, particularly within the Lower Talang Akar Formation (LTAF) interval, is frequently encountered in this field, with coal serving as the source of in-situ CO2 content formation. Consequently, CO2 content can be identified using the Amplitude Versus Offset (AVO) analysis method, allowing an assessment of its influence on the percentage presence of CO2. The data used in this study consist of well data, including log data (Density, Porosity, Vp & Vs Sonic/DT), Drill Stem Test (DST) data, well marker data resulting from stratigraphic sequence interpretation, and well reports. The AVO method to be employed encompasses a wide-angle range, approximately 0°-45°. The results obtained from this research using the AVO method, after AVO modelling on well data, indicate that the dominant AVO response within the LTAF interval is class 4 AVO. AVO analysis results regarding AVO presence indicate that reservoir intervals containing CO2 content will exhibit a class 4 AVO response, and an increase in fluid content percentage will result in a more positive intercept and a more negative gradient (towards class 2 AVO). In terms of facies and depth variations, the AVO attribute response consistently demonstrates an increase in intercept and a decrease in gradient with rising CO2 levels.

Abstract In this article, we introduce a novel Bayesian Max-EWMA control chart under various loss functions to concurrently monitor the mean and variance of a normally distributed process. The Bayesian Max-EWMA control chart exhibit strong overall performance in detecting shifts in both mean and dispersion across various magnitudes. To evaluate the performance of the proposed control chart, we employ Monte Carlo simulation methods to compute their run length characteristics. We conduct an extensive comparative analysis, contrasting the run length performance of our proposed charts with that of existing ones. Our findings highlight the heightened sensitivity of Bayesian Max-EWMA control chart to shifts of diverse magnitudes. Finally, to illustrate the efficacy of our Bayesian Max-EWMA control chart using various loss functions, we present a practical case study involving the hard-bake process in semiconductor manufacturing. Our results underscore the superior performance of the Bayesian Max-EWMA control chart in detecting out-of-control signals.

Abstract Quality control often employs memory-type control charts, including the exponentially weighted moving average (EWMA) and Shewhart control charts, to identify shifts in the location parameter of a process. This article pioneers a new Bayesian Adaptive EWMA (AEWMA) control chart, built on diverse loss functions (LFs) such as the square error loss function (SELF) and the Linex loss function (LLF). The proposed chart aims to enhance the process of identifying small to moderate as well as significant shifts in the mean, signifying a notable advancement in the field of quality control. These are implemented utilizing an informative prior for both posterior and posterior predictive distributions, employing various paired ranked set sampling (PRSS) schemes. The effectiveness of the suggested chart is appraised using average run length (ARL) and the standard deviation of run length (SDRL). Monte Carlo simulations are employed to contrast the recommended approach against other control charts. The outcomes demonstrate the dignitary performance of the recommended chart in identifying out-of-control signals, especially applying PRSS designs, in comparison to simple random sampling (SRS). Finally, a practical application was conducted in the semiconductor manufacturing context to appraise the efficacy of the offered chart using various paired ranked set sampling strategies. The results reveal that the suggested control chart performed well in capturing the out-of-control signals far better than the already in use control charts. Overall, this study interposes a new technique with diverse LFs and PRSS designs, improving the precision and effectiveness in detecting process mean shifts, thereby contributing to advancements in quality control and process monitoring.

The aim of this study was to evaluate the degree of the evaluation of the educational management program in light of the academic accreditation standards in the universities of Jordan in Irbid from the point of view of the faculty members. The researchers used the descriptive analytical approach and the study sample consisted of (17) faculty members specialized in educational administration. The results showed that the degree of evaluation educational administration program in the light of academic accreditation standards from the viewpoint of faculty members was average (3.47), and a mean of (0.46) and the results showed that there were no significant in estimates of faculty members to the degree of evaluation of the educational administration program due to variables (gender, academic rank, and experience), the recommendations include working on improving the educational administration program in light of the academic accreditation standards in terms of the content and goals, study plan.

This systematic literature review examines the development of Technological, Pedagogical and Content Knowledge (TPACK) in mathematics education and its impact on teaching. The study analyzed 25 peer-reviewed articles published between January of 2018 and December of 2022 from the Scopus and ERIC databases to answer four research questions. The results reveal that most studies on TPACK have been conducted in Turkey, the United States, Costa Rica, and Malaysia, with both in-service and pre-service mathematics teachers as subjects. The majority of the studies focus on the use of digital technology and identify factors that hinder the development of TPACK among mathematics teachers, including lack of confidence, resistance to change, lack of institutional support, and inadequate resources. The study recommends active participation in pedagogical technology training for teachers and prospective teachers to improve mathematics teaching. Although the use of technology in teaching and learning offers many benefits, it also presents challenges that need to be addressed to effectively integrate technology into mathematics teaching and enhance the development of TPACK among mathematics teachers.

This paper seeks to explore the challenges faced by excellent teachers in preparing for authentic assessment in their English as a Second Language (ESL) classrooms. This qualitative case study involving six excellent teachers was based on purposive sampling. Data sources in this study used classroom observation and a series of semi-structured interviews with the excellent teachers. Thematic analysis was used to identify emerging themes from the codes gathered from the interviews and observations. Data showed that excellent teachers faced multiple challenges prior to preparing for authentic assessment. They have also been using variations of authentic assessment to help children in their learning besides accurate documentation and extensive reading from around the globe to equip themselves with the current knowledge. Since there is no clear guideline for teachers who practice authentic assessment in their classes, this study provided some insights on the preparations and the use of authentic assessment as part of their teaching and learning process.

Abstract This study introduces the Bayesian adaptive exponentially weighted moving average (AEWMA) control chart within the framework of measurement error, examining two separate loss functions: the squared error loss function and the linex loss function. We conduct an analysis of the posterior and posterior predictive distributions utilizing a conjugate prior. In the presence of measurement error (ME), we employ a linear covariate model to assess the control chart's effectiveness. Additionally, we explore the impacts of measurement error by investigating multiple measurements and a method involving linearly increasing variance. We conduct a Monte Carlo simulation study to assess the control chart's performance under ME, examining its run length profile. Subsequently, we offer a specific numerical instance related to the hard-bake process in semiconductor manufacturing, serving to verify the functionality and practical application of the suggested Bayesian AEWMA control chart when confronted with ME.

Abstract The article introduces a novel Bayesian AEWMA Control Chart that integrates different loss functions (LFs) like the square error loss function and Linex loss function under an informative prior for posterior and posterior predictive distributions, implemented across diverse ranked set sampling (RSS) designs. The main objective is to detect small to moderate shifts in the process mean, with the average run length and standard deviation of run length serving as performance measures. The study employs a hard bake process in semiconductor production to demonstrate the effectiveness of the proposed chart, comparing it with existing control charts through Monte Carlo simulations. The results underscore the superiority of the proposed approach, particularly under RSS designs compared to simple random sampling (SRS), in identifying out-of-control signals. Overall, this study contributes a comprehensive method integrating various LFs and RSS schemes, offering a more precise and efficient approach for detecting shifts in the process mean. Real-world applications highlight the heightened sensitivity of the suggested chart in identifying out-of-control signals compared to existing Bayesian charts using SRS.

Abstract In recent times, there has been a growing focus among researchers on memory-based control charts. The Exponentially Weighted Moving Average (EWMA) and Cumulative Sum (CUSUM) charts and the adaptive control charting approaches got the attention. Control charts are commonly employed to oversee processes, assuming the monitored variable follows a normal distribution. However, it's worth noting that this assumption does not hold true in many real-world situations. The use of the algebraic expression for normalization, which can be used for all kinds of skewed distributions with a closed-form distribution function, using the proposed continuous function to adapt a smoothing constant, motivates this study. In the present manuscript, we design an EWMA statistic-based adaptive control chart to monitor the irregular variations in the mean of two parametric Weibull distribution and use Hasting approximation for normalization. The adaptive control charts are used to update the smoothing constant according to the estimated shift. Here we use the proposed continuous function to adapt the smoothing constant. The average run length and standard deviation of run length are calculated under different parameter settings. The effectiveness of the proposed chart is argued in terms of ARLs over the considered EWMA chart through Monte-Carlo (MC) simulation method. The proposed chart is examined, followed by a real data set to demonstrate the design and application procedures.

Some previous studies tried to improve students' understanding of the concept of the composition of functions. However, only some research results still examine efforts to increase student understanding of the concept taught online, especially by utilizing the STEAM-integrated Flip Flop learning model. Therefore, the purpose of this study is to analyze the level of effectiveness of the application of the STEAM-integrated Flip Flop model in increasing students' understanding of composition function material taught online. To answer the purpose of the study, the authors compared the model with the Flipped Learning model and the conventional learning model. The quantitative method with a pretest-posttest control group design model is employed in the present study by engaging 90 Indonesian senior high school students. This study finds that the STEAM-integrated Flip Flop model is significantly better at improving students' understanding of the composition of function concepts than conventional models. However, the same result is not found if compared with the Flipped Learning model. Though the average score of the STEAM-integrated Flip Flop model is better than the Flipped Learning model, the difference is not significant. This study also describes the related syntax of the STEAM-integrated Flip Flop model that high school math teachers can use to teach better composition functions online.

Abstract Control charts, including exponentially moving average (EWMA) , are valuable for efficiently detecting small to moderate shifts. This study introduces a Bayesian EWMA control chart that employs ranked set sampling (RSS) with known prior information and two distinct loss functions (LFs), the Square Error Loss function (SELF) and the Linex Loss function (LLF), for posterior and posterior predictive distributions. The chart's performance is assessed using average run length (ARL) and standard deviation of run length (SDRL) profiles, and it is compared to the Bayesian EWMA control chart based on simple random sampling (SRS). The results indicate that the proposed control chart detects small to moderate shifts more effectively. The application in semiconductor manufacturing provides concrete evidence that the Bayesian EWMA control chart, when implemented with RSS schemes, demonstrates a higher degree of sensitivity in detecting deviations from normal process behavior. Comparison to the Bayesian EWMA control chart using SRS, it exhibits a superior ability to identify and flag instances where the manufacturing process is going out of control. This heightened sensitivity is critical for promptly addressing and rectifying issues, which ultimately contributes to improved quality control in semiconductor production.

Abstract Adaptive EWMA (AEWMA) control charts have gained remarkable recognition by monitoring productions over a wide range of shifts. The adaptation of computational statistic as per system shift is the main aspect behind the proficiency of these charts. In this paper, a function-based AEWMA multivariate control chart is suggested to monitor the stability of the variance–covariance matrix for normally distributed process control. Our approach involves utilizing an unbiased estimator applying the EWMA statistic to estimate the process shift in real-time and adapt the smoothing or weighting constant using a suggested continuous function. Preferably, the Monte Carlo simulation method is utilized to determine the characteristics of the suggested AEWMA chart in terms of proficient detection of process shifts. The underlying computed results are compared with existing EWMA and existing AEWMA charts and proved to outperform in providing quick detection for different sizes of shifts. To illustrate its real-life application, the authors employed the concept in the bimetal thermostat industry dataset. The proposed research contributes to statistical process control and provides a practical tool for the solution while monitoring covariance matrix changes.

Online Distance Learning (ODL) is a prominent method used in conducting online class for institutions of higher learning (IHLs) in Malaysia since COVID-19 pandemic. Therefore, it is vital to understand students’ emotional engagement during online class to improve learning. This study focuses on university’s students’ preference in participating during online class and their emotional reactions by identifying their positive or negative feelings toward online class participation. A quantitative method was used and questionnaire was addressed to 163 Information Science Studies students from College of Computing, Informatics and Mathematics, Universiti Teknologi MARA (UiTM) Negeri Sembilan Branch, Rembau Campus. The results showed that 65.6% of students prefer to participate actively during online class with positive feelings as revealed by the study where 17.8% students enjoy being acknowledged and feels appreciated if lecturer call upon their name in class to share their opinions and ideas or answer questions given to them. In addition, 14% students stated this action can boost their confidence and enhance their communication skills. These findings are in line with previous research that showed interpersonal relationships and interaction between students and lecturers have a pivotal role in inducing students’ confidence and involvement. Active communication between lecturers and students can promotes students’ sense of belonging which make them more interested in capturing the information and knowledge convey in class. Hence, the findings may spark interests for lecturers or educators to engage students learning so active learning session can be developed and benefits both parties. Thus, may give positive influence on lecturers-students relationship.