In the current digital era, education system has witness tremendous growth in data storage and efficient retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and information. The complexity of the data is an important issue as educational data consists of structural as well as non-structural type which includes various text editors like node pad, word, PDF files, images, video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of learning platform like Moodle have implemented to integrate the requirement of educators, administrators and learner. Although this type of platforms are indeed a great support of educators, still mining of the large data is required to uncover various interesting patterns and facts for decision making process for the
benefits of the students.
The e-learning contained many educational resources are generally used in learning systems like Moodle, It’s free open source software packages designed and flexible platform to create Learning Objects (LOs) and users’ accounts. The author demonstrates how to use semantic web technologies to improve online learning environments and bridge the gap between learners and LOs. The ontological construction presented here helps formalize LOs context as a complex interplay of different learning-related elements and shows how we can use semantic annotation to interrelate diverse between learner and LOs. On top of this construction, the author implemented several feedback channels for educators to improve the delivery of future Web-based learning. The particular aim of this paper was to provide a solution based in the Moodle Platform. The main idea behind the approach presented here is that ontology which can not only be useful as a learning instrument but it can also be employed to assess students’ skills. For it, each student is prompted to express his/her beliefs by building own discipline-related ontology through an application displayed in the interface of Moodle. This paper presents the ontology for an e-Learning System, which arranges metadata, and defines the relationships of metadata, which are about learning objects; belong to academic courses and user profiles. This ontology has been incorporated as a critical part of the proposed architecture. By this ontology, effective retrieval of learning content, customizing Learning Management System (LMS) is expected. Metadata used in this paper are based on current metadata standards. This ontology specified in human and machine-readable formats. In implementing it, several APIs were defined to manage the ontology. They were introduced into a typical LMS such as Moodle. Proposed ontology maps user preferences with learning content to satisfy learner requirements. These learning objects are presented to the learner based on ontological relationships. Hence it increases the usability and customizes the LMS. In conclusion, ontologies have a range of potential benefits and applications in further and higher education, including the sharing of information across e-learning systems, providing frameworks for learning object reuse, and enabling information between learner and system parts.
E Learning and Learning Management Systems Advantages, Disadvantages and Sugg...ijtsrd
With the developing technology, different e learning concept has entered our lives. This new education model, which is very different from traditional education administration, has been adopted by the education community and learners. Thanks to this model that provides internet based training, it is possible to receive or give training without the concept of time and space. However, when the issue is a critical area such as education, this new method needs to be discussed and analyzed. First, the concept of e learning and learning management systems in the infrastructure of this new education model should be understood in detail. Determining the software modules that LMSs consisting of internet based software is a guide in this study. In addition, the advantages and disadvantages of this new education model were evaluated and presented item by item. At the end of the study, suggestions were given to the individuals or institutions who were educated with e learning model or developed LMS software. Gülleman Erdal | Erdal Erdal "E-Learning and Learning Management Systems: Advantages, Disadvantages and Suggestions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd36911.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/36911/elearning-and-learning-management-systems-advantages-disadvantages-and-suggestions/gülleman-erdal
Although of the semantic web technologies utilization in the learning development field is a new research area, some authors have already proposed their idea of how an effective that operate. Specifically, from analysis of the literature in the field, we have identified three different types of existing applications that actually employ these technologies to support learning. These applications aim at: Enhancing the learning objects reusability by linking them to an ontological description of the domain, or, more generally, describe relevant dimension of the learning process in an ontology, then; providing a comprehensive authoring system to retrieve and organize web material into a learning course, and constructing advanced strategies to present annotated resources to the user, in the form of browsing facilities, narrative generation and final rendering of a course. On difference with the approaches cited above, here we propose an approach that is modeled on narrative studies and on their transposition in the digital world. In the rest of the paper, we present the theoretical basis that inspires this approach, and show some examples that are guiding our implementation and testing of these ideas within e-learning. By emerging the idea of the ontologies are recognized as the most important component in achieving semantic interoperability of e-learning resources. The benefits of their use have already been recognized in the learning technology community. In order to better define different aspects of ontology applications in e-learning, researchers have given several classifications of ontologies. We refer to a general one given in that differentiates between three dimensions ontologies can describe: content, context, and structure. Most of the present research has been dedicated to the first group of ontologies. A well-known example of such an ontology is based on the ACM Computer Classification System (ACM CCS) and defined by Resource Description Framework Schema (RDFS). It’s used in the MOODLE to classify learning objects with a goal to improve searching. The chapter will cover the terms of the semantic web and e-learning systems design and management in e-learning (MOODLE) and some of studies depend on e-learning and semantic web, thus the tools will be used in this paper, and lastly we shall discuss the expected contribution. The special attention will be putted on the above topics.
A SURVEY AND COMPARETIVE ANALYSIS OF E-LEARNING PLATFORM (MOODLE AND BLACKBOARD)paperpublications3
Abstract: This paper presents an evaluation of open source e-learning platforms with the aim of finding the most suitable platform for extending to an adaptive one. The extended platform will be utilized in an operational teaching environment. Therefore, the overall functionality of the platform is as important as the adaptation capabilities, and the evaluation treats both issues in this paper .in this paper we will explain the proper and best learning platform for Users . In this we will compare one of the best learning platforms (Moodle and Blackbox) both are all of them best virtual learning platform. We will compare both virtual system its functionality and using best tool. This paper is focused on the Moodle Architecture and comparative study of Moodle, thus we discusses comparisons it between different virtual learning platform at last conclusion we will describe which learning platform is best for users.Keywords: E-learning, Blackboard, Moodle, tools, function, methodology.
Title: A SURVEY AND COMPARETIVE ANALYSIS OF E-LEARNING PLATFORM (MOODLE AND BLACKBOARD)
Author: Kanak Sachan, Dr. Rajiv Singh
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
The e-learning contained many educational resources are generally used in learning systems like Moodle, It’s free open source software packages designed and flexible platform to create Learning Objects (LOs) and users’ accounts. The author demonstrates how to use semantic web technologies to improve online learning environments and bridge the gap between learners and LOs. The ontological construction presented here helps formalize LOs context as a complex interplay of different learning-related elements and shows how we can use semantic annotation to interrelate diverse between learner and LOs. On top of this construction, the author implemented several feedback channels for educators to improve the delivery of future Web-based learning. The particular aim of this paper was to provide a solution based in the Moodle Platform. The main idea behind the approach presented here is that ontology which can not only be useful as a learning instrument but it can also be employed to assess students’ skills. For it, each student is prompted to express his/her beliefs by building own discipline-related ontology through an application displayed in the interface of Moodle. This paper presents the ontology for an e-Learning System, which arranges metadata, and defines the relationships of metadata, which are about learning objects; belong to academic courses and user profiles. This ontology has been incorporated as a critical part of the proposed architecture. By this ontology, effective retrieval of learning content, customizing Learning Management System (LMS) is expected. Metadata used in this paper are based on current metadata standards. This ontology specified in human and machine-readable formats. In implementing it, several APIs were defined to manage the ontology. They were introduced into a typical LMS such as Moodle. Proposed ontology maps user preferences with learning content to satisfy learner requirements. These learning objects are presented to the learner based on ontological relationships. Hence it increases the usability and customizes the LMS. In conclusion, ontologies have a range of potential benefits and applications in further and higher education, including the sharing of information across e-learning systems, providing frameworks for learning object reuse, and enabling information between learner and system parts.
E Learning and Learning Management Systems Advantages, Disadvantages and Sugg...ijtsrd
With the developing technology, different e learning concept has entered our lives. This new education model, which is very different from traditional education administration, has been adopted by the education community and learners. Thanks to this model that provides internet based training, it is possible to receive or give training without the concept of time and space. However, when the issue is a critical area such as education, this new method needs to be discussed and analyzed. First, the concept of e learning and learning management systems in the infrastructure of this new education model should be understood in detail. Determining the software modules that LMSs consisting of internet based software is a guide in this study. In addition, the advantages and disadvantages of this new education model were evaluated and presented item by item. At the end of the study, suggestions were given to the individuals or institutions who were educated with e learning model or developed LMS software. Gülleman Erdal | Erdal Erdal "E-Learning and Learning Management Systems: Advantages, Disadvantages and Suggestions" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd36911.pdf Paper URL : https://www.ijtsrd.com/computer-science/other/36911/elearning-and-learning-management-systems-advantages-disadvantages-and-suggestions/gülleman-erdal
Although of the semantic web technologies utilization in the learning development field is a new research area, some authors have already proposed their idea of how an effective that operate. Specifically, from analysis of the literature in the field, we have identified three different types of existing applications that actually employ these technologies to support learning. These applications aim at: Enhancing the learning objects reusability by linking them to an ontological description of the domain, or, more generally, describe relevant dimension of the learning process in an ontology, then; providing a comprehensive authoring system to retrieve and organize web material into a learning course, and constructing advanced strategies to present annotated resources to the user, in the form of browsing facilities, narrative generation and final rendering of a course. On difference with the approaches cited above, here we propose an approach that is modeled on narrative studies and on their transposition in the digital world. In the rest of the paper, we present the theoretical basis that inspires this approach, and show some examples that are guiding our implementation and testing of these ideas within e-learning. By emerging the idea of the ontologies are recognized as the most important component in achieving semantic interoperability of e-learning resources. The benefits of their use have already been recognized in the learning technology community. In order to better define different aspects of ontology applications in e-learning, researchers have given several classifications of ontologies. We refer to a general one given in that differentiates between three dimensions ontologies can describe: content, context, and structure. Most of the present research has been dedicated to the first group of ontologies. A well-known example of such an ontology is based on the ACM Computer Classification System (ACM CCS) and defined by Resource Description Framework Schema (RDFS). It’s used in the MOODLE to classify learning objects with a goal to improve searching. The chapter will cover the terms of the semantic web and e-learning systems design and management in e-learning (MOODLE) and some of studies depend on e-learning and semantic web, thus the tools will be used in this paper, and lastly we shall discuss the expected contribution. The special attention will be putted on the above topics.
A SURVEY AND COMPARETIVE ANALYSIS OF E-LEARNING PLATFORM (MOODLE AND BLACKBOARD)paperpublications3
Abstract: This paper presents an evaluation of open source e-learning platforms with the aim of finding the most suitable platform for extending to an adaptive one. The extended platform will be utilized in an operational teaching environment. Therefore, the overall functionality of the platform is as important as the adaptation capabilities, and the evaluation treats both issues in this paper .in this paper we will explain the proper and best learning platform for Users . In this we will compare one of the best learning platforms (Moodle and Blackbox) both are all of them best virtual learning platform. We will compare both virtual system its functionality and using best tool. This paper is focused on the Moodle Architecture and comparative study of Moodle, thus we discusses comparisons it between different virtual learning platform at last conclusion we will describe which learning platform is best for users.Keywords: E-learning, Blackboard, Moodle, tools, function, methodology.
Title: A SURVEY AND COMPARETIVE ANALYSIS OF E-LEARNING PLATFORM (MOODLE AND BLACKBOARD)
Author: Kanak Sachan, Dr. Rajiv Singh
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
ATTITUDES OF SAUDI UNIVERSITIES FACULTY MEMBERS TOWARDS USING LEARNING MANAGE...Hisham Hussein
The research aims to identify the Attitudes of faculty members at Saudi Universities towards using E-learning Management System JUSUR, which follows the National Center for E-learning. A descriptive analysis was used as a research methodology. (90) participants in this research were asked to complete a 5-point Likert scale questionnaire, which consists of (34) items, classified in three main categories, and (2) items as probe statements. Validity and reliability of the questionnaire were ensured. Statistical treatments such as percentages, means, frequencies, and analysis of variance ANOVA were conducted. The results showed a positive Attitudes of the members of the faculty at Saudi University towards E-learning management system JUSUR, although it has not activated in a sufficient way yet, the results showed how their needs for training in using the system and in particular learning content management and file sharing, forums, and Questions Bank. Moreover, results showed no difference in attitudes towards using the system among the faculty members regarding gender or the types of colleges humanitarian, scientific and health. The paper has 9 tables, 9 shapes, and 20 references.
http://www.tojet.net/articles/v10i2/1025.pdf
Load balancing clustering on moodle LMS to overcome performance issue of e-le...TELKOMNIKA JOURNAL
In dealing with the rapid growth of digitalization, the e-learning system has become a mandatory component of any Higher Education (HE) to serve academic processes requests. Along with the increasing number of users, the need for service availability and capabilities of eLearning are increasing day by day. The organization should always look for strategies to keep the eLearning always able to meet these demands. This report presents the implementation of Load Balancing Clustering (LBC) mechanism applied to Moodle LMS in an HE Institution to deal with the poor performance issues. By utilizing existing tools such as HAProxy and keepalived, the implemented LBC configuration delivers a qualified e-learning system performance. Both qualitative and quantitative parameters convince better performance than before. In four months of the operation there is no user complaint received. Meanwhile, in the current semester has been running for two months, the up-time is 99.8 % of 52.685 minutes operational time.
Review of monitoring tools for e learning platformsijcsit
The advancement of e-learning technologies has made it viable for developments in education and
technology to be combined in order to fulfil educational needs worldwide. E-learning consists of informal
learning approaches and emerging technologies to support the delivery of learning skills, materials,
collaboration and knowledge sharing. E-learning is a holistic approach that covers a wide range of
courses, technologies and infrastructures to provide an effective learning environment. The Learning
Management System (LMS) is the core of the entire e-learning process along with technology, content, and
services. This paper investigates the role of model-driven personalisation support modalities in providing
enhanced levels of learning and trusted assimilation in an e-learning delivery context. We present an
analysis of the impact of an integrated learning path that an e-learning system may employ to track
activities and evaluate the performance of learners.
E-Learning is transfer of skills and knowledge by
the computer and network enabled. It includes out of & in
classroom educational experiences with the help of
technology. Early E-Learning systems are based on computer
based learning& training often which attempted to replicate
autocratic teaching styles where the role of the e-Learning
systems was to transfer knowledge, as opposed to this
systems developed later which were based on computer
supportive collaborative learning which encouraged the
shared development of knowledge.
A Survey on E-Learning System with Data MiningIIRindia
E-learning process has been widely used in university campus and educational institutions are playing vital role to enhance the skill set of students. Modern E-learning done by many electronic devices, such as smartphones, Tabs, and so on, on existing E-learning tools is insufficient to achieve the purpose of online training of education. This paper presents a survey of online e-Learning authoring tools for creating and integrating reusable e-learning tool for generation and enhancing existing learning resources with them. The work concentrates on evaluation of the existing e-learning tools a, and authoring tools that have shown good performance in the past for online learners. This survey work takes more than 20 online tools that deal with the educational sector mechanism, for the purpose of observations, and the outcome were analyzed. The findings of this paper are the main reason for developing a new tool, and it shows that educators can enhance existing learning resources by adding assessment resources, if suitable authoring tools are provided. Finally, the different factors that assure the reusability of the created new e-learning tool has been analysed in this paper.E-learning environment is a guide for both students and tutorial management system. The useful on the e-learning system for apart from students and distance learning students. The purpose of using e-learning environment for online education system, developed in data mining for more number of clustering servers and resource chain has been good.
Big data integration for transition from e-learning to smart learning framework eraser Juan José Calderón
Big data integration for transition from e-learning to smart learning framework . Dr. Prakash Kumar Udupi Mr. Puttaswamy Malali Mr. Herald Noronha Department of Computing Department of Computing Department of Computing Middle East College Middle East College Middle East College .
Neural Network Model for Predicting Students' Achievement in Blended Courses ...ijaia
Educator’s knowledge about the likely students’ achievement in blended courses prior to sitting for
examinations provides room for early intervention on students’ learning process, especially to those at risk.
Unfortunately, Leaning Management Systems (LMSs), Moodle in particular lacks an environment to assist
educators access such knowledge from time to time before undertaking their examinations. This raised the
need to propose a model, of which from time to time would be providing the likely students’ achievement
based on activities in Moodle and previous achievement, taking a case of postgraduate programmes at the
University of Dar es Salaam.
This study applied artificial neural networks in building a prediction model. Simulations were conducted in
Matrix Laboratory (MATLAB) utilizing seventy eight instances (78) of students’ logs of three blended
courses extracted from Moodle for 2013/2014 and 2014/2015 academic years.
Mean Square Error (MSE) and Coefficient of Determination (R
2
) performance metrics were used to find
the best prediction model considering ten possible models. The study revealed a model with architecture of
4:10:1 trained with Bayesian Regularization (BR) to be the best model resulting to least MSE of 0.0170 and
high R
2
of 0.93 on training. During testing, the model successfully predicted 78% of the students’
achievement with risk and pass status.
ATTITUDES OF SAUDI UNIVERSITIES FACULTY MEMBERS TOWARDS USING LEARNING MANAGE...Hisham Hussein
The research aims to identify the Attitudes of faculty members at Saudi Universities towards using E-learning Management System JUSUR, which follows the National Center for E-learning. A descriptive analysis was used as a research methodology. (90) participants in this research were asked to complete a 5-point Likert scale questionnaire, which consists of (34) items, classified in three main categories, and (2) items as probe statements. Validity and reliability of the questionnaire were ensured. Statistical treatments such as percentages, means, frequencies, and analysis of variance ANOVA were conducted. The results showed a positive Attitudes of the members of the faculty at Saudi University towards E-learning management system JUSUR, although it has not activated in a sufficient way yet, the results showed how their needs for training in using the system and in particular learning content management and file sharing, forums, and Questions Bank. Moreover, results showed no difference in attitudes towards using the system among the faculty members regarding gender or the types of colleges humanitarian, scientific and health. The paper has 9 tables, 9 shapes, and 20 references.
http://www.tojet.net/articles/v10i2/1025.pdf
Load balancing clustering on moodle LMS to overcome performance issue of e-le...TELKOMNIKA JOURNAL
In dealing with the rapid growth of digitalization, the e-learning system has become a mandatory component of any Higher Education (HE) to serve academic processes requests. Along with the increasing number of users, the need for service availability and capabilities of eLearning are increasing day by day. The organization should always look for strategies to keep the eLearning always able to meet these demands. This report presents the implementation of Load Balancing Clustering (LBC) mechanism applied to Moodle LMS in an HE Institution to deal with the poor performance issues. By utilizing existing tools such as HAProxy and keepalived, the implemented LBC configuration delivers a qualified e-learning system performance. Both qualitative and quantitative parameters convince better performance than before. In four months of the operation there is no user complaint received. Meanwhile, in the current semester has been running for two months, the up-time is 99.8 % of 52.685 minutes operational time.
Review of monitoring tools for e learning platformsijcsit
The advancement of e-learning technologies has made it viable for developments in education and
technology to be combined in order to fulfil educational needs worldwide. E-learning consists of informal
learning approaches and emerging technologies to support the delivery of learning skills, materials,
collaboration and knowledge sharing. E-learning is a holistic approach that covers a wide range of
courses, technologies and infrastructures to provide an effective learning environment. The Learning
Management System (LMS) is the core of the entire e-learning process along with technology, content, and
services. This paper investigates the role of model-driven personalisation support modalities in providing
enhanced levels of learning and trusted assimilation in an e-learning delivery context. We present an
analysis of the impact of an integrated learning path that an e-learning system may employ to track
activities and evaluate the performance of learners.
E-Learning is transfer of skills and knowledge by
the computer and network enabled. It includes out of & in
classroom educational experiences with the help of
technology. Early E-Learning systems are based on computer
based learning& training often which attempted to replicate
autocratic teaching styles where the role of the e-Learning
systems was to transfer knowledge, as opposed to this
systems developed later which were based on computer
supportive collaborative learning which encouraged the
shared development of knowledge.
A Survey on E-Learning System with Data MiningIIRindia
E-learning process has been widely used in university campus and educational institutions are playing vital role to enhance the skill set of students. Modern E-learning done by many electronic devices, such as smartphones, Tabs, and so on, on existing E-learning tools is insufficient to achieve the purpose of online training of education. This paper presents a survey of online e-Learning authoring tools for creating and integrating reusable e-learning tool for generation and enhancing existing learning resources with them. The work concentrates on evaluation of the existing e-learning tools a, and authoring tools that have shown good performance in the past for online learners. This survey work takes more than 20 online tools that deal with the educational sector mechanism, for the purpose of observations, and the outcome were analyzed. The findings of this paper are the main reason for developing a new tool, and it shows that educators can enhance existing learning resources by adding assessment resources, if suitable authoring tools are provided. Finally, the different factors that assure the reusability of the created new e-learning tool has been analysed in this paper.E-learning environment is a guide for both students and tutorial management system. The useful on the e-learning system for apart from students and distance learning students. The purpose of using e-learning environment for online education system, developed in data mining for more number of clustering servers and resource chain has been good.
Big data integration for transition from e-learning to smart learning framework eraser Juan José Calderón
Big data integration for transition from e-learning to smart learning framework . Dr. Prakash Kumar Udupi Mr. Puttaswamy Malali Mr. Herald Noronha Department of Computing Department of Computing Department of Computing Middle East College Middle East College Middle East College .
Neural Network Model for Predicting Students' Achievement in Blended Courses ...ijaia
Educator’s knowledge about the likely students’ achievement in blended courses prior to sitting for
examinations provides room for early intervention on students’ learning process, especially to those at risk.
Unfortunately, Leaning Management Systems (LMSs), Moodle in particular lacks an environment to assist
educators access such knowledge from time to time before undertaking their examinations. This raised the
need to propose a model, of which from time to time would be providing the likely students’ achievement
based on activities in Moodle and previous achievement, taking a case of postgraduate programmes at the
University of Dar es Salaam.
This study applied artificial neural networks in building a prediction model. Simulations were conducted in
Matrix Laboratory (MATLAB) utilizing seventy eight instances (78) of students’ logs of three blended
courses extracted from Moodle for 2013/2014 and 2014/2015 academic years.
Mean Square Error (MSE) and Coefficient of Determination (R
2
) performance metrics were used to find
the best prediction model considering ten possible models. The study revealed a model with architecture of
4:10:1 trained with Bayesian Regularization (BR) to be the best model resulting to least MSE of 0.0170 and
high R
2
of 0.93 on training. During testing, the model successfully predicted 78% of the students’
achievement with risk and pass status.
An Efficient Methodology To Develop A Secured E-Learning System Using Cloud C...IJMERJOURNAL
ABSTRACT: Now-a-days, each and every action involved in our life becomes computerized in order to reduce the time, complexity and manual power. The education systems are also being computerized, to train the students in a much efficient way. This system is termed as E-Learning. E-Learning is an Internet-based learning process, in which the Internet technology is used to design, implement, manage and extend learning, which will improve the efficiency of learning. Learning, Teaching and Training are intensely connected components, which are all included in the development of E-Learning system. Cloud Computing provides an efficient platform to support the E-Learning systems, as it can be dramatically changes over time .In this paper, an overview on the new emerging E-Learning system , utilization of the SAAS (Software as a Service) and the methodology to test the efficiency of the person in a secured way are described.
An Overview of Criteria for Selecting an LMS.docx.David Brooks
Paper presented at Second Canadian International Conference on Advances in Education,Teaching & Technology 2017, 29-31 July, 2017, Toronto, Canada
This letter is to inform you that the scientific committee has selected your abstract for oral presentation in the Second Canadian International Conference on Advances in Education,Teaching & Technology 2017 (EduTeach2017) which will be held on 29-31 July 2017, at the International Living & Learning Center, Ryerson University, Toronto, Canada.
Developing online learning resources: Big data, social networks, and cloud co...eraser Juan José Calderón
"Developing online learning resources: Big data, social netorks, and cloud computing to support pervasive knowledge" de Muhammad Anshari & Yabit Alas1 & Lim Sei Guan
Published online: 21 May 2015 # Springer Science+Business Media New York 2015
Abstract
Utilizing online learning resources (OLR) from multi channels in learning activities promise extended benefits from traditional based learning-centred to a collaborative based learning-centred that emphasises pervasive learning anywhere and anytime. While compiling big data, cloud computing, and semantic web into OLR offer a broader spectrum of pervasive knowledge acquisition to enrich users’ experience in learning. In conventional learning practices, a student is perceived as a recipient of information and knowledge. However, nowadays students are empowered to involve in learning processes that play an active role in creating, extracting, and improving OLR collaborative learning platform and knowledge sharing as well as distributing. Researchers have employed contents analysis for reviewing literatures in peer-reviewed journals and interviews with the teachers who utilize OLR. In fact, researchers propose pervasive knowledge can address the need of integrating technologies like cloud computing, big data, Web 2.0, and Semantic Web. Pervasive knowledge redefines value added, variety, volume, and velocity of OLR, which is flexible in terms of resources adoption, knowledge acquisition, and technological implementation.
Recent Trends in E-Learning and Technologies IIJSRJournal
This work centers around the various advances accessible to help instructing and learning in e-Learning frameworks whose significance for schooling educators and framework designers is obvious. It is important to decide the most fitting e-learning advances to help the individual necessities in instructing, which make it conceivable to give the best learning freedoms to understudies, considering the current circumstance where instructive frameworks have quick requests got from the Covid 19 pandemic, which makes homeroom based instructive practices offer way to far off exercises. There are as of now drifts in the improvement of an assortment of accessible advances which might be outlined in Web environments and Virtual Reality among other arising advances; subsequently, the choice to utilize a specific innovation should be founded on strong exploration and obvious proof. This article audits a considerable lot of these e-Learning framework innovations and gives data, about their utilization, openings and patterns being developed.
Using data mining in e learning-a generic framework for military educationElena Susnea
Susnea E. (2013). Using Data Mining in eLearning: A Generic Framework for Military Education, in Proceedings of "The International Scientific Conference eLearning and Software for Education", Iss. 01 (pp. 411-415).
COMPARING THE CHALLENGES OF IMPLEMENTING WEB-BASED AND TRADITIONAL TEACHING S...adeij1
Web-based teaching systems have several advantages and have the potential to benefit education greatly. It
is significant to carefully consider learners' and instructors' specific needs and circumstances when
deciding whether to use these systems. Using web-based and traditional teaching methods may be
appropriate to provide a well-rounded educational experience. It may be appropriate to use a combination
of web-based and traditional teaching methods to provide a well-rounded educational experience. Webbased teaching systems have the potential to greatly benefit education in developing countries by
increasing access to quality education and reducing the cost of delivering education. However, there are
also several challenges to implementing these systems in developing countries, such as limited
infrastructure and resources, limited access to technology, and low digital literacy. The purpose of this
review article is to analyse and contrast the efficacy of web-based teaching systems with traditional
teaching systems, assess their respective advantages and disadvantages, identify the factors that influence
their effectiveness, and conclude that web-based teaching systems offer certain benefits over traditional
teaching systems, including greater flexibility, convenience, and the capacity to deliver multimedia content.
However, traditional teaching systems also have advantages, such as the ability to provide face-to-face
interaction and immediate feedback. This review paper examines the factors that impact the efficacy of
both systems, such as the system's design, the quality of the educational materials, and the proficiency of
the instructor. Both systems have their strengths and weaknesses, and the best approach depends on the
specific needs and circumstances of the learner and the instructor
Comparing the Challenges of Implementing Web-Based and Traditional Teaching S...adeij1
Web-based teaching systems have several advantages and have the potential to benefit education greatly. It is significant to carefully consider learners' and instructors' specific needs and circumstances when deciding whether to use these systems. Using web-based and traditional teaching methods may be appropriate to provide a well-rounded educational experience. It may be appropriate to use a combination of web-based and traditional teaching methods to provide a well-rounded educational experience. Webbased teaching systems have the potential to greatly benefit education in developing countries by increasing access to quality education and reducing the cost of delivering education. However, there are also several challenges to implementing these systems in developing countries, such as limited infrastructure and resources, limited access to technology, and low digital literacy. The purpose of this review article is to analyse and contrast the efficacy of web-based teaching systems with traditional teaching systems, assess their respective advantages and disadvantages, identify the factors that influence their effectiveness, and conclude that web-based teaching systems offer certain benefits over traditional teaching systems, including greater flexibility, convenience, and the capacity to deliver multimedia content. However, traditional teaching systems also have advantages, such as the ability to provide face-to-face interaction and immediate feedback. This review paper examines the factors that impact the efficacy of both systems, such as the system's design, the quality of the educational materials, and the proficiency of the instructor. Both systems have their strengths and weaknesses, and the best approach depends on the specific needs and circumstances of the learner and the instructor.
In the era of data-driven warfare, the integration of big data and machine learning (ML) techniques has
become paramount for enhancing defence capabilities. This research report delves into the applications of
big data and ML in the defence sector, exploring their potential to revolutionize intelligence gathering,
strategic decision-making, and operational efficiency. By leveraging vast amounts of data and advanced
algorithms, these technologies offer unprecedented opportunities for threat detection, predictive analysis,
and optimized resource allocation. However, their adoption also raises critical concerns regarding data
privacy, ethical implications, and the potential for misuse. This report aims to provide a comprehensive
understanding of the current state of big data and ML in defence, while examining the challenges and
ethical considerations that must be addressed to ensure responsible and effective implementation.
Cloud Computing, being one of the most recent innovative developments of the IT world, has been
instrumental not just to the success of SMEs but, through their productivity and innovative contribution to
the economy, has even made a remarkable contribution to the economic growth of the United States. To
this end, the study focuses on how cloud computing technology has impacted economic growth through
SMEs in the United States. Relevant literature connected to the variables of interest in this study was
reviewed, and secondary data was generated and utilized in the analysis section of this paper. The findings
of this paper revealed that there have been meaningful contributions that the usage of virtualization has
made in the commercial dealings of small firms in the United States, and this has also been reflected in the
economic growth of the country. This paper further revealed that as important as cloud-based software is,
some SMEs are still skeptical about how it can help improve their business and increase their bottom line
and hence have failed to adopt it. Apart from the SMEs, some notable large firms in different industries,
including information and educational services, have adopted cloud computing technology and hence
contributed to the economic growth of the United States. Lastly, findings from our inferential statistics
revealed that no discernible change has occurred in innovation between small and big businesses in the
adoption of cloud computing. Both categories of businesses adopt cloud computing in the same way, and
their contribution to the American economy has no significant difference in the usage of virtualization.
Energy-constrained Wireless Sensor Networks (WSNs) have garnered significant research interest in
recent years. Multiple-Input Multiple-Output (MIMO), or Cooperative MIMO, represents a specialized
application of MIMO technology within WSNs. This approach operates effectively, especially in
challenging and resource-constrained environments. By facilitating collaboration among sensor nodes,
Cooperative MIMO enhances reliability, coverage, and energy efficiency in WSN deployments.
Consequently, MIMO finds application in diverse WSN scenarios, spanning environmental monitoring,
industrial automation, and healthcare applications.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication. IJCSIT publishes original research papers and review papers, as well as auxiliary material such as: research papers, case studies, technical reports etc.
With growing, Car parking increases with the number of car users. With the increased use of smartphones
and their applications, users prefer mobile phone-based solutions. This paper proposes the Smart Parking
Management System (SPMS) that depends on Arduino parts, Android applications, and based on IoT. This
gave the client the ability to check available parking spaces and reserve a parking spot. IR sensors are
utilized to know if a car park space is allowed. Its area data are transmitted using the WI-FI module to the
server and are recovered by the mobile application which offers many options attractively and with no cost
to users and lets the user check reservation details. With IoT technology, the smart parking system can be
connected wirelessly to easily track available locations.
Welcome to AIRCC's International Journal of Computer Science and Information Technology (IJCSIT), your gateway to the latest advancements in the dynamic fields of Computer Science and Information Systems.
Computer-Assisted Language Learning (CALL) are computer-based tutoring systems that deal with
linguistic skills. Adding intelligence in such systems is mainly based on using Natural Language
Processing (NLP) tools to diagnose student errors, especially in language grammar. However, most such
systems do not consider the modeling of student competence in linguistic skills, especially for the Arabic
language. In this paper, we will deal with basic grammar concepts of the Arabic language taught for the
fourth grade of the elementary school in Egypt. This is through Arabic Grammar Trainer (AGTrainer)
which is an Intelligent CALL. The implemented system (AGTrainer) trains the students through different
questions that deal with the different concepts and have different difficulty levels. Constraint-based student
modeling (CBSM) technique is used as a short-term student model. CBSM is used to define in small grain
level the different grammar skills through the defined skill structures. The main contribution of this paper
is the hierarchal representation of the system's basic grammar skills as domain knowledge. That
representation is used as a mechanism for efficiently checking constraints to model the student knowledge
and diagnose the student errors and identify their cause. In addition, satisfying constraints and the number
of trails the student takes for answering each question and fuzzy logic decision system are used to
determine the student learning level for each lesson as a long-term model. The results of the evaluation
showed the system's effectiveness in learning in addition to the satisfaction of students and teachers with its
features and abilities.
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This research aims to further understanding in the field of continuous authentication using behavioural
biometrics. We are contributing a novel dataset that encompasses the gesture data of 15 users playing
Minecraft with a Samsung Tablet, each for a duration of 15 minutes. Utilizing this dataset, we employed
machine learning (ML) binary classifiers, being Random Forest (RF), K-Nearest Neighbors (KNN), and
Support Vector Classifier (SVC), to determine the authenticity of specific user actions. Our most robust
model was SVC, which achieved an average accuracy of approximately 90%, demonstrating that touch
dynamics can effectively distinguish users. However, further studies are needed to make it viable option
for authentication systems. You can access our dataset at the following
link:https://github.com/AuthenTech2023/authentech-repo
This paper discusses the capabilities and limitations of GPT-3 (0), a state-of-the-art language model, in the
context of text understanding. We begin by describing the architecture and training process of GPT-3, and
provide an overview of its impressive performance across a wide range of natural language processing
tasks, such as language translation, question-answering, and text completion. Throughout this research
project, a summarizing tool was also created to help us retrieve content from any types of document,
specifically IELTS (0) Reading Test data in this project. We also aimed to improve the accuracy of the
summarizing, as well as question-answering capabilities of GPT-3 (0) via long text
In the realm of computer security, the importance of efficient and reliable user authentication methods has
become increasingly critical. This paper examines the potential of mouse movement dynamics as a
consistent metric for continuous authentication. By analysing user mouse movement patterns in two
contrasting gaming scenarios, "Team Fortress" and "Poly Bridge," we investigate the distinctive
behavioral patterns inherent in high-intensity and low-intensity UI interactions. The study extends beyond
conventional methodologies by employing a range of machine learning models. These models are carefully
selected to assess their effectiveness in capturing and interpreting the subtleties of user behavior as
reflected in their mouse movements. This multifaceted approach allows for a more nuanced and
comprehensive understanding of user interaction patterns. Our findings reveal that mouse movement
dynamics can serve as a reliable indicator for continuous user authentication. The diverse machine
learning models employed in this study demonstrate competent performance in user verification, marking
an improvement over previous methods used in this field. This research contributes to the ongoing efforts to
enhance computer security and highlights the potential of leveraging user behavior, specifically mouse
dynamics, in developing robust authentication systems.
Image segmentation and classification tasks in computer vision have proven to be highly effective using neural networks, specifically Convolutional Neural Networks (CNNs). These tasks have numerous
practical applications, such as in medical imaging, autonomous driving, and surveillance. CNNs are capable
of learning complex features directly from images and achieving outstanding performance across several
datasets. In this work, we have utilized three different datasets to investigate the efficacy of various preprocessing and classification techniques in accurssedately segmenting and classifying different structures
within the MRI and natural images. We have utilized both sample gradient and Canny Edge Detection
methods for pre-processing, and K-means clustering have been applied to segment the images. Image
augmentation improves the size and diversity of datasets for training the models for image classification.
This work highlights transfer learning’s effectiveness in image classification using CNNs and VGG 16 that
provides insights into the selection of pre-trained models and hyper parameters for optimal performance.
We have proposed a comprehensive approach for image segmentation and classification, incorporating preprocessing techniques, the K-means algorithm for segmentation, and employing deep learning models such
as CNN and VGG 16 for classification.
The security of Electric Vehicle (EV) charging has gained momentum after the increase in the EV adoption
in the past few years. Mobile applications have been integrated into EV charging systems that mainly use a
cloud-based platform to host their services and data. Like many complex systems, cloud systems are
susceptible to cyberattacks if proper measures are not taken by the organization to secure them. In this
paper, we explore the security of key components in the EV charging infrastructure, including the mobile
application and its cloud service. We conducted an experiment that initiated a Man in the Middle attack
between an EV app and its cloud services. Our results showed that it is possible to launch attacks against
the connected infrastructure by taking advantage of vulnerabilities that may have substantial economic and
operational ramifications on the EV charging ecosystem. We conclude by providing mitigation suggestions
and future research directions.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
The AIRCC's International Journal of Computer Science and Information Technology (IJCSIT) is devoted to fields of Computer Science and Information Systems. The IJCSIT is a open access peer-reviewed scientific journal published in electronic form as well as print form. The mission of this journal is to publish original contributions in its field in order to propagate knowledge amongst its readers and to be a reference publication.
This paper describes the outcome of an attempt to implement the same transitive closure (TC) algorithm
for Apache MapReduce running on different Apache Hadoop distributions. Apache MapReduce is a
software framework used with Apache Hadoop, which has become the de facto standard platform for
processing and storing large amounts of data in a distributed computing environment. The research
presented here focuses on the variations observed among the results of an efficient iterative transitive
closure algorithm when run against different distributed environments. The results from these comparisons
were validated against the benchmark results from OYSTER, an open source Entity Resolution system. The
experiment results highlighted the inconsistencies that can occur when using the same codebase with
different implementations of Map Reduce.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
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Multilevel Analysis of Student's Feedback Using Moodle Logs in Virtual Cloud Environment
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 9, No 5, October 2017
DOI:10.5121/ijcsit.2017.9502 15
MULTILEVEL ANALYSIS OF STUDENT’S
FEEDBACKUSING MOODLE LOGS IN VIRTUAL
CLOUD ENVIRONMENT
Ashok Verma1
, Sumangla Rathore2
, Santosh Vishwakarma3
and Shubham
Goswami4
1
Department of Computer Science & Engineering, Sir Padampat Singhania University,
Udaipur, Rajasthan, India
2
Department of Computer Science & Engineering, Sir Padampat Singhania University,
Udaipur, Rajasthan,India
3
Department of Computer Science & Engineering,Gyan Ganga Institute of
Technology & Sciences, Jabalpur, India
4
Department of Computer Science & Engineering, Sir Padampat Singhania University,
Udaipur, Rajasthan, India
ABSTRACT
In the current digital era, education system has witness tremendous growth in data storage and efficient
retrieval. Many Institutes have very huge databases which may be of terabytes of knowledge and
information. The complexity of the data is an important issue as educational data consists of structural as
well as non-structural type which includes various text editors like node pad, word, PDF files, images,
video, etc. The problem lies in proper storage and correct retrieval of this information. Different types of
learning platform like Moodle have implemented to integrate the requirement of educators, administrators
and learner. Although this type of platforms are indeed a great support of educators, still mining of the
large data is required to uncover various interesting patterns and facts for decision making process for the
benefits of the students.
In this research work, different data mining classification models are applied to analyse and predict
students’ feedback based on their Moodle usage data. The models described in this paper surely assist the
educators, decision maker, mentors to early engage with the issues as address by students. In this research,
real data from a semester has been experimented and evaluated. To achieve the better classification
models, discretization and weight adjustment techniques have also been applied as part of the pre –
processing steps. Finally, we conclude that for efficient decision making with the student’s feedback the
classifier model must be appropriate in terms of accuracy and other important evaluation measures. Our
experiments also shows that by using weight adjustment techniques like information gain and support
vector machines improves the performance of classification models.
KEYWORDS
Educational Data, Educational Data Mining,LMS, Moodle, Feedback system, weight adjustment
techniques.
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017
1. INTRODUCTION
The use of web-based education systems or e
last years, spurred by the fact that neither students nor teachers are bound to any specific location
and that this form of computer-
platform. In particular, collaborative and communication tools are also
educational contexts and as a result. Learning Management Systems (LMSs) are becoming much
more common in universities, community colleges, schools, and businesses, and are even used by
individual instructors in order to add web te
face-to-face courses. LMSs can offer a great variety of channels and workspaces to facilitate
information sharing and communication among participants in a course. They let educators
distribute information to students, produce content material, prepare assignments and tests,
engage in discussions, manage distance classes and enable collaborative learning with forums,
chats, file storage areas, news services, etc. Some examples of commercial systems are
Blackboard and TopClass while some examples of free systems are Ilias
Cloud computing can store a huge amount of educational resources and provide infrastructure,
platform, and application services for users instead of letting users sav
can also provide unlimited computing power for the completion
[1][2].
Nowadays, one of the most commonly used Learning Management System is Modular Object
Oriented Dynamic Learning Environment (
the creation of powerful, flexible and engaging
figure shows an architecture of Moodle system which is based on a layered approach.
Figure 1.Architecture
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017
based education systems or e-learning systems has grown exponentially in the
spurred by the fact that neither students nor teachers are bound to any specific location
-based education is virtually independent of a specific hardware
platform. In particular, collaborative and communication tools are also becoming widely used in
educational contexts and as a result. Learning Management Systems (LMSs) are becoming much
more common in universities, community colleges, schools, and businesses, and are even used by
individual instructors in order to add web technology to their courses and supplement traditional
face courses. LMSs can offer a great variety of channels and workspaces to facilitate
information sharing and communication among participants in a course. They let educators
on to students, produce content material, prepare assignments and tests,
engage in discussions, manage distance classes and enable collaborative learning with forums,
chats, file storage areas, news services, etc. Some examples of commercial systems are
ackboard and TopClass while some examples of free systems are Ilias,Claroline and Moodle.
Cloud computing can store a huge amount of educational resources and provide infrastructure,
platform, and application services for users instead of letting users save them in their devices. It
can also provide unlimited computing power for the completion of various types of
Nowadays, one of the most commonly used Learning Management System is Modular Object
Oriented Dynamic Learning Environment (Moodle), a free learning management system enabling
the creation of powerful, flexible and engaging online courses and experiences. The following
figure shows an architecture of Moodle system which is based on a layered approach.
Figure 1.Architecture of Moodle System
International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017
16
learning systems has grown exponentially in the
spurred by the fact that neither students nor teachers are bound to any specific location
based education is virtually independent of a specific hardware
becoming widely used in
educational contexts and as a result. Learning Management Systems (LMSs) are becoming much
more common in universities, community colleges, schools, and businesses, and are even used by
chnology to their courses and supplement traditional
face courses. LMSs can offer a great variety of channels and workspaces to facilitate
information sharing and communication among participants in a course. They let educators
on to students, produce content material, prepare assignments and tests,
engage in discussions, manage distance classes and enable collaborative learning with forums,
chats, file storage areas, news services, etc. Some examples of commercial systems are
,Claroline and Moodle.
Cloud computing can store a huge amount of educational resources and provide infrastructure,
e them in their devices. It
f various types of application
Nowadays, one of the most commonly used Learning Management System is Modular Object
Moodle), a free learning management system enabling
The following
figure shows an architecture of Moodle system which is based on a layered approach.
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017
17
Moodle (Modular Object-Oriented Dynamic Learning Environment) is a mainstream open-source
learning management system widely welcomed due to its simple and clear operation as well as
flexible space expanding capability [24]. The typical working environment is a server with
LAMP, short for Linux operation system, Apache server, Mysql database and PHP scripting
language. This architecture could optimize Moodle and makes it more stable and safer. A Moodle
Virtual Cluster is constructed through virtualization technology in cloud computing to spread
excessive load in single server of Moodle and avoid aforesaid shortcomings in physical cluster.
Virtual cluster can dynamically allocate resources on demands, thus utilizing resources efficiently
and constructing an elastic computing architecture [5][17].
Moodle has benefited many Universities and colleges around the world. Moodle allows teachers
to assess hundreds and thousands of students’ performance as it allows the instructors to track
students’ grades, personal information, academic results, number of tests taken, and other
activities. This data is usually termed as Educational data. The data collected from Students' is
huge and manually extracting any useful information from such huge dataset is not an easy task.
One such approach which can be used here is Data Mining. Data Mining is a technique of
extracting useful and hidden information from large databases. It is also termed as a "Knowledge
discovery" process. This knowledge once mined can be used to increase revenues, sales, cut costs
or both. [19].
In the past few years, researchers have largely focused on using Data Mining techniques such as
classification, clustering, association rule mining to analyse educational data; i.e. to improve the
educational system. These methods have been proven immensely useful in analysing students'
learning behaviour and performance. Data mining techniques have been successfully applied to
educational data; to understand students' learning issues, recommendation system: where new
courses are recommended on the basis of their performance, feedback for teachers, etc. Though
there are several tools which can be used for analysis; but they have not given much insight into
students' behaviour. The educational data can be mined to understand students’ behaviour using
data mining techniques; this is termed as Educational Data Mining.
2. RELATED WORK
The problem of high computational load in Moodle server has been addressed by Guo et al. [1].
They proposed that the centralized server load must be allocated to several other servers in terms
of virtual clusters. They perform concurrent access pressure test to evaluate the virtual cloud
clusters performance and found that it improves transaction capability of the system.
Chen et al. [4] proposed an efficient resource management system for on-line virtual clusters
provision, aiming to provide immediately-available virtual clusters for academic users.
Particularly, they investigated two crucial problems: efficient VM image management and
intelligent resource mapping, either of them has remarkable impact on the performance of the
system.
Another important work carried out by [20] about the usage of data mining applied for
personalization in web environments. They also develop tools for web based learning
environment primarily focused on evaluating learning process of the educational system.
A similar work is carried out by [21] which focus on web-based technology that relates
affordability of accessing the ubiquitous Web and the simplicity of deploying and maintaining
4. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017
18
resources. They discussed some data mining and machine learning techniques that could be used
to enhance web-based learning environments for the educator to better evaluate the leaning
process, as well as for the learners to help them in their learning endeavour.
Another approach as suggested by Alves et al. [16] is to extends a recent comparative study
covering four different courses lectured at the Polytechnic of Porto - School of Engineering, in
respect to the usage of a particular Learning Management System, i.e .Moodle, and its impact on
students' results.
A similar approach was proposed by Daraghmi et al. [23]. They suggested that a new method for
Moodle a web based interface supporting a wide range of activities including forums, contents,
and assignments is provided to assist both the teachers and the students. However, limitations of
the file-size acceptable for uploads, weakness in the assessment procedure, complicated task of
replacing an existing file, and the lowest visiting rate of the traditional discussion module are
some major deficiencies in the traditional Moodle module. They also integrated Moodle with
Facebook to increase the visiting rate of the traditional discussion Moodle module, hence,
increasing the students' motivation to ask questions and the interaction among the students. The
performance and the usability of the new module were evaluated and promising results were
obtained.
Another seminal work by Nagi&Suesawaluk [18], suggested that virtual learning environment is
designed to help educators create online courseware with opportunities for rich interaction.
Interactivity is becoming a key facet of eLearning. Moodle logs all activities including views and
posts for all learning objects hosted in the system. It provides different statistics to help the
content experts to improve the quality of eLearning courseware. They also describes the use of
automated, scalable real-time containing data of all activity for four major ICT courses offered as
a part of the Master Degree eLearning program at Assumption University of Thailand.
Recently Holbl et al. [19] presented experience with learning management system Moodle when
used in their educational process. They discussed the use of a feedback form enabling students to
asses and comment courses. Additionally, the results of a questionnaire compiled to gain data on
student experiences with Moodle with focus on features of the platform and specific privacy
concerns are presented. Further, the relation between the experience gained with the course and
the questionnaire results is described. Students were asked about Moodle features they use and
specific privacy concerns, including visibility of profiles, results and grades. Also students'
relation to giving feedback feature of Moodle user’s analysed.
Another important work on web usage mining with multilevel analysis and data pre-processing is
given by Sael et al. [20]. This research illustrates the potential of Web Usage Mining on e-
Learning domain. They used educational data mining techniques to analyse learners' behaviour,
to help in learning evaluation and to enhance the structure of a given course. They focused on the
pre-processing task, which is considered as the most crucial phase in the whole process. They
also presented multidimensional graphics in order to understand users' accesses. These
aggregated variables provide teachers and tutors with interesting knowledge about students'
learning process according to different levels of content accessed.
Recently an important work is carried out by Pong &Rungworawut [21] which focus on the new
pattern of teaching evaluation. The research carried out by them also presents analysis model for
5. International Journal of Computer Science & Information Technology (IJCSIT) Vol 10, No 5, October 2017
19
teaching evaluation from answering and posting a comment to discussion in form of open-end
question obtained from Moodle LMS forum using data mining techniques. The techniques extract
classification of attitudes that are defined positive and negative attitude from students to
instructor for improvement of learning and teaching.
Another important paper is given by Gil et al. [22]. They proposed a new way to verify the
identity of users in learning management systems used at Higher Education. The starting point is
to study the needs demanded by the new Distance Education model. After the study of possible
security risks in the new environment, they conclude that the use of biometrics tools provide a
good mechanism to check the user identity. In this article the authors show how to integrate a
fingerprint verification system in an open source LMS called Moodle as a reliable method for
achieving user authentication. The system was used successfully for a group of Industrial
Engineering students at UNED for accessing their Electronic laboratory exams. They also
analysed the students' attitudes faced with a new way of control access both from the technical
and personal points of view.
Based on the extensive literature survey carries out, our proposed work focus on first; the pre-
processing step with the Moodle corpus as an important and key aspects before analysing the
students feedback pattern and second; the recommendation based as the solution of the problems
identified.
3. METHODOLOGY
For assessment of our approach, we utilized the information from the Moodle framework.
Moodle, like most LMSs, records every one of the understudies' utilization data in log files as
well as straightforwardly in a database. There exists large number of interrelated relations in the
Moodle database. Be that as it may, this data is not required thus it is additionally important to
change over the valuable information to the required arrangement utilized for the mining purpose.
Therefore, Moodle information must be pre-processed to change over it to the adoptable format
for further processing and mining. Then, various data mining algorithms (classification
algorithms in our case) will be executed to find hidden patterns and data inside the info of interest
for the instructor. So, this mining method consists of three phases of knowledge discovery
process: pre-processing, data processing, and post-processing. Next there is an
overview in further detail relating to how these steps have been perform with the Rapidminer tool
used in this research.
In the pre-processing step, the user created data files from the Moodle database have been
imported in excel worksheet.Our mining tool additionally splits the data file into parts, such as
training and testing files. Table 1 shows the list of attributes used for processing of the Moodle
data. The feedback of students have been assessed with course name as identification of course,
N_post as the total number of posts submitted by student in a current semester, N_post sub is the
attribute which signify the number of posts related to teaching feedback and N_post teacher
specify the post which relates teachers feedback.
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Table 1. Attributes used by students in Moodle Logs
Attributes used for students
Name Description
Course Identification number of the course
N_post No of posts send to Moodle
N_post_sub No of subject related posts send to Moodle
N_post_teacer No of teacher related posts send to Moodle
As specify in the Figure 2, Moodle database has been integrated and several preprocessing steps
have been performed as part of preprocessing. The major steps includes tokenization, stop word
removal, stemming and generate n-grams during pre-processing of data. Tokenization [3] refers
to process of taking character sequence from defined document unit and breaking it into words,
symbols, phrases and numbers called tokens. Stop word removal [4] filter out the words that have
no values for retrieval purpose. Stemming [5] perform replacement of all the variations of the
words with its root word. The variant words may be plurals, gerund forms, prefixes, suffixes etc.
a stem word can represents all of its variants that reduces the size of dictionary containing all
words of document collection. In our analysis Porter algorithm found best because it produces
maximum number of tokens.
Figure 2 Import of Moodle database and preprocessing
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The output from the process as shown in Figure 2 generates the document vector and a word list.
We store the word list and document vector generated by above step for future use. The document
vector is further process through weight adjustment as shown in Figure 3 below,
Figure3 Term weight adjustment by Information Gain and SVM
We deploy two weight adjustment methods for our document vector which strive the purpose of
ranking the terms according to their discriminating power and normalize the term weight with
compare to the reference values. First the weight by Information Gain method uses the
information gain ratio of label attribute to calculate the value of un-label attribute and assigns the
weight accordingly. Second, we used SVM method for weight adjustment which computes
attributes weights with the coefficients of the normal vector of a linear SVM. The weight
modification procedure brings about a sensible decrease in the modelling costs. This process
generates two weighting tables as shown in Figure 3.
Once we have the updated document vector and attribute weights, we evaluated four state of the
art classification models to understand which give the best accuracy as shown in Figure 4. The
algorithms used are K-NN, Decision Tree, Random Forest,Generalized Linear Modeland
Naive Bayes. The descriptions of the models are as following:
Random forest is an ensemble learning method for classification, in which it constructs multiple
of unpruned classification trees in the training phase, by bootstrap sampling method on the
training data. The final predicted output for a random selected feature is given by finding the
mean from all unpruned classification trees in the testing phase [18].
Naive Bayes classification is the extended form of Bayesian classifiers which include naïve
assumption too. Bayesian classifiers are statistical classifiers, which is based on Bayes’ theorem.
Bayesian classifiers can predict probability that a given sample belongs to a particular class, i.e.
can predict class membership probabilities. According to the naïve assumption, the changes in an
attribute value on a given class are independent of the changes in the values of the other
attributes. This assumption is also known as ‘class conditional independence’ [19].
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Suppose there are k different classes denoted as C1, C2 … Ck. Let X = {x1, x2 … xn}, depicting
n measured values of the n attributes, A1, A2 . . . An respectively. Then X is predicted to belong
to the class Ci if and only if
P (Ci|X) > P (Cj|X) for 1 ≤ j ≤ m, j ≠ i.
Where P(C|X) is the posterior probability of class C conditioned on predictor X, P(C) is the prior
probability of class C, P(X|C) is the probability of predictor X conditioned on class C and P(X) is
the prior probability of predictor X. By Bayes’ theorem:
P (Ci|X) = (P (X|Ci) P (Ci)) / P(X)
Rule induction uses sequential covering algorithm to extract the “if, then” rule, and they are
directly extracted from the training data set. These rules are learned sequentially. One rule covers
multiple examples present in the database hence, termed as the sequential covering algorithm.
The collection of rules extracted represents full model. [4]
Decision trees are classifying model which can be directly transformed into a set of IF-THEN
rules that are one of the most popular forms of knowledge representation, due to their simplicity
and comprehensibility.
Figure 4: Various Classification Models
Generalized linear model perform predictions for a variable Y using a predictor variable X; this is
termed as simple linear regression. We model the relationship between the two independent and
dependent variables. Generalized linear model is an extension of Linear Regression which allows
variables with error distribution models other than allowing only variables with Normal
Distribution as in the case of Linear Regression. In other words, we can say that GL Model is the
generalized version of Linear Regression. GL model performs classification by making
predictions for an unknown object for its target class. Mathematically, GL model is expressed as:
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=
The parameters, E(Y) denotes the expected value/ target class for Y, Xβ represents the predictor
in linear form,g is the link function used by GL model for prediction.
4. RESULT AND ANALYSIS
The Moodle database used in our experimentation consists of around 1500 records of University
students. This record mimics various feedbacks related to various subjects and teachers. In this
paper, the open source data mining tool Rapid Miner is used for experimentation. The split ration
of 70:30 is used for training and testing of model performance. The models used in our
experimentation have correctly classified their problems according to the feedback entered by
them for various issues. It is very useful to analyse what issues students' face in their subjects and
how it can be resolved. These predictions are automatically made by the system and no manual
effort is required. The analysis shows that there are some common subjects' problems which
students' face in their college curriculum. These issues must be resolved for their better academic
career and future. Different classification models such as Naive Bayes, K-NN, Decision Tree,
Random Forest, Generalized linear model are applied in the Moodle dataset after pre-processing
and a comparative study has been made on the basis of their performance and accuracy in
making predictions.
The table below shows the comparative study for various classification algorithms along with
their performance parameters such as accuracy, classification error rate, Kappa, precision and
recall.
Table2. Performance Table
Classifier
Naive
Bayes
K-NN
Decision
Tree
Random
Forest
Generalized Linear
Model
Accuracy 96.00% 98.50% 98.12% 93.87% 99.00%
Classification error 4.00% 1.50% 1.88% 6.13% 1.00%
Kappa 0.935 0.975 0.969 0.894 0.983
Weighted mean recall 97.24% 98.80% 98.38% 91.87% 98.89%
Weighted mean
precision
96.29% 98.60% 98.36% 96.76% 99.38%
We can see from the above table that GL Model has given best performance in terms of achieving
high accuracy and low classification error rate. K-NN, Decision Tree and Naive Bayes have also
achieved high accuracy rates. Random forest classifier uses combination of decision trees with a
random function for classifying data has given a little high error rate as it requires more features
for data classification. This may not be appropriate in classifying students' subjects’ problems
from the Moodle dataset as we emphasize on the problems rather than different features.
Classifiers which can effectively handle polynomial data have achieved high accuracy and better
results.
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The graphs below shows the analysis for different classifiers using performance parameters -
Figure 5. Performance measure of classifier using Accuracy
Accuracy is defined as how correct the predictions are made by the system. We can see from the
above graph that GL Model has achieved highest accuracy of 99.90%. Other classifiers have also
achieved high accuracy results.
Figure 6 Performance measure of classifier using Classification Error
Classification error rate is the percentage of incorrect predictions made by the system. The graph
above shows that GL Model has given least classification error rate of 1%, as it achieved high
accuracy rate. Other classifiers have also given less error rate; where only random forest has
given little high error as it achieved low accuracy.
Random
Forest
Naive
Bayes
Decision
Tree
K-NN
GL
Model
Accuracy 93.87% 96.00% 98.12% 98.50% 99.00%
90%
92%
94%
96%
98%
100%
Accuracy
Levels
(%)
GL
Model
K-NN
Decisio
n Tree
Naive
Bayes
Rando
m
Forest
Classification error 1.00% 1.50% 1.88% 4.00% 6.13%
0.00%
1.00%
2.00%
3.00%
4.00%
5.00%
6.00%
7.00%
Classification
Error
Levels
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Figure 7. Performance measure of classifier using Kappa
Kappa statistics is a popular measure to check how correct the predictions are made by the
system. It is considered more robust as compared to accuracy; as it also considers correct
predictions made by ‘chance’ by the system. It compares the accuracy of predictions by the
system with a random system. The analysis shows that GL Model has gained highest Kappa value
of 0.983 which is close to 1. Other classifiers have also achieved close to 1; high Kappa values.
Figure 8. Performance measure of classifier using Kappa
Random
Forest
Naive
Bayes
Decision
Tree
K-NN GL Model
Kappa 0.894 0.935 0.969 0.975 0.983
0.88
0.9
0.92
0.94
0.96
0.98
1
Kappa
Measures
Random
Forest
Naive
Bayes
Decision
Tree
K-NN
GL
Model
Weighted mean recall 91.87% 97.24% 98.38% 98.80% 98.89%
90.00%
92.00%
94.00%
96.00%
98.00%
100.00%
Weighted
mean
recall
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Figure 9 Performance measure of classifier using Weighted Mean Precision
Recall and Precision are also important measures to analyse the performance of data mining
models. The weighted mean recall and precision for each individual class is calculated. The above
analysis shows that GL Model has achieved high mean value of 98%.
5. CONCLUSION
In this paper we had analysed student’s feedback Moodle data with different data mining
classification models with term weight adjustment schemes. It has been found that some
algorithms shows better performance after applying various pre-processing tasks such as
tokenization, stop word removal, stemming, etc. Further, by using weight adjustment by support
vector machine and information gain also improves in terms of accuracy and other important
evaluation measures. Although we have applied the techniques individually, but these models can
also be applied in multi model scenario to obtain interesting patterns and knowledge in more
accurate and faster way. The results obtain in our experiments are very useful to classify students
problems as well as they are useful to detect other interesting patterns about the source of Moodle
data. The main objective of our work was to improve the quality of student learning and to timely
provide feasible solutions for their different problems.
In future experiments, we intend to measures on other important issues related to student profile,
curriculum choices etc. The classification model used in our work will also be evaluated in terms
of compressibility. By this we will be able to specify about the quantity and quality of the data
can affect the performance of the algorithms. This paper can be further extended to increase the
accuracy percentage by using several other classifiers at different numbers of folds. Finally, in
order to prove the acceptability of the model accuracy, we look forwardto deploy the model with
much larger dataset in real scenarios.
.
Naive
Bayes
Random
Forest
Decisio
n Tree
K-NN
GL
Model
Weighted mean
precision 96.29% 96.76% 98.36% 98.60% 99.38%
96.00%
97.00%
98.00%
99.00%
100.00%
Weighted
Mean
Precision
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