The document discusses reinforcement learning (RL) and its potential applications in education. It begins with an introduction to RL, describing the state-action-reward framework and key algorithms like Q-learning and deep Q-networks. It then outlines several open applications of RL in education, including using RL-based robots in classrooms to monitor student performance, developing educational games with RL, and building an intelligent admission recommender system. The document concludes by noting challenges in real-world deployment of RL in educational institutions.
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.
INFORMATIZATION LEVEL ASSESSMENT FRAMEWORK AND EDUCATIONAL POLICY IMPLICATIONSijmpict
Seeing the informatization as a measure of the educational policy, we propose an informatization level
assessment framework and introduce a composite indicator – Education Informatization Index, calculated
as a weighted sum by applying the Rank-Order Centroid method for weight designation. Although it is made up of only two main categories (Educational Policy Implementation subindex and Educational Policy Creation subindex) and a total of six individual indicators, it captures well all the socio-political flows in the educational sphere in the Republic of Macedonia in the past five-year period. Namely, a slight decline of the value of the Education Informatization Index can be observed in 2013 in comparison to 2012, and in
2016 in comparison to 2015. Whereas only the value of the implementation subindex suffered in the first case, the value of the creation subindex suffered, as well, in the latter. Having in mind that policymakers can assess the improvement of a country over time, the methodology and the results can aid in making knowledgeable decisions or in establishing greater commitment to incorporating ICT into the education
system.
AI-Empowered Learning Models in Economy 5.0: Fostering Meaning Creation Beyon...IJCI JOURNAL
Economy 5.0 signifies a transformative era with profound implications for human development and education. This article examines emerging learning models underpinning Economy 5.0, exploring their impact on politics, personal growth, and global education ecosystems. The paradigm shift in economic evolution prompts a reevaluation of the nexus between politics and personal development, with learning acting as a catalyst for societal and individual transformation. A global perspective on AI in education policies underscores the geopolitical significance of AI-related technologies, reshaping knowledge dissemination through innovative learning platforms and Learning DAOs. Blockchain-based Agile Learning DAOs (BALD) are introduced as a mechanism that revolutionizes content creation with transparency and ethical considerations. Ethical learning, privacy, and addressing information bias emerge as central themes, with AI enhancing personhood. The roles of educators as guides remain pivotal. The future of learning in Economy 5.0 necessitates a balanced partnership between humanity and technology, grounded in ethics and human potential.
What is needed for successful Cloud Computing implementation in education?TheSoFGr
School on the Cloud (SoC),
ICT Key Action 3 European Project - With the support of the Lifelong Learning Programme of the European Union
Author: Karl Donert
Abstract: This deliverable is the publication based on research undertaken before the 3rd Summit Meeting of the School on the Cloud Project. It is based on literature research and surveys of project participants.
The publication considers the current needs for the development of Cloud Computing in European education. It examines some of the ongoing barriers to the implementation of Cloud Computing in education and explores leadership and policy issues.
The publication led to the development and launch of a Brussels Declaration for a Cloud Computing Strategy for European Education.
Blockchain and machine learning in education: a literature reviewIAESIJAI
There is a growing influence around the use of technology in education, with many solutions already being implemented and many others being explored. Using various forms of technology to assist the educational process has increased dramatically in the previous decade in education systems in many respects. Both machine learning and the blockchain have had a significant impact on education. The purpose of this study is to conduct a literature review on the application of machine learning and blockchain technology in educational institutions. Additionally, this study examines the potential applications, benefits, and challenges those educational institutions may face as a result of using machine learning and blockchain technologies. Using machine learning and blockchain in educational systems will have a positive impact on the entire educational process and student achievement. Researchers, academics, and practitioners will benefit from this study to focus on a wider range of educational applications and solve the related issues of machine learning and blockchain technology in the education sector.
Education Management Information System Role in Educational Development A Cas...ijtsrd
This study purposes to determine the impact of Education Management Information System variables such as system efficiency, service efficiency, information efficiency, and technical efficiency on the educational system in UNRWA schools in Lebanon. The authors employed an analytical descriptive approach by creating a questionnaire in Google Forms and disseminating it to the study sample participants via the WhatsApp application for data collection. The survey includes 1440 professional female and male teachers who demonstrated expertise in the 2022 academic year. However, the author only collected 128 questionnaires from teachers, which were studied and analyzed by the SPSS application. The findings of this analysis show that teachers have a good level of awareness of the factors of the information management system in general. The findings also revealed a statistically significant association between education information management system parameters and educational system development. Khalil Nabil Khalil | Mohammad Youssef Farhat "Education Management Information System Role in Educational Development: A Case Study of UNRWA Schools in Lebanon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd57387.pdf Paper URL: https://www.ijtsrd.com.com/computer-science/other/57387/education-management-information-system-role-in-educational-development-a-case-study-of-unrwa-schools-in-lebanon/khalil-nabil-khalil
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.
INFORMATIZATION LEVEL ASSESSMENT FRAMEWORK AND EDUCATIONAL POLICY IMPLICATIONSijmpict
Seeing the informatization as a measure of the educational policy, we propose an informatization level
assessment framework and introduce a composite indicator – Education Informatization Index, calculated
as a weighted sum by applying the Rank-Order Centroid method for weight designation. Although it is made up of only two main categories (Educational Policy Implementation subindex and Educational Policy Creation subindex) and a total of six individual indicators, it captures well all the socio-political flows in the educational sphere in the Republic of Macedonia in the past five-year period. Namely, a slight decline of the value of the Education Informatization Index can be observed in 2013 in comparison to 2012, and in
2016 in comparison to 2015. Whereas only the value of the implementation subindex suffered in the first case, the value of the creation subindex suffered, as well, in the latter. Having in mind that policymakers can assess the improvement of a country over time, the methodology and the results can aid in making knowledgeable decisions or in establishing greater commitment to incorporating ICT into the education
system.
AI-Empowered Learning Models in Economy 5.0: Fostering Meaning Creation Beyon...IJCI JOURNAL
Economy 5.0 signifies a transformative era with profound implications for human development and education. This article examines emerging learning models underpinning Economy 5.0, exploring their impact on politics, personal growth, and global education ecosystems. The paradigm shift in economic evolution prompts a reevaluation of the nexus between politics and personal development, with learning acting as a catalyst for societal and individual transformation. A global perspective on AI in education policies underscores the geopolitical significance of AI-related technologies, reshaping knowledge dissemination through innovative learning platforms and Learning DAOs. Blockchain-based Agile Learning DAOs (BALD) are introduced as a mechanism that revolutionizes content creation with transparency and ethical considerations. Ethical learning, privacy, and addressing information bias emerge as central themes, with AI enhancing personhood. The roles of educators as guides remain pivotal. The future of learning in Economy 5.0 necessitates a balanced partnership between humanity and technology, grounded in ethics and human potential.
What is needed for successful Cloud Computing implementation in education?TheSoFGr
School on the Cloud (SoC),
ICT Key Action 3 European Project - With the support of the Lifelong Learning Programme of the European Union
Author: Karl Donert
Abstract: This deliverable is the publication based on research undertaken before the 3rd Summit Meeting of the School on the Cloud Project. It is based on literature research and surveys of project participants.
The publication considers the current needs for the development of Cloud Computing in European education. It examines some of the ongoing barriers to the implementation of Cloud Computing in education and explores leadership and policy issues.
The publication led to the development and launch of a Brussels Declaration for a Cloud Computing Strategy for European Education.
Blockchain and machine learning in education: a literature reviewIAESIJAI
There is a growing influence around the use of technology in education, with many solutions already being implemented and many others being explored. Using various forms of technology to assist the educational process has increased dramatically in the previous decade in education systems in many respects. Both machine learning and the blockchain have had a significant impact on education. The purpose of this study is to conduct a literature review on the application of machine learning and blockchain technology in educational institutions. Additionally, this study examines the potential applications, benefits, and challenges those educational institutions may face as a result of using machine learning and blockchain technologies. Using machine learning and blockchain in educational systems will have a positive impact on the entire educational process and student achievement. Researchers, academics, and practitioners will benefit from this study to focus on a wider range of educational applications and solve the related issues of machine learning and blockchain technology in the education sector.
Education Management Information System Role in Educational Development A Cas...ijtsrd
This study purposes to determine the impact of Education Management Information System variables such as system efficiency, service efficiency, information efficiency, and technical efficiency on the educational system in UNRWA schools in Lebanon. The authors employed an analytical descriptive approach by creating a questionnaire in Google Forms and disseminating it to the study sample participants via the WhatsApp application for data collection. The survey includes 1440 professional female and male teachers who demonstrated expertise in the 2022 academic year. However, the author only collected 128 questionnaires from teachers, which were studied and analyzed by the SPSS application. The findings of this analysis show that teachers have a good level of awareness of the factors of the information management system in general. The findings also revealed a statistically significant association between education information management system parameters and educational system development. Khalil Nabil Khalil | Mohammad Youssef Farhat "Education Management Information System Role in Educational Development: A Case Study of UNRWA Schools in Lebanon" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-7 | Issue-3 , June 2023, URL: https://www.ijtsrd.com.com/papers/ijtsrd57387.pdf Paper URL: https://www.ijtsrd.com.com/computer-science/other/57387/education-management-information-system-role-in-educational-development-a-case-study-of-unrwa-schools-in-lebanon/khalil-nabil-khalil
E-LEARNING READINESS ASSESSMENT TOOL FOR PHILIPPINE HIGHER EDUCATION INSTITUT...IJITE
The growth of internet technologies changed learning strategies globally. The Philippines is no exemption.
Due to its usefulness and potential, E-learning is becoming popular. But before these benefits would be
enjoyed, it is very important for an institution to be assessed. This is to identify the needs and factors that
directly affect their readiness. This study presents a readiness assessment tool for Philippine Higher
Education Institutions. It also serves as a needs assessment instrument.
E-Learning Readiness Assessment Tool for Philippine Higher Education Institut...IJITE
The growth of internet technologies changed learning strategies globally. The Philippines is no exemption. Due to its usefulness and potential, E-learning is becoming popular. But before these benefits would be enjoyed, it is very important for an institution to be assessed. This is to identify the needs and factors that directly affect their readiness. This study presents a readiness assessment tool for Philippine Higher Education Institutions. It also serves as a needs assessment instrument.
E-Learning Readiness Assessment Tool for Philippine Higher Education Institut...IJITE
The growth of internet technologies changed learning strategies globally. The Philippines is no exemption.
Due to its usefulness and potential, E-learning is becoming popular. But before these benefits would be
enjoyed, it is very important for an institution to be assessed. This is to identify the needs and factors that
directly affect their readiness. This study presents a readiness assessment tool for Philippine Higher
Education Institutions. It also serves as a needs assessment instrument.
Framework for Securing Educational E-Government Serviceijcisjournal
Enhancement in technology is leading to a change in the way governments, individuals, institutions and
business entities provide quality services to the citizen. Today's education system plays crucial role for
developing cognizance in society so e-government service is obliged to integrate with educational system.
In this work we proposed a novel framework for integrating educational service within e-government
services. One of the main tasks of this paper is to explore or propose a Secure Examination Management
System (SEMS). The system has been designed using cryptographic primitives, which enables students to
take the exam from anywhere. The student is allowed to take the exam after he gives his necessary
authentication details. In SEMS, it is important to exclude false students while ensuring the privacy for the
honest students. It allows evaluators to share student examination papers for evaluation with proper
authentication. This is done using digital signatures, authentication and confidentiality provided by public
key cryptographic system.
The Big data concept emerged to meet the growing demands in analysing large
volumes of fast moving, heterogeneous and complex data, which traditional data
analysis systems could not manage further. The application of big data technology
across various sectors of the economy has aided better utilization of multiple data
collated and hence decision making. Organizations no longer base operations on
assumptions or constructed models solely, but can make inferences from generated
data. Educational organizations are more efficient and the pedagogical processes
more effective, when multiple streams of data can be collated from the various
personnel and facilitators involved. This data when analysed, maximizes the
performance of administrators andrecipients alike. This paper looks at the
components and techniques in bigdata technology, and how it can be implemented in
the education system for effective administration and delivery
Reinforcement learning is the next revolution in artificial intelligence (AI). As a feedback-driven and agent-based learning technology stack that is suitable for dynamic environments, reinforcement learning methodologies leverage self-learning capabilities and multi-agent potential to address issues that are unaddressed by other AI techniques. In contrast, other machine learning, AI techniques like supervised learning and unsupervised learning are limited to handling one task at a given time.
With the advent of Artificial General Intelligence (AGI), reinforcement learning becomes important in addressing other challenges like multi-tasking of intelligent applications across different ecosystems. The technology appears set to drive the adoption of AGI technologies, with companies futureproofing their AGI roadmaps by leveraging reinforcement learning techniques.
This report provides an analysis of the startups focused on reinforcement learning techniques across industries. To purchase the complete report visit https://www.researchonglobalmarkets.com/reinforcement-learning-startup-ecosystem-analysis.html.
Netscribes offers customizations to this report depending on your specific needs. To request a customized report, contact info@netscribes.com.
To purchase the full report, write to us at info@netscribes.com
THE USE OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES DEVELOPMENTEEIJ journal
Artificial intelligence has been an eye-popping word that is impacting every industry in the world. With the
rise of such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards continuous development. From the
definition, the welfare of human beings is the core of continuous development. Continuous development is
useful only when ordinary people’s lives are improved whether in health, education, employment,
environment, equality or justice. Securing decent jobs is a key enabler to promote the components of
continuous development, economic growth, social welfare and environmental sustainability. The human
resources are the precious resource for all nations. The high unemployment and underemployment rates
especially in youth is a great threat affecting the continuous economic development of many countries and
is influenced by investment in education, and quality of living.
THE USE OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES DEVELOPMENTEEIJ journal
Artificial intelligence has been an eye-popping word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards continuous development. From the definition, the welfare of human beings is the core of continuous development. Continuous development is useful only when ordinary people’s lives are improved whether in health, education, employment, environment, equality or justice. Securing decent jobs is a key enabler to promote the components of continuous development, economic growth, social welfare and environmental sustainability. The human resources are the precious resource for all nations. The high unemployment and underemployment rates especially in youth is a great threat affecting the continuous economic development of many countries and is influenced by investment in education, and quality of living.
A Literature Survey on Mobile-Learning Management SystemsAM Publications
In today’s digital era, due to development in wireless communication technology, proliferation of electronics gazettes usage especially smartphones is at peak point across the globe. Without smartphone, most of the individuals feel inconvenience in their daily routine India is not an exception for it, today; India has the second-biggest smartphone market in terms of active unique smartphone users, which crossed 220 million users. These innovations and developments in mobile technologies have an impact on education and learning systems which in turn resulted into the potential to develop an education system that enables individuals and groups to learn bypassing the time and place constraints. This paper gives a glimpse on characteristics, elements, security risks, design issues and challenges of mobile learning management system.
Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...IJAEMSJORNAL
Technology has advanced significantly from the analogue period to the digital era. Digital Learning Technology (DLT) is a learning paradigm based on the use of ubiquitous latest technologies, by using smart devices. It can be described as a learning environment that is assisted in daily life by wireless networks, mobile, and embedded computers. It aims to offer content and interaction to students wherever they are, at any time. The learning process has advanced thanks to the technology revolution, which has also fundamentally altered how knowledge is shared and learned. At present, there exist other frameworks too, but they are centered towards different paradigms, and point of view pertaining to DLT with its emphasis on Telecommunication Sector has not been taken into consideration. As, existing frameworks are centered towards different environments hence there exists a need to add dimensions of Empowered Learner, Digital Citizen, Knowledge Curator, Innovative Designer, Computational Thinker and Creator, Communicator & Global Collaborator. These have not been integrated together in existing available research. The study will ascertain level of knowledge of DLT and examined factors which affect the adoption rate, use, and role of DLT in telecoms setups. The results of this research will help create a framework that, if used in any academic or learning setting in a technology-based firm.
TVET Training Trends 2023 - Vocational TrainingShashank Adiga
I am delighted to welcome you to today's presentation on "TVET Training Trends 2023." Technical and Vocational Education and Training (TVET) plays a pivotal role in shaping a skilled workforce, fostering economic development, and addressing the ever-evolving demands of the job market. As we embark on this journey through the latest trends in TVET for the year 2023, we will explore the transformative changes taking place in the realm of vocational education. These trends are not merely theoretical concepts but are substantiated by recent statistics, indicating their relevance and impact on the global workforce. In this presentation, we will delve into four key trends and provide insightful analysis for each.
Soft computing SC is an emerging branch of computer science that is tolerant to imprecise and uncertain problems with partial truth. It is one of the front running technologies which is defining the future of computing. The different components of SC are used in the development of computing systems that can easily perform difficult tasks without the need of human beings. Education in soft computing is a mean for promoting science and innovation in a changing society. This paper is a primer on the applications of soft computing in education. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Soft Computing in Education: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49261.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/49261/soft-computing-in-education-a-primer/matthew-n-o-sadiku
Integration of Metaverse Technology Into the Police Academy Education and Tra...ijejournal
This study explores the potential of integrating the metaverse into police colleges for education and training purposes. The metaverse, considered a futuristic educational platform, is investigated for its viability. The study employs a two-pronged approach involving a review of 13 relevant studies and a survey to gather qualitative data on metaverse usage. The findings reveal a strong willingness to embrace the metaverse, dissatisfaction with current police education programs, inadequate preparedness of cadets after training, and various potential applications of the metaverse in police education, sports training, military training, professional policing, and data management. Recommendations include metaverse adoption in police institutions, thorough risk assessment, and comprehensive training for educators.
Integration of Metaverse Technology Into the Police Academy Education and Tra...ijejournal
This study explores the potential of integrating the metaverse into police colleges for education and training purposes. The metaverse, considered a futuristic educational platform, is investigated for its viability. The study employs a two-pronged approach involving a review of 13 relevant studies and a survey to gather qualitative data on metaverse usage. The findings reveal a strong willingness to embrace the metaverse, dissatisfaction with current police education programs, inadequate preparedness of cadets after training, and various potential applications of the metaverse in police education, sports training, military training, professional policing, and data management. Recommendations include metaverse adoption in police institutions, thorough risk assessment, and comprehensive training for educators.
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.
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The growth of internet technologies changed learning strategies globally. The Philippines is no exemption.
Due to its usefulness and potential, E-learning is becoming popular. But before these benefits would be
enjoyed, it is very important for an institution to be assessed. This is to identify the needs and factors that
directly affect their readiness. This study presents a readiness assessment tool for Philippine Higher
Education Institutions. It also serves as a needs assessment instrument.
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The growth of internet technologies changed learning strategies globally. The Philippines is no exemption. Due to its usefulness and potential, E-learning is becoming popular. But before these benefits would be enjoyed, it is very important for an institution to be assessed. This is to identify the needs and factors that directly affect their readiness. This study presents a readiness assessment tool for Philippine Higher Education Institutions. It also serves as a needs assessment instrument.
E-Learning Readiness Assessment Tool for Philippine Higher Education Institut...IJITE
The growth of internet technologies changed learning strategies globally. The Philippines is no exemption.
Due to its usefulness and potential, E-learning is becoming popular. But before these benefits would be
enjoyed, it is very important for an institution to be assessed. This is to identify the needs and factors that
directly affect their readiness. This study presents a readiness assessment tool for Philippine Higher
Education Institutions. It also serves as a needs assessment instrument.
Framework for Securing Educational E-Government Serviceijcisjournal
Enhancement in technology is leading to a change in the way governments, individuals, institutions and
business entities provide quality services to the citizen. Today's education system plays crucial role for
developing cognizance in society so e-government service is obliged to integrate with educational system.
In this work we proposed a novel framework for integrating educational service within e-government
services. One of the main tasks of this paper is to explore or propose a Secure Examination Management
System (SEMS). The system has been designed using cryptographic primitives, which enables students to
take the exam from anywhere. The student is allowed to take the exam after he gives his necessary
authentication details. In SEMS, it is important to exclude false students while ensuring the privacy for the
honest students. It allows evaluators to share student examination papers for evaluation with proper
authentication. This is done using digital signatures, authentication and confidentiality provided by public
key cryptographic system.
The Big data concept emerged to meet the growing demands in analysing large
volumes of fast moving, heterogeneous and complex data, which traditional data
analysis systems could not manage further. The application of big data technology
across various sectors of the economy has aided better utilization of multiple data
collated and hence decision making. Organizations no longer base operations on
assumptions or constructed models solely, but can make inferences from generated
data. Educational organizations are more efficient and the pedagogical processes
more effective, when multiple streams of data can be collated from the various
personnel and facilitators involved. This data when analysed, maximizes the
performance of administrators andrecipients alike. This paper looks at the
components and techniques in bigdata technology, and how it can be implemented in
the education system for effective administration and delivery
Reinforcement learning is the next revolution in artificial intelligence (AI). As a feedback-driven and agent-based learning technology stack that is suitable for dynamic environments, reinforcement learning methodologies leverage self-learning capabilities and multi-agent potential to address issues that are unaddressed by other AI techniques. In contrast, other machine learning, AI techniques like supervised learning and unsupervised learning are limited to handling one task at a given time.
With the advent of Artificial General Intelligence (AGI), reinforcement learning becomes important in addressing other challenges like multi-tasking of intelligent applications across different ecosystems. The technology appears set to drive the adoption of AGI technologies, with companies futureproofing their AGI roadmaps by leveraging reinforcement learning techniques.
This report provides an analysis of the startups focused on reinforcement learning techniques across industries. To purchase the complete report visit https://www.researchonglobalmarkets.com/reinforcement-learning-startup-ecosystem-analysis.html.
Netscribes offers customizations to this report depending on your specific needs. To request a customized report, contact info@netscribes.com.
To purchase the full report, write to us at info@netscribes.com
THE USE OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES DEVELOPMENTEEIJ journal
Artificial intelligence has been an eye-popping word that is impacting every industry in the world. With the
rise of such advanced technology, there will be always a question regarding its impact on our social life,
environment and economy thus impacting all efforts exerted towards continuous development. From the
definition, the welfare of human beings is the core of continuous development. Continuous development is
useful only when ordinary people’s lives are improved whether in health, education, employment,
environment, equality or justice. Securing decent jobs is a key enabler to promote the components of
continuous development, economic growth, social welfare and environmental sustainability. The human
resources are the precious resource for all nations. The high unemployment and underemployment rates
especially in youth is a great threat affecting the continuous economic development of many countries and
is influenced by investment in education, and quality of living.
THE USE OF ARTIFICIAL INTELLIGENCE IN HUMAN RESOURCES DEVELOPMENTEEIJ journal
Artificial intelligence has been an eye-popping word that is impacting every industry in the world. With the rise of such advanced technology, there will be always a question regarding its impact on our social life, environment and economy thus impacting all efforts exerted towards continuous development. From the definition, the welfare of human beings is the core of continuous development. Continuous development is useful only when ordinary people’s lives are improved whether in health, education, employment, environment, equality or justice. Securing decent jobs is a key enabler to promote the components of continuous development, economic growth, social welfare and environmental sustainability. The human resources are the precious resource for all nations. The high unemployment and underemployment rates especially in youth is a great threat affecting the continuous economic development of many countries and is influenced by investment in education, and quality of living.
A Literature Survey on Mobile-Learning Management SystemsAM Publications
In today’s digital era, due to development in wireless communication technology, proliferation of electronics gazettes usage especially smartphones is at peak point across the globe. Without smartphone, most of the individuals feel inconvenience in their daily routine India is not an exception for it, today; India has the second-biggest smartphone market in terms of active unique smartphone users, which crossed 220 million users. These innovations and developments in mobile technologies have an impact on education and learning systems which in turn resulted into the potential to develop an education system that enables individuals and groups to learn bypassing the time and place constraints. This paper gives a glimpse on characteristics, elements, security risks, design issues and challenges of mobile learning management system.
Adoption of Digital Learning Technology: An Empirical Analysis of the Determi...IJAEMSJORNAL
Technology has advanced significantly from the analogue period to the digital era. Digital Learning Technology (DLT) is a learning paradigm based on the use of ubiquitous latest technologies, by using smart devices. It can be described as a learning environment that is assisted in daily life by wireless networks, mobile, and embedded computers. It aims to offer content and interaction to students wherever they are, at any time. The learning process has advanced thanks to the technology revolution, which has also fundamentally altered how knowledge is shared and learned. At present, there exist other frameworks too, but they are centered towards different paradigms, and point of view pertaining to DLT with its emphasis on Telecommunication Sector has not been taken into consideration. As, existing frameworks are centered towards different environments hence there exists a need to add dimensions of Empowered Learner, Digital Citizen, Knowledge Curator, Innovative Designer, Computational Thinker and Creator, Communicator & Global Collaborator. These have not been integrated together in existing available research. The study will ascertain level of knowledge of DLT and examined factors which affect the adoption rate, use, and role of DLT in telecoms setups. The results of this research will help create a framework that, if used in any academic or learning setting in a technology-based firm.
TVET Training Trends 2023 - Vocational TrainingShashank Adiga
I am delighted to welcome you to today's presentation on "TVET Training Trends 2023." Technical and Vocational Education and Training (TVET) plays a pivotal role in shaping a skilled workforce, fostering economic development, and addressing the ever-evolving demands of the job market. As we embark on this journey through the latest trends in TVET for the year 2023, we will explore the transformative changes taking place in the realm of vocational education. These trends are not merely theoretical concepts but are substantiated by recent statistics, indicating their relevance and impact on the global workforce. In this presentation, we will delve into four key trends and provide insightful analysis for each.
Soft computing SC is an emerging branch of computer science that is tolerant to imprecise and uncertain problems with partial truth. It is one of the front running technologies which is defining the future of computing. The different components of SC are used in the development of computing systems that can easily perform difficult tasks without the need of human beings. Education in soft computing is a mean for promoting science and innovation in a changing society. This paper is a primer on the applications of soft computing in education. Matthew N. O. Sadiku | Uwakwe C. Chukwu | Abayomi Ajayi-Majebi | Sarhan M. Musa "Soft Computing in Education: A Primer" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-6 | Issue-2 , February 2022, URL: https://www.ijtsrd.com/papers/ijtsrd49261.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/49261/soft-computing-in-education-a-primer/matthew-n-o-sadiku
Integration of Metaverse Technology Into the Police Academy Education and Tra...ijejournal
This study explores the potential of integrating the metaverse into police colleges for education and training purposes. The metaverse, considered a futuristic educational platform, is investigated for its viability. The study employs a two-pronged approach involving a review of 13 relevant studies and a survey to gather qualitative data on metaverse usage. The findings reveal a strong willingness to embrace the metaverse, dissatisfaction with current police education programs, inadequate preparedness of cadets after training, and various potential applications of the metaverse in police education, sports training, military training, professional policing, and data management. Recommendations include metaverse adoption in police institutions, thorough risk assessment, and comprehensive training for educators.
Integration of Metaverse Technology Into the Police Academy Education and Tra...ijejournal
This study explores the potential of integrating the metaverse into police colleges for education and training purposes. The metaverse, considered a futuristic educational platform, is investigated for its viability. The study employs a two-pronged approach involving a review of 13 relevant studies and a survey to gather qualitative data on metaverse usage. The findings reveal a strong willingness to embrace the metaverse, dissatisfaction with current police education programs, inadequate preparedness of cadets after training, and various potential applications of the metaverse in police education, sports training, military training, professional policing, and data management. Recommendations include metaverse adoption in police institutions, thorough risk assessment, and comprehensive training for educators.
Similar to Reinforcement Learning in Education 4.0: Open Applications and Deployment Challenges (20)
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.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
Online aptitude test management system project report.pdfKamal Acharya
The purpose of on-line aptitude test system is to take online test in an efficient manner and no time wasting for checking the paper. The main objective of on-line aptitude test system is to efficiently evaluate the candidate thoroughly through a fully automated system that not only saves lot of time but also gives fast results. For students they give papers according to their convenience and time and there is no need of using extra thing like paper, pen etc. This can be used in educational institutions as well as in corporate world. Can be used anywhere any time as it is a web based application (user Location doesn’t matter). No restriction that examiner has to be present when the candidate takes the test.
Every time when lecturers/professors need to conduct examinations they have to sit down think about the questions and then create a whole new set of questions for each and every exam. In some cases the professor may want to give an open book online exam that is the student can take the exam any time anywhere, but the student might have to answer the questions in a limited time period. The professor may want to change the sequence of questions for every student. The problem that a student has is whenever a date for the exam is declared the student has to take it and there is no way he can take it at some other time. This project will create an interface for the examiner to create and store questions in a repository. It will also create an interface for the student to take examinations at his convenience and the questions and/or exams may be timed. Thereby creating an application which can be used by examiners and examinee’s simultaneously.
Examination System is very useful for Teachers/Professors. As in the teaching profession, you are responsible for writing question papers. In the conventional method, you write the question paper on paper, keep question papers separate from answers and all this information you have to keep in a locker to avoid unauthorized access. Using the Examination System you can create a question paper and everything will be written to a single exam file in encrypted format. You can set the General and Administrator password to avoid unauthorized access to your question paper. Every time you start the examination, the program shuffles all the questions and selects them randomly from the database, which reduces the chances of memorizing the questions.
An Approach to Detecting Writing Styles Based on Clustering Techniquesambekarshweta25
An Approach to Detecting Writing Styles Based on Clustering Techniques
Authors:
-Devkinandan Jagtap
-Shweta Ambekar
-Harshit Singh
-Nakul Sharma (Assistant Professor)
Institution:
VIIT Pune, India
Abstract:
This paper proposes a system to differentiate between human-generated and AI-generated texts using stylometric analysis. The system analyzes text files and classifies writing styles by employing various clustering algorithms, such as k-means, k-means++, hierarchical, and DBSCAN. The effectiveness of these algorithms is measured using silhouette scores. The system successfully identifies distinct writing styles within documents, demonstrating its potential for plagiarism detection.
Introduction:
Stylometry, the study of linguistic and structural features in texts, is used for tasks like plagiarism detection, genre separation, and author verification. This paper leverages stylometric analysis to identify different writing styles and improve plagiarism detection methods.
Methodology:
The system includes data collection, preprocessing, feature extraction, dimensional reduction, machine learning models for clustering, and performance comparison using silhouette scores. Feature extraction focuses on lexical features, vocabulary richness, and readability scores. The study uses a small dataset of texts from various authors and employs algorithms like k-means, k-means++, hierarchical clustering, and DBSCAN for clustering.
Results:
Experiments show that the system effectively identifies writing styles, with silhouette scores indicating reasonable to strong clustering when k=2. As the number of clusters increases, the silhouette scores decrease, indicating a drop in accuracy. K-means and k-means++ perform similarly, while hierarchical clustering is less optimized.
Conclusion and Future Work:
The system works well for distinguishing writing styles with two clusters but becomes less accurate as the number of clusters increases. Future research could focus on adding more parameters and optimizing the methodology to improve accuracy with higher cluster values. This system can enhance existing plagiarism detection tools, especially in academic settings.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
HEAP SORT ILLUSTRATED WITH HEAPIFY, BUILD HEAP FOR DYNAMIC ARRAYS.
Heap sort is a comparison-based sorting technique based on Binary Heap data structure. It is similar to the selection sort where we first find the minimum element and place the minimum element at the beginning. Repeat the same process for the remaining elements.
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
6th International Conference on Machine Learning & Applications (CMLA 2024) will provide an excellent international forum for sharing knowledge and results in theory, methodology and applications of on Machine Learning & Applications.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Student information management system project report ii.pdf
Reinforcement Learning in Education 4.0: Open Applications and Deployment Challenges
1. International Journal of Computer Science & Information Technology (IJCSIT) Vol 15, No 3, June 2023
DOI: 10.5121/ijcsit.2023.15304 47
REINFORCEMENT LEARNING IN EDUCATION 4.0:
OPEN APPLICATIONS AND DEPLOYMENT
CHALLENGES
Delali Kwasi Dake
Department of ICT Education, University of Education, Winneba, Ghana
ABSTRACT
Education 4.0 involves adopting technology in teaching and learning to drive innovation and growth
across academic institutions. Artificial Intelligence and Machine Learning are frontrunners in Education
4.0, having already impacted diverse sectors globally. Since the COVID-19 pandemic, the conventional
method of teaching and learning has become unpopular among institutions and is currently being replaced
with intelligent educational data pattern identification and online learning. The teacher-
centredpedagogical paradigm has significantly shifted to a learner-centredpedagogywith the emergenceof
Education 4.0. Reinforcement Learning has been deployed successfully in diverse sectors, and the
educational domain should not be an exemption. This survey discusses Reinforcement Learning, a
feedback-based machine learning technique, with application modules in the academicfield. Each module
is analysed for the state-action-reward implementation policies with relevant features that define
individual use cases. The survey primarily examined the classroom, admission, e-learning, library and
game development modules. In addition, the survey heightened the foreseeable challenges in the real-world
deployment of Reinforcement Learning in educational institutions.
KEYWORDS
Education 4.0, Reinforcement Learning, Smart Campus, Intelligent Objects, Artificial Intelligence,
Machine Learning
1. INTRODUCTION
Education which involves teaching and learning, has seen growth in varying pedagogy and
technological integration in recent years[1]. As higher educational institutions (HEIs) prioritised
a learner-centred approach[2, 3] to pedagogy and align institutional policies with the fourth
industrial revolution (Industry 4.0), all disruptive technologies must be fully exploited. Disruptive
technologies are emerging innovations with cutting-edge features to drive new markets and
applications [4]. Artificial Intelligence (AI), a focal point of this review, is a key driver in the
fourth industrial revolution, and its integration transcends diverse application domains, including
education.
HEIs has seen a staggering increase in learner enrollment globally. The UNESCO (2020) report
shows that student enrollment doubled in the last two decades, and the gross enrollment rate
increased to 40% from 19% between 2000 and 2020. The quality of education, even as access to
HEIs rises, needs to be protected. The high quality expected is usually seen when educational
resources are sufficient, class sizes are minimal, learners' supervision is intensive, the student-
teacher ratio is standard and governmental policies are progressive [5]. The struggle to achieve
quality education amidst enrollment increases was exacerbated further by the COVID-19
pandemic[6].In March 2020, 1.5 billion students globally were affected by the temporal closure of
schools (UNESCO, 2020). At the time of closure, COVID-19's policy measures to prevent the
spread of the virus, including social and physical distancing, were strictly enforced [7].During the
2. International Journal of Computer Science & Information Technology (IJCSIT) Vol 15, No 3, June 2023
48
pandemic's peak, in-person teaching and learning were discontinued, and most HEIs hastily
migrated to eLearning platforms[8].The devastating pandemic exposed HEIs to technological and
infrastructural deficits but provided an opportunity for HEIs to adopt proactive policies and
integrate disruptive technologies in the educational ecosystem.
The following outlines guide the survey:
A brief discussion of RL algorithms
Open application of RL in education
Deployment challenges of RL in education
2. ARTIFICIAL INTELLIGENCE, INDUSTRY 4.0 AND EDUCATION 4.0
Human intelligence forms the foundation for Artificial Intelligence (AI) and Machine Learning
(ML), where algorithms perform intelligent tasks with features that mimic human cognitive
functions[9, 10].While AI involves computers' ability to emulate human thought, ML uses
algorithms to learn from data and automatically expose hidden patterns[11]. Industry 4.0 and
Education 4.0 has AI and ML as primary technologies but with diverse application deployments.
Industry 4.0 enables interconnected computers, intelligent machines, and smart objects to
communicate and effect data-driven decisions with little or no human involvement[12, 13].
Industry 4.0 are cyber-physical systems with disruptive technologies characterised by higher
productivity, efficiency, analytics and automation [13]. In the smart manufacturing value chain,
the lifecycle of products and supply networks are vertically and horizontally integrated with
modern control systems, sensors and technology.
Education 4.0, which is essential to this study, is closely related to Industry 4.0. Education 4.0 is
a new paradigm which redefines teaching and learning to match the needs and technological
advancement in Industry 4.0. The innovation-producing education, Education 4.0, transitioned
from Education 1.0 as the download education to Education 2.0 as the open-access education and
Education 3.0 as the knowledge-producing education[12, 14]. Education 4.0 contains 21st
-century
skills with collaboration, innovation, creativity, communication, critical thinking and problem-
solving as learning strategies [15]. The deployment of technology, especially 3D printing, AI,
Virtual and Augmented Reality (VAR) and the Internet of Things (IoT) in Education 4.0 will
form the bases for the smart classrooms of the future. The AI aspect remains pivotal to this study,
specifically ML in Education 4.0.
3. MACHINE LEARNING
In ML, there are three main categories of algorithms: Supervised, Unsupervised and
Reinforcement Learning[16]. Supervised learning uses a labelled dataset where the predictive
output of the training data is tagged with the correct answer [16, 17]. Successful prediction is
only possible in supervised implementations when enough data is trained and tested for
respective algorithms. In contrast, unsupervised learning uses unlabelled dataset to deduce
clusters and expose hidden similarity patterns [16, 17].The clusters formed after unsupervised
implementations become predictive labels for classification. Reinforcement Learning, the focal
point of this review, involves actions exerted in an environment with rewards as guiding
references to the agents[16 -18].
3. International Journal of Computer Science & Information Technology (IJCSIT) Vol 15, No 3, June 2023
49
3.1. Reinforcement Learning
In Reinforcement Learning (RL) model, software agents play a central role in changing the state
of the environment from 𝑠 to 𝑠′ using action 𝑎 and receive a reward, 𝑟. As illustrated in Figure 1,
the agent uses the trial and error principle with exploitation-exploration trade-off to select actions
for future or immediate rewards [19, 20].
Figure 1. Reinforcement Learning [18]
In modelling the state-action-reward of a RL agent and its policy 𝜋, the Markov Decision Process
(MDP) for decision-theoretic planning de facto standard formalism is utilised as shown in
Algorithm 1 [21].
Algorithm 1 Markov Decision Process (MDP)
An MDP is a 5-tuple (𝑆, 𝐴, 𝑃, 𝑅, 𝛾), where;
𝑆 is a set of states
𝐴 is a set of actions
𝑃 (𝑠, 𝑎, 𝑠′) is the probability that action 𝑎 in state 𝑠 at time 𝑡
will lead to state 𝑠^′ at time 𝑡 + 1
𝑅 (𝑠, 𝑎, 𝑠′) is the immediate reward received after a transition
from state 𝑠 to 𝑠′, due to action 𝑎
𝛾 is the discounted factor which is used to generate a discounted
reward
3.1.1. Reinforcement Learning Algorithms
RL algorithms are classified into two main classes: model-based (indirect) and model-free
(direct) methods. Further, we have single and multi-agent RL algorithms based on the number of
agents [22, 23]. Model-based algorithms maintain the agent's transition dynamics by utilising a
model that influences future state, action, and reward. Model-based RL algorithms struggle to
achieve asymptotic application performances but are data-efficient[24]. Model-free algorithms
use trial and error for optimal actions without a model. Model-free algorithms are common with
Q-learning and policy optimisation approaches [25]. Even though model-free methods are data-
dependent, they have low computational complexity [25].
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The single-agent RL (SARL), as depicted in Figure 2, has only one agent interacting in the
environment. Q-learning are off-policy and value-based algorithm that improves its policy in a
discrete action space. Unlike Q-learning, the State-Action-Reward-State-Action (SARSA) are on-
policy algorithms that use the same policy for acting and updating the value function during
training [26, 27].
Figure 2. Single-agent RL
The Deep Q-Network (DQN) and the Deep Deterministic Policy Gradient (DDPG) SARL
algorithms have become increasingly prominent due to artificial neural networks. The DQN is a
combination of Q-learning and deep neural networks for discrete action space; whiles DDPG is
for continuous action space [26, 27].
The multi-agent RL (MARL), as shown in Figure 3, has multiple agents interacting with the
environment. In a MARL environment, the agents can be either cooperative or competitive.
Cooperative agents help to improve group utility, whiles competitive agents are selfish [22, 23].
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Figure 3. Multi-agent RL
The multi-agent Deep Deterministic Policy gradient (MADDPG) is the most dominant MARL
algorithm that extends the DDPG algorithm for continuous action space [28]. The MADDPG is
based on DDPG's actor-critic model, where the actor is the policy network and the critic the Q-
value for training the actor network[29].
4. OPEN APPLICATION OF REINFORCEMENT LEARNING IN EDUCATION
This section focused on the relevant application integration of RL in education. The objective is
to propose each module and examine relevant functionalities.
4.1. RL- Based Robots in Classroom
The traditional classroom is an in-person mode of teaching and learning where the instructor
regulates the flow of knowledge and information[30]. The traditional classroom has a fixed
space, usually with a formal arrangement of tables and chairs. This conventional space improves
active learning where students are attentive during lesson delivery with proactive feedback
sessions and greater class engagements[31]. Even though the traditional classroom approach to
teaching and learning is recently underutilised, especially with the devastating COVID-19
pandemic, its benefits are largely significant. One beneficial aspect of a conventional classroom
is the growth of interpersonal relationships. Secondly, some relevant courses, including aspects of
medicine, engineering, agriculture and mathematics, are best taught in a traditional classroom
setting. RL-based physical robots embedded with RL algorithms has interesting modules in a
conventional classroom. The robot is either a learner robot or an instructor robot that replaces the
teacher in the classroom. As shown in Figure 4, the learner robotsare personal agents fitted in
the classroom for continuous monitoring of student's performance and behaviour. The robot using
RL algorithms understands the learner and provides responsive solutions with detailed, intelligent
analytics for the instructor, parents and school authorities. In a classroom, the state which forms
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the environment varies from one student to another. As the robots perceive the state of a student,
an action is recommended by the robots. The implemented action leads the student to a new state
and the robot has a reward. The reward can be positive or negative. A positive reward indicates
that, the action of the robot changes the state of the learner to a better one. A negative reward is a
punishment which indicates a poor response from the student after the action was implemented.
Figure 4. Learner robot in the classroom
Until the robot fully understands the learner using exploration and exploitation, the state of the
student and the reward to the robot changes frequently. As depicted in equation 1, each student
will have a state, 𝑠 at every time slot 𝑡. A student's state in the classroom varies, as shown in
Figure 5. Examples include sad, happy, angry, quiet, attentive, noisy, etc.
𝑠𝑡𝑎𝑡𝑒 𝑠𝑝𝑎𝑐𝑒 = 𝑠1 𝑡 𝑠2 𝑡 𝑠3 𝑡 ………… 𝑠𝑛 𝑡 (1)
The action space determines the new state of the learner. In equation 2, the robot's action will
change the student's state to a new one, 𝑠′. As illustrated in Figure 5, a robot’s actions include
video, animation, text, temperature, equation etc.
𝑎𝑐𝑡𝑖𝑜𝑛 𝑠𝑝𝑎𝑐𝑒 = 𝑎1 𝑡 𝑎2 𝑡 𝑎3 𝑡 ………… 𝑎𝑛 𝑡 (2)
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Figure 5. Examples of state-action activities
4.2. Educational Game Development for Students
Game development is one of the essential applications of Reinforcement Learning. The use of RL
algorithms for game development gained popularity when DeepMind’s AlphaGo-trained RL
agent in 2016 defeated Lee Se-dol, the 18-times Go player champion in the world [32].In the
educational context, Serious Games[33] are entertaining, engaging, and immersive, with
embedded problem-solving design to improve students' learning strategies. The Serious Games
modules have rewards based on challenges. In addition, such games support multiplayer
participation, which enforces the concept of collaborative learning. Games played in a group are
interesting, competitive, and engaging, accelerating the appreciation of specific educational
content[34]. The primary attributes of single-agent and multi-agent RL algorithms, including
state, action and reward, has influenced their usage in educational game programming[35].A RL
technique in educational game development is even more gratifying since it uses unlabeled
training data in its state-action-reward paradigm [35, 36].Dobrovsky et al.,[37] RL-based Serious
Games framework, as shown in Figure 6, has fundamental elements in educational game
development.
Figure 6. RL-based Serious Game Framework
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The generic framework [37] offers default functionality and components for RL-based
educational games. The framework's primary component is the controller, referred to as the game
interface. The controller’s major function is to receive learner actions and execute control. The
domain expert implements the interaction and learning acquisition level and sets the learning
objective. The game state of the player forms the learning environment, which is crucial in
evaluating the learning objective of the game. The reward function of the learner is based on the
actions taken and the positive development in changing the state, 𝑠, to a new state, 𝑠′.
4.3. Intelligent Admission Recommender System
Admissions are central to every educational institution. The objective of most institutions is to
admit the best students into the appropriate programmes. Secondly, institutions are supposed to
enrol the right number of students based on the availability of facilities and instructors.
Furthermore, the admissions team should have firsthand information of the behaviour of students
admitted for proactive counselling. The manual admission procedure is problematic, especially in
a twenty-first century dominated by AI applications. In Education 4.0, technological integration
and automation in learner admission are linked directly to Industry 4.0 to reduce undesirable
unemployment figures[38]. The intelligent admission system, as illustrated in Figure 7, has an
input component that forms the new students' state. The new students' state includes historical
behaviour, grades and other relevant information. The agent that serves as the central engine of
the framework receives the input and recommends a policy-informed action. The output modules
guide the action recommended by the agent.
Figure 7. Intelligent Students Admission Framework
The output modules has four essential components. The past and current student analytical data
module containsintelligent information about past and present students. This analytical module
includes the profession of past students, grade performance, programmes studied, behaviour and
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their admission data. The available school resources module informs the admission teamof the
maximum number of studentswho can be admitted to a particular programme. The number of
instructors module also helps the admission team to know the availability of academic staff in the
institution for quality learner education. This helps enrol the right number of new students to
different programmes. Available programme module guides in course availability and the number
of applicants left. The agent receives a positive reward when the student is admitted into the right
programme based on available analytics but a punishment when the recommendation by the agent
results in admission problems.
4.4. Smart Library Management
Libraries are central to every educational institution. Primary, libraries support teaching and
learning with a collection of books, journals, digital content and media that are accessible easily.
However, the increasing number of students and changing pedagogy have necessitated the
incorporation of technology into library management[39].The modern-day Internet of Things
(IoT) enabled library consists of sensing technologies for object connectivity in tracking library
facilities and generating big data for analytics. Book recommendation, seat management, stock
control, security and user profiles in a modern-day library are integrated with AI. The intelligent
library goes beyond the physical space of learning to online mediums and analytics that drive
personalised learning[40]. RL agents in a library can be deployed as physical robots embedded
with RL algorithms or as software agents on smart devices.
Figure 8. Intelligent Library Management
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As illustrated in Figure 8, the RL-based intelligent library management consists of three primary
users who form the state and are part of the environment. The librarian plays a vital role in
managing library resources and library users. The emotional state of the librarian varies from
happy to sad mood. In addition, the RL agent receives other relevant state information from the
librarian concerning stock management, shelve management, behaviour and attendance. The RL
agent recommends actions using past library analytics, user and stock management to determine
the new state of the librarian. The effect of the proposed action determines the reward of the RL
agent. The user module of the intelligent library system is fundamental in promoting learning.
The library user has diverse states, including behavioural, preferential and academic. The RL
agent recommends books to the user based on the past library analytics of the user. In addition,
the seating space based on availability, user preference and behavior is also enforced as an action.
The new state of the user after the recommended action varies from positive to negative
feedback, guiding the RL agent on the next action. Security in the library pertains to theft and
other dangers threatening library resources and users. The awareness and state of the security
personnel is central to the environment. Past library analytics concerning security lapses and
detection also forms a significant component in the library environment. The RL agent
recommends actions to the security personnel based on past library analytics of user behaviour
that leads to theft and security signals that should not be ignored. The security personnel enforces
the recommended action with a changing state. The actions of the security personnel can be
positive or negative. This informs the next recommendation by the RL agent.
4.5. Automation of E-learning Platforms
The COVID-19 pandemic has increased the use of electronic learning platforms globally[41,
42]Whether it's an online learning platform or a learning management system, incorporating
intelligent analytics into previously intractable learner patterns is critical in modern-day
education. The proliferation of the internet, affordable digital devices, user-friendly learning
management systems (LMS) and multimedia has strengthened the adoption of e-learning
platforms in educational institutions. The adoption of e-learning also provides Big Data sources,
which are essential in developing an analytic engine that covers various aspects of the learning
experience.Integrating RL-based software agents in e-learning platformsgoes beyond
conventional analysis to learning perfection without class labels and feature selection. As
illustrated in Figure 9, the RF-based intelligent e-learning system has two primary users, student
and teacher.
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Figure 9. Intelligent E-learning System
The student has diverse input states based on interaction with the e-learning system. The state of
the student and the teacher in an e-learning system changes significantly after interacting with the
system. As teachers set up educational content that seeks to cover the curriculum, the need for
intelligent analytics is relevant for personalised learning and collaborative studies. The modern-
day instructor relies on analytics from learner interactions with the e-learning modules for
reflective practice.The agent's action in an intelligent E-learning system is based on four
modules: the past analytics module, the learning module, the evaluation module and the content
module. The content module includes multimedia elements such as text, animation, video, audio
and images. The agent's action on content selection depends on the past analytics module and the
state of the learner and teacher. The evaluation module contains assessment strategies for specific
learners based on the current state and the past analytics module. The evaluation engine
determines learners preferred means of assessment to improve strengths and suggest areas of
improvement. The agent receives a positive reward when the suggested evaluation leads to
performance and skills enhancement. The learning module can be teacher-centred, learner-
centred and collaborative based on the past analytics modules and the learner's current state. The
agent's goal is to recommend the best learner module for respective students and create analytics
based on rewards and punishment from learner performances. The RF-based intelligent e-
learning system agent proposes actions based on the four modules after receiving state inputs
from the learner and teacher. Implementing the agent's action based on the modules leads to an
improved or worst state for the learner and teacher.The agent receives a reward or punishment
which informs the next action using the exploration and exploitation RL mechanism.
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5. DEPLOYMENT CHALLENGES OF RL IN EDUCATION
Reinforcement Learning, as discussed, has application deployments that will advance Education
4.0 with intelligent AI integrations and modules. However, RL implementation has foreseeable
challenges that need solutions generally and in the context of education.
5.1. Availability of Data
Reinforcement Learning algorithms perform better with large data samples [43, 44]. Exploration
in RL increases the chances of higher agents' reward, but this trade-off with the exploitation
mechanism is data intensive. Furthermore, RL agents learn incrementally with data until mastery
before proposing intelligent and efficient actions. In an uncertain environment with sequential
decision enforcement, RL algorithms are data-dependent and rely mainly on the state space data
definitions. In educational institutions, effective data aggregation remains a problem, especially
in developing countries[45]. Aside the volumes of data expected, the variety of data requires well
structured record keeping to enable state-action-reward policy definitions with the right RL
algorithm. The velocity of data generation also requires best data storage architectures to avoid
missing values that affects the performance of the RL algorithms.
5.2. Partially Observable Environment
The non-stationary nature of objects in some detectable environments has made it impossible for
RL agents to observe some state space features fully. Partially Observable Markov Decision
Process (POMDP) is a crucial challenge in modelling RL environment and, if not addressed, can
lead to agents performing poorly in the environment[46].In POMDP, the agents' observation
𝑥 ∈ 𝑋 is separate from the state with 𝑂(𝑥 ∣ 𝑠) as observation function with the probability of
observing 𝑥 in the environment 𝑠 [43]. Even though fully observable environments are formalised
as MDPs, most environments are partially observable, where it is difficult for an agent to obtain
knowledge of the environment's full state or transition probabilities. In an educational setting, the
agent cannot fully observe the environment. In RL-based robots in a classroom module, for
instance, there are no observations on the mental state of the learners but rather sensors that pick
the facial emotions. Addressing POMDP in RL is an ongoing research challenge, with several
proposed models stacking observation as full state.
5.3. Curse of Dimensionality
In RL modelling, the state and action space representation grows exponentially when exploring
optimal control in high-dimensional spaces, and the phenomenon is described as Bellman’s curse
of dimensionality[47]. The curse of dimensionality is unavoidable in an environment with diverse
and growing features and data. As the dimensionality increases, more data points are needed to
model good performances of the environment, and the situation is considered a curse. The curse
of dimensionality has negative implications on memory and predominantly affects the effective
deployment of RL applications[48]. In an educational setting, the environment monitored has
various features and data that increase the state and action representation of the RL agent. The
curse of dimensionality is an ongoing research difficulty in RL, and efficient solutions are
required before real-world application deployments.
5.4. Coordination in Multi-Agents
Whether cooperative or competitive, multi-agents in RL have significant advantages, including
faster task execution, performance improvement, resource sharing and intrigue competition.
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Aside from scalability that leads to the curse of dimensionality [23], coordination among agents
for cooperative and competitive tasks is problematic. In multi-agent systems (MASs), the goal is
for the agents to become autonomous in task execution. The independent agents in MASs are also
expected to communicate and share ideas with other agents to achieve a goal. Even with
centralised training and decentralised execution, improper agent coordination can lead to the
selfish depletion of MASs resources and the crashing of MAS applications.
6. CONCLUSION
Education 4.0 is driving derivatives to transform the future of teaching and learning with
advanced and emerging technologies. Coupled with the tragic COVID-19 pandemic, educational
reforms and policies have tilted to technological integration that will discontinue conventional
educational applications. Even though traditional educationhas significant advantages, the need
for intelligent analytics and predictive education cannot be over-emphasised. Supervised and
unsupervised ML algorithm has been discussed, and applications are deduced in literature to
cover diverse domain aspects of education. The challenge, however, is the discussion and
implementation of Reinforcement Learning, which involves incremental learning of agents in an
environment to the mastery level.
Reinforcement Learning leads to autonomy in educational applications and modules where
personalised, collaborative and reflective practices are deduced with agents mastering their
environment. The survey discussed critical deployment areas of RL learning in education with
diagrammatic modules that identify state-action-reward policies of the agents. The modules
discussed include RL agents in the classroom, admission, game development, library and e-
learning system. These modules are central in Education 4.0 and relevant to all stakeholders in
education. Aside from the application modules, the anticipated challenges to successful RL
algorithm implementation in educational institutions were also considered.
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16. International Journal of Computer Science & Information Technology (IJCSIT) Vol 15, No 3, June 2023
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AUTHOR
Delali Kwasi Dake, PhD is a Senior Lecturer in the department of ICT Education at the
University of Education, Winneba, Ghana. His research interests include educational
data mining, artificial intelligence, sentiment analysis, software-defined networking, and
Internet of Things.