This research aims to design a quality management tool in education. The methodology comprises two stages: first, the construction of the decision tree model, and second, the efficiency evaluation. For the validation and development of this research, the data modelled corresponds to the standardized exams for higher education in Colombia of ninety industrial engineering degrees. Among the results, the citizenship skills (CC_PRO) generate the most significant contribution to the model. On the other hand, the written communication competence (CE_PRO) generates a minor contribution to the model. In addition, the most relevant result of the research is the design and validation of a tool to estimate educational efficiency using the efficiency analysis tree (EAT) and data enveloping analysis (DEA) models. The proposed tool allows the generation of specific targets to increase the level of efficiency of universities through the nodes of the decision tree, which contributes to the spectrum of knowledge on models for educational management. In conclusion, this research presents a tool for the management of educational processes through the analysis of efficiency using EAT, estimating the efficiency of universities and setting the foundations for forecasting future efficiency scenarios.
New Fuzzy Model For quality evaluation of e-Training of CNC Operatorsinventionjournals
The quality of e-learning is a very important issue, especially when production technologies are concerned. This paper introduces a new fuzzy model for e-learning quality evaluation. All uncertainties and consequent imprecision are modeled by triangular fuzzy numbers. The quality of CNC e-learning process is determined by using the fuzzy logic IF-THEN rules. The proposed method derives an aggregated satisfaction value both for the participants as well as the trainers.The authors introduce a genuine metric interval for the objective evaluation of E-learning effect. The OLS regression model estimates the magnitude and polarity of Elearning effect on participants` perception of the training quality. The predicted coefficient of E-learning effect on the overall quality of CNC training is estimated to be14.88 measurement points with a negative impact on overall satisfaction. These novel findings shed a new light on the quantitative effect of E-learning on CNC machine training and contribute to the contemporary scientific literature within the research area.The developed model is illustrated by real-life data from secondary technological schools from central Serbia
New Fuzzy Model for quality evaluation of E-Training of CNC Operatorsinventionjournals
This document proposes a new fuzzy model for evaluating the quality of e-learning training for CNC operators. It begins by discussing the importance of continuous education in production technologies like CNC. It then reviews existing literature on evaluating e-learning quality and identifies uncertainties in criteria weights and values. The document goes on to introduce assumptions of the proposed model which uses fuzzy set theory to represent uncertainties. Criteria weights are determined using fuzzy AHP based on linguistic assessments from trainers. An example application evaluates participant satisfaction with e-learning training quality. Finally, regression analysis estimates the isolated effect of e-learning on training quality perceptions.
A Literature Survey on Student Profile Management SystemIRJET Journal
This document provides a literature review on student profile management systems. It discusses 10 academic papers related to developing a student profile system that allows educational institutions to efficiently store and access student records and profiles. The key aspects covered include using data mining and machine learning to classify students, implementing cloud-based student profile systems, ensuring security and privacy of student data stored in the cloud, and optimizing costs for cloud-based student data management systems. The goal of the literature review is to better understand existing approaches to developing an effective student profile management system.
The document analyzes the efficiency of graduate programs at colleges of the University of Basra using data envelopment analysis (DEA). DEA was used to measure the efficiency of 13 colleges over 3 academic years based on inputs like faculty members and students enrolled in graduate programs, and outputs like graduates, research publications, and promotions.
The results found that colleges of Engineering, Education for Pure Sciences, Fine Arts, and Education for Girls achieved full efficiency in all 3 years. The other colleges' efficiency varied between incomplete and full inefficiency. Inefficient colleges were identified and the colleges they could follow, like Engineering or Education for Pure Sciences, to improve efficiency. The study aims to help colleges invest resources optimally to increase outputs of the
MINING DISCIPLINARY RECORDS OF STUDENT WELFARE AND FORMATION OFFICE: AN EXPLO...IJITCA Journal
This document discusses using data mining techniques like decision trees and CHAID algorithms to analyze disciplinary records from a university's Student Welfare and Formation Office. It aims to identify relationships between student demographics and offense categories to develop an efficient remediation plan. The study uses Data Envelopment Analysis to evaluate the efficiency of different colleges in minimizing student offenses. Classification decision trees and CHAID analysis identify that most students commit minor offenses regardless of attributes like gender or year level. The results will inform a Student Offenses Remediation System to efficiently address issues and improve university services.
MINING DISCIPLINARY RECORDS OF STUDENT WELFARE AND FORMATION OFFICE: AN EXPLO...IJITCA Journal
Data mining is the process of analyzing large datasets, understanding their patterns and discovering useful
information from a large amount of data. Decision tree as one of the common algorithm of data mining is a
tree structure entailing of internal and terminal nodes which process the data to eventually produce a
classification. Classification is the process of dividing a dataset together in a high-class set such that the
members of each set are nearby as expected to one another, and different groups are as far as expected
from one another, where distance is measured with respect to the specific variable(s) you are trying to
predict. Data Envelopment Analysis is a technique wherein the productivity of a unit is evaluated by
equating the volume/amount of output(s) in relation to the volume/amount of input(s) used. The
performance of a unit is calculated by equating its efficiency with the best-perceived performance in the
data set. In this study, a model for measuring the efficiency of Decision Making Units will be presented,
along with related methods of implementation and interpretation. DEA assesses and evaluates the
efficiency of a unit dubbed as Decision-Making Units or DMU. There are many classification techniques
and algorithms but the research study used decision tree using CHAID algorithms. Classification decision
tree algorithm using CHAID as data mining technique identifies the relationship between the demographic
profile of the students and the category of offenses. Cross tabulation is a tool used to analyze categorical
data. It is a type of table in a matrix format that shows the multivariate occurrence dissemination of the
variables and delivers a basic picture of the interrelation between two variables. Both CHAID algorithm
and cross tabulation obtained the same results implying that higher percentage of students commit minor
offenses regardless of college, gender, year level, month and course. The CHAID algorithm used in a
software application Student Offenses Remediation System (STORES) serves as remediation plan for the
university. Further studies should be conducted to identify the effectiveness of the remediation plan by
conducting an empirical investigation on the rule set and/or implement another algorithm to determine the
program efficiency.
Dimensions of Enterprise Resource Planning Systems Success in Public and Priv...paperpublications3
Abstract: Enterprise Resource Planning (ERP) systems have been adopted and implemented in the Kenyan higher education sector, with their success being described in many ways that one. Empirical studies have identified Quality, use, and benefits dimensions as suitable descriptors of success of ERP systems. This study used the results of a cross-sectional survey conducted in selected public and private universities in Kenya, coupled with theories and literature from existing Information System (IS) success models, to examine the effect of these dimensions on success of ERP systems. An understanding of ERP systems success dimensions will help to appreciate how each dimension fit in the higher education sector and provide a basis from which mitigation mechanisms can be employed to ensure success. There is need for universities to match their expectations on ERP systems with efficiency, assurance, accuracy, coupled with good support service by experienced professionals that will ensure the desired level quality is guaranteed. Engaging end-users during implementation and providing adequate training to employees have a direct impact on productive use of the ERP system. In addition, universities also need to define the strategic goals clearly before embarking on implementation, such that the process can always be steered towards the realization of benefits associated with the ERP system.
Keywords: ERP Success; Information Quality; Net Benefits; Service Quality; System Quality; Use.
Title: Dimensions of Enterprise Resource Planning Systems Success in Public and Private Universities in Kenya
Author: Anthony Njina, Dr. Mike Iravo, Dr. Michael Kimwele
ISSN 2350-1049
International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)
Paper Publications
Efficiency of Women’s Technical Institutions By Using Bcc Model Through Dea A...IOSR Journals
This paper conducts an application of the DEA Methodology in the assessment of the performance of JNTUH Colleges the indicators included the Faculty, Students, Infrastructure and Placements of the technical Institutions. The results reveal those institutions that more efficiently carry out these activities. The proposed method has been used for selection of quality attributes in technical education setting the performance of an institute is likely to be influenced by quality of teacher, quality of students, infrastructure administration, extent of training and placement and many others. It is felt that quality and performance evaluation is necessary not only for appraisal but it is also required to improve overall service quality. Finally we discuss about the existence of differences in the strengths and weaknesses between the technical institutions.
New Fuzzy Model For quality evaluation of e-Training of CNC Operatorsinventionjournals
The quality of e-learning is a very important issue, especially when production technologies are concerned. This paper introduces a new fuzzy model for e-learning quality evaluation. All uncertainties and consequent imprecision are modeled by triangular fuzzy numbers. The quality of CNC e-learning process is determined by using the fuzzy logic IF-THEN rules. The proposed method derives an aggregated satisfaction value both for the participants as well as the trainers.The authors introduce a genuine metric interval for the objective evaluation of E-learning effect. The OLS regression model estimates the magnitude and polarity of Elearning effect on participants` perception of the training quality. The predicted coefficient of E-learning effect on the overall quality of CNC training is estimated to be14.88 measurement points with a negative impact on overall satisfaction. These novel findings shed a new light on the quantitative effect of E-learning on CNC machine training and contribute to the contemporary scientific literature within the research area.The developed model is illustrated by real-life data from secondary technological schools from central Serbia
New Fuzzy Model for quality evaluation of E-Training of CNC Operatorsinventionjournals
This document proposes a new fuzzy model for evaluating the quality of e-learning training for CNC operators. It begins by discussing the importance of continuous education in production technologies like CNC. It then reviews existing literature on evaluating e-learning quality and identifies uncertainties in criteria weights and values. The document goes on to introduce assumptions of the proposed model which uses fuzzy set theory to represent uncertainties. Criteria weights are determined using fuzzy AHP based on linguistic assessments from trainers. An example application evaluates participant satisfaction with e-learning training quality. Finally, regression analysis estimates the isolated effect of e-learning on training quality perceptions.
A Literature Survey on Student Profile Management SystemIRJET Journal
This document provides a literature review on student profile management systems. It discusses 10 academic papers related to developing a student profile system that allows educational institutions to efficiently store and access student records and profiles. The key aspects covered include using data mining and machine learning to classify students, implementing cloud-based student profile systems, ensuring security and privacy of student data stored in the cloud, and optimizing costs for cloud-based student data management systems. The goal of the literature review is to better understand existing approaches to developing an effective student profile management system.
The document analyzes the efficiency of graduate programs at colleges of the University of Basra using data envelopment analysis (DEA). DEA was used to measure the efficiency of 13 colleges over 3 academic years based on inputs like faculty members and students enrolled in graduate programs, and outputs like graduates, research publications, and promotions.
The results found that colleges of Engineering, Education for Pure Sciences, Fine Arts, and Education for Girls achieved full efficiency in all 3 years. The other colleges' efficiency varied between incomplete and full inefficiency. Inefficient colleges were identified and the colleges they could follow, like Engineering or Education for Pure Sciences, to improve efficiency. The study aims to help colleges invest resources optimally to increase outputs of the
MINING DISCIPLINARY RECORDS OF STUDENT WELFARE AND FORMATION OFFICE: AN EXPLO...IJITCA Journal
This document discusses using data mining techniques like decision trees and CHAID algorithms to analyze disciplinary records from a university's Student Welfare and Formation Office. It aims to identify relationships between student demographics and offense categories to develop an efficient remediation plan. The study uses Data Envelopment Analysis to evaluate the efficiency of different colleges in minimizing student offenses. Classification decision trees and CHAID analysis identify that most students commit minor offenses regardless of attributes like gender or year level. The results will inform a Student Offenses Remediation System to efficiently address issues and improve university services.
MINING DISCIPLINARY RECORDS OF STUDENT WELFARE AND FORMATION OFFICE: AN EXPLO...IJITCA Journal
Data mining is the process of analyzing large datasets, understanding their patterns and discovering useful
information from a large amount of data. Decision tree as one of the common algorithm of data mining is a
tree structure entailing of internal and terminal nodes which process the data to eventually produce a
classification. Classification is the process of dividing a dataset together in a high-class set such that the
members of each set are nearby as expected to one another, and different groups are as far as expected
from one another, where distance is measured with respect to the specific variable(s) you are trying to
predict. Data Envelopment Analysis is a technique wherein the productivity of a unit is evaluated by
equating the volume/amount of output(s) in relation to the volume/amount of input(s) used. The
performance of a unit is calculated by equating its efficiency with the best-perceived performance in the
data set. In this study, a model for measuring the efficiency of Decision Making Units will be presented,
along with related methods of implementation and interpretation. DEA assesses and evaluates the
efficiency of a unit dubbed as Decision-Making Units or DMU. There are many classification techniques
and algorithms but the research study used decision tree using CHAID algorithms. Classification decision
tree algorithm using CHAID as data mining technique identifies the relationship between the demographic
profile of the students and the category of offenses. Cross tabulation is a tool used to analyze categorical
data. It is a type of table in a matrix format that shows the multivariate occurrence dissemination of the
variables and delivers a basic picture of the interrelation between two variables. Both CHAID algorithm
and cross tabulation obtained the same results implying that higher percentage of students commit minor
offenses regardless of college, gender, year level, month and course. The CHAID algorithm used in a
software application Student Offenses Remediation System (STORES) serves as remediation plan for the
university. Further studies should be conducted to identify the effectiveness of the remediation plan by
conducting an empirical investigation on the rule set and/or implement another algorithm to determine the
program efficiency.
Dimensions of Enterprise Resource Planning Systems Success in Public and Priv...paperpublications3
Abstract: Enterprise Resource Planning (ERP) systems have been adopted and implemented in the Kenyan higher education sector, with their success being described in many ways that one. Empirical studies have identified Quality, use, and benefits dimensions as suitable descriptors of success of ERP systems. This study used the results of a cross-sectional survey conducted in selected public and private universities in Kenya, coupled with theories and literature from existing Information System (IS) success models, to examine the effect of these dimensions on success of ERP systems. An understanding of ERP systems success dimensions will help to appreciate how each dimension fit in the higher education sector and provide a basis from which mitigation mechanisms can be employed to ensure success. There is need for universities to match their expectations on ERP systems with efficiency, assurance, accuracy, coupled with good support service by experienced professionals that will ensure the desired level quality is guaranteed. Engaging end-users during implementation and providing adequate training to employees have a direct impact on productive use of the ERP system. In addition, universities also need to define the strategic goals clearly before embarking on implementation, such that the process can always be steered towards the realization of benefits associated with the ERP system.
Keywords: ERP Success; Information Quality; Net Benefits; Service Quality; System Quality; Use.
Title: Dimensions of Enterprise Resource Planning Systems Success in Public and Private Universities in Kenya
Author: Anthony Njina, Dr. Mike Iravo, Dr. Michael Kimwele
ISSN 2350-1049
International Journal of Recent Research in Interdisciplinary Sciences (IJRRIS)
Paper Publications
Efficiency of Women’s Technical Institutions By Using Bcc Model Through Dea A...IOSR Journals
This paper conducts an application of the DEA Methodology in the assessment of the performance of JNTUH Colleges the indicators included the Faculty, Students, Infrastructure and Placements of the technical Institutions. The results reveal those institutions that more efficiently carry out these activities. The proposed method has been used for selection of quality attributes in technical education setting the performance of an institute is likely to be influenced by quality of teacher, quality of students, infrastructure administration, extent of training and placement and many others. It is felt that quality and performance evaluation is necessary not only for appraisal but it is also required to improve overall service quality. Finally we discuss about the existence of differences in the strengths and weaknesses between the technical institutions.
Implementation of AHP-MAUT and AHP-Profile Matching Methods in OJT Student Pl...Gede Surya Mahendra
ABSTRACT
To improve the quality and quality of employment, OJT is very much needed by Monarch Bali students, but the process, which is still manual, makes decisions that are taken less fast, accurate, effective and efficient. In line with the roadmap of Monarch Bali, it is necessary to develop an automation system to be able to improve the performance of decision making for OJT student placement by making a DSS. The method used in this research is AHP-MAUT and AHP-PM. The decision makers in this study were 3 people, and out of a total of 500 OJT students, 8 OJT students for F&B class, 12 OJT students for Housekeeping class, 13 OJT students for Catering class, and 17 OJT students for Food Management class with a total of 50 OJT students. Implementation of AHP-MAUT, OJT students from the F&B class with the code StudentD04 have the highest preference value of 0.5724, and OJT students from the beverage class with the code StudentA02 have a preference value of 4.1155 calculated using AHP-PM, each being ranked first.
Keywords:
Analytical Hierarchy Process, Multi-Attribute Utility Theory, Profile Matching,
CRISP-DM,
On the Job Training
Using Multi-Criteria Decision and knowledge representation methodologies for ...Nit Celesc
This document describes a methodology used to evaluate innovation projects for an electric power company in Brazil. It uses multi-criteria decision making (MCDM) and the analytic hierarchy process (AHP) to establish criteria and weights for evaluating projects. Key criteria included originality, applicability, relevance, and cost reasonableness. The methodology decomposed the evaluation hierarchy and used pairwise comparisons to determine relative importance of criteria. This allowed establishing priority scores for projects and simulating different evaluation scenarios. The results provided an effective tool to support the company's evaluation and selection of research and development projects.
This study focuses on determining a working ‘selection criteria model’ that will help Information
Technology (IT) companies choose the right candidates to work on their IT projects in areas such as system
design, requirement gathering and management,
The document contains summaries of several research papers related to information technology and its applications in business and education. The papers discuss topics like ERP systems in organizations, ICT adoption in SMEs, using ERP and JIT together, cloud-based learning systems, modeling ICT facilities in higher education, and the potential of social media in supply chain management. One paper specifically analyzes student perceptions of service facilities and their impact on satisfaction at a university.
Modeling the Student Success or Failure in Engineering at VUT Using the Date ...IJRTEMJOURNAL
The success or failure of students is a concern for every academic institution, college, university,
governments and students themselves. This paper presents a model to determine the propensity of a student to
succeed in the Electrical Engineering Department at Vaal University of Technology. Firstly, various machine
learning algorithms which can be used in modelling and in predicting student success or failure are discussed as
well a new algorithm called the date band algorithm. Secondly, the concept of an academic model is also
discussed. This model defines the domain and focus of data used to make preditcions. The academic model consists
of the subject, the lecturer and the sudent each of which has various attributes. One of the attributes discussed in
this paper is the popularity index which is a measure of cohesiveness of the model.The Date Band Algorithm is
presented among others in the development of the model. In this algorithm, predictions are made to optimize the
performance of academic environment, thereby impacting on the choices of funders when they support students.
Modeling the Student Success or Failure in Engineering at VUT Using the Date ...journal ijrtem
The success or failure of students is a concern for every academic institution, college, university,
governments and students themselves. This paper presents a model to determine the propensity of a student to
succeed in the Electrical Engineering Department at Vaal University of Technology. Firstly, various machine
learning algorithms which can be used in modelling and in predicting student success or failure are discussed as
well a new algorithm called the date band algorithm. Secondly, the concept of an academic model is also
discussed. This model defines the domain and focus of data used to make preditcions. The academic model consists
of the subject, the lecturer and the sudent each of which has various attributes. One of the attributes discussed in
this paper is the popularity index which is a measure of cohesiveness of the model.The Date Band Algorithm is
presented among others in the development of the model. In this algorithm, predictions are made to optimize the
performance of academic environment, thereby impacting on the choices of funders when they support students.
Analyzing the solutions of DEA through information visualization and data min...Gurdal Ertek
Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, Smart DEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for bench marking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.
http://research.sabanciuniv.edu.
Validity of a graph-based automatic assessment system for programming assign...IJECEIAES
Programming is a very complex and challenging subject to teach and learn. A strategy guaranteed to deliver proven results has been intensive and continual training. However, this strategy holds an extra workload for the teachers with huge numbers of programming assignments to evaluate in a fair and timely manner. Furthermore, under the current coronavirus (COVID-19) distance teaching circumstances, regular assessment is a fundamental feedback mechanism. It ensures that students engage in learning as well as determines the extent to which they reached the expected learning goals, in this new learning reality. In sum, automating the assessment process will be particularly appreciated by the instructors and highly beneficial to the students. The purpose of this paper is to investigate the feasibility of automatic assessment in the context of computer programming courses. Thus, a prototype based on merging static and dynamic analysis was developed. Empirical evaluation of the proposed grading tool within an introductory C-language course has been presented and compared to manually assigned marks. The outcomes of the comparative analysis have shown the reliability of the proposed automatic assessment prototype.
UNIVERSITY ADMISSION SYSTEMS USING DATA MINING TECHNIQUES TO PREDICT STUDENT ...IRJET Journal
This document summarizes a research study that aimed to predict student performance and support decision making for university admission systems using data mining techniques. The study analyzed data from 2,039 students at a university in Saudi Arabia to compare the predictive power of different data mining classification models (ANN, decision trees, SVM, naive Bayes). It found that a student's score on the pre-admission Scholastic Proficiency Admission Test was the best predictor of their first year GPA. Based on this, the university adjusted its admission criteria to give greater weight to this pre-admission test score. After making this change, the number of students with high GPAs increased while the number with low GPAs decreased.
Enhancing software development cost control by forecasting the cost of rework...nooriasukmaningtyas
Industrial reports show massive cost overruns associated with software
projects. The cost of software reworks constitutes a large portion of the
overall cost, reflecting a substantial challenge in cost control. Earned value
management (EVM) is the most recognized model for project cost control.
However, it shows many limitations in forecasting the software project cost,
leading to a considerable challenge in cost control. Nevertheless, the major
EVM limitation found is its inability to forecast the cost of software rework.
This research investigated the factors affecting this limitation and suggests an
enhanced EVM model. The significant contribution of this research is its
incorporation of software-related factors into the EVM model. We
introduced the software rework index (SRI), which is incorporated into the
traditional EVM model to enhance its predictability of the software project
cost at completion, including the rework cost. We defined the SRI in terms of
two factors: product functional complexity and the team competency.
Finally, we evaluated the proposed model using a dataset drawn from five
actual projects. The results showed a significant enhancement in forecasting
accuracy.
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
THE USE OF COMPUTER-BASED LEARNING ASSESSMENT FOR PROFESSIONAL COURSES: A STR...IAEME Publication
Background/Objectives: While the increase in classroom technology, it is necessary to examine how assessment is administered through technology. The purpose of this study is to understand how students and faculty are perceived and examine the effectiveness of the computer-based assessment in professional education courses (Educational Technology) at Northern Iloilo Polytechnic State College, Iloilo, Philippines. Methods: The research design utilized in this study is mixed-method research. A computer-based assessment was utilized to assess students' performance in educational technology. This instrument was validated, and pilot tested to establish reliability. Each campus of NIPSC selected ten students of 70 as respondents during Academic Year 2016-2017. Frequency count, mean, standard deviation, and Wilcoxon signed-rank test were statistical tools used for data analyses. Findings: The study's finding showed a high score of students in the posttest ensured better performance of the students in educational technology. The increase in the posttest per performance level of the students was due to an accurate measure of what they have learned in educational technology. The majority of students users agreed that online assessment was fasters than the paper and pencil form. Also, users agreed that online assessment is contemporary and more systematic. They also stated that online assessment is consistent with the teaching style, but they are less anxious. Furthermore, according to faculty and students, ninety percent (90%) believed that computer-based assessment accurately measures what they are teaching and what they learned in school, respectively. Novelty: With the current situation that the education system is in new normal, computer-based learning is important in flexible learning. And assessment using technology is a great help to both faculty and students. Thus, state universities and colleges (SUCs) should adopt this innovation to help teaching and learning.
An Application Of The Data Envelopment Analysis Method To Evaluate The Perfor...Karin Faust
This document summarizes an analysis of the performance of university departments at the University of Zaragoza in Spain using data envelopment analysis (DEA). The study uses DEA to evaluate departments based on inputs like staff, budgets, and capital assets, as well as outputs related to teaching and research activities. It analyzes 52 departments, identifying the most efficient ones and discussing differences between departments in different subject areas. The results are intended to inform management and decision making at the university.
Design of an environmental management information system for the Universidad ...nooriasukmaningtyas
This article presents the design, development and implementation of a software tool, serving as an alternative to the problems involving management, control and reporting of processes within the institutional plan for environmental management (known as plan institucional de gestión ambiental (PIGA) by its Spanish acronym) for the Universidad Distrital Francisco José de Caldas. The software is focused on carrying out such processes to the automation setting, based on the extreme programming (XP) Agile methodology that mainly centers on the continuous development of the customer requirements to offer a more assertive tool, in line with the plan institucional de gestión ambiental in Spanish (PIGA) processes. The result is a complete satisfaction of users and a highly usable, adaptable and efficient software, inherently optimizing and automating the environmental management processes of the PIGA program. This work delivers an applet that meets the design and implementation requirements of environmental management policies. The proposed tool manages to reduce process-related times by 97%, therefore, allowing to aim efforts in other missional functions and increase the overall value offer of the organization.
Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise ...Waqas Tariq
The creation of goods and services requires changing the expended resources into the output goods and services. How efficiently we transform these input resources into goods and services depends on the productivity of the transformation process. However, it has been observed there is always a vagueness or imprecision associated with the values of inputs and outputs. Therefore, it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. The present paper, applies fuzzy set theory to measure and compare productivity performance of transformation processes when numerical data cannot be specified in exact terms. The approach makes it possible to measure and compare productivity of organizational units (including non-government and non-profit entities) when the expert inputs can not be specified as exact scalar quantities. The model has been applied to compare productivity of different branches of a company.
Study of Lean construction tools for Workflow Improvement -A ReviewIRJET Journal
This document summarizes several research studies on improving workflow in construction projects using lean tools like value stream mapping (VSM). It discusses how VSM has been used to identify waste and inefficiencies in processes like structural masonry work. The studies found that VSM helps visualize information and material flows to reduce wait times and optimize value-adding activities. Implementing lean principles through tools such as VSM can help construction projects improve workflow and productivity.
Textbooks for Responsible Data Analysis in ExcelNathan Garrett
With 27 million users, Excel (Microsoft Corporation, Seattle, WA) is the most common
business data analysis software. However, audits show that almost all complex spreadsheets
have errors. The author examined textbooks to understand why responsible data analysis is
taught. A purposeful sample of 10 textbooks was coded, and then compared against
spreadsheet development best practices. The results show a wide range of approaches, and
reveal that none of the 10 books fully cover the methodologies needed to create wellrounded
Excel data analysts. There is a need to re-evaluate the teaching approaches being
used in office application courses
This document describes the development of a web-based staff appraisal management system for the Federal University of Technology in Owerri, Nigeria. The system aims to automate the performance appraisal process for academic staff, which was previously done manually and led to issues like inappropriate assessments and a lack of data integrity. The objectives are to identify weaknesses in the existing system, design input-output modules, code the system modules, test the system, and verify it. The document outlines the methodology, design models including flowcharts and entity relationship diagrams, major outputs, and conclusions that recommend adopting the automated system to improve the staff appraisal process.
Evaluation of E-Learning Web Sites Using Fuzzy Axiomatic Design Based ApproachGurdal Ertek
High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is
applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations
for future research are offered.
http://research.sabanciuniv.edu.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
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Implementation of AHP-MAUT and AHP-Profile Matching Methods in OJT Student Pl...Gede Surya Mahendra
ABSTRACT
To improve the quality and quality of employment, OJT is very much needed by Monarch Bali students, but the process, which is still manual, makes decisions that are taken less fast, accurate, effective and efficient. In line with the roadmap of Monarch Bali, it is necessary to develop an automation system to be able to improve the performance of decision making for OJT student placement by making a DSS. The method used in this research is AHP-MAUT and AHP-PM. The decision makers in this study were 3 people, and out of a total of 500 OJT students, 8 OJT students for F&B class, 12 OJT students for Housekeeping class, 13 OJT students for Catering class, and 17 OJT students for Food Management class with a total of 50 OJT students. Implementation of AHP-MAUT, OJT students from the F&B class with the code StudentD04 have the highest preference value of 0.5724, and OJT students from the beverage class with the code StudentA02 have a preference value of 4.1155 calculated using AHP-PM, each being ranked first.
Keywords:
Analytical Hierarchy Process, Multi-Attribute Utility Theory, Profile Matching,
CRISP-DM,
On the Job Training
Using Multi-Criteria Decision and knowledge representation methodologies for ...Nit Celesc
This document describes a methodology used to evaluate innovation projects for an electric power company in Brazil. It uses multi-criteria decision making (MCDM) and the analytic hierarchy process (AHP) to establish criteria and weights for evaluating projects. Key criteria included originality, applicability, relevance, and cost reasonableness. The methodology decomposed the evaluation hierarchy and used pairwise comparisons to determine relative importance of criteria. This allowed establishing priority scores for projects and simulating different evaluation scenarios. The results provided an effective tool to support the company's evaluation and selection of research and development projects.
This study focuses on determining a working ‘selection criteria model’ that will help Information
Technology (IT) companies choose the right candidates to work on their IT projects in areas such as system
design, requirement gathering and management,
The document contains summaries of several research papers related to information technology and its applications in business and education. The papers discuss topics like ERP systems in organizations, ICT adoption in SMEs, using ERP and JIT together, cloud-based learning systems, modeling ICT facilities in higher education, and the potential of social media in supply chain management. One paper specifically analyzes student perceptions of service facilities and their impact on satisfaction at a university.
Modeling the Student Success or Failure in Engineering at VUT Using the Date ...IJRTEMJOURNAL
The success or failure of students is a concern for every academic institution, college, university,
governments and students themselves. This paper presents a model to determine the propensity of a student to
succeed in the Electrical Engineering Department at Vaal University of Technology. Firstly, various machine
learning algorithms which can be used in modelling and in predicting student success or failure are discussed as
well a new algorithm called the date band algorithm. Secondly, the concept of an academic model is also
discussed. This model defines the domain and focus of data used to make preditcions. The academic model consists
of the subject, the lecturer and the sudent each of which has various attributes. One of the attributes discussed in
this paper is the popularity index which is a measure of cohesiveness of the model.The Date Band Algorithm is
presented among others in the development of the model. In this algorithm, predictions are made to optimize the
performance of academic environment, thereby impacting on the choices of funders when they support students.
Modeling the Student Success or Failure in Engineering at VUT Using the Date ...journal ijrtem
The success or failure of students is a concern for every academic institution, college, university,
governments and students themselves. This paper presents a model to determine the propensity of a student to
succeed in the Electrical Engineering Department at Vaal University of Technology. Firstly, various machine
learning algorithms which can be used in modelling and in predicting student success or failure are discussed as
well a new algorithm called the date band algorithm. Secondly, the concept of an academic model is also
discussed. This model defines the domain and focus of data used to make preditcions. The academic model consists
of the subject, the lecturer and the sudent each of which has various attributes. One of the attributes discussed in
this paper is the popularity index which is a measure of cohesiveness of the model.The Date Band Algorithm is
presented among others in the development of the model. In this algorithm, predictions are made to optimize the
performance of academic environment, thereby impacting on the choices of funders when they support students.
Analyzing the solutions of DEA through information visualization and data min...Gurdal Ertek
Data envelopment analysis (DEA) has proven to be a useful tool for assessing efficiency or productivity of organizations, which is of vital practical importance in managerial decision making. DEA provides a significant amount of information from which analysts and managers derive insights and guidelines to promote their existing performances. Regarding to this fact, effective and methodologic analysis and interpretation of DEA solutions are very critical. The main objective of this study is then to develop a general decision support system (DSS) framework to analyze the solutions of basic DEA models. The paper formally shows how the solutions of DEA models should be structured so that these solutions can be examined and interpreted by analysts through information visualization and data mining techniques effectively. An innovative and convenient DEA solver, Smart DEA, is designed and developed in accordance with the proposed analysis framework. The developed software provides a DEA solution which is consistent with the framework and is ready-to-analyze with data mining tools, through a table-based structure. The developed framework is tested and applied in a real world project for bench marking the vendors of a leading Turkish automotive company. The results show the effectiveness and the efficacy of the proposed framework.
http://research.sabanciuniv.edu.
Validity of a graph-based automatic assessment system for programming assign...IJECEIAES
Programming is a very complex and challenging subject to teach and learn. A strategy guaranteed to deliver proven results has been intensive and continual training. However, this strategy holds an extra workload for the teachers with huge numbers of programming assignments to evaluate in a fair and timely manner. Furthermore, under the current coronavirus (COVID-19) distance teaching circumstances, regular assessment is a fundamental feedback mechanism. It ensures that students engage in learning as well as determines the extent to which they reached the expected learning goals, in this new learning reality. In sum, automating the assessment process will be particularly appreciated by the instructors and highly beneficial to the students. The purpose of this paper is to investigate the feasibility of automatic assessment in the context of computer programming courses. Thus, a prototype based on merging static and dynamic analysis was developed. Empirical evaluation of the proposed grading tool within an introductory C-language course has been presented and compared to manually assigned marks. The outcomes of the comparative analysis have shown the reliability of the proposed automatic assessment prototype.
UNIVERSITY ADMISSION SYSTEMS USING DATA MINING TECHNIQUES TO PREDICT STUDENT ...IRJET Journal
This document summarizes a research study that aimed to predict student performance and support decision making for university admission systems using data mining techniques. The study analyzed data from 2,039 students at a university in Saudi Arabia to compare the predictive power of different data mining classification models (ANN, decision trees, SVM, naive Bayes). It found that a student's score on the pre-admission Scholastic Proficiency Admission Test was the best predictor of their first year GPA. Based on this, the university adjusted its admission criteria to give greater weight to this pre-admission test score. After making this change, the number of students with high GPAs increased while the number with low GPAs decreased.
Enhancing software development cost control by forecasting the cost of rework...nooriasukmaningtyas
Industrial reports show massive cost overruns associated with software
projects. The cost of software reworks constitutes a large portion of the
overall cost, reflecting a substantial challenge in cost control. Earned value
management (EVM) is the most recognized model for project cost control.
However, it shows many limitations in forecasting the software project cost,
leading to a considerable challenge in cost control. Nevertheless, the major
EVM limitation found is its inability to forecast the cost of software rework.
This research investigated the factors affecting this limitation and suggests an
enhanced EVM model. The significant contribution of this research is its
incorporation of software-related factors into the EVM model. We
introduced the software rework index (SRI), which is incorporated into the
traditional EVM model to enhance its predictability of the software project
cost at completion, including the rework cost. We defined the SRI in terms of
two factors: product functional complexity and the team competency.
Finally, we evaluated the proposed model using a dataset drawn from five
actual projects. The results showed a significant enhancement in forecasting
accuracy.
Software Cost Estimation Using Clustering and Ranking SchemeEditor IJMTER
Software cost estimation is an important task in the software design and development process.
Planning and budgeting tasks are carried out with reference to the software cost values. A variety of
software properties are used in the cost estimation process. Hardware, products, technology and
methodology factors are used in the cost estimation process. The software cost estimation quality is
measured with reference to the accuracy levels.
Software cost estimation is carried out using three types of techniques. They are regression based
model, anology based model and machine learning model. Each model has a set of technique for the
software cost estimation process. 11 cost estimation techniques fewer than 3 different categories are
used in the system. The Attribute Relational File Format (ARFF) is used maintain the software product
property values. The ARFF file is used as the main input for the system.
The proposed system is designed to perform the clustering and ranking of software cost
estimation methods. Non overlapped clustering technique is enhanced with optimal centroid estimation
mechanism. The system improves the clustering and ranking process accuracy. The system produces
efficient ranking results on software cost estimation methods.
THE USE OF COMPUTER-BASED LEARNING ASSESSMENT FOR PROFESSIONAL COURSES: A STR...IAEME Publication
Background/Objectives: While the increase in classroom technology, it is necessary to examine how assessment is administered through technology. The purpose of this study is to understand how students and faculty are perceived and examine the effectiveness of the computer-based assessment in professional education courses (Educational Technology) at Northern Iloilo Polytechnic State College, Iloilo, Philippines. Methods: The research design utilized in this study is mixed-method research. A computer-based assessment was utilized to assess students' performance in educational technology. This instrument was validated, and pilot tested to establish reliability. Each campus of NIPSC selected ten students of 70 as respondents during Academic Year 2016-2017. Frequency count, mean, standard deviation, and Wilcoxon signed-rank test were statistical tools used for data analyses. Findings: The study's finding showed a high score of students in the posttest ensured better performance of the students in educational technology. The increase in the posttest per performance level of the students was due to an accurate measure of what they have learned in educational technology. The majority of students users agreed that online assessment was fasters than the paper and pencil form. Also, users agreed that online assessment is contemporary and more systematic. They also stated that online assessment is consistent with the teaching style, but they are less anxious. Furthermore, according to faculty and students, ninety percent (90%) believed that computer-based assessment accurately measures what they are teaching and what they learned in school, respectively. Novelty: With the current situation that the education system is in new normal, computer-based learning is important in flexible learning. And assessment using technology is a great help to both faculty and students. Thus, state universities and colleges (SUCs) should adopt this innovation to help teaching and learning.
An Application Of The Data Envelopment Analysis Method To Evaluate The Perfor...Karin Faust
This document summarizes an analysis of the performance of university departments at the University of Zaragoza in Spain using data envelopment analysis (DEA). The study uses DEA to evaluate departments based on inputs like staff, budgets, and capital assets, as well as outputs related to teaching and research activities. It analyzes 52 departments, identifying the most efficient ones and discussing differences between departments in different subject areas. The results are intended to inform management and decision making at the university.
Design of an environmental management information system for the Universidad ...nooriasukmaningtyas
This article presents the design, development and implementation of a software tool, serving as an alternative to the problems involving management, control and reporting of processes within the institutional plan for environmental management (known as plan institucional de gestión ambiental (PIGA) by its Spanish acronym) for the Universidad Distrital Francisco José de Caldas. The software is focused on carrying out such processes to the automation setting, based on the extreme programming (XP) Agile methodology that mainly centers on the continuous development of the customer requirements to offer a more assertive tool, in line with the plan institucional de gestión ambiental in Spanish (PIGA) processes. The result is a complete satisfaction of users and a highly usable, adaptable and efficient software, inherently optimizing and automating the environmental management processes of the PIGA program. This work delivers an applet that meets the design and implementation requirements of environmental management policies. The proposed tool manages to reduce process-related times by 97%, therefore, allowing to aim efforts in other missional functions and increase the overall value offer of the organization.
Measurement and Comparison of Productivity Performance Under Fuzzy Imprecise ...Waqas Tariq
The creation of goods and services requires changing the expended resources into the output goods and services. How efficiently we transform these input resources into goods and services depends on the productivity of the transformation process. However, it has been observed there is always a vagueness or imprecision associated with the values of inputs and outputs. Therefore, it becomes hard for a productivity measurement expert to specify the amount of resources and the outputs as exact scalar numbers. The present paper, applies fuzzy set theory to measure and compare productivity performance of transformation processes when numerical data cannot be specified in exact terms. The approach makes it possible to measure and compare productivity of organizational units (including non-government and non-profit entities) when the expert inputs can not be specified as exact scalar quantities. The model has been applied to compare productivity of different branches of a company.
Study of Lean construction tools for Workflow Improvement -A ReviewIRJET Journal
This document summarizes several research studies on improving workflow in construction projects using lean tools like value stream mapping (VSM). It discusses how VSM has been used to identify waste and inefficiencies in processes like structural masonry work. The studies found that VSM helps visualize information and material flows to reduce wait times and optimize value-adding activities. Implementing lean principles through tools such as VSM can help construction projects improve workflow and productivity.
Textbooks for Responsible Data Analysis in ExcelNathan Garrett
With 27 million users, Excel (Microsoft Corporation, Seattle, WA) is the most common
business data analysis software. However, audits show that almost all complex spreadsheets
have errors. The author examined textbooks to understand why responsible data analysis is
taught. A purposeful sample of 10 textbooks was coded, and then compared against
spreadsheet development best practices. The results show a wide range of approaches, and
reveal that none of the 10 books fully cover the methodologies needed to create wellrounded
Excel data analysts. There is a need to re-evaluate the teaching approaches being
used in office application courses
This document describes the development of a web-based staff appraisal management system for the Federal University of Technology in Owerri, Nigeria. The system aims to automate the performance appraisal process for academic staff, which was previously done manually and led to issues like inappropriate assessments and a lack of data integrity. The objectives are to identify weaknesses in the existing system, design input-output modules, code the system modules, test the system, and verify it. The document outlines the methodology, design models including flowcharts and entity relationship diagrams, major outputs, and conclusions that recommend adopting the automated system to improve the staff appraisal process.
Evaluation of E-Learning Web Sites Using Fuzzy Axiomatic Design Based ApproachGurdal Ertek
High quality web site has been generally recognized as a critical enabler to conduct online business. Numerous studies exist in the literature to measure the business performance in relation to web site quality. In this paper, an axiomatic design based approach for fuzzy group decision making is adopted to evaluate the quality of e-learning web sites. Another multi-criteria decision making technique, namely fuzzy TOPSIS, is
applied in order to validate the outcome. The methodology proposed in this paper has the advantage of incorporating requirements and enabling reductions in the problem size, as compared to fuzzy TOPSIS. A case study focusing on Turkish e-learning websites is presented, and based on the empirical findings, managerial implications and recommendations
for future research are offered.
http://research.sabanciuniv.edu.
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Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Embedded machine learning-based road conditions and driving behavior monitoringIJECEIAES
Car accident rates have increased in recent years, resulting in losses in human lives, properties, and other financial costs. An embedded machine learning-based system is developed to address this critical issue. The system can monitor road conditions, detect driving patterns, and identify aggressive driving behaviors. The system is based on neural networks trained on a comprehensive dataset of driving events, driving styles, and road conditions. The system effectively detects potential risks and helps mitigate the frequency and impact of accidents. The primary goal is to ensure the safety of drivers and vehicles. Collecting data involved gathering information on three key road events: normal street and normal drive, speed bumps, circular yellow speed bumps, and three aggressive driving actions: sudden start, sudden stop, and sudden entry. The gathered data is processed and analyzed using a machine learning system designed for limited power and memory devices. The developed system resulted in 91.9% accuracy, 93.6% precision, and 92% recall. The achieved inference time on an Arduino Nano 33 BLE Sense with a 32-bit CPU running at 64 MHz is 34 ms and requires 2.6 kB peak RAM and 139.9 kB program flash memory, making it suitable for resource-constrained embedded systems.
Advanced control scheme of doubly fed induction generator for wind turbine us...IJECEIAES
This paper describes a speed control device for generating electrical energy on an electricity network based on the doubly fed induction generator (DFIG) used for wind power conversion systems. At first, a double-fed induction generator model was constructed. A control law is formulated to govern the flow of energy between the stator of a DFIG and the energy network using three types of controllers: proportional integral (PI), sliding mode controller (SMC) and second order sliding mode controller (SOSMC). Their different results in terms of power reference tracking, reaction to unexpected speed fluctuations, sensitivity to perturbations, and resilience against machine parameter alterations are compared. MATLAB/Simulink was used to conduct the simulations for the preceding study. Multiple simulations have shown very satisfying results, and the investigations demonstrate the efficacy and power-enhancing capabilities of the suggested control system.
Neural network optimizer of proportional-integral-differential controller par...IJECEIAES
Wide application of proportional-integral-differential (PID)-regulator in industry requires constant improvement of methods of its parameters adjustment. The paper deals with the issues of optimization of PID-regulator parameters with the use of neural network technology methods. A methodology for choosing the architecture (structure) of neural network optimizer is proposed, which consists in determining the number of layers, the number of neurons in each layer, as well as the form and type of activation function. Algorithms of neural network training based on the application of the method of minimizing the mismatch between the regulated value and the target value are developed. The method of back propagation of gradients is proposed to select the optimal training rate of neurons of the neural network. The neural network optimizer, which is a superstructure of the linear PID controller, allows increasing the regulation accuracy from 0.23 to 0.09, thus reducing the power consumption from 65% to 53%. The results of the conducted experiments allow us to conclude that the created neural superstructure may well become a prototype of an automatic voltage regulator (AVR)-type industrial controller for tuning the parameters of the PID controller.
An improved modulation technique suitable for a three level flying capacitor ...IJECEIAES
This research paper introduces an innovative modulation technique for controlling a 3-level flying capacitor multilevel inverter (FCMLI), aiming to streamline the modulation process in contrast to conventional methods. The proposed
simplified modulation technique paves the way for more straightforward and
efficient control of multilevel inverters, enabling their widespread adoption and
integration into modern power electronic systems. Through the amalgamation of
sinusoidal pulse width modulation (SPWM) with a high-frequency square wave
pulse, this controlling technique attains energy equilibrium across the coupling
capacitor. The modulation scheme incorporates a simplified switching pattern
and a decreased count of voltage references, thereby simplifying the control
algorithm.
A review on features and methods of potential fishing zoneIJECEIAES
This review focuses on the importance of identifying potential fishing zones in seawater for sustainable fishing practices. It explores features like sea surface temperature (SST) and sea surface height (SSH), along with classification methods such as classifiers. The features like SST, SSH, and different classifiers used to classify the data, have been figured out in this review study. This study underscores the importance of examining potential fishing zones using advanced analytical techniques. It thoroughly explores the methodologies employed by researchers, covering both past and current approaches. The examination centers on data characteristics and the application of classification algorithms for classification of potential fishing zones. Furthermore, the prediction of potential fishing zones relies significantly on the effectiveness of classification algorithms. Previous research has assessed the performance of models like support vector machines, naïve Bayes, and artificial neural networks (ANN). In the previous result, the results of support vector machine (SVM) were 97.6% more accurate than naive Bayes's 94.2% to classify test data for fisheries classification. By considering the recent works in this area, several recommendations for future works are presented to further improve the performance of the potential fishing zone models, which is important to the fisheries community.
Electrical signal interference minimization using appropriate core material f...IJECEIAES
As demand for smaller, quicker, and more powerful devices rises, Moore's law is strictly followed. The industry has worked hard to make little devices that boost productivity. The goal is to optimize device density. Scientists are reducing connection delays to improve circuit performance. This helped them understand three-dimensional integrated circuit (3D IC) concepts, which stack active devices and create vertical connections to diminish latency and lower interconnects. Electrical involvement is a big worry with 3D integrates circuits. Researchers have developed and tested through silicon via (TSV) and substrates to decrease electrical wave involvement. This study illustrates a novel noise coupling reduction method using several electrical involvement models. A 22% drop in electrical involvement from wave-carrying to victim TSVs introduces this new paradigm and improves system performance even at higher THz frequencies.
Electric vehicle and photovoltaic advanced roles in enhancing the financial p...IJECEIAES
Climate change's impact on the planet forced the United Nations and governments to promote green energies and electric transportation. The deployments of photovoltaic (PV) and electric vehicle (EV) systems gained stronger momentum due to their numerous advantages over fossil fuel types. The advantages go beyond sustainability to reach financial support and stability. The work in this paper introduces the hybrid system between PV and EV to support industrial and commercial plants. This paper covers the theoretical framework of the proposed hybrid system including the required equation to complete the cost analysis when PV and EV are present. In addition, the proposed design diagram which sets the priorities and requirements of the system is presented. The proposed approach allows setup to advance their power stability, especially during power outages. The presented information supports researchers and plant owners to complete the necessary analysis while promoting the deployment of clean energy. The result of a case study that represents a dairy milk farmer supports the theoretical works and highlights its advanced benefits to existing plants. The short return on investment of the proposed approach supports the paper's novelty approach for the sustainable electrical system. In addition, the proposed system allows for an isolated power setup without the need for a transmission line which enhances the safety of the electrical network
Bibliometric analysis highlighting the role of women in addressing climate ch...IJECEIAES
Fossil fuel consumption increased quickly, contributing to climate change
that is evident in unusual flooding and draughts, and global warming. Over
the past ten years, women's involvement in society has grown dramatically,
and they succeeded in playing a noticeable role in reducing climate change.
A bibliometric analysis of data from the last ten years has been carried out to
examine the role of women in addressing the climate change. The analysis's
findings discussed the relevant to the sustainable development goals (SDGs),
particularly SDG 7 and SDG 13. The results considered contributions made
by women in the various sectors while taking geographic dispersion into
account. The bibliometric analysis delves into topics including women's
leadership in environmental groups, their involvement in policymaking, their
contributions to sustainable development projects, and the influence of
gender diversity on attempts to mitigate climate change. This study's results
highlight how women have influenced policies and actions related to climate
change, point out areas of research deficiency and recommendations on how
to increase role of the women in addressing the climate change and
achieving sustainability. To achieve more successful results, this initiative
aims to highlight the significance of gender equality and encourage
inclusivity in climate change decision-making processes.
Voltage and frequency control of microgrid in presence of micro-turbine inter...IJECEIAES
The active and reactive load changes have a significant impact on voltage
and frequency. In this paper, in order to stabilize the microgrid (MG) against
load variations in islanding mode, the active and reactive power of all
distributed generators (DGs), including energy storage (battery), diesel
generator, and micro-turbine, are controlled. The micro-turbine generator is
connected to MG through a three-phase to three-phase matrix converter, and
the droop control method is applied for controlling the voltage and
frequency of MG. In addition, a method is introduced for voltage and
frequency control of micro-turbines in the transition state from gridconnected mode to islanding mode. A novel switching strategy of the matrix
converter is used for converting the high-frequency output voltage of the
micro-turbine to the grid-side frequency of the utility system. Moreover,
using the switching strategy, the low-order harmonics in the output current
and voltage are not produced, and consequently, the size of the output filter
would be reduced. In fact, the suggested control strategy is load-independent
and has no frequency conversion restrictions. The proposed approach for
voltage and frequency regulation demonstrates exceptional performance and
favorable response across various load alteration scenarios. The suggested
strategy is examined in several scenarios in the MG test systems, and the
simulation results are addressed.
Enhancing battery system identification: nonlinear autoregressive modeling fo...IJECEIAES
Precisely characterizing Li-ion batteries is essential for optimizing their
performance, enhancing safety, and prolonging their lifespan across various
applications, such as electric vehicles and renewable energy systems. This
article introduces an innovative nonlinear methodology for system
identification of a Li-ion battery, employing a nonlinear autoregressive with
exogenous inputs (NARX) model. The proposed approach integrates the
benefits of nonlinear modeling with the adaptability of the NARX structure,
facilitating a more comprehensive representation of the intricate
electrochemical processes within the battery. Experimental data collected
from a Li-ion battery operating under diverse scenarios are employed to
validate the effectiveness of the proposed methodology. The identified
NARX model exhibits superior accuracy in predicting the battery's behavior
compared to traditional linear models. This study underscores the
importance of accounting for nonlinearities in battery modeling, providing
insights into the intricate relationships between state-of-charge, voltage, and
current under dynamic conditions.
Smart grid deployment: from a bibliometric analysis to a surveyIJECEIAES
Smart grids are one of the last decades' innovations in electrical energy.
They bring relevant advantages compared to the traditional grid and
significant interest from the research community. Assessing the field's
evolution is essential to propose guidelines for facing new and future smart
grid challenges. In addition, knowing the main technologies involved in the
deployment of smart grids (SGs) is important to highlight possible
shortcomings that can be mitigated by developing new tools. This paper
contributes to the research trends mentioned above by focusing on two
objectives. First, a bibliometric analysis is presented to give an overview of
the current research level about smart grid deployment. Second, a survey of
the main technological approaches used for smart grid implementation and
their contributions are highlighted. To that effect, we searched the Web of
Science (WoS), and the Scopus databases. We obtained 5,663 documents
from WoS and 7,215 from Scopus on smart grid implementation or
deployment. With the extraction limitation in the Scopus database, 5,872 of
the 7,215 documents were extracted using a multi-step process. These two
datasets have been analyzed using a bibliometric tool called bibliometrix.
The main outputs are presented with some recommendations for future
research.
Use of analytical hierarchy process for selecting and prioritizing islanding ...IJECEIAES
One of the problems that are associated to power systems is islanding
condition, which must be rapidly and properly detected to prevent any
negative consequences on the system's protection, stability, and security.
This paper offers a thorough overview of several islanding detection
strategies, which are divided into two categories: classic approaches,
including local and remote approaches, and modern techniques, including
techniques based on signal processing and computational intelligence.
Additionally, each approach is compared and assessed based on several
factors, including implementation costs, non-detected zones, declining
power quality, and response times using the analytical hierarchy process
(AHP). The multi-criteria decision-making analysis shows that the overall
weight of passive methods (24.7%), active methods (7.8%), hybrid methods
(5.6%), remote methods (14.5%), signal processing-based methods (26.6%),
and computational intelligent-based methods (20.8%) based on the
comparison of all criteria together. Thus, it can be seen from the total weight
that hybrid approaches are the least suitable to be chosen, while signal
processing-based methods are the most appropriate islanding detection
method to be selected and implemented in power system with respect to the
aforementioned factors. Using Expert Choice software, the proposed
hierarchy model is studied and examined.
Enhancing of single-stage grid-connected photovoltaic system using fuzzy logi...IJECEIAES
The power generated by photovoltaic (PV) systems is influenced by
environmental factors. This variability hampers the control and utilization of
solar cells' peak output. In this study, a single-stage grid-connected PV
system is designed to enhance power quality. Our approach employs fuzzy
logic in the direct power control (DPC) of a three-phase voltage source
inverter (VSI), enabling seamless integration of the PV connected to the
grid. Additionally, a fuzzy logic-based maximum power point tracking
(MPPT) controller is adopted, which outperforms traditional methods like
incremental conductance (INC) in enhancing solar cell efficiency and
minimizing the response time. Moreover, the inverter's real-time active and
reactive power is directly managed to achieve a unity power factor (UPF).
The system's performance is assessed through MATLAB/Simulink
implementation, showing marked improvement over conventional methods,
particularly in steady-state and varying weather conditions. For solar
irradiances of 500 and 1,000 W/m2
, the results show that the proposed
method reduces the total harmonic distortion (THD) of the injected current
to the grid by approximately 46% and 38% compared to conventional
methods, respectively. Furthermore, we compare the simulation results with
IEEE standards to evaluate the system's grid compatibility.
Enhancing photovoltaic system maximum power point tracking with fuzzy logic-b...IJECEIAES
Photovoltaic systems have emerged as a promising energy resource that
caters to the future needs of society, owing to their renewable, inexhaustible,
and cost-free nature. The power output of these systems relies on solar cell
radiation and temperature. In order to mitigate the dependence on
atmospheric conditions and enhance power tracking, a conventional
approach has been improved by integrating various methods. To optimize
the generation of electricity from solar systems, the maximum power point
tracking (MPPT) technique is employed. To overcome limitations such as
steady-state voltage oscillations and improve transient response, two
traditional MPPT methods, namely fuzzy logic controller (FLC) and perturb
and observe (P&O), have been modified. This research paper aims to
simulate and validate the step size of the proposed modified P&O and FLC
techniques within the MPPT algorithm using MATLAB/Simulink for
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Efficiency analysis trees as a tool to analyze the quality of university education
1. International Journal of Electrical and Computer Engineering (IJECE)
Vol. 13, No. 4, August 2023, pp. 4412~4421
ISSN: 2088-8708, DOI: 10.11591/ijece.v13i4.pp4412-4421 4412
Journal homepage: http://ijece.iaescore.com
Efficiency analysis trees as a tool to analyze the quality of
university education
Rohemi Zuluaga-Ortiz1
, Alicia Camelo-Guarín2
, Enrique Delahoz-Domínguez3
1
Faculty of Engineering, Universidad del Sinú, Cartagena, Colombia
2
Faculty of Military Sciences, Escuela Militar de Cadetes General José María Córdova, Bogotá, Colombia
3
Faculty of Engineering, Universidad Tecnológica de Bolívar, Cartagena, Colombia
Article Info ABSTRACT
Article history:
Received Jul 21, 2022
Revised Oct 6, 2022
Accepted Nov 21, 2022
This research aims to design a quality management tool in education. The
methodology comprises two stages: first, the construction of the decision
tree model, and second, the efficiency evaluation. For the validation and
development of this research, the data modelled corresponds to the
standardized exams for higher education in Colombia of ninety industrial
engineering degrees. Among the results, the citizenship skills (CC_PRO)
generate the most significant contribution to the model. On the other hand,
the written communication competence (CE_PRO) generates a minor
contribution to the model. In addition, the most relevant result of the
research is the design and validation of a tool to estimate educational
efficiency using the efficiency analysis tree (EAT) and data enveloping
analysis (DEA) models. The proposed tool allows the generation of specific
targets to increase the level of efficiency of universities through the nodes of
the decision tree, which contributes to the spectrum of knowledge on models
for educational management. In conclusion, this research presents a tool for
the management of educational processes through the analysis of efficiency
using EAT, estimating the efficiency of universities and setting the
foundations for forecasting future efficiency scenarios.
Keywords:
Data enveloping analysis
Decision trees
Education management
Efficiency
Learning analytics
This is an open access article under the CC BY-SA license.
Corresponding Author:
Enrique Delahoz-Dominguez
Faculty of Engineering, Universidad Tecnológica de Bolivar
Cartagena, Colombia
Email: edelahoz@utb.edu.co
1. INTRODUCTION
The knowledge acquired through education quickly becomes a powerful tool that provides benefits,
either as a personal competitive advantage or for the development of organizations [1]. However, a problem
currently arising for organizations can be identified as an opportunity or a threat. As is well known,
knowledge evolves, and it is necessary to be on the frontier of knowledge, so if organizations are not aware
of the new waves of knowledge, this will become a threat; in contrast, if the organizations are aware of the
new research, this will be an opportunity [2].
For their part, it is the duty of higher education institutions (HEI) to provide organizations with
professionals with training framed in the frontier of knowledge; therefore, it is of great relevance to generate
tools that support the management and evaluation of educational processes. In the case of Colombia, the
Colombian Institute for the Evaluation of Education Instituto Colombiano para la evaluación de la educación
(ICFES) has standardized tests to evaluate and monitor students’ academic performance in secondary
education, Saber 11, and university education, Saber PRO. Thus, ICFES uses the added value approach,
considering that a student's capacities at the end of a university program are not only a consequence of their
2. Int J Elec & Comp Eng ISSN: 2088-8708
Efficiency analysis trees as a tool to analyze the quality of university education (Rohemi Zuluaga-Ortiz)
4413
passage through the HEI but also due to their previous knowledge foundations. Since 2012, the ICFES has
developed a method called relative contribution (RC), evolving the concept of added value to measure the
quality of education in Colombia.
Usually, methods for estimating technical efficiency in production systems use an efficiency frontier
scheme to compare productive units’ performance and the deviations associated with inefficient units.
However, data envelopment analysis (DEA) models suffer from overfitting problems when estimating
efficiency parameters, resulting in conservative models that underestimate the technical efficiency of
decision-making units (DMUs). Therefore, becoming models that describe very well the current situation but
do not allow a generalized adjustment for the whole system. Consequently, with the rise of machine learning
techniques, econometric models are increasingly required to predict future scenarios so that the results of an
efficiency model serve as support for objective decision-making and not only for the description of a past
situation. Therefore, the approach proposed in this research for analyzing the academic performance of
universities relies on using the efficiency analysis trees (EAT) approach to demonstrate the advantages of
creating an efficiency frontier through the decision trees technique and will also allow determining the
efficiency for out-of-sample DMUs.
Consequently, measuring the quality and efficiency of education today is a challenge for many
researchers [3]–[5] due to the number of variables associated with the economic, psychosocial,
sociodemographic, environmental, institutional, ethical, spiritual and cultural contexts of each individual
[6]–[9]. Therefore, it is essential to manage educational processes in the most effective way possible through
reliable techniques, tools and methodologies, which have been studied in Colombia [10], [11]. Unlike models
previously developed in this field, the present research proposes to measure the efficiency and productivity of
universities in Colombia in engineering careers from the EAT. This computational library was created by
[12], and to date, it has no real implementations in the literature related to efficiency analysis issues.
Consequently, this research seeks to validate the new EAT model and compare the findings with the classic
efficiency model DEA.
DEA is a non-parametric method to measure the efficiency and productivity of decision-making
units. For instance, [13] considers that the purpose of the DEA technique is to analyze the level of efficiency
of the study units (also known as DMUs, decision-making units). The tool’s core focuses on analyzing
various inputs to generate desired outputs, as long as they are under equal conditions (ensuring that the
assumption of homogeneity is met). In summary, the DEA technique estimates the observations’ efficiency
levels, taking into account the deviations in the production frontier (isoquant curve formed by the inputs and
outputs of the system). The DEA model is non-parametric and, as has been mentioned, by means of the
production frontier analysis, it estimates the efficiency of the DMUs [14], [15]; then, using the DMUs, the
efficient frontier is constructed taking into account the estimated efficiency levels. On the other hand, several
authors claim that the DEA tool is suitable for estimating the performance of DMUs in the public and private
sectors [16].
Therefore, statistical inference is possible based on current point estimates resulting from the DEA.
However, this model suffers from an overfitting problem since it underestimates the technical inefficiency of
the observations, generating estimated frontiers always located below the theoretical frontiers (underlying)
[17]. Therefore, DEA can correctly describe the situation from the point of view of efficiency evaluation, but
it cannot provide adequate generalization. Thus, DEA determines the efficiency scores but cannot give details
of the factors related to inefficiency; therefore, this research seeks to compare the analysis carried out by the
DEA against a more recent and little-studied model, the EAT.
Decision tree (DT) models belong to the family of supervised machine learning models and their
structure is similar to that of a tree, in this model the leaves correspond to the classification of the output and
the branches are the ramifications of the input variable that defines the classification or regression response
[18], [19]. In Figure 1, a decision tree model is illustrated that starts with predictor X; if predictor X has the
characteristic t, it goes to predictor Y, and if X has the characteristic f, it goes to W. In the same way, each of
the new predictors is branched until finding the response of the model that will be either C1 or C2.
The EAT is a new technique proposed by and based on the adaptation of the classification and
regression trees (CART) proposed by [20] for the estimation of production frontiers. This new technique
allows to calculate the production frontier taking into account the common assumptions for efficiency
analysis, using an approach that does not require a specific distribution over the behavior of the data and
results in a step function as a predictor.
The new specifications of the EAT model are related to the free disposal hull (FDH) technique.
However, EAT makes use of cross-validation to avoid overfitting that may occur at the time of model
construction [20]. Additionally, the construction of the EAT model is performed taking into account the
mean square error and the use of stopping rules taking into account the number of individuals within the
node, thus avoiding the generation of empty nodes, otherwise a response without inputs is obtained.
3. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4412-4421
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Figure 1. Graphic representation of a decision tree note. Taken from [21]
Unlike CART, EAT by estimating maximum trends instead of averages, guarantees the FDH model
criterion and thus succeeds in calculating the production frontier. In EAT, the MSE minimization is the
criteria for selecting the predictors in each node, generating binary partitions recursively (the training sample)
until a significant partitioning is no longer possible or by a stopping rule. However, similar to the graphical
representation of decision tree models, the visual representation of the model is a tree starting at a root node,
branching to intermediate nodes and ending at its leaves.
The new EAT technique divides the inputs of the model into two binary responses, it should be
noted that each new response is constant. In this sense, the inputs’ evolution behaves like a step function.
Then, FDH and EAT generate the efficiency estimation similarly by using a production frontier. However,
the EAT model avoids the overfitting presented in the FDH model by using cross-validation and overfitting
[20]. Thus, the fusion between the assumption of the FDH model (free disposal) and the construction
performed by the EAT technique contributes to the data analysis by widening the spectrum of knowledge for
efficiency analysis.
On the other hand, the research of [12] through the mean square error, bias and absolute bias,
affirms that the EAT model performs better than the FDH model. For the mean squared error of the EAT
model, they present performances that outperformed the FDH model between 13% and 70% in the
simulations. Additionally, the authors show that as the size of the individuals increased, the mean squared
error measure decreased. Also, an interesting advantage of the EAT model is that it allows graphically
representing the production frontier generated by the trees. Thus, this tool becomes a proposal for the visual
analysis of efficiency. Finally, the evaluation of the input variables is generated on the basis of the predictive
importance of the variables of interest.
2. METHOD
The methodology research seeks to estimate the efficiency of universities that offer industrial
engineering programs. The research methodology comprises two parts as shown in Figure 2: constructing the
decision tree model and evaluating the efficiency and, additionally, a comparison with the classical model of
efficiency analysis. The data used in this research were taken from the Mendeley research repository by [22].
It should be noted that from this database, only the results of the industrial engineering program were
selected as shown in Table 1. Initially, the information was reviewed and pre-processed to obtain helpful
information for the system. Consequently, eliminating the categorical variables that did not add valuable
information and homogenizing the useful numerical variables.
Additionally, the EAT methodology proposes training a prediction model (decision tree) before
constructing the efficient frontier. In this order of ideas, for this research, it is established that the predictor
variables are QR, CS, ENG, WC, and CR, while the output variables are FEP, MSST, and DPLS. However, it
is important to highlight that the production frontier has the same configuration as the prediction model
(decision tree). Finally, for the data analysis and the construction of the models, R software [23] was used
and the EAT package [11] for constructing the efficiency model employing a decision tree.
4. Int J Elec & Comp Eng ISSN: 2088-8708
Efficiency analysis trees as a tool to analyze the quality of university education (Rohemi Zuluaga-Ortiz)
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Figure 2. Research methodology
Table 1. Information from the research data
Variable Full Name Mean Standard Deviation
QR Quantitative reasoning 77.42 22.67
CS Citizenship skills 62.20 27.67
ENG English 59.19 28.99
WC Writing communication 67.50 25.49
CR Critical reading 53.70 30.00
FEP Formulation of engineering projects 145.48 40.12
MSST Mathematical and statistical scientific thinking 133.71 12.99
DPLS Design of production and logistics systems 147.80 16.50
3. RESULTS
3.1. Stage 1: decision tree
The decision tree model uses parameters numStop (number of observations-DMUs-in a node to
perform a partition) and fold (number of partitions of the dataset to make cross-validation during pruning).
This model configuration comprises nine leaf nodes (leaves), providing a lesser complex model than a
traditional efficiency model. Consequently, creating a training dataset (training: 70%) and, subsequently, a
test dataset (test: 30%). Thus, Table 2 presents the cross-validation results, evidencing that the numStop and
fold that minimize the root mean squares error (RMSE) are 6 and 5, respectively. Thus, the selected model
must be branched, and within its configuration, it is found that it has 12 internal nodes, which are partitioned
as shown in Table 3.
Table 2. Cross-validation of the decision tree model
numStop Fold RMSE Leaves
6 5 38.79 9
3 5 39.05 13
3 6 39.05 13
4 4 39.06 11
5 4 39.06 10
4 5 39.06 11
5 5 39.06 10
4 6 39.06 11
3 4 39.10 12
6 4 39.10 8
Table 3. Levels at the frontiers of the leaf nodes
Leaf node MSST DPLS FEP
1 174.12 192.94 187.24
2 154.80 160.33 170.50
3 120.25 132.88 134.88
4 119.25 147.67 142.00
5 123.59 147.67 142.00
6 128.00 147.67 142.00
7 128.00 147.67 142.29
8 128.00 147.67 145.11
9 130.39 150.02 149.34
10 128.00 147.67 147.00
11 128.00 147.70 145.10
12 128.00 147.70 145.10
Therefore, assuming that the decision tree model considers the predictors (independent variables) to
predict the variables of interest (dependent variables), it is crucial to observe each academic entry’s
contribution to the academic skills output as shown in Figure 3. The input-level citizenship skills (CS_PRO)
5. ISSN: 2088-8708
Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4412-4421
4416
generate the most significant contribution to the model, while the written communication skill (WC_PRO)
generate the most negligible contribution to the model. From an academic approach, the results evidence the
importance of basic reading and citizenship skills to generate integral learning in engineering students.
Figure 1. Importance of the model variables
3.2. Stage 2: efficiency analysis
Consequently, Table 4 compares the efficiency model’s results using decision trees EAT and the
classical efficiency model DEA. However, in general, it is observed that the results of the classical model
have a higher efficiency level than the decision tree model for both the constant scale constant return to scale
(CRS) and the variable scale variable return to scale (VRS).
Table 4. Results of the efficiency models
Metric CRS VRS Scale performance
EAT DEA EAT DEA EAT DEA
Efficient number 2 (2.17%) 8 (8.70%) 11 (11.95%) 82 (89.13%) 6 (6.52%) 8 (8.69%)
Mean 0.59 0.80 0.91 0.99 0.66 0.82
Deviation 0.11 0.11 0.08 0.05 0.14 0.11
Minimum 0.44 0.61 0.68 0.72 0.47 0.62
Quartile 2 0.56 0.79 0.91 1.00 0.64 0.80
Quartile 3 0.64 0.88 0.97 1.00 0.74 0.90
On the other hand, Figure 4 shows the concentration of the distribution of the efficiency data for the
CEAT and DEA models, evidencing that for the CEAT model, the data distribution concentrates on the left
side. In contrast, the data distribution is concentrated on the right side of the DEA model. Thus, indicating
that the DEA model has a higher efficiency level than the CEAT model.
Figure 4. Density of the initial model efficiency
6. Int J Elec & Comp Eng ISSN: 2088-8708
Efficiency analysis trees as a tool to analyze the quality of university education (Rohemi Zuluaga-Ortiz)
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Similarly, Figure 5 shows the concentration of the distribution of the efficiency data for the EAT
and FDH models, evidencing a high concentration on the right side for the FDH model. In contrast, for the
EAT model, the efficiency data tends to be distributed between 0.8 and 1.0, indicating that the FDH model
has a higher average number of efficient units compared to the EAT model. The difference in the model’s
distribution suggests a trend of the CEAT to underestimate the efficiency scores. Additionally, it is vital to
understand the information provided by the decision tree model of Figure 6; for this, Table 5 shows evidence
that of the 23 nodes of the model, there are only eight nodes that contain efficient units for the CRS model
and 13 nodes with efficient units for the VRS model. In addition, the average of the study variables per node
is presented, taking into account only the DMUs found in that node. This efficiency structure provides a
broader perspective of the efficiency results by characterizing each node as an efficiency cluster in which it is
possible to determine which DMUs shares similar performances.
Figure 5. EAT vs FDH efficiency-density
Figure 2. Decision tree for the construction of the DEA model notation: Node id (Id), node error (R), number
of node observations (n (t)), node predictor variable and node predictions (y)
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Int J Elec & Comp Eng, Vol. 13, No. 4, August 2023: 4412-4421
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Table 5. Characterization of the decision tree nodes
Node CR CS ENG WC QR MSST DPLS FEP DMUs EFF (CRS) EFF (VRS)
1 53.94 52.52 60.08 52.48 67.79 133.71 147.80 150.83 92 2 (2%) 6 (7%)
2 48.34 47.82 54.44 49.73 62.46 128.33 140.72 145.24 71 2 (3%) 5 (7%)
3 72.85 68.41 79.17 61.77 85.79 151.89 171.71 169.73 21 0 (0%) 1 (5%)
4 42.89 42.17 48.21 47.52 54.93 122.30 134.67 138.88 37 2 (5%) 3 (8%)
5 54.28 53.97 61.21 52.14 70.66 134.89 147.31 152.16 34 0 (0%) 2 (6%)
6 39.87 40.37 45.59 46.89 52.55 121.24 132.40 136.41 23 2 (9%) 2 (9%)
7 49.07 45.74 53.52 48.19 59.08 124.48 138.90 143.31 14 0 (0%) 1 (7%)
8 35.12 33.11 41.52 39.75 48.44 118.88 130.92 134.12 8 2 (25%) 1 (13%)
9 41.43 44.03 47.64 51.45 55.11 122.49 132.90 137.33 14 0 (0%) 1 (7%)
10 25.13 17.63 35.63 40.50 59.13 119.25 132.88 134.88 1 1 (100%) 0 (0%)
11 36.86 35.72 41.50 40.34 46.81 118.55 130.58 134.36 8 1 (13%) 1 (13%)
12 35.16 35.29 40.39 40.07 43.74 117.18 130.11 133.16 6 1 (17%) 0 (0%)
13 41.96 36.98 44.83 41.14 56.02 122.67 131.99 137.96 2 0 (0%) 1 (100%)
14 40.99 40.23 44.45 48.13 55.19 121.93 127.47 132.42 6 0 (0%) 1 (17%)
15 41.75 46.88 50.04 53.94 55.05 122.91 136.98 141.01 8 0 (0%) 0 (0%)
16 40.12 47.57 49.69 55.58 49.12 120.60 138.32 138.25 4 0 (0%) 0 (0%)
17 43.39 46.20 50.39 52.30 60.99 125.22 135.64 143.77 4 0 (0%) 0 (0%)
18 49.24 45.16 55.16 49.24 56.98 122.38 136.74 142.56 8 0 (0%) 0 (0%)
19 48.84 46.52 51.34 46.78 61.87 127.27 141.79 144.30 6 0 (0%) 1 (17%)
20 48.10 44.88 55.72 47.89 57.21 122.54 135.94 141.93 7 0 (0%) 0 (0%)
21 57.24 47.12 51.24 58.71 55.41 121.24 142.29 147.00 1 0 (0%) 0 (0%)
22 46.33 43.10 43.45 51.19 52.74 123.31 132.36 136.06 1 0 (0%) 0 (0%)
23 48.39 45.17 57.76 47.34 57.95 122.42 136.54 142.91 6 0 (0%) 0 (0%)
In addition, one of the advantages of this methodology for the efficiency analysis is the prediction of
future efficiency scores; therefore, it is possible to have complete management of the process and take
preventive actions in the face of various scenarios. Therefore, the predictive model built has five predictor
variables (QR, CS, ENG, WC, and CR) and three variables of interest (FEP, MSST, and DPLS).
Additionally, a random forest model is constructed to compare the results of the tree model. However, the
results show that the tree model has a higher error level due to the simplicity of the model construction. In
contrast, the error level of the random forest model is much lower due to its robust construction. Table 6
presents the performance metrics of the decision tree model used to build the efficiency frontier of the
system.
Table 6. Performance of the models in the evaluation
Metric Decision Tree model Random Forest model
MSST DPLS FEP MSST DPLS FEP
RMSE 18.16 17.47 16.15 5.62 9.12 5.69
RSquared 0.70 0.66 0.67 0.93 0.87 0.90
MAE 14.21 13.99 12.31 4.82 7.31 4.40
4. DISCUSSION
Education is a series of sequential and evolutionary activities of great relevance for the development
of societies in political, economic, technological and knowledge matters. However, objective and
reproducible tools to estimate efficiency in educational processes are scarce in the literature. Consequently,
this research presents a tool for managing educational processes through efficiency analysis using EAT and
DEA and comparing the EAT model with the FDH model.
Consequently, the literature indicates that there will be overfitting problems for estimating
efficiency through the DEA and FDH methods [12], [24]. Conversely, the EAT model does not have
problems with overfitting by implementing cross-validation and pruning. In addition, the estimation of the
production frontier and the relative position of the DMUs in the EAT model is much closer to the theoretical
production function (frontier) than in the FDH model. However, the above is reachable because the tree's
growth occurs evolutionarily. Besides, the fitting process is achieved by minimizing the MSE and using
stopping rules linked to the size of the database, avoiding empty leaf nodes [25].
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For its part, it is necessary to mention that the importance of the tool lies in being useful for
decision-making in educational environments. This tool helps to identify through two powerful techniques
(machine learning and decision tree) the universities' efficiency level. The result of this tool is a tree structure
consisting of a number of nodes representing a level of efficiency calculated from the specific characteristics
of the universities that are part of the node. In this vein, the nodes can be called the goals that the universities
must achieve to increase efficiency, considering that the nodes generate information about the skills each
university must improve.
The results presented in Table 4 show that in each EAT model, the amount and level of efficiency of
the DMUs is lower than in the classic DEA models. In addition, DEA models traditionally reach efficiency
analysis, and the EAT model generates a greater contribution by providing a prediction tool for a more
complete management of educational processes. In addition, an important finding is a piece of objective
evidence that EAT models are stricter than traditional DEA models.
Besides, the variables with greater relevance for predicting the response variables FEP, MSST and
DPLS; QR, CR, CS and ENG; in contrast, the WC according to the model has no relevance as a predictor. On
the other hand, this research identified that universities with the highest levels (≥77.85) in QR could achieve
a high level of efficiency with results in their specific skills MSST, DPLS and FEP of 174.1, 192.9 and
187.2, respectively. While those universities with a level lower than 77.85 in QR should strengthen their level
of CR skill as a second option, if the performance exceeds 45.23 points, their performance in the MSST,
DPLS and FEP skills likely is 130.4, 150, 149.3. On the other hand, if the score in their CR skill is less than
45.23, universities should strengthen their level in CS in such a way that by generating a score higher than
39, the performance in the MSST, DPLS and FEP skills is 128, 147.7 and 145.1, respectively. In summary,
this research proposes a tool that eliminates the risk of overfitting by articulating machine learning
techniques and DEA, estimating the efficiency and productivity of engineering degrees. Thus, setting a
reproducible and replicable procedure to evaluate efficiency in other sectors different than education.
5. CONCLUSION
The research objective was to create a tool that contributes to the spectrum of knowledge about
models for educational management. Consequently, the result of the research is a tool to estimate educational
efficiency through the EAT and DEA models. Thus, considering the EAT model as an adaptation of the
decision trees for estimating production frontiers, DEA is the classic linear programming model for
efficiency analysis. Consequently, the decision tree model with the best performance considers a minimum
number of six observations to perform a partition in a node; the number of partitions of the dataset to perform
cross-validation during pruning is five; the number of nodes is nine, and the value of the RMSE is 38.79
(lesser than outputs standard deviation). It is essential to highlight that this model, compared with the
traditional DEA model, has less complexity and provides better forecasting results.
Regarding the efficiency models, the production frontier of the EAT model in its constant scale
constructed by the decision tree model presents an efficiency level of 2.17% (2 efficient DMUs). For the
variable scale, the EAT model has an efficiency level of 11.95% (11 efficient DMUs). In contrast, the classic
DEA model in its constant scale provides an efficiency level of 8.70% (8 efficient DMUs), and in its variable
scale, its efficiency level is 89.13% (82 efficient DMUs). In addition, it is observed that the scale
performance levels for the EAT and DEA models are 6.52% (6 efficient DMUs) and 8.69% (8 efficient
DMUs), respectively. It should be noted that this tool is much stricter than conventional efficiency models
because in the first instance, variables relevant to the problem must be selected; in addition, the set of
observed data must be correctly listed in a way that avoids the problem of instability in the decision trees due
to noise factors and predictors not relevant to the problem. Additionally, the robustness of the model is
generated by performing the partition of the nodes to construct the efficiency frontier so that only partitions
of predictors will be made a whose contribution to the explanation of the response variable is significant.
Finally, the EAT model for this research has a lower efficiency level than the traditional DEA model due to
the model's rigour to avoid overfitting.
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BIOGRAPHIES OF AUTHORS
Rohemi A. Zuluaga Ortiz has a master’s degree in engineering from the
Universidad Tecnológica de Bolívar (UTB), and an Industrial Engineer from the Universidad
Tecnológica de Bolívar (UTB). He is currently a professor at the University of Sinú. His areas
of research are efficiency and learning analytics. He can be contacted at email:
rohemi.zuluaga@unisinu.edu.co.
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Alicia Camelo Guarín has a Ph.D. in Social Sciences, Social Communicator and
Master in Communication Management from the Universidad del Norte. With experience in
research of educational, Military Sciences, entertainment and communication strategies for
social change. University professor and methodological consultant in the design of
experiments, conducting scientific studies, validation and dissemination of results. She can be
contacted at email: aliciacameloguarin@hotmail.com.
Enrique De La Hoz is a Ph.D. candidate in Networks and information systems
from the Universitat Oberta de Catalunya (UOC), and received a master's degree in Operations
Research from the Universitat de Barcelona and the UPC. He is currently a professor at the
Technological University of Bolívar. His research areas are learning analytics,
recommendation systems and large-scale data mining. He can be contacted at email:
edelahoz@utb.edu.co.