The objective of this study is to analyze and discuss the metrics of the predictive model using the K-nearest neighbor (K-NN) learning algorithm, which will be applied to the data on the perception of engineering students on the quality of the virtual administrative service, such as part of the methodology was analyzed the indicators of accuracy, precision, sensitivity and specificity, from the obtaining of the confusion matrix and the receiver operational characteristic (ROC) curve. The collected data were validated through Cronbach's Alpha, finding consistency values higher than 0.9, which allows to continue with the analysis. Through the predictive model through the Matlab R2021a software, it was concluded that the average metrics for all classes are optimal, presenting a precision of 92.77%, sensitivity 86.62%, and specificity 94.7%; with a total accuracy of 85.5%. In turn, the highest level of the area under the curve (AUC) is 0.98, which is why it is considered an optimal predictive model. Having carried out this study, it is possible to contribute significantly to the decision-making of the higher institution in relation to the improvement of the quality of the virtual administrative service.
Rodmunkong, T., Wannapiroon, P. and Nilsook, P. (2015) Development of Information Management Systems
in accordance with the Thai Qualifications Framework for Higher Education via Cloud Computing
The sixth International e-Learning Conference 2015 (IEC2015), July 20-21, 2015,BITEC Bangna, Bangkok, Thailand.
The Management of Information Systems and Its Impact on the Quality of Servic...Eswar Publications
This study aimed to identify the impact of management information systems (MIS) techniques on the quality of services provided at the University of Tabuk from the perspective of students. To achieve the goals of the study, one question were developed and distributed on a random sample of 244 students at the University of Tabuk in the Kingdom of Saudi Arabia. The Statistical Package of Social Sciences (SPSS, V.16) was used to analyze the data of the question.
Evaluate and improve high school students for some skills using quality funct...Rania Elrifai
5. RECOMMENDATIONS
Due to the needing for the inevitable to raise the level of academic must attention means that enable to improve the level of education , which has become the other nations advanced dramatically despite the fact that these methods do not cost a lot of money, time and effort g spite of the results that will return us over time, which will advance our Islamic nation, especially the Arab world General, and therefore we recommend in our study that the characteristics we have learned to be starting out in the early stages, even before the beginning of the school stage( kindergarten),
for example, enable the child to display a simple subject and talk about it in front of his colleagues earned many skills, including the courageous confrontation, connect with others and instil confidence that makes a strong base for the personal leadership of the children, and do not forget that the child is affected by the environment around them, so take care of this opportunity to improve our future generations, through our study, there are some of the things want to be displayed and recommend as the following:
2. Use laptops instead of bags that weigh weights strain our children as well as the ease to download the curriculum according to each level
3. The use of laptop computers to facilitate the process of Internet connectivity and to keep pace with scientific development.
4. Focus on the internet linking and connection with as consideration a part of topics of courses inside the
International Journal of Science and Engineering Applications
Volume 7–Issue 10,401-405, 2018, ISSN:-2319–7560
405
curriculum for raising the education quality level of each level.
5. Concern on these skills from the beginning of the school at first stage as consider the basic level is the base for future a generation.
6. Mainstreaming the introduction of these skills in the early stages of the study as consider there are available applications but need funder and support from the government.
7. Support the confidence of children through the presentation in front of his colleagues and his parents are present.
6. ACKNOWLEDGMENT
In this study, I would like to thank Dr. Galal Abdella. Assistant Professor of the faculty of the Faculty of Engineering, Benghazi University, Department of Industrial and Manufacturing Engineering, who taught us this scientific material in the master degree in 2014 AD, which he has provided us with a lot of information and which has always been a race for creative ideas. By equipping students to the level of international universities. All thanks and appreciation
THE IMPLEMENTATION OF MODEL SERVICE ORIENTED ARCHITECTURE TO ANALYZE STUDENT ...AM Publications
The application of Service Oriented Architecture (SOA) model may integrate one information system to others. Also, SOA can handle the complexity of hardware platforms, software, and other services development in the college. There are many services systems exist in the university, for instance student satisfaction system by questionnaire. Student satisfaction is very important to be used as an evaluation of the college. Student satisfaction was analyzed using Importance Performance Analysis (IPA) by using quadrants for each competency. This study discusses the application of SOA and IPA model to analyze student satisfaction towards the course. Student satisfaction data was taken using questionnaires sent to the student satisfaction information system for 70 respondents for each course. It is found that sending the data using SOA and JavaScript Object Notation (JSON) is very efficient with the average value of page generation time for 1,5414 seconds. In addition, the analysis of student satisfaction towards each course is ranged from 3,72 to 4,09 with a satisfaction scale of 1 - 5. Therefore, the level of student satisfaction at the course is categorized in the satisfactory scale.
Rodmunkong, T., Wannapiroon, P. and Nilsook, P. (2015) Development of Information Management Systems
in accordance with the Thai Qualifications Framework for Higher Education via Cloud Computing
The sixth International e-Learning Conference 2015 (IEC2015), July 20-21, 2015,BITEC Bangna, Bangkok, Thailand.
The Management of Information Systems and Its Impact on the Quality of Servic...Eswar Publications
This study aimed to identify the impact of management information systems (MIS) techniques on the quality of services provided at the University of Tabuk from the perspective of students. To achieve the goals of the study, one question were developed and distributed on a random sample of 244 students at the University of Tabuk in the Kingdom of Saudi Arabia. The Statistical Package of Social Sciences (SPSS, V.16) was used to analyze the data of the question.
Evaluate and improve high school students for some skills using quality funct...Rania Elrifai
5. RECOMMENDATIONS
Due to the needing for the inevitable to raise the level of academic must attention means that enable to improve the level of education , which has become the other nations advanced dramatically despite the fact that these methods do not cost a lot of money, time and effort g spite of the results that will return us over time, which will advance our Islamic nation, especially the Arab world General, and therefore we recommend in our study that the characteristics we have learned to be starting out in the early stages, even before the beginning of the school stage( kindergarten),
for example, enable the child to display a simple subject and talk about it in front of his colleagues earned many skills, including the courageous confrontation, connect with others and instil confidence that makes a strong base for the personal leadership of the children, and do not forget that the child is affected by the environment around them, so take care of this opportunity to improve our future generations, through our study, there are some of the things want to be displayed and recommend as the following:
2. Use laptops instead of bags that weigh weights strain our children as well as the ease to download the curriculum according to each level
3. The use of laptop computers to facilitate the process of Internet connectivity and to keep pace with scientific development.
4. Focus on the internet linking and connection with as consideration a part of topics of courses inside the
International Journal of Science and Engineering Applications
Volume 7–Issue 10,401-405, 2018, ISSN:-2319–7560
405
curriculum for raising the education quality level of each level.
5. Concern on these skills from the beginning of the school at first stage as consider the basic level is the base for future a generation.
6. Mainstreaming the introduction of these skills in the early stages of the study as consider there are available applications but need funder and support from the government.
7. Support the confidence of children through the presentation in front of his colleagues and his parents are present.
6. ACKNOWLEDGMENT
In this study, I would like to thank Dr. Galal Abdella. Assistant Professor of the faculty of the Faculty of Engineering, Benghazi University, Department of Industrial and Manufacturing Engineering, who taught us this scientific material in the master degree in 2014 AD, which he has provided us with a lot of information and which has always been a race for creative ideas. By equipping students to the level of international universities. All thanks and appreciation
THE IMPLEMENTATION OF MODEL SERVICE ORIENTED ARCHITECTURE TO ANALYZE STUDENT ...AM Publications
The application of Service Oriented Architecture (SOA) model may integrate one information system to others. Also, SOA can handle the complexity of hardware platforms, software, and other services development in the college. There are many services systems exist in the university, for instance student satisfaction system by questionnaire. Student satisfaction is very important to be used as an evaluation of the college. Student satisfaction was analyzed using Importance Performance Analysis (IPA) by using quadrants for each competency. This study discusses the application of SOA and IPA model to analyze student satisfaction towards the course. Student satisfaction data was taken using questionnaires sent to the student satisfaction information system for 70 respondents for each course. It is found that sending the data using SOA and JavaScript Object Notation (JSON) is very efficient with the average value of page generation time for 1,5414 seconds. In addition, the analysis of student satisfaction towards each course is ranged from 3,72 to 4,09 with a satisfaction scale of 1 - 5. Therefore, the level of student satisfaction at the course is categorized in the satisfactory scale.
International Journal on Web Service Computing (IJWSC)ijwscjournal
A Knowledge Management (KM) System plays a crucial role in every industry as well as in Higher Learning Institutions. A RESTful resource is anything that is addressable over the Web. The resources can be accessed and transferred between clients and server. The resources can be accessed and transferred
between clients and servers. Based on our earlier research works, we have developed a comprehensive KM System framework, evaluation method, mult-dimensional metric model and useful metrics which are helpful to assess any given knowledge management system. In this proposed work, we first describe the actual implementation steps for building the KM System metric database using the multi-dimensional metric model. Secondly we describe the approaches for designing a multi-dimensional Restful Resources and Web Services using the mutli-dimensional metric model and demonstrate how the KM system can be ranked and rated for its effectiveness using WAM and RESTful Resources.
KM System Evaluation using Four dimensional Metric Model, Database and RESTfu...ijwscjournal
A Knowledge Management (KM) System plays a crucial role in every industry as well as in Higher Learning Institutions. A RESTful resource is anything that is addressable over the Web. The resources can be accessed and transferred between clients and server. The resources can be accessed and transferred between clients and servers. Based on our earlier research works, we have developed a comprehensive KM System framework, evaluation method, mult-dimensional metric model and useful metrics which are helpful to assess any given knowledge management system. In this proposed work, we first describe the actual implementation steps for building the KM System metric database using the multi-dimensional metric model. Secondly we describe the approaches for designing a multi-dimensional Restful Resources and Web Services using the mutli-dimensional metric model and demonstrate how the KM system can be ranked and rated for its effectiveness using WAM and RESTful Resources.
Predicting the Presence of Learning Motivation in Electronic Learning: A New ...TELKOMNIKA JOURNAL
Research affirms that electronic learning (e-learning) has a great deal of advantages to learning process, it’s provides learners with flexibility in terms of time, place, and pace. That easiness was not be a guarantee of a good learning outcomes though they got the same learning material and course, because the fact is the outcomes was not the same from one to another and even not satisfy enough. One of prerequisite to the successful of e-learning process is the presence of learning motivation and it was not easy to identify. We propose a novel model to predicting the presence of learning motivation in e-learning using those attributes that have been identified in previous research. This model has been built using WEKA toolkit by comparing fourteen algorithms for Tree Classifier and ten-fold cross validation testing methods to process 3.200 of data sets. The best accuracy reached at 91.1% and identified four parameters to predict the presence of learning motivation in e-learning, and aim to assist teachers in identify whether student needs motivation or does not. This study also confirmed that Tree Classifier still has the best accuracy to predict and classify academic performance, it was reached average 90.48% for fourteen algorithm for accuracy value.
Qualitative Analysis of Electronic Class Record.pdfChristopher Lee
The Electronic Class Record (E-Class Record) from Department of Education
(DepEd) is an Information Software used by Faculty Members in different schools
depending on their level status of the Students at present. This Information System tool
is designed to facilitate proper evaluations, assessment reports and effectively getting
the total progress and performance grading results exclusively for the Senior High
School Students in Diliman College - Quezon City.
The E-Class Record which is formatted by using a Microsoft Excel spreadsheet
file document gives the teachers an important grading information and evaluations on
which class recording of grades are now more reliable and efficient.
The construction of the Qualitative research study is for me to gather the
descriptive analysis statements made from the respondents’ point of view, and how they
have been assessed and satisfied by their own experiences in using the Electronic Class
Record (E-Class Record) effectively and efficiently.
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.
A Model for Usability Evaluation of Learning Management SystemsIJITE
Learning management systems (LMSs) are increasingly popular in education. The usability of these
systems must be examined to ensure they meet the needs of both students and faculty. To address the
limitations of existing models for evaluating the usability of LMS, this research proposes a new model that
combines objective measures collected from eye-tracking with subjective measures collected from
retrospective think-aloud protocols and questionnaires. This model evaluates the effectiveness, efficiency,
satisfaction, and learnability of LMSs. The proposed model was applied to study the usability of the LMS
used at King Saud University (Blackboard) and was compared with other existing usability evaluation
models. The findings indicate that the proposed model fulfilled its intended purpose and provided more
comprehensive results than existing models.
A MODEL FOR USABILITY EVALUATION OF LEARNING MANAGEMENT SYSTEMSIJITE
Learning management systems (LMSs) are increasingly popular in education. The usability of these
systems must be examined to ensure they meet the needs of both students and faculty. To address the
limitations of existing models for evaluating the usability of LMS, this research proposes a new model that
combines objective measures collected from eye-tracking with subjective measures collected from
retrospective think-aloud protocols and questionnaires. This model evaluates the effectiveness, efficiency,
satisfaction, and learnability of LMSs. The proposed model was applied to study the usability of the LMS
used at King Saud University (Blackboard) and was compared with other existing usability evaluation
models. The findings indicate that the proposed model fulfilled its intended purpose and provided more
comprehensive results than existing models.
A MODEL FOR USABILITY EVALUATION OF LEARNING MANAGEMENT SYSTEMSIJITE
Learning management systems (LMSs) are increasingly popular in education. The usability of these
systems must be examined to ensure they meet the needs of both students and faculty. To address the
limitations of existing models for evaluating the usability of LMS, this research proposes a new model that
combines objective measures collected from eye-tracking with subjective measures collected from
retrospective think-aloud protocols and questionnaires. This model evaluates the effectiveness, efficiency,
satisfaction, and learnability of LMSs. The proposed model was applied to study the usability of the LMS
used at King Saud University (Blackboard) and was compared with other existing usability evaluation
models. The findings indicate that the proposed model fulfilled its intended purpose and provided more
comprehensive results than existing models.
ACCEPTABILITY OF K12 SENIOR HIGH SCHOOL STUDENTS ACADEMIC PERFORMANCE MONITOR...IJITE
The K to 12 Basic Education program uses standards and a competency-based grading system. These are
found in the curriculum guides. All grades will be based on the weighted raw score of the learners’
summative assessments. Senior High School Students have been graded on three categories the written
work, performance tasks, and quarterly assessments. Technology plays a substantial role in helping
teachers in the progress, communication, application, and grading of assessment tasks. The correlational
aspect aims to establish the degree to which the variables of on the level of compliance of the developed
application affects and influences the level of acceptance of the system as perceived by the respondents.
From the level of acceptance of the system when it comes to its performance efficiency is directly affected
by the level of compliance of the system in its compatibility and reliability. This means that changes on the
identified variables may directly affect the variables on the level of acceptance of the system. The findings
of significant difference on the perceptions of the IT expert and the Users on the level of acceptance of the
system simply imply that the IT experts and the users does not share similar perceptions on the system. This
means that there is a significant difference on the level of acceptance of the system as perceived by the
users and the IT experts.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
School management system project Report.pdfKamal Acharya
Education system forms the backbone of every nation. And hence it is important to provide a strong educational foundation to the young generation to ensure the development of open-minded global citizens securing the future for everyone. Advanced technology available today can play a crucial role in streamlining education-related processes to promote solidarity among students, teachers and the school staff. School Management System(SMS) consists of tasks such as registering students, attendance record keeping to control absentees, producing report cards, producing official transcript, preparing timetable and producing different reports for teachers, officials from Dr.Mohiuddin Education foundation and other stakeholders. Automation is the utilization of technology to replace human with a machine that can perform more quickly and more continuously. By automating SMS documents that took up many large storage rooms can be stored on few disks. Transcript images can be annotate. It reduces the time to retrieve old transcripts from hours to seconds.
Combining Two Models of Successful Information System MeasurementTELKOMNIKA JOURNAL
This paper purposes is to measure successful of Academic Advisory information system by combining two models of information system measurement. DeLone & McLean IS Success Model use to measure the successful of system while COBIT framework is to measure system maturity level. Result of this research showed that the successful of Academic Advisory IS affected by User Satifaction, Quality of Service, Quality of System while Maturity level at 3.7. The result also showed there’s a relation between level of maturity system with the success of system.
Our Journal has became fully open access Journal. It’s publishes Differential Equations, Operations Research Mathematical Economics etc.UMJ is an original research in Pure and Applied Mathematics and Statistics. UMJ coverage extends to Operations Research, Mathematical Economics, Mathematics Biology and Computer Science.
Development of depth map from stereo images using sum of absolute differences...nooriasukmaningtyas
This article proposes a framework for the depth map reconstruction using stereo images. Fundamentally, this map provides an important information which commonly used in essential applications such as autonomous vehicle navigation, drone’s navigation and 3D surface reconstruction. To develop an accurate depth map, the framework must be robust against the challenging regions of low texture, plain color and repetitive pattern on the input stereo image. The development of this map requires several stages which starts with matching cost calculation, cost aggregation, optimization and refinement stage. Hence, this work develops a framework with sum of absolute difference (SAD) and the combination of two edge preserving filters to increase the robustness against the challenging regions. The SAD convolves using block matching technique to increase the efficiency of matching process on the low texture and plain color regions. Moreover, two edge preserving filters will increase the accuracy on the repetitive pattern region. The results show that the proposed method is accurate and capable to work with the challenging regions. The results are provided by the Middlebury standard dataset. The framework is also efficiently and can be applied on the 3D surface reconstruction. Moreover, this work is greatly competitive with previously available methods.
Model predictive controller for a retrofitted heat exchanger temperature cont...nooriasukmaningtyas
This paper aims to demonstrate the practical aspects of process control theory for undergraduate students at the Department of Chemical Engineering at the University of Bahrain. Both, the ubiquitous proportional integral derivative (PID) as well as model predictive control (MPC) and their auxiliaries were designed and implemented in a real-time framework. The latter was realized through retrofitting an existing plate-and-frame heat exchanger unit that has been operated using an analog PID temperature controller. The upgraded control system consists of a personal computer (PC), low-cost signal conditioning circuit, national instruments USB 6008 data acquisition card, and LabVIEW software. LabVIEW control design and simulation modules were used to design and implement the PID and MPC controllers. The performance of the designed controllers was evaluated while controlling the outlet temperature of the retrofitted plate-and-frame heat exchanger. The distinguished feature of the MPC controller in handling input and output constraints was perceived in real-time. From a pedagogical point of view, realizing the theory of process control through practical implementation was substantial in enhancing the student’s learning and the instructor’s teaching experience.
More Related Content
Similar to K-NN supervised learning algorithm in the predictive analysis of the quality of the university administrative service in the virtual environment
International Journal on Web Service Computing (IJWSC)ijwscjournal
A Knowledge Management (KM) System plays a crucial role in every industry as well as in Higher Learning Institutions. A RESTful resource is anything that is addressable over the Web. The resources can be accessed and transferred between clients and server. The resources can be accessed and transferred
between clients and servers. Based on our earlier research works, we have developed a comprehensive KM System framework, evaluation method, mult-dimensional metric model and useful metrics which are helpful to assess any given knowledge management system. In this proposed work, we first describe the actual implementation steps for building the KM System metric database using the multi-dimensional metric model. Secondly we describe the approaches for designing a multi-dimensional Restful Resources and Web Services using the mutli-dimensional metric model and demonstrate how the KM system can be ranked and rated for its effectiveness using WAM and RESTful Resources.
KM System Evaluation using Four dimensional Metric Model, Database and RESTfu...ijwscjournal
A Knowledge Management (KM) System plays a crucial role in every industry as well as in Higher Learning Institutions. A RESTful resource is anything that is addressable over the Web. The resources can be accessed and transferred between clients and server. The resources can be accessed and transferred between clients and servers. Based on our earlier research works, we have developed a comprehensive KM System framework, evaluation method, mult-dimensional metric model and useful metrics which are helpful to assess any given knowledge management system. In this proposed work, we first describe the actual implementation steps for building the KM System metric database using the multi-dimensional metric model. Secondly we describe the approaches for designing a multi-dimensional Restful Resources and Web Services using the mutli-dimensional metric model and demonstrate how the KM system can be ranked and rated for its effectiveness using WAM and RESTful Resources.
Predicting the Presence of Learning Motivation in Electronic Learning: A New ...TELKOMNIKA JOURNAL
Research affirms that electronic learning (e-learning) has a great deal of advantages to learning process, it’s provides learners with flexibility in terms of time, place, and pace. That easiness was not be a guarantee of a good learning outcomes though they got the same learning material and course, because the fact is the outcomes was not the same from one to another and even not satisfy enough. One of prerequisite to the successful of e-learning process is the presence of learning motivation and it was not easy to identify. We propose a novel model to predicting the presence of learning motivation in e-learning using those attributes that have been identified in previous research. This model has been built using WEKA toolkit by comparing fourteen algorithms for Tree Classifier and ten-fold cross validation testing methods to process 3.200 of data sets. The best accuracy reached at 91.1% and identified four parameters to predict the presence of learning motivation in e-learning, and aim to assist teachers in identify whether student needs motivation or does not. This study also confirmed that Tree Classifier still has the best accuracy to predict and classify academic performance, it was reached average 90.48% for fourteen algorithm for accuracy value.
Qualitative Analysis of Electronic Class Record.pdfChristopher Lee
The Electronic Class Record (E-Class Record) from Department of Education
(DepEd) is an Information Software used by Faculty Members in different schools
depending on their level status of the Students at present. This Information System tool
is designed to facilitate proper evaluations, assessment reports and effectively getting
the total progress and performance grading results exclusively for the Senior High
School Students in Diliman College - Quezon City.
The E-Class Record which is formatted by using a Microsoft Excel spreadsheet
file document gives the teachers an important grading information and evaluations on
which class recording of grades are now more reliable and efficient.
The construction of the Qualitative research study is for me to gather the
descriptive analysis statements made from the respondents’ point of view, and how they
have been assessed and satisfied by their own experiences in using the Electronic Class
Record (E-Class Record) effectively and efficiently.
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.
A Model for Usability Evaluation of Learning Management SystemsIJITE
Learning management systems (LMSs) are increasingly popular in education. The usability of these
systems must be examined to ensure they meet the needs of both students and faculty. To address the
limitations of existing models for evaluating the usability of LMS, this research proposes a new model that
combines objective measures collected from eye-tracking with subjective measures collected from
retrospective think-aloud protocols and questionnaires. This model evaluates the effectiveness, efficiency,
satisfaction, and learnability of LMSs. The proposed model was applied to study the usability of the LMS
used at King Saud University (Blackboard) and was compared with other existing usability evaluation
models. The findings indicate that the proposed model fulfilled its intended purpose and provided more
comprehensive results than existing models.
A MODEL FOR USABILITY EVALUATION OF LEARNING MANAGEMENT SYSTEMSIJITE
Learning management systems (LMSs) are increasingly popular in education. The usability of these
systems must be examined to ensure they meet the needs of both students and faculty. To address the
limitations of existing models for evaluating the usability of LMS, this research proposes a new model that
combines objective measures collected from eye-tracking with subjective measures collected from
retrospective think-aloud protocols and questionnaires. This model evaluates the effectiveness, efficiency,
satisfaction, and learnability of LMSs. The proposed model was applied to study the usability of the LMS
used at King Saud University (Blackboard) and was compared with other existing usability evaluation
models. The findings indicate that the proposed model fulfilled its intended purpose and provided more
comprehensive results than existing models.
A MODEL FOR USABILITY EVALUATION OF LEARNING MANAGEMENT SYSTEMSIJITE
Learning management systems (LMSs) are increasingly popular in education. The usability of these
systems must be examined to ensure they meet the needs of both students and faculty. To address the
limitations of existing models for evaluating the usability of LMS, this research proposes a new model that
combines objective measures collected from eye-tracking with subjective measures collected from
retrospective think-aloud protocols and questionnaires. This model evaluates the effectiveness, efficiency,
satisfaction, and learnability of LMSs. The proposed model was applied to study the usability of the LMS
used at King Saud University (Blackboard) and was compared with other existing usability evaluation
models. The findings indicate that the proposed model fulfilled its intended purpose and provided more
comprehensive results than existing models.
ACCEPTABILITY OF K12 SENIOR HIGH SCHOOL STUDENTS ACADEMIC PERFORMANCE MONITOR...IJITE
The K to 12 Basic Education program uses standards and a competency-based grading system. These are
found in the curriculum guides. All grades will be based on the weighted raw score of the learners’
summative assessments. Senior High School Students have been graded on three categories the written
work, performance tasks, and quarterly assessments. Technology plays a substantial role in helping
teachers in the progress, communication, application, and grading of assessment tasks. The correlational
aspect aims to establish the degree to which the variables of on the level of compliance of the developed
application affects and influences the level of acceptance of the system as perceived by the respondents.
From the level of acceptance of the system when it comes to its performance efficiency is directly affected
by the level of compliance of the system in its compatibility and reliability. This means that changes on the
identified variables may directly affect the variables on the level of acceptance of the system. The findings
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witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
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A Model for Predicting Students’ Academic Performance using a Hybrid of K-mea...Editor IJCATR
Higher learning institutions nowadays operate in a more complex and competitive due to a high demand from prospective
students and an emerging increase of universities both public and private. Management of Universities face challenges and concerns of
predicting students’ academic performance in to put mechanisms in place prior enough for their improvement. This research aims at
employing Decision tree and K-means data mining algorithms to model an approach to predict the performance of students in advance
so as to devise mechanisms of alleviating student dropout rates and improve on performance. In Kenya for example, there has been
witnessed an increase student enrolling in universities since the Government started free primary education. Therefore the Government
expects an increased workforce of professionals from these institutions without compromising quality so as to achieve its millennium
development and vision 2030. Backlog of students not finishing their studies in stipulated time due to poor performance is another
issue that can be addressed from the results of this research since predicting student performance in advance will enable University
management to devise ways of assisting weak students and even make more decisions on how to select students for particular courses.
Previous studies have been done Educational Data Mining mostly focusing on factors affecting students’ performance and also used
different algorithms in predicting students’ performance. In all these researches, accuracy of prediction is key and what researchers
look forward to try and improve.
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K-NN supervised learning algorithm in the predictive analysis of the quality of the university administrative service in the virtual environment
1. Indonesian Journal of Electrical Engineering and Computer Science
Vol. 25, No. 1, January 2022, pp. 521~528
ISSN: 2502-4752, DOI: 10.11591/ijeecs.v25.i1.pp521-528 521
Journal homepage: http://ijeecs.iaescore.com
K-NN supervised learning algorithm in the predictive analysis
of the quality of the university administrative service in the
virtual environment
Omar Freddy Chamorro Atalaya1
, Guillermo Morales-Romero2
, Adrián Quispe-Andía2
, Beatriz
Caycho-Salas3
, Elizabeth Katerin Auqui-Ramos2
, Primitiva Ramos-Salazar4
, Carlos Palacios-Huaraca5
1
Faculty of Engineering and Management, National Technological University of Lima Sur, Lima, Perú
2
Facultad de Ciencias, Universidad Nacional de Educación Enrique Guzmán y Valle, Lima, Perú
3
Facultad de Ciencias Empresariales, Universidad Nacional de Educación Enrique Guzmán y Valle, Lima, Perú
4
Facultad de Tecnología, Universidad Nacional de Educación Enrique Guzmán y Valle, Lima, Perú
5
Departamento de Ciencias, Universidad Tecnológica del Perú, Lima, Perú
Article Info ABSTRACT
Article history:
Received Jul 25, 2021
Revised Oct 14, 2021
Accepted Nov 26, 2021
The objective of this study is to analyze and discuss the metrics of the
predictive model using the K-nearest neighbor (K-NN) learning algorithm,
which will be applied to the data on the perception of engineering students
on the quality of the virtual administrative service, such as part of the
methodology was analyzed the indicators of accuracy, precision, sensitivity
and specificity, from the obtaining of the confusion matrix and the receiver
operational characteristic (ROC) curve. The collected data were validated
through Cronbach's Alpha, finding consistency values higher than 0.9, which
allows to continue with the analysis. Through the predictive model through
the Matlab R2021a software, it was concluded that the average metrics for
all classes are optimal, presenting a precision of 92.77%, sensitivity 86.62%,
and specificity 94.7%; with a total accuracy of 85.5%. In turn, the highest
level of the area under the curve (AUC) is 0.98, which is why it is
considered an optimal predictive model. Having carried out this study, it is
possible to contribute significantly to the decision-making of the higher
institution in relation to the improvement of the quality of the virtual
administrative service.
Keywords:
K-nearest neighbor
Predictive analytics
Quality of service
Supervised learning
Virtuality
This is an open access article under the CC BY-SA license.
Corresponding Author:
Omar Freddy Chamorro Atalaya
Faculty of Engineering and Management, National Technological University of Lima Sur
Av. Central y Av. Bolivar, Sector 3, Grupo 1A-03, Villa El Salvador, Lima, Perú
Email: ochamorro@untels.edu.pe
1. INTRODUCTION
Today it is essential that the various organizations have the tools to capture, analyze and adapt to
changes; even more so in this environment of high competition in institutions, the culture of organizational
learning is considered key for decision-making based on the achievement of goals, to cement permanence in
the market and to transcend it [1], [2]. In the virtual context in which we find ourselves, educational
institutions apply techniques to understand the modeling of their users' perception of the quality of the
service they provide, be it academic, pedagogical or administrative [3], [4]. The modeling of user behavior is
one of the techniques most used by organizations, with the development of personalized content according to
the level of user-platform interaction as its main motivation [5], [6].
In this educational field, the objective is to facilitate learning activities for the user, access to
information in an agile way and the management of the required resources [7]. Improving the levels of
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personalization of the content associated with an information system generates a positive perception for the
user, making the performance of the services provided more efficient [8], [9]. At present, the development of
information technologies allows to store and manage large amounts of data, this applied in virtual education
environments generates the possibility of personalizing the content presented to users, through classification
models [10], [11].
The classification models are supported by the definition of a priori groups so that through a machine
learning model and with known information inputs, the unit of study can be classified into a specific group, in
this technique dependent variables are defined and independent [12], [13]. Automatic learning or machine
learning, belongs to a branch of artificial intelligence that is based on algorithms that allow modifying the
behavior of data based on experience or knowledge acquired autonomously, in order to facilitate the extraction
of users. of relevant information [14], [15]. This technique groups together a wide range of algorithms focused
on solving various problems, such as: selection of characteristics, classification, grouping or imputation of data,
among others [16], [17]. Likewise, the chosen algorithm depends on the type of information analyzed, this
allows obtaining higher quality information and improving processing times [18], [19].
Among the algorithms most used in automatic learning for classification models, is the K-nearest
neighbors (K-NN), given its simplicity and efficiency to detect and classify elements in categories [20], [21].
This supervised learning algorithm is made up of several descriptive attributes and a single objective attribute
(also called class) [22]. The parameter k in K-NN refers to the number of neighbors with which the belonging
to a category is defined, this parameter is usually determined empirically, depending on the problem it is
tested with different values of K, choosing the parameter with the best performance in precision [23], [24].
Given what has been described, the present study aims to analyze and discuss the metrics of the predictive
model obtained through the supervised learning algorithm K-NN, also known as medium K-NN, applied to
the data of the perception of the quality of the virtual administrative service by engineering students, for
which the indicators of accuracy, precision, sensitivity and specificity will be analyzed, based on obtaining
the confusion matrix and the receiver operational characteristic (ROC) curve, the purpose of this analysis is
to provide information quality and relevant to university managers to improve decision-making.
2. RESEARCH METHOD
The level of research, according to the degree of measurement and analysis of the information is
descriptive, it is based on analyzing and discussing the metrics of the predictive model obtained through the
medium K-NN supervised learning algorithm, applied to the data of the perception of the quality of the
virtual administrative service by engineering students. Thus, the methodology is based on the construction of
a predictive model through the execution of MATLAB software. Figure 1 shows the proposed methodology
according to the supervised learning algorithm medium K-NN.
Figure 1. Methodology of the proposed medium K-NN algorithm
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The data referring to the perception of the quality of the virtual administrative service were collected
by means of the survey technique, and the collection instrument is the questionnaire, with responses on a
Likert scale ranging from 1 to 4, which represent the levels from dissatisfied to very satisfied, these levels of
satisfaction in the analysis will be represented as the classes of the model. The survey was carried out
virtually due to the context of the health emergency declared by Covid-19, and was applied to all 651
students from the seventh to the tenth cycle, belonging to professional engineering schools, this criterion is
part of a regulation established and approved by the university. As part of the methodology, the data
collected is validated through Cronbach's Alpha coefficient using the SPSS software; once this analysis has
been carried out, it is observed in Table 1 that the values obtained show a high homogeneity and equivalence
of the response of all the indicators, since "values greater than 0.9 indicate a great consistency of the
elements of the scale" [25]. Likewise, Table 1 shows the indicators called predictors of the quality of the
administrative service. Based on these results, the analysis can be continued.
Table 1. Value of Alpha Cronbach’s
Code Indicators Alpha Cronbach’s
I1 Efficient work 0.944
I2 Timely attention 0.941
I3 Relevant information provided 0.942
O1 Quality care 0.935
3. RESULTS AND DISCUSSION
3.1. Determination of the predictive model
This is the training stage, where a classification algorithm builds a model by analyzing or learning
from a set of training data. Thus, for the determination of the predictive model, the data collected based on
the first 3 indicators shown was used. In Table 1, which are called predictors, while the indicator that
represents the quality of the virtual administrative service is represented by 01. Thus, through the MATLAB
software and through the Classification Learner tool and Statistics and Machine Learning Toolbox 12.1, the
best type of predictive model determined by the validation of the accuracy is identified. The results generated
by the Matlab R2021a software are shown in Table 2.
According to Table 2, of all the learning algorithms, the best type of classification model was
granted by the supervised learning algorithm K-NN or medium K-NN, with a validation of 85.5%. Regarding
the validation percentage in [21] it is pointed out that, by obtaining 77.85% precision, it can be stated that the
K-NN algorithm is capable of classifying well the unbalanced data with the most optimal values. As
indicated in [26], the results of the classification and forecasting process show good results in terms of
precision when using the medium K-NN algorithm, representing benefits in the ease of interpretation and
comparability of the results, in this case with 91% accuracy exceeds 7.1% linear and quadratic classifier
performance. Contextualizing this result in the university environment, for the educational institution it is
important to know the different ways in which the student relates to the educational process and much better
if it can take actions to facilitate learning and student satisfaction, re-conceptualizing the management virtual
environments of the educational service.
Table 2. Results of classification learner
Algorithm Accuracy (validation)
Medium K-NN 85.5%
Bilayered neural network 85.4%
Bagged trees 85.3%
3.2. Results of the predictive model metrics
The confusion matrix is a useful tool to analyze how well a classification model can correctly predict
outcomes in a large number of classes [27]. As indicated in [28] the K-NN algorithm requires knowing in
advance the value of k to determine the K closest neighbors, the confusion matrix being a common procedure
to evaluate different K-NN configurations. Described in the previous paragraph, Figure 2 shows the confusion
matrix according to the number of observations, this matrix contains information about the predictions made
by the classification system and reports the number of false negatives (FNR) and true positives (TPR), which
shows the closeness between the levels of satisfaction predicted (predicted class) by the model with respect to
its true value (true class). Rajagopal et al. in [19] it is indicated that the TPR defines the number of positive
samples correctly classified as positive and the FNR is the number of positive examples incorrectly classified
as negative, these 2 configurations plus the positive values predicted (PPV) and false obtained rate (FDR),
shown in Figure 3, will define the performance metrics of the predictive model.
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In Figure 2, it is highlighted that of the 4 classes on which the predictive model acted, class 1 shows
the highest percentage of sensitivity, this means that the predictive model has the ability to discriminate
between a true positive (TP) of a false negative (FN) in this class (satisfaction level: dissatisfied), in this case
it is 89.7%; in other words, the model was only 10.3% confused, a considerably low rate. Although all the
levels are high, we can say that the lowest level of sensitivity of the predictive model is shown in class 2
(level of satisfaction: not very satisfied), whose value is 82.2%. Likewise, in Figure 2, it is highlighted that to
the right of the confusion matrix the rate of TPR and the rate of FNR are shown for each class. Regarding the
validation of true positives and false negatives, Mhaske-Dhamdhere and Vanjale in [29], the medium
algorithm K-NN, allowed to know that out of 160 emails from computer engineering students there are true
positives of 67% and 80%, these values are very high, they are due according to users to the quality of the
service in terms of email, such as technical parameters and structure.
In Figure 3, a second confusion matrix is shown in which its main diagonal values indicate the
precision of the predictive model for each class. Figure 3 shows that the predictive model for class 3
(satisfaction level: satisfied) shows the best sensitivity rate, with a precision rate of 89.9%, thus being the
highest among the other classes. While the lowest level of sensitivity of the predictive model is shown in
class 3 (level of satisfaction: very satisfied), whose value is 80.2%. Although the predictive model for class 3
(satisfaction level: satisfied) shows a sensitivity rate of 85.6% (Figure 2), in this case it shows a precision rate
of 89.9%, which indicates that the level of dispersion of the data used is very low; answering that if the
dispersion is low the precision is high. Likewise, one aspect to highlight is that in the lower part of the
confusion matrix of Figure 3, it should be noted that the PPV and the FDR are shown for each class.
Figure 2. Confusion matrix based on PPV and FDR rates
Figure 3. Confusion matrix based on PPV and FDR rates
As noted, the four ranking possibilities of any intrusion detection study determine meaningful
performance metrics, such as accuracy (A), precision (P), sensitivity (S), and specificity (R). Table 3 shows
the metrics of the predictive model, for each class, which shows that the four metrics show relatively high
values in the 4 classes. In general, the accuracy of the predictive model is 85.5%.
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In relation to the sensitivity indicator of the predictive model using the supervised learning
algorithm K-NN, Figure 4 shows the response that Matlab provides for each class under study with its
corresponding ROC graph, for each class; ROC validation represents the relationship between the sensitivity
and specificity indicators; in this case the ROC of class 1 (unsatisfied) is displayed. In Figure 4, the
discrimination threshold is shown, which is 0.90 for the rate of true positives and 0.02 for the rate of false
positives, showing an AUC of 0.98, being a value almost optimal, very close to one. In the same way, the
ROC of class 4 is shown (very satisfied), in Figure 5 the discrimination threshold is shown, which is 0.89 for
the rate of true positives and 0.03 for the rate of false positives, evidencing an AUC of 0.96, as indicated, the
closest value is 1, the model is much more optimal.
Table 3. Results of classification learner
Class
Metrics
Sensitivity Sensitivity Accuracy Precision
1 89.66% 98.07% 97.11% 85.71%
2 82.19% 92.44% 89.49% 81.45%
3 85.63% 91.63% 88.83% 89.94%
4 89.00% 96.67% 95.66% 80.18%
Figure 4. Validation ROC Curve for class 1 Figure 5. Validation ROC Curve for class 4
Regarding ROC validation, Susheelamma and Ravikuma in [22] it is indicated that there is an
improvement in ROC performance, thus reflecting that the proposed learning model improves the score of
measure F by 24.45%, 26.65%, and 18. 96% on the existing learning model; with this validation an average
improvement of 23.35% of the existing model is achieved. Finally, after validating the collected data, and
proceeding with obtaining the predictive model through the Matlab R2021a software, it was concluded that
the average metrics for all its classes were optimal, presenting an accuracy of 92.77%, sensitivity of 86.62%
and specificity of 94.7%; with an overall accuracy of 85.5%. In turn, the highest level of AUC is 0.98, thus
being considered an optimal predictive model.
Regarding what was obtained in [1] it is highlighted that, from the perspective of innovation, the
results of these studies achieve great changes, they also allow delegating functions, promoting skills, and
encouraging continuous updating; all this from the visionary leadership approach. Likewise, what was
obtained in [22] indicates that the proposed model accurately predicts even for the early day (that is, during 0
and 1 days), it also efficiently predicts the days after the end of the course and achieves better results than
training with legacy data. As indicated in [30] there is a strong tendency to predict student performance in
college. For what research about predicting the behavior of students in the academic environment, it is very
interesting, because within an organization the quality of service is essential, since user satisfaction depends
on it. The results Ghouch et al. in [31] indicate that the integration of the K-NN algorithm in the educational
environment allows searching for students with similar behaviors, which will offer, on the one hand, a
learning path adapted to the student's profile, and based on the experiences of others. students with similar
behaviors by observing and analyzing their learning footprints, and, on the other hand, overcomes the
limitations of the K-NN algorithm in terms of computational time and memory.
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4. CONCLUSION
In relation to the results obtained, an optimal predictive model is evidenced, with an accuracy of
85.5% making use of the supervised learning algorithm K-NN, likewise, the application of the cross-
validation procedure shows optimal results in the experimentation of the K-algorithm. NN finding all the
model metrics (sensitivity, specificity, accuracy, and precision) in the 4 classes with relatively high values,
said this, the results will allow to establish the grouping of engineering students who can reach a level of
satisfaction based on the indicators called predictors, by means of which the authorities will be able to make
timely decisions to improve the percentage of satisfied students and reduce the percentage of dissatisfied
students in relation to the quality of the virtual administrative service, also these classification techniques
allow to extract relevant information from interested parties, from higher quality and even less time. The
research also provides added value, since it provides the scientific and academic community with a
methodology to classify participating students in virtual environments, identifying the relationship between
the predictive elements and the results of the satisfaction of the quality of the administrative service, which is
provides in this context virtually.
ACKNOWLEDGEMENTS
The authors wish to recognize and thank the National Technological University of South Lima for
their support of this investigation.
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BIOGRAPHIES OF AUTHORS
Omar Freddy Chamorro Atalaya is an electronic engineer, graduated from the
National University of Callao (UNAC), with a Master's degree in Systems Engineering and a
doctoral student at the Faculty of Administrative Sciences at UNAC. Researcher recognized
by CONCYTEC (National Council for Science, Technology and Technological Innovation).
Research professor at the Universidad Nacional Tecnológica de Lima Sur (UNTELS), in the
Associate category, he teaches courses on automatic process control and industrial automation,
and design of control panels and electrical control. He is the author of scientific articles
indexed to Scopus and WoS. He is a reviewer for scientific articles for journals indexed to
Scopus. He is a speaker at scientific conferences, in areas such as Data Science, Machine
Learning, Natural Language Processing, and Sentiment Analysis. He can be contacted at
email: ochamorroa@untels.edu.pe
Guillermo Morales Romero External Evaluator in University Higher Education
at SINEACE, Doctor in Educational Sciences, Master in Systems Engineering, Master in
Public Management, Master in Educational Management, Bachelor of Mathematics and
Computer Science, Lawyer with specialist in computer auditing, computer security, X cycle
Systems Engineering of UNFV, Professional with 24 years of experience in University
Teaching in the careers of Engineering, Computer Science, Law, Mathematics Education,
Statistics. Teacher in the different Postgraduate Schools of Public and Private Universities of
the Country. Author of scientific articles in journals indexed in databases: Scopus, Wos,
Latindex and Others. Organizer of National and International Events, National and
International Speaker. Double Blind Pair Reviewer of different national and international
scientific journals. Member of the Scientific Committee of the Scientific Journal: Ciencia &
Sociedad Autonomous University Tomás Frías de Bolivia; Member of the Scientific
Committee of the Multidisciplinary Magazine Ciencia Latina. Mexico. Member of the Lima
Chamber of Commerce and the Lima Bar Association. Former specialist in the AGEBAT Area
of UGEL 05 - MINEDU. He can be contacted at email: gmorales@une.edu.pe
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Adrián Quispe Andía Doctor in Educational Sciences, with a Master's Degree in
University Didactics, a Bachelor of Computer Science from the Universidad Nacional Mayor
de San Marcos and a Bachelor of Education specializing in mathematics from the Enrique
Guzmán y Valle University, with completed studies in Systems Engineering at the National
University of San Marcos. With more than 25 years of experience as an undergraduate and
graduate university teacher in different public and private institutions. My line of research is
oriented to University Higher Education, author of books on statistics applied to scientific
research at national and international level, I carry out research projects with competitive
funds and participate in national and international conferences as a speaker, in addition to
publishing articles scientists in international journals and indexed in high-impact repositories
such as Scopus. He can be contacted at email: aquispe@une.edu.pe.
Beatriz Caycho Salas Doctor in educational sciences, Master in Educational
Management, graduate of master's degree in administration, Bachelor of Administration,
Teacher of Primary Education, graduate of the professional education career with the mention
of social sciences and educational administration, professional with 27 years of experience in
University Teaching in the careers of administration, international business and undergraduate
education. Postgraduate teaching experience: Public Management. Co-author of university
texts. She can be contacted at email: bcaycho@une.edu.pe
Elizabeth Katerin Auqui Ramos Magister in the mention of University
Teaching, with completed doctorate studies in the mention of Educational Sciences, studies in
Industrial Engineering. Proficiency in English at an intermediate level, Italian and Quechua at
a basic level, graduates in didactics of mathematics and pedagogical management and
innovation for educational quality and competitiveness, with 11 years of experience in
teaching at higher and university levels. She can be contacted at email: eauqui@une.edu.pe
Primitiva Ramos Salazar with completed doctoral studies in educational
sciences, Master's degree in educational sciences, completed Master's studies in
administration, Bachelor's degree in textile technology, with 23 years of experience in
teaching technical-productive education and 7 years in university teaching. She can be
contacted at email: pramos@une.edu.pe
Carlos Palacios Huaraca Doctor of Educational Sciences, Master of
Mathematical Education, Bachelor of Mathematics and Computer Science, second specialty in
Mathematics Didactics with 11 years of experience in University Teaching in Engineering and
Education careers. He can be contacted at email: c18125@utp.edu.pe