Semantically Enchanced Personalised Adaptive E-Learning for General and Dysle...Eswar Publications
E-learning plays an important role in providing required and well formed knowledge to a learner. The medium of e- learning has achieved advancement in various fields such as adaptive e-learning systems. The need for enhancing e-learning semantically can enhance the retrieval and adaptability of the learning curriculum. This paper provides a semantically enhanced module based e-learning for computer science programme on a learnercentric perspective. The learners are categorized based on their proficiency for providing personalized learning environment for users. Learning disorders on the platform of e-learning still require lots of research. Therefore, this paper also provides a personalized assessment theoretical model for alphabet learning with learning objects for
children’s who face dyslexia.
AN ADAPTIVE REUSABLE LEARNING OBJECT FOR E-LEARNING USING COGNITIVE ARCHITECTUREacijjournal
Nowadays, a huge amount of ambiguous e-learning materials are available in World Wide Web
irrespective of various objectives. These digital educational resources can be reused and shared from
centralized online repository and it will avoid the redundant learning material. The main goal is to design
consistent adaptable e-learning course material for web-based education system with emphasis on the
quality of learning. This can be done by organizing learning object in a prescribed manner and it can be
reused in feature. Such reusable learning objects are enhanced further to become adaptive reusable
learning objects that are virtually cognitive and responsive towards the specific needs of the user/customer.
This paper proposes the cognitive architecture to offer an adaptive reusable objects (RLO) based on
individual profile of e-learner besides their cognitive behaviour while learning.
Big data integration for transition from e-learning to smart learning framework eraser Juan José Calderón
Big data integration for transition from e-learning to smart learning framework . Dr. Prakash Kumar Udupi Mr. Puttaswamy Malali Mr. Herald Noronha Department of Computing Department of Computing Department of Computing Middle East College Middle East College Middle East College .
Learner Ontological Model for Intelligent Virtual Collaborative Learning Envi...ijceronline
An enacting approach to intelligent virtual collaborative learning model is explored through the lens of critical ontology. This ontological model enables to reuse of the domain knowledge and to make the knowledge explicitly available to the agent working as an Expert System, which uses the operational knowledge in collaborative learning environment. This ontological model used by the agent to identify the preliminary competency level of the user. This environment offers personalized education to each learner in accordance with his/her learning preferences, and learning capabilities. Here the factors considered to identify the learning capability taken are demographic profile, age, family profile, basic educational qualification and basic competency scale. The conception of heuristics is then used by the agent to determine the effectiveness of the learner by referring the different parameters of the learner available in the ontological model.To help getting over this, the paper describes the experience on using an ontological model for collaborative learning to relate and integrate the history of the learner by maintaining the history of learner in collaborative learning environment that will be used by the Multi-Objective Grey Situation Decision Making Theory to infer the understanding level of user and produces the conditional content to the user
This paper aims to provide main advance in the delivering techniques which are adapting to learner using multiagent system. Including models and the corresponding methods.It focuses on both datamining and e-learning. Multiagent system is a computer programming based system which is composed by multiple interacting computer programs.MAS can be used to solve the program that are complex or seems impossible for an indivisual program to solve.Multiagent system composed of various entities that have different information or diverging interest.In multiagent system agents are computer program that act on behalf of the users to solve a computer program.
Towards a new ontology of the Moroccan Post-baccalaureate learner profile for...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Semantically Enchanced Personalised Adaptive E-Learning for General and Dysle...Eswar Publications
E-learning plays an important role in providing required and well formed knowledge to a learner. The medium of e- learning has achieved advancement in various fields such as adaptive e-learning systems. The need for enhancing e-learning semantically can enhance the retrieval and adaptability of the learning curriculum. This paper provides a semantically enhanced module based e-learning for computer science programme on a learnercentric perspective. The learners are categorized based on their proficiency for providing personalized learning environment for users. Learning disorders on the platform of e-learning still require lots of research. Therefore, this paper also provides a personalized assessment theoretical model for alphabet learning with learning objects for
children’s who face dyslexia.
AN ADAPTIVE REUSABLE LEARNING OBJECT FOR E-LEARNING USING COGNITIVE ARCHITECTUREacijjournal
Nowadays, a huge amount of ambiguous e-learning materials are available in World Wide Web
irrespective of various objectives. These digital educational resources can be reused and shared from
centralized online repository and it will avoid the redundant learning material. The main goal is to design
consistent adaptable e-learning course material for web-based education system with emphasis on the
quality of learning. This can be done by organizing learning object in a prescribed manner and it can be
reused in feature. Such reusable learning objects are enhanced further to become adaptive reusable
learning objects that are virtually cognitive and responsive towards the specific needs of the user/customer.
This paper proposes the cognitive architecture to offer an adaptive reusable objects (RLO) based on
individual profile of e-learner besides their cognitive behaviour while learning.
Big data integration for transition from e-learning to smart learning framework eraser Juan José Calderón
Big data integration for transition from e-learning to smart learning framework . Dr. Prakash Kumar Udupi Mr. Puttaswamy Malali Mr. Herald Noronha Department of Computing Department of Computing Department of Computing Middle East College Middle East College Middle East College .
Learner Ontological Model for Intelligent Virtual Collaborative Learning Envi...ijceronline
An enacting approach to intelligent virtual collaborative learning model is explored through the lens of critical ontology. This ontological model enables to reuse of the domain knowledge and to make the knowledge explicitly available to the agent working as an Expert System, which uses the operational knowledge in collaborative learning environment. This ontological model used by the agent to identify the preliminary competency level of the user. This environment offers personalized education to each learner in accordance with his/her learning preferences, and learning capabilities. Here the factors considered to identify the learning capability taken are demographic profile, age, family profile, basic educational qualification and basic competency scale. The conception of heuristics is then used by the agent to determine the effectiveness of the learner by referring the different parameters of the learner available in the ontological model.To help getting over this, the paper describes the experience on using an ontological model for collaborative learning to relate and integrate the history of the learner by maintaining the history of learner in collaborative learning environment that will be used by the Multi-Objective Grey Situation Decision Making Theory to infer the understanding level of user and produces the conditional content to the user
This paper aims to provide main advance in the delivering techniques which are adapting to learner using multiagent system. Including models and the corresponding methods.It focuses on both datamining and e-learning. Multiagent system is a computer programming based system which is composed by multiple interacting computer programs.MAS can be used to solve the program that are complex or seems impossible for an indivisual program to solve.Multiagent system composed of various entities that have different information or diverging interest.In multiagent system agents are computer program that act on behalf of the users to solve a computer program.
Towards a new ontology of the Moroccan Post-baccalaureate learner profile for...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Development of Intelligent Multi-agents System for Collaborative e-learning S...journalBEEI
The aim of this paper is the introduction of intelligence in e-learning collaborative system. In such system, the tutor plays an important role to facilitate collaboration between users and boost less active among them to get more involved for good pedagogical action. However, the problem lies in the large number of platform users, and the tutor tasks become difficult if not impossible. Therefore, we used fuzzy logic technics in order to solve this problem by automating tutor tasks and creating an artificial agent. This agent is elaborate in basing on the learners activities, especially the assessment of their collaborative behaviors. After the implementation of intelligent collaborative system by using Moodle platform, we have tested it. The reader will discover our approach and relevant results.
A Method of Designing Student Model in Ubiquitous Environment ijujournal
Context-aware ubiquitous learning combines context-awareness with wireless and mobile technologies to
observe the situation of students in the real world and provides personalized guidance accordingly.
Student Model creates student's history logs automatically and maintains history of subject content
requested. It also offers information on student's hardware capabilities, students preferences, knowledge
level and student status. This information can be utilized to respond to new student's request from previous
similar request. A Ubiquitous student model aims to identify student’s needs, characteristics and situations.
In this paper, we have proposed a method of designing student model, that provides personalized subject
content adaptation.
Information security approach in open distributed multi agent virtual learnin...ijcsit
This paper presented the main information, security problems and threats in open multi-agent distributed
e-learning information systems and Proposed various approaches to solve information security attacks in
virtual learning environment using service oriented architecture which based on multi-agent information
systems architecture, the solution on the multi-agent learning information system implementation based on
the implementation of two types of systems the first system with the centralized mobile agent information
security management and the second system with decentralized mobile agents security management, and
proposed the migration behavior simulation for their active software components (software agents) .
Ubiquitous learning allows students to learn at any time and any place. This educational activity is possible to be performed by various types of students and to operate on various devices, networks and environments, where the system understands the study pattern and behaviour of the students. Adaptivity plays an important role in Ubiquitous learning, aiming at providing students with adaptive and
personalized learning material and information at the right place and the right time. Student's history logs
is automatically created and maintained by the student history database that maintains student's history of subject content requested. This offers information on student's hardware capabilities, students preferences,
knowledge level and student status. This information can be utilized to respond to new student's request with subject content created from previous similar request. A Ubiquitous learning student model aims to identify students needs, characteristics and situations. We use C-IOB (Context-Information, Observation
and Belief) model to process the context of the student, formulate the observation and use the observations
to generate beliefs. The belief generated by C-IOB model is based on adaptation decision and subject analysis from student history database, which are able to detect the real-world learning status of students.
Designed method has been illustrated for students with divergent knowledge levels, by considering complete course material of a subject, Communication Protocols offered at graduate level.
Random forest application on cognitive level classification of E-learning co...IJECEIAES
The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through E-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with random forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level .The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level.
A Review on Introduction to Reinforcement Learningijtsrd
This paper aims to introduce, review and summarize the basic concepts of reinforcement learning. It will provide an introduction to reinforcement learning in machine learning while covering reinforcement learning workflow, types, methods and algorithms used in it. Shreya Khare | Yogeshchandra Puranik "A Review on Introduction to Reinforcement Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42498.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42498/a-review-on-introduction-to-reinforcement-learning/shreya-khare
Achieving Highly Effective Personalized Learning through Learning ObjectsBabatunde Ishola
A personalized learning system is one in which the information delivered to learners is customized to fit their personal or environmental preferences. Despite the existence of some evidence of the value of personalized learning, there is, to date no widely used personalized learning systems. This paper argues that the primary reason is because of the absence of repositories with the requisite properties. The paper presents the four conditions that any system used for personalized learning delivery would need to have for
it to be effective. The paper then describes the architectural features that such a system must also have.
Comparative evaluation of four multi label classification algorithms in class...csandit
The classification of learning objects (LOs) enables users to search for, access, and reuse them
as needed. It makes e-learning as effective and efficient as possible. In this article the multilabel
learning approach is represented for classifying and ranking multi-labelled LOs, whereas
each LO might be associated with multiple labels as opposed to a single-label approach. A
comprehensive overview of the common fundamental multi-label classification algorithms and
metrics will be discussed. In this article, a new multi-labelled LOs dataset will be created and
extracted from ARIADNE Learning Object Repository. We experimentally train four effective
multi-label classifiers on the created LOs dataset and then, assess their performance based on
the results of 16 evaluation metrics. The result of this article will answer the question of: what is
the best multi-label classification algorithm for classifying multi-labelled LOs?
Learner Model's Utilization in the e-Learning Environments
Vija VAGALE and Laila NIEDRITE
Faculty of Computing, University of Latvia, Raina boulv. 19, Riga, Latvia
Abstract. In the field of personalized systems big role is granted to the adaptive elearning environments. The task of these systems is very important and complicated. With their participation the learner gains exactly the knowledge he
needs most, and the system adapts to user needs, expectations and his individual features. In this kind of systems information about learner is saved in the learner model also known as the user model and student model. For the system to be able to perceive and analyze user activities correctly, is necessary to define what kind of information about the learner has to be saved. The article gives an overview about already existing learner models, their utilization methods and also it offers their comparison according to various criteria.
Development of Intelligent Multi-agents System for Collaborative e-learning S...journalBEEI
The aim of this paper is the introduction of intelligence in e-learning collaborative system. In such system, the tutor plays an important role to facilitate collaboration between users and boost less active among them to get more involved for good pedagogical action. However, the problem lies in the large number of platform users, and the tutor tasks become difficult if not impossible. Therefore, we used fuzzy logic technics in order to solve this problem by automating tutor tasks and creating an artificial agent. This agent is elaborate in basing on the learners activities, especially the assessment of their collaborative behaviors. After the implementation of intelligent collaborative system by using Moodle platform, we have tested it. The reader will discover our approach and relevant results.
A Method of Designing Student Model in Ubiquitous Environment ijujournal
Context-aware ubiquitous learning combines context-awareness with wireless and mobile technologies to
observe the situation of students in the real world and provides personalized guidance accordingly.
Student Model creates student's history logs automatically and maintains history of subject content
requested. It also offers information on student's hardware capabilities, students preferences, knowledge
level and student status. This information can be utilized to respond to new student's request from previous
similar request. A Ubiquitous student model aims to identify student’s needs, characteristics and situations.
In this paper, we have proposed a method of designing student model, that provides personalized subject
content adaptation.
Information security approach in open distributed multi agent virtual learnin...ijcsit
This paper presented the main information, security problems and threats in open multi-agent distributed
e-learning information systems and Proposed various approaches to solve information security attacks in
virtual learning environment using service oriented architecture which based on multi-agent information
systems architecture, the solution on the multi-agent learning information system implementation based on
the implementation of two types of systems the first system with the centralized mobile agent information
security management and the second system with decentralized mobile agents security management, and
proposed the migration behavior simulation for their active software components (software agents) .
Ubiquitous learning allows students to learn at any time and any place. This educational activity is possible to be performed by various types of students and to operate on various devices, networks and environments, where the system understands the study pattern and behaviour of the students. Adaptivity plays an important role in Ubiquitous learning, aiming at providing students with adaptive and
personalized learning material and information at the right place and the right time. Student's history logs
is automatically created and maintained by the student history database that maintains student's history of subject content requested. This offers information on student's hardware capabilities, students preferences,
knowledge level and student status. This information can be utilized to respond to new student's request with subject content created from previous similar request. A Ubiquitous learning student model aims to identify students needs, characteristics and situations. We use C-IOB (Context-Information, Observation
and Belief) model to process the context of the student, formulate the observation and use the observations
to generate beliefs. The belief generated by C-IOB model is based on adaptation decision and subject analysis from student history database, which are able to detect the real-world learning status of students.
Designed method has been illustrated for students with divergent knowledge levels, by considering complete course material of a subject, Communication Protocols offered at graduate level.
Random forest application on cognitive level classification of E-learning co...IJECEIAES
The e-learning is the primary method of learning for most learners after the regular academics studies. The knowledge delivery through E-learning technologies increased exponentially over the years because of the advancement in internet and e-learning technologies. Knowledge delivery to some people would never have been possible without the e-learning technologies. Most of the working professional do focused studies for carrier advancement, promotion or to improve the domain knowledge. These learner can find many free e-learning web sites from the internet easily in the domain of interest. However it is quite difficult to find the best e-learning content suitable for their learning based on their domain knowledge level. User spent most of the time figuring out the right content from a plethora of available content and end up learning nothing. An intelligent framework using machine learning algorithms with random forest Classifier is proposed to address this issue, which classifies the e-learning content based on its difficulty levels and provide the learner the best content suitable based on the knowledge level .The frame work is trained with the data set collected from multiple popular e-learning web sites. The model is tested with real time e-learning web sites links and found that the e-contents in the web sites are recommended to the user based on its difficulty levels as beginner level, intermediate level and advanced level.
A Review on Introduction to Reinforcement Learningijtsrd
This paper aims to introduce, review and summarize the basic concepts of reinforcement learning. It will provide an introduction to reinforcement learning in machine learning while covering reinforcement learning workflow, types, methods and algorithms used in it. Shreya Khare | Yogeshchandra Puranik "A Review on Introduction to Reinforcement Learning" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42498.pdf Paper URL: https://www.ijtsrd.comcomputer-science/artificial-intelligence/42498/a-review-on-introduction-to-reinforcement-learning/shreya-khare
Achieving Highly Effective Personalized Learning through Learning ObjectsBabatunde Ishola
A personalized learning system is one in which the information delivered to learners is customized to fit their personal or environmental preferences. Despite the existence of some evidence of the value of personalized learning, there is, to date no widely used personalized learning systems. This paper argues that the primary reason is because of the absence of repositories with the requisite properties. The paper presents the four conditions that any system used for personalized learning delivery would need to have for
it to be effective. The paper then describes the architectural features that such a system must also have.
Comparative evaluation of four multi label classification algorithms in class...csandit
The classification of learning objects (LOs) enables users to search for, access, and reuse them
as needed. It makes e-learning as effective and efficient as possible. In this article the multilabel
learning approach is represented for classifying and ranking multi-labelled LOs, whereas
each LO might be associated with multiple labels as opposed to a single-label approach. A
comprehensive overview of the common fundamental multi-label classification algorithms and
metrics will be discussed. In this article, a new multi-labelled LOs dataset will be created and
extracted from ARIADNE Learning Object Repository. We experimentally train four effective
multi-label classifiers on the created LOs dataset and then, assess their performance based on
the results of 16 evaluation metrics. The result of this article will answer the question of: what is
the best multi-label classification algorithm for classifying multi-labelled LOs?
Learner Model's Utilization in the e-Learning Environments
Vija VAGALE and Laila NIEDRITE
Faculty of Computing, University of Latvia, Raina boulv. 19, Riga, Latvia
Abstract. In the field of personalized systems big role is granted to the adaptive elearning environments. The task of these systems is very important and complicated. With their participation the learner gains exactly the knowledge he
needs most, and the system adapts to user needs, expectations and his individual features. In this kind of systems information about learner is saved in the learner model also known as the user model and student model. For the system to be able to perceive and analyze user activities correctly, is necessary to define what kind of information about the learner has to be saved. The article gives an overview about already existing learner models, their utilization methods and also it offers their comparison according to various criteria.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
eTeacher: Providing personalized assistance to e-learning students. Silvia Sc...eraser Juan José Calderón
eTeacher: Providing personalized assistance to e-learning students
Silvia Schiaffino *, Patricio Garcia, Analia Amandi
ISISTAN Research Institute – Fac. Cs. Exactas - UNCPBA, Campus Universitario, Paraje Arroyo Seco, 7000 Tandil, Buenos Aires, Argentina
CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
abstract
In this paper we present eTeacher, an intelligent agent that provides personalized assistance to e-learning students. eTeacher observes a student’s behavior while he/she is taking
online courses and automatically builds the student’s profile. This profile comprises the
student’s learning style and information about the student’s performance, such as exercises
done, topics studied, exam results. In our approach, a student’s learning style is automatically detected from the student’s actions in an e-learning system using Bayesian networks.
Then, eTeacher uses the information contained in the student profile to proactively assist
the student by suggesting him/her personalized courses of action that will help him/her
during the learning process. eTeacher has been evaluated when assisting System Engineering students and the results obtained thus far are promising.
The e-learning contained many educational resources are generally used in learning systems like Moodle, It’s free open source software packages designed and flexible platform to create Learning Objects (LOs) and users’ accounts. The author demonstrates how to use semantic web technologies to improve online learning environments and bridge the gap between learners and LOs. The ontological construction presented here helps formalize LOs context as a complex interplay of different learning-related elements and shows how we can use semantic annotation to interrelate diverse between learner and LOs. On top of this construction, the author implemented several feedback channels for educators to improve the delivery of future Web-based learning. The particular aim of this paper was to provide a solution based in the Moodle Platform. The main idea behind the approach presented here is that ontology which can not only be useful as a learning instrument but it can also be employed to assess students’ skills. For it, each student is prompted to express his/her beliefs by building own discipline-related ontology through an application displayed in the interface of Moodle. This paper presents the ontology for an e-Learning System, which arranges metadata, and defines the relationships of metadata, which are about learning objects; belong to academic courses and user profiles. This ontology has been incorporated as a critical part of the proposed architecture. By this ontology, effective retrieval of learning content, customizing Learning Management System (LMS) is expected. Metadata used in this paper are based on current metadata standards. This ontology specified in human and machine-readable formats. In implementing it, several APIs were defined to manage the ontology. They were introduced into a typical LMS such as Moodle. Proposed ontology maps user preferences with learning content to satisfy learner requirements. These learning objects are presented to the learner based on ontological relationships. Hence it increases the usability and customizes the LMS. In conclusion, ontologies have a range of potential benefits and applications in further and higher education, including the sharing of information across e-learning systems, providing frameworks for learning object reuse, and enabling information between learner and system parts.
CONSTRAINT-BASED AND FUZZY LOGIC STUDENT MODELING FOR ARABIC GRAMMARijcsit
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.
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.
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.
Applying adaptive learning by integrating semantic and machine learning in p...IJECEIAES
Adaptive learning is one of the most widely used data driven approach to teaching and it received an increasing attention over the last decade. It aims to meet the student’s characteristics by tailoring learning courses materials and assessment methods. In order to determine the student’s characteristics, we need to detect their learning styles according to visual, auditory or kinaesthetic (VAK) learning style. In this research, an integrated model that utilizes both semantic and machine learning clustering methods is developed in order to cluster students to detect their learning styles and recommend suitable assessment method(s) accordingly. In order to measure the effectiveness of the proposed model, a set of experiments were conducted on real dataset (Open University Learning Analytics Dataset). Experiments showed that the proposed model is able to cluster students according to their different learning activities with an accuracy that exceeds 95% and predict their relative assessment method(s) with an average accuracy equals to 93%.
1. Learner behavior Prediction in Adaptive and Intelligent
Web Base Educational Systems (AIWBES):
Early educational systems that have been implemented using technology were
called Computer Aided Instruction (CAI). These systems used programmed instruction
paradigms that only contained domain knowledge. A recent shift has seen Intelligent
Tutoring Systems (ITS) become more popular. ITS, in contrast to CAI, incorporate both
AI techniques and model based systems. These systems integrate three main model
components (i) an expert module, which contains domain knowledge and reasoning
knowledge to solve problems, (ii) knowledge about the learner (student), in the form of
a learner model, and (iii) knowledge about the learning strategy, in form of pedagogical
(tutoring) module. AI techniques, on the other hand, are used to simulate activities
related to the delivery or tutor, such as coaching learners and/or diagnosing their
misconception. In addition to introducing AI techniques in such systems, adaptation has
also been incorporated in ITS. Such adaptive systems are called Adaptive Intelligent
Tutoring Systems (AITS). These systems use the individual student knowledge to adapt
interactions to student requirements and preferences. It might be worth mentioning that
ITS were first built as standalone single user systems, and have progressed to more
multi-user environments.
With the increasing use of the internet, web based ITS emerged, where multitudes of
learners, in different locations, can interact on these systems. In addition, collaboration
and sharing the knowledge between users, and knowledge acquisition from the net are
among the features of web based systems. Web based educational systems that display
adaptation are referred to in the literature as Adaptive and Intelligent Web-Based
Education Systems (AIWBES). Among the major technologies deployed with in
AIWBES are the (i) intelligent tutoring, (ii) adaptive hypermedia and (iii) intelligent
monitoring [Brusilovsky99; Brusilovsky03].These technologies are outlined in the
following:
Intelligent tutoring
Intelliegnt tutoring is concerned with simiulating the tutor to guide the
learner to follow a cetain sequence in the curriculum, and in solving subsequent
problems. This is in addition to discovering and diagnosing the learners’ bugs and
mistakes. The application domains that are cateorized under this technology are (i)
curriculum sequencing, (ii) interactive problem solving support and (iii) intelligent
solution analysis.
Curriculum sequencing (pedagogical strategy): Curriculum sequencing is used
to guide the learner to find the optimal path through learning material. It determines the
sequence of knowledge units or objects to learn, and the sequence of tasks (examples,
questions, problems) to present to the learner. ELM-ART tutor [Weber01] and KBS-
Hyperbook [Henze01] are two systems hat apply curriculum sequencing.
Interactive problem solving support: Systems using this technology guide the
learner while he/she is solving a problem. This can be achieved by either providing a
hint to execute the next step, or by choosing and presenting more relevant examples.
ActiveMath [Melis01] and ELM-ART tutor are examples of applying interactive
problem solving support technologies.
2. Intelligent solution analysis: The major role of this technology is concerned with
analyzing the learner’s solution to any given problem. The learner (student) model is
updated if the final answer is either correct or wrong. If the solution is correct, the
system infers the appropriate knowledge. On the other hand, if the solution is wrong, it
analyzes the answer and determines the nature of error. The system tries to identify the
incorrect knowledge snipple responsible for this error (knowledge diagnosis). German
Tutor [Heift01] and ELM-ART tutor are among the systems that apply Intelligent
solution analysis.
Intelligent solution analysis technology is used in building and updating the learner
(student) model. The literature generally divides the learner (student) model into
overlay and buggy models.
• The overlay model is based on the assumption that the learner knowledge is a
subset of expert knowledge. This model stores historical data about the
knowledge the learner has mastered. This data is very useful in some
technologies like curriculum sequence and adaptive navigation support.
• The buggy model considers the learners’ knowledge as a perturbation of the
expert’s knowledge. This model contains a list of predefined misconceptions
(bugs) describing errors observed by the learner. This model is the foundation
of intelligent solution analysis.
Adaptive Hypermedia technologies
Hypertext is a non-sequential method for representing and accessing
information. In a hypertext document, information is stored as a network of nodes that
are connected by hypertext links (hyperlinks). A hyperlink is a connection between a
source node and a destination node. The source node is referred to as an anchor. The
anchor can be a word, a phrase, an icon, a button, or an image. The selection of a
hyperlink allows for a jump to another part of the document, or even to another
document. In other words, hypertext links are used to facilitate navigation between
nodes. A hypermedia system is an extension of this principle to integrate elements of
multimedia, allowing selection of animation, video and sound from within the
document. There are two main components of a hypertext system that can be adapted;
the hypertext links and the information contained in the nodes. Adaptation of hypertext
links mainly affects navigation within a hypertext system, while adaptation of the nodes
themselves affects the presentation of information. These two forms of adaptation are
usually referred to adaptive navigation support and adaptive presentation respectively.
Adaptive navigation support: Assistance to learners in hyperspace, through
orientation and navigation, in adaptive navigation support is achieved by changing the
appearance of visible links. Adaptively sorting, annotating, or partly hiding the links of
the current page to narrow the choice of where to go next are among some of the
examples of is an example of navigation support in adaptive hypermedia system..
Adaptive navigation support shares the same goal with curriculum sequencing (helping
the learner to find the optimal path through the learning material). However, it is less
directive and more cooperative than traditional sequencing. It guides the students while
leaving them the choice of the next knowledge item to be learned and next problem to
be solved. ELM-ART tutor, ActiveMath and KBS Hyperbook are three examples of
applying adaptive navigation support. This support is achieved using adaptive link
annotation based on the master level and inter-relations of concepts. ML tutor [Smith02]
uses link sorting and generation to implement adaptive navigation support.
3. Adaptive Presentation: In adaptive presentation information presented to the user is
tailored to his/her needs. This implies that, expert users receive more detailed and in
depth information, while novices receive additional explanation. ActiveMath is an
example of applying adaptive presentation technique.
Intelligent monitoring
Intelligent monitoring technology is based on the ability to compare records of
different learners. In this comparision, mismatched entries are identified. The goal is to
identify the learners who have learning records different from those of their peers. This
applies to both troubled learners, who need more help, or bright learners, who need
larger challenges. Intelligent class monitoring systems use AI techniques (mainly data
mining and machine learning) to select the different learners who need more attention.
They also infer the learning material segments that are either too easy, too hard or
confusing. Logic ITA [Yacef04] is an example for systems using intelligent monitoring.
Romero [Romero03] also demonstrates genatic algorithms for data mining technique in
context of intelligent monitoring.
Systems in this area can be classified into two categories. The first focuses on the
application of data mining techniques (especially association rules) on hypermedia
systems to discover the relationships between the learner’s knowledge level and the
difficulty level of the presented concept. The designer uses the discovered relations to
reconstruct the learning material to be more effective [Romero03; DeBra01]. The
second category applies different data mining techniques on systems that perform
learner diagnosis for bugs, and can infer the concepts responsible on these bugs
[Mercoren03; Yacef04; Mercoren05]. The bugs and the concepts are the main features
used in clustering techniques to organize the learners into clusters. The teacher can see
these clusters, and hence provide more attention to certain clusters which has more
mistakes. In addition, the same data and the learner marks are used in classification
techniques to predict the final learner grade. Association rules have been used to
determine the relationships between mistakes and concepts, and mistakes and each
other. This information can be used to predict the sequence of bugs, and learning
material developers could incorporate this information to give proactive feedback to
learners.
Student Prediction in AIWBES
Most of the existing AIWBES focus on providing a measure of the behavior of the
learner. The tutor thereon takes this measure and redefines a delivery mechanism
suitable for a given behavior. However, e-learning systems provide an opportunity to
collect information as the learner is progressing through the material. This information
could allow the system to realign the educational procedure promptly and hence
improve the learning outcome. In order to achieve this AIWBES need to demonstrate
two features
1. Predicting learner behavior. This prediction would help in adapting the learning
material to each individual learner. It would also assist in speeding up the
diagnosis of the learner’s progress. Prediction is possible through the group
analysis of the learner’s records. Once the learners are clustered into groups,
their behavior can be generalized, and a profile can be generated for each group.
New learners can be dynamically assigned to these groups based on their
individual profile.
4. 2. Learner cognitive modeling. The buggy and overlay model is limited in their
ability to account for the learners’ intention or their personal problem solving
style. Augmenting them with a cognitive model would allow systems to estimate
the learner’s progress through modeling cognitive behavior in addition to his/her
knowledge and misconceptions. These cognitive models would be based on
psychology theories [Taatgen06; Ritter06] integrated with cognitive models of
human behavior [Wang04; Chiew04; Wang03]. This model will contain
knowledge for diagnosing and explaining the learning errors experienced by
each individual learner. The knowledge representation of the cognitive models
consists of concepts and rules describing different ways to solve the associated
goals. In addition, this representation includes bug rules and bug concepts
describing errors observed by other learners. Information regarding the time
taken to solve problems, number of trials, in addition to the experience of the
learner with similar situations would be incorporated in developing this model.
These augments can represent the cognitive ability of the student. This
information is useful in predicting the learner’s behavior over time.
This approach would enhance the use of intelligent tutoring technologies in AIWBES.
In addition the prediction of learner behavior would be the foundation for implementing
adaptive hypermedia. If the learner is classified within a cluster that experiences a
specific problem in a certain concept, the presentation of this concept will be enhanced
by adding more details. On the other hand, if a certain concept is not used, it will be
removed. In addition, the utilization of the cognitive model can help isolate individual
problem compared to group problems. Group problems are more probably related to the
presented material.
The system approach to predict the learner behavior can be summarized in the following
steps
1- Cluster learners using all available data and features in the learner models (overlay,
buggy and cognitive models).
2- For each cluster, apply association rules technique to generalize and find the relations
between the presented material (the difficulty level, structure, and time of presentation)
and the cognitive ability, number of bugs and knowledge level.
3- For each cluster, apply association rules technique to generalize and find the relations
between the presented question (the difficulty level, type, time available to solve,
number of available trials) and the cognitive ability, number of bugs and knowledge
level.
4- For each cluster, apply association rules technique to determine the relations between
the pedagogical strategy and the cognitive ability, number of bugs and knowledge level.
5- Translate the resulted relations to adaptive rules applied on the presented material,
questions and the learning strategy.
6- Applying classification techniques on new student records to determine his/her cluster.
7- Adapt the learning material, the generated questions and the learning strategy to the
learner cluster.
5. Proposed System Architecture
Learning objects
Repository Learning Material module
Classifier
(determine the
Adaptor
student clusters and
module
its characteristics)
and Translator
(translate the result Student
relations to adaptive model
rules) Pedagogical
strategy
module
Evaluator
and Student
Updater Interaction Question
Module Analyzer generation module
Module
Diagnosis Expert
Log file
Module Module
Students’ interactions database
Group analyzer
Figure 1: Proposed System Block Diagram
The block diagram of the proposed system is illustrated in Figure 1. The various
modules of this system can be categorized into four distinct groups:
1- Delivery modules that contain the learning material module and question generation
module.
6. 2- Control modules that contain (i) adaptor module, which adapts the deliver modules
according to learner model, (ii) pedagogical module, that controls the delivering process,
and (iii) translator module, that adapts the deliver modules according to the cluster of the
student.
3- Analysis modules that contain (i) learner interaction analyzer module, that deals with
individual interaction and (ii) the group analyzer, that deals with interactions of learners’
group.
4- Storage units that contain learning objects repository, log files, students’ interactions
database and student model.
7. Thesis Schedule
Activity Semester Semester Semester Semester Semester Semester Semester Semester
1 2 3 4 5 6 7 8
Survey1
Domain selection2
Web implementation3
Learner data collection procedures4
Implementing data mining5
Translate the extracted information to adaptation rules 6
Learner clustering algorithm7
Performance evaluation8
Reporting
Thesis writing
1
Surveying data mining techniques and how they have been used in the literature for the monitoring process. The , cognitive model and the features can be used to differentiate
between learners
2
Choosing the domain and determining the method of knowledge representation, how to generate problems, how to generate the correct answer and how to define bugs.
3
Implementation of the system on the web.
4
Accumulating data about the learners
5
Choosing and implementing the data mining technique to extract useful information and to cluster learners.
6
Translate the extracted information to rules that will be used to adapt the question generation module, learning material and pedagogical strategy module according to each
cluster.
7
Applying clustering and classification techniques to define the cluster of a new learner.
8
Evaluate the performance of the users after adaptation of the delivery materials and the pedagogical strategy according to the prediction of his cluster.
8. References
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