Performance Evaluation Matrix Research Articles

Construction of on-site DNA pre-treatment device and rapid electrochemical biosensor set for Escherichia coli detection in milk

Restaurants and cafeterias are constantly exposed to bacterial contamination. In such environments, it is highly needed the rapid bacteria detection system with simple pre-treatment device. To solve this problem, this study presented the pre-treatment device and Rapid electrochemical (REC) biosensor set for detecting Escherichia coli (E. coli) gene in milk sample. The pre-treatment device is consisted of tetraethyl orthosilicate (TEOS)-coated bead that operated with sonication method, and it can effectively remove the E. coli residues and extract E. coli DNA well. By analyzing the sequences of various foodborne pathogens, we selected the highly variable region sequences of E. coli DNA to use as the bioprobe for REC biosensor. The selected thiol-modified bioprobe was immobilized onto the fabricated biosensor electrode. For detecting the extracted E. coli gene fragment rapidly, the alternating current electrothermal flow (ACEF) technique was introduced to REC-biosensor. ACEF technique minimize the target-bioprobe binding time to 10 mins. Using the extracted E. coli DNA fragments with the pre-treatment device, the proposed REC-biosensor shows the limit of detection (LOD) in milk samples was 9.235 × 10-5 ng/μL, and the target detection accuracy was 92.5 %, with a maximal error rate up to 6.730 %. Moreover, the biosensor demonstrated high selectivity in electrochemical performance evaluation (Matrix effect = 7.425 %). The developed pre-treatment device provides simple and quick preprocessing time (within 20 min) of E. coli, and REC biosensor provides the rapid detection of E. coli (within 10 min). In conclusion, the proposed pre-treatment device and biosensor set can be easily applied to other types of bacteria, virus or germs in the near future.

Performance Management Decision-Making Model: Case Study on Foreign Language Learning Curriculums

Foreign language learning courses can be regarded as a service operation system, and a complete foreign language learning course performance evaluation model can help improve the effectiveness of student learning. The performance evaluation matrix (PEM) is an excellent tool for evaluation and resource management decision making, and the administrator uses the satisfaction and the importance indices to establish evaluation coordinate points based on the rules of statistical testing. The coordinate points of all service items are plotted into the PEM to grasp the full picture and to make decisions on what to improve or to consider resource transfers so as to elevate the overall satisfaction of the entire service. However, plotting all the coordinate points on the PEM can only be performed by programming, which will lead to limitations in practice. Therefore, instead of the above evaluation rules, this article uses the confidence intervals of decision-making indicators to form a validity evaluation table, to decide which teaching service items should be improved, maintained, or transferred to improve the satisfaction of the entire service system. This form of performance evaluation can be completed using any commonly used word-processing software, so it is easy to apply and promote. Finally, this article provides an applied example to illustrate the proposed method.

Comparative Analysis of Naïve Bayes and K-Nearest Neighbors Algorithms in Predicting Public Interest in Electric Motorcycles

Concerns about global warming and the need for sustainable transport solutions have led to the emergence of electric vehicles as an alternative to conventional vehicles. These vehicles offer cleaner and more efficient transportation, especially in urban areas. However, high costs in some countries, such as Indonesia, hinder their adoption. Electric motorcycle companies use previously recorded data to predict customer interest in purchasing their products. Based on these forecasts, business owners make decisions about the quantity of goods to supply. Researchers are increasingly using machine learning algorithms to study consumer behavior and predict demand for a variety of products, including electric motorcycles. The researchers have used this data-based method to analyze large amounts of consumer data, including online survey responses and reviews, as well as other sources. The study aims to perform a comparative analysis of the performance of the Naïve Bayes and K-Nearest Neighbors algorithms to predict public interest in electric motorcycles in Labuhanbatu district. We will perform a comparative analysis based on the performance evaluation matrix (accuracy, precision, recall, and f1 score) to determine the most suitable algorithm. The phases of the research methods included: data collection, exploratory data analysis, data preprocessing, splitting data, implementation of naïve bayes and k-nearest neighbors, and evaluation. The results showed that Naïve Bayes achieved an accuracy of 31.13%, precision of 83.33%, recall of 4.63%, and f1-score of 8.77%. In contrast, K-Nearest Neighbors attains an accuracy of 71.52% and a precision of 71.52%, recall 100%, and f1 score of 83,40%. According to the research results, K-nearest neighbors showed much better results in terms of accuracy, recall, and F1 scores. These results demonstrate that K-nearest neighbor is more effective in detecting public interest in electric motorcycles, and it strikes a better balance between identifying all positive cases and avoiding predictive errors.

Comparative Analysis of Machine Learning Algorithm Performance in Predicting Stunting in Toddlers

Stunting is a condition where the growth of children and toddlers is stunted, which causes children to be shorter than they should be. In the long term, stunting can reduce reproductive health, study concentration, and work productivity, thereby causing significant state losses. The prevalence of stunting in Indonesia, which is still above 20 percent, shows that there are still chronic nutritional problems among toddlers. To prevent this from happening, identification as early as possible can be done using machine learning for predictions. The aim of this research is to conduct a comparative analysis of the performance of machine learning algorithms for predicting stunting in toddlers. Random Forest, K-Nearest Neighbors, and Extreme Gradient Boosting are the algorithms that are compared for their performance. The performance of each algorithm is measured using evaluation matrices such as accuracy, precision, recall, and f1-score. The research method starts with data collection, data preprocessing, data splitting, application of machine learning algorithms, evaluation of algorithm performance, and comparison of results. The performance evaluation matrix measurement results show that Random Forest has an accuracy of 99.95%, precision of 99.89%, recall of 99.94%, and f1-score of 99.91%. K-Nearest Neighbors has an accuracy of 99.93%, precision of 99.87%, recall of 99.88%, and f1-score of 99.88%. Meanwhile, Extreme Gradient Boosting has an accuracy of 99.36%, precision of 98.86%, recall of 98.95%, and f1-score of 98.90%. From the results of all performance evaluation matrices, it can be concluded that the random forest algorithm is the best algorithm for predicting stunting in toddlers.

Development of performance evaluation matrix for farmer producer organizations in India

Development of performance evaluation matrix for farmer producer organizations in India

Fuzzy Decision-Making and Resource Management Model of Performance Evaluation Indices

The Performance Evaluation Matrix (PEM) is an excellent decision-making tool for assessment and resource management. Satisfaction Index and Importance Index are two important evaluation indicators of construction and PEM. Managers can decide whether the service item needs to be improved based on the Satisfaction Index of the service item. When resources are limited, managers can determine the priority of improving the service item based on the Importance Index. In order to avoid the risk of misjudgment caused by sample errors and meet the needs of enterprises’ rapid decision-making, this study proposed a fuzzy test built on the confidence intervals of the above two key indicators to decide whether essential service items should be improved and determine the priority of improvement. Since the fuzzy test was relatively complex, this study further came up with fuzzy evaluation values and fuzzy evaluation critical values of service items following fuzzy testing rules. Besides, evaluation rules were established to facilitate industrial applications. This approach can be completed with any common word processing software, so it is relatively convenient in application and easy to manage. Finally, an application example was presented in this paper to explain the applicability of the proposed approach.

Integrating Importance-Satisfaction Model and Performance Evaluation Matrix to improve service quality

Purpose: The Taiwanese coffee market is growing every year, together with progressive changes in national habits and the concept of drinking coffee. This study examines a particular coffee studio and explores its service quality within a competitive environment as a basis for improvement. Research methodology: The design of service elements for the questionnaire was based on the relevant literature. The study identified 19 quality attributes as survey items, all of which were then subsumed under the Importance-Satisfaction Model (I-S Model) and Performance Evaluation Matrix (PEM) for service quality. Results: There are 8 service elements falling in “Excellent area,” 3 service elements falling in “To be improved area,” 3 service elements falling in “Surplus area,” and 6 service elements falling in “Care-free area” as shown by the results of Importance-Satisfaction Model introduction. With regard to the Performance Evaluation Matrix which are introduced later on, 2 service elements fall in “Priority improvement area,” 17 service elements fall in “Improvement area,” and 1 service element falls in “Maintenance area.” Limitations: The results of this study are only applicable to the analysis of the coffee studio survey, and cannot be extrapolated to explain improvements in service quality across the entire coffee industry. Contribution: It is expected that items in dire need of improvement can be confirmed by introducing a service quality model to enhance service quality, build a corporate image, and increase competitive advantage.

Spectral analysis and Bi-LSTM deep network-based approach in detection of mild cognitive impairment from electroencephalography signals.

Mild cognitive impairment (MCI) is a neuropsychological syndrome that is characterized by cognitive impairments. It typically affects adults 60years of age and older. It is a noticeable decline in the cognitive function of the patient, and if left untreated it gets converted to Alzheimer's disease (AD). For that reason, early diagnosis of MCI is important as it slows down the conversion of the disease to AD. Early and accurate diagnosis of MCI requires recognition of the clinical characteristics of the disease, extensive testing, and long-term observations. These observations and tests can be subjective, expensive, incomplete, or inaccurate. Electroencephalography (EEG) is a powerful choice for the diagnosis of diseases with its advantages such as being non-invasive, based on findings, less costly, and getting results in a short time. In this study, a new EEG-based model is developed which can effectively detect MCI patients with higher accuracy. For this purpose, a dataset consisting of EEG signals recorded from a total of 34 subjects, 18 of whom were MCI and 16 control groups was used, and their ages ranged from 40 to 77. To conduct the experiment, the EEG signals were denoised using Multiscale Principal Component Analysis (MSPCA), and to increase the size of the dataset Data Augmentation (DA) method was performed. The tenfold cross-validation method was used to validate the model, moreover, the power spectral density (PSD) of the EEG signals was extracted from the EEG signals using three spectral analysis methods, the periodogram, welch, and multitaper. The PSD graphs of the EEG signals showed signal differences between the subjects of control and the MCI group, indicating that the signal power of MCI patients is lower compared to control groups. To classify the subjects, one of the best classifiers of deep learning algorithms called the Bi-directional long-short-term-memory (Bi-LSTM) was used, and several machine learning algorithms, such as decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN). These algorithms were trained and tested using the extracted feature vectors from the control and the MCI groups. Additionally, the values of the coefficient matrix of those algorithms were compared and evaluated with the performance evaluation matrix to determine which one performed the best overall. According to the experimental results, the proposed deep learning model of multitaper spectral analysis approach with Bi-LSTM deep learning algorithm attained the highest number of correctly classified samples for diagnosing MCI patients and achieved a remarkable accuracy compared to the other proposed models. The achieved classification results of the deep learning model are reported to be 98.97% accuracy, 98.34% sensitivity, 99.67% specificity, 99.70% precision, 99.02% f1 score, and 97.94% Matthews correlation coefficient (MCC).

Decision-Making Model of Performance Evaluation Matrix Based on Upper Confidence Limits

A performance evaluation matrix (PEM) is an evaluation tool for assessing customer satisfaction and the importance of service items across various services. In addition, inferences based on point estimates of sample data can increase the risk of misjudgment due to sampling errors. Thus, this paper creates a decision-making model for a performance evaluation matrix based on upper confidence limits to provide various service operating systems for performance evaluation and decision making. The concept is that through the gap between customer satisfaction and the level of importance of each service item, we are able to identify critical-to-quality (CTQ) service items requiring improvement. Many studies have indicated that customer satisfaction and the importance of service items follow a beta distribution, and based on the two parameters of this distribution, the proposed indices for customer satisfaction and the importance of service items represent standardization. The vertical axis of a PEM represents the importance index; the horizontal axis represents the satisfaction index. Since these two indices have unknown parameters, this paper uses the upper confidence limit of the satisfaction index to find out the CTQ service items and the upper confidence limit of the importance index to determine the order of improvement priority for each service item. This paper then establishes a decision-making model for a PEM based on the above-mentioned decision-making rules. Since all decision-making rules proposed in this paper are established through upper confidence limits, the risk of misjudgment caused by sampling errors can be reduced. Finally, this article uses a practical example to illustrate how to use a PEM to find CTQ service items and determine the order of improvement priority for these service items that need to be improved.

Using Statistical Test Method to Establish a Decision Model of Performance Evaluation Matrix

Many studies have pointed out that the Performance Evaluation Matrix (PEM) is a convenient and useful tool for the evaluation, analysis, and improvement of service operating systems. All service items of the operating system can collect customer satisfaction and importance through questionnaires and then convert them into satisfaction indices and importance indices to establish PEM and its evaluation rules. Since the indices have unknown parameters, if the evaluation is performed directly by the point estimates of the indices, there will be a risk of misjudgment due to sampling error. In addition, most of the studies only determine the critical-to-quality (CTQ) that needs to be improved, and do not discuss the treatment rules in the case of limited resources nor perform the confirmation after improvement. Therefore, to address similar research gaps, this paper proposed the unbiased estimators of these two indices and determined the critical-to-quality (CTQ) service items which need to be improved through the one-tailed statistical hypothesis test by building a PEM method of the satisfaction index. In addition, through the one-tailed statistical hypothesis test method of the importance index, the improvement priority of service items was determined under the condition of limited resources. Confirmation of the effect on improvement is an important step in management. Thus, this paper adopted a statistical two-tailed hypothesis test to verify whether the satisfaction of all the CTQ service items that need to be improved was enhanced. Since the method proposed in this paper was established through statistical hypothesis tests, the risk of misjudgment due to sampling error could be reduced. Obviously, reducing the misjudgment risk is the advantage of the method in this paper. Based on the precondition, utilizing the model in this study may assist the industries to determine CTQ rapidly, implement the most efficient improvement under the condition of limited resources and also confirm the improvement effect at the same time. Finally, a case study of computer-assisted language learning system (CALL System) was used to illustrate a way to apply the model proposed in this paper.

Dynamic Candidate Solution Boosted Beluga Whale Optimization Algorithm for Biomedical Classification

In many fields, complicated issues can now be solved with the help of Artificial Intelligence (AI) and Machine Learning (ML). One of the more modern Metaheuristic (MH) algorithms used to tackle numerous issues in various fields is the Beluga Whale Optimization (BWO) method. However, BWO has a lack of diversity, which could lead to being trapped in local optimaand premature convergence. This study presents two stages for enhancing the fundamental BWO algorithm. The initial stage of BWO’s Opposition-Based Learning (OBL), also known as OBWO, helps to expedite the search process and enhance the learning methodology to choose a better generation of candidate solutions for the fundamental BWO. The second step, referred to as OBWOD, combines the Dynamic Candidate Solution (DCS) and OBWO based on the k-Nearest Neighbor (kNN) classifier to boost variety and improve the consistency of the selected solution by giving potential candidates a chance to solve the given problem with a high fitness value. A comparison study with present optimization algorithms for single-objective bound-constraint optimization problems was conducted to evaluate the performance of the OBWOD algorithm on issues from the 2022 IEEE Congress on Evolutionary Computation (CEC’22) benchmark test suite with a range of dimension sizes. The results of the statistical significance test confirmed that the proposed algorithm is competitive with the optimization algorithms. In addition, the OBWOD algorithm surpassed the performance of seven other algorithms with an overall classification accuracy of 85.17% for classifying 10 medical datasets with different dimension sizes according to the performance evaluation matrix.

Performance Evaluation of Machine Learning Techniques (MLT) for Heart Disease Prediction

The leading cause of death worldwide today is heart disease (HD). The heart is recognised as the second‐most significant organ behind the brain. A successful outcome of treatment can be improved by an early diagnosis which can significantly reduce the chance of death in health care. In this paper, we proposed a method to predict heart disease. We used various machine learning algorithms (MLA), namely, logistic regression (LR), k‐nearest neighbor (KNN), support vector machine (SVM), Naive Bayes (NB), random forest (RF), and decision tree (DT). With the testing data set, we evaluated the model’s accuracy in heart disease prediction. When compared to the other five models, the random forest and k‐nearest neighbor approaches perform better. With a 99.04% accuracy rate, the k‐nearest neighbor algorithm and random forest provide the best match to the data as compared to other algorithms. Six feature selection algorithms were used for the performance evaluation matrix. MCC parameters for accuracy, precision, recall, and F measure are used to evaluate models.

Performance appraisal of factors contributing to the development of aquaculture

Recent declining trends in landings from capture fisheries and increasing population has raised serious concerns about the food and nutritional security of millions of people. Aquaculture is thus seen as a viable alternative to fill the gap in supply of fish. Extension services in aquaculture provide adequate support in furthering the development of the sector. The current study focuses on identification of key factors for the development of aquaculture sector in Kerala, India. A questionnaire based survey was carried out among 550 randomly selected respondents of five selected districts of Kerala, India. The respondents composed of beneficiaries of Agricultural Technology Management Agency (ATMA), staff of the Department of Fisheries (DoF), ATMA Governing Board (GB) members and ATMA Management Committee (MC) members. The major aquaculture development indicators closely associated with extension services were identified. The major indicators included extension contact, information acquisition, information dissemination, ATMA activities and expectations. Similarly sub indicators were identified and were used for the formulation of the performance evaluation matrix. Farmers’ score for extension contact (24.54%), farm information acquisition (49.23%) and expectation score (71.39%) indicated enough scope for improvement which implied lack of sufficient contact with resource persons, improper mechanism of obtaining information and high expectation from the extension service providers. Officials’ score on extension contact (36%), farm information dissemination score (49%) and expectation (53%) indicated low contact with the farmers, moderate information dissemination and fairly high level of expectation in providing efficient extension services to farmers. Officials’ score on farm information acquisition (62.5%) and ATMA activities (57.46%) showed that they need to vigorously conduct extension activities and acquire farm related information from traditional and modern mass media sources. The scores obtained for key performance appraisal indicators reflected scope for improvement in terms of the selected indicators. The findings could be utilised for developing improved policy measures for strengthening aquaculture extension system that could lead to a boom in aquaculture development.

A Performance Evaluation Matrix for Measuring the Life Satisfaction of Older Adults Using eHealth Wearables.

eHealth wearables can track users’ state of health, record their physiological data, and facilitate self-care. In this study, we examined whether they enhance older adults’ casual exercise willingness and life satisfaction. After reviewing the related literature, the performance and satisfaction of elements for older adults to use eHealth Wearables were determined. The elements were derived from the means–end chain analysis. Three dimensions, product attributes, beneficial consequences, personal values, and responding elements, were identified first. The Performance Evaluation Matrix (PEM) was then established to determine the elements to be improved. A total of 250 questionnaires were distributed, out of which 206 valid questionnaires were completed and returned. In the proposed PEM, the product attributes that were in the priority improvement zone were accessibility, learnability, usability, affordability, positioning, pedometer, heart rate monitor, and data feedback. These elements are the most essential properties in need of improvement.

Development and Application of a Performance Evaluation Matrix: A Case Study on Exploring the Items Considered Critical to Quality

Abstract A performance evaluation matrix is based on questionnaires collected from users or customers to pinpoint the performance of a system operation and furthermore to locate the items that need to be improved to upgrade the performance of system operations. To achieve this, this article proposes a discrimination index and applies statistics inference to deduce confidence intervals of the discrimination index. Meanwhile, we refer to Buckley’s fuzzy testing method to construct a fuzzy affiliation function and address a fuzzy evaluation criterion for exploring the items considered critical to quality to overcome the complicated problems of the questionnaires’ sampling error and interviewees’ fuzzy linguistics. The advantages of the method in this article are that it keeps the simple filling pattern of Likert’s scale and provides efficacy of data collection. Subsequently, we reduce the fuzzy linguistics and questionnaires’ sampling error by statistics inference and fuzzy hypothesis testing. Finally, we use a case study with the computer-assisted language learning system to demonstrate an application of the proposed method.

Constructing Fuzzy Hypothesis Methods to Determine Critical-To-Quality Service Items

This paper constructs a performance evaluation matrix (PEM) with beta distribution. Beta is between zero and one, making it a suitable indicator to describe customer ratings of importance and satisfaction from 0% to 100%. According to the spirit of ceaseless improvement put forward by total quality management, the average ratings are set as the standard, and then the coordinates of each satisfaction and importance item is located in the performance areas. This makes it easy to identify critical-to-quality items that require improvement. However, the data collection method of questionnaires inevitably involves sampling error, and the opinions of customers are often ambiguous. To solve these problems, we constructed a fuzzy testing method based on confidence intervals. The use of confidence intervals decreases the chance of misjudgment caused by sampling errors, and more precisely gets closer to customers’ voices. This result is more reasonable than the traditional statistical testing principle. The proposed methods are applied to assessment of a computer-assisted language learning (CALL) system to display their competence.

Short-Term Wind Speed Forecasting With Principle-Subordinate Predictor Based on Conv-LSTM and Improved BPNN

To improve the accuracy of short term wind speed forecasting, a novel principle-subordinate prediction model based on Conv-LSTM network and BPNN is designed in this paper. The proposed model combines deep learning algorithms and improved neural network to deal with the problem of wind speed forecasting. In this model, the prediction sequence of each subseries is obtained by Conv-LSTM to form more smoother and characteristic series; the BPNN, optimized by MWOA (Modified Whale Optimization Algorithm), is trained with the reconstruction sequence, which processed for the prediction sequence by invert-EMD (inverse empirical model decomposition). Before prediction, singular spectrum analysis (SSA) and complete ensemble empirical model decomposition adaptive noise (CEEMDAN) are adopted to de-noise and decompose the original wind speed data into several subseries. This process is beneficial to improve the ratio of signal to noise and simply the features of wind speed data. In addition, the Conv-LSTM is tested on three datasets, the results proved that data process is advantaged to obtain higher quality wind speed datasets, and convolutional layer can deep extract the characteristics of each subseries, it can facilitate LSTM to make accurate prediction. The final prediction results, compared with other five different models, demonstrate that the proposed model can achieve higher precision. Such as the performance evaluation matrix (MAPE = 2.62%, RMSE = 0.151) are smallest obtained from experiments on three wind different wind speed datasets.

Fuzzy test model for performance evaluation matrix of service operating systems

The performance evaluation matrix is a useful and convenient tool for collecting user opinions and identifying service items that are in need of improvement in operating systems. It has been used in many studies looking for ways to increase the response willingness of customers and solve the problem of fuzziness in user opinions. Likert scales are simple and can increase the response willingness of customers and the efficiency of data collection. In contrast, fuzzy linguistic scales can more accurately capture the voice of the customer, but responding to them is a more complex matter and lowers the response willingness of customers. Confidence intervals can lower the risk of misjudgment caused by sampling errors in user opinion collection; the smaller the confidence intervals are, the lower the risk of misjudgment is. We therefore developed the Buckley fuzzy test methods that maintain the simplicity offered by Likert scales in collecting data. We used the indices confidence intervals derived from the data to construct fuzzy membership functions and develop our fuzzy test methods. Clearly, those approaches solve the problem of fuzziness in customer opinions without affecting the response willingness of customers. Those methods are grounded in confidence intervals, which reduce the risk of misjudgment caused by sampling errors. Furthermore, when confidence intervals are relatively wide due to cost and time considerations, the Buckley fuzzy test methods overcome this limitation by swiftly identifying service items that are in need of improvement in an operating system, as well as prioritizing service items using pair-wise comparisons of the influence indices of the service items. This enables the identification of the service items that should take priority when resources are limited. Finally, we used a case study involving a computer-assisted language learning system to demonstrate the applicability of the proposed approach.

Developing a Fuzzy Verification Method of Performance Improvement Using Satisfaction Index

Performance evaluation matrix (PEM) has captured many scholars’ attention and has been widely used to a variety of industry fields to evaluate their efficiency performance. The data mainly come from the collection of questionnaires in which customers’ varied opinions are enquired; however, Likert scale will be modified to fuzzy linguistic scale due to some uncertainty aspect in the linguistic data. In contrast, the major flaw of some researches was that they failed to present a verification approach with statistical inference to confirm the effectiveness of the improvement. Regarding to the related issues, Chen et al. (2018a) use discrimination index in reliance section to establish fuzzy membership function; subsequently, they propose fuzzy hypothesis testing to locate critical-to-quality (CTQ) so as to make further improvement. Following the same logic, this paper uses the identical process to develop the fuzzy verification method of the improvement efficiency for CTQ. Finally, this paper puts forward a numerical example to demonstrate application of the proposed verification model. The result means that there is sufficient evidence showing that performance improvement is poor and non-effective. Thus, manager should adopt otherwise measures to improve. Obviously, the method proposed particularly highlights the reduction of sampling error, shies away from the complexity inherent in fuzzy semantic collection, overcomes the uncertainty in the collected data, and increases the reliability of evaluation method.

Fuzzy Evaluation Model for Enhancing E-Learning Systems

As the environment and information-technology conditions of the Internet of Things matured, various applications were launched. In education, e-learning is promoted so that students’ learning is no longer restricted to the classroom. E-learning schedules are flexible, and learners’ commuting costs are low. Apparently, improving the quality of e-learning systems can enhance learners’ learning effectiveness, satisfaction, engagement, and learning efficacy. A performance evaluation matrix is a useful tool for collecting users’ opinions to assess the performance of an operating system, and it is widely used to evaluate and improve performance in numerous industries and organizations. Therefore, this study used this matrix to construct a model for evaluation and analysis, providing suggestions on improving e-learning systems. This approach maintained the simple response model of Likert scales, which increases the efficiency and accuracy of data collection. Furthermore, the fuzzy membership function of the discriminant index was constructed based on the confidence interval, thereby solving the problems of sampling error and the complexity of collecting fuzzy linguistic data. Besides, we simplified calculations by standardizing test statistics to increase evaluation efficiency. As a result, this study improved the quality of e-learning system, enhanced users’ learning effectiveness, satisfaction, and engagement, and achieved the goal of sustainability.