Dynamic learning environment has emerged as a powerful platform in a modern e-learning system. The learning situation that constantly changing has forced the learning platform to adapt and personalize its learning resources for students. Evidence suggested that adaptation and personalization of e-learning systems (APLS) can be achieved by utilizing learner modeling, domain modeling, and instructional modeling. In the literature of APLS, questions have been raised about the role of individual characteristics that are relevant for adaptation. With several options, a new problem has been raised where the attributes of students in APLS often overlap and are not related between studies. Therefore, this study proposed a list of learner model attributes in dynamic learning to support adaptation and personalization. The study was conducted by exploring concepts from the literature selected based on the best criteria. Then, we described the results of important concepts in student modeling and provided definitions and examples of data values that researchers have used. Besides, we also discussed the implementation of the selected learner model in providing adaptation in dynamic learning.
If i upload it, will they come using lazy user theory to explain student use ...
This document summarizes a study that examined student use of optional online learning resources based on the theory of lazy user behavior. The study surveyed 55 undergraduate business students about their perceptions and use of different online services like groupware and online courses. The results indicated that students' decisions about using administrative online services were strongly influenced by minimizing effort, but effort minimization was less of a factor for more learning-focused services. The findings provide insights into how students allocate effort across different online learning options.
Assessing the Readiness to Adopt E-Learning among Industrial Training Institu...
E-learning is using computer and Internet to learn part of or full course whether it is in school, college or in any educational training. The use of e-learning has gained momentum in recent years contrasting to the under valuations done in the previous years. This study was undertaken to assess the readiness of the Industrial Training Institute (ITI) students to adopt e-learning platform. The constructs attitude, perceived ease of use, perceived benefits and behavioural intention from Technology Adoption Model are studied to attain the objective. It is a descriptive research and the population used for this study is Industrial Training Institutes (ITI) in Tamil Nadu. The data was collected from sample of 267 students. The findings of the research infer that the ITI students are willing to adopt the e-learning platform.
Effectiveness of computer supported cooperative learning
This study investigated the effectiveness of different computer-supported cooperative learning strategies (STAD, Jigsaw II, and TAI) on senior secondary students' physics performance in Nigeria. 167 students from 4 classes participated. Students were assigned to learn about equilibrium of forces and simple harmonic motion using either an independent computer-assisted instruction or one of the cooperative strategies supported by a computer program. Pre- and post-tests were used to measure performance. The study found that the cooperative learning strategies enhanced performance more than independent instruction. Academic ability also influenced performance, but gender did not. This provides support for using computer-assisted instruction within cooperative learning settings to improve physics learning.
020. students’ attitude and behavioural intention on adoption of internet for...
This document summarizes a study that examined students' attitudes and behavioral intentions regarding adopting the internet for learning. The study surveyed 200 undergraduate students at Al-Hikmah University in Nigeria. The findings showed that perceived usefulness was the strongest determinant for adopting the internet for learning. Students' attitudes were also found to significantly influence their adoption of the internet. However, facilitating conditions did not significantly impact adoption. The study aims to better understand factors influencing internet adoption for education among Nigerian students.
Dr. Chuck Holt and Dr. Amy Burkman, NATIONAL FORUM OF EDUCATIONAL ADMINISTRAT...
Dr. Chuck Holt and Dr. Amy Burkman, NATIONAL FORUM OF EDUCATIONAL ADMINISTRATION AND SUPERVISION JOURNAL, 30(3) 2013
Dr. David E. Herrington, Invited Guest Editor, NFEAS JOURNAL, 30(3) 2013
Dr. William Allan Kritsonis, Editor-in-Chief (Since 1982)
Implementing e-learning in far western region of Nepal_ Crimson Publishers
Implementing e-learning in far western region of Nepal by Gajendra Sharma* and Mahesh Prashad Bhatta in Crimson Publishers: Electronics and Telecommunications
The rapid developments of internet and communication technologies have materially altered many characteristics and concepts of the learning environment. E-learning has started to make way into developing countries and is believed to have huge potential for governments struggling to meet a growing demand for education while facing shortage of expert teachers, shortage of update textbooks and limited teaching materials. The objective of this study is to determine the major challenges of implementing e-learning systems in far western region of Nepal. The results of this study will serve as a basic for improving higher education in developing countries. There are many commercial or free e-learning systems available on the market. Most of these e-learning systems provide lot of functionality and modules. Some courses are completely based on e-learning resources instead of traditional learning model. E-learning system also offers graphs and charts of student’s results. This system is based on linear workflow. That means students can see new learning resources and tests only after previous was done. Students can also create their own learning plan by defining dates. System is able to export this plan into general calendar format or remind students via e-mail.
https://crimsonpublishers.com/cojec/fulltext/COJEC.000514.php
For More open access journals in Crimson Publishers
please click on link: https://crimsonpublishers.com
For More Articles on Electronics and Telecommunications
Please click on: https://crimsonpublishers.com/cojec
E-learning Information Technology Based on Ontology Driven Learning Engine
Based on the experience of using the “Moodle”, the application of new ontology-based intelligent information technologies is proposed. In the article, proposed is a new e-learning information technology based on an ontology driven learning engine, which is matched with modern pedagogical technologies. With the help of proposed engine and developed question database we have conducted an experiment, where students were tested. The developed ontology driven system of e-learning facilitates the creation of favorable conditions for the development of personal qualities and creation of a holistic understanding of the subject area among students throughout the educational process.
This survey analyzed the usage of information and communication technologies (ICT) among different groups at Angola High Polytechnic School. 441 participants including teachers, administrative staff, and students completed a questionnaire. The most commonly used devices for internet access were mobile phones, laptops, and tablets. The widest used ICT tools were social networks like Facebook and file sharing tools. Among teachers, social networks, file sharing tools, and wikis were most familiar. Students were most familiar with social networks, file sharing tools, wikis, and learning management systems like Moodle. The survey provided insight into the adoption of various ICT tools to support teaching and learning at this institution.
4.[31 39]towards a model of e-learning in nigerian higher institutionsAlexander Decker
This document summarizes an ongoing study to develop an e-learning model for higher education institutions in Nigeria using an evolutionary software modeling approach. The study aims to introduce a flexible model that can be adapted over time based on user feedback, rather than using distinct phases that do not allow for changes. The model is being developed and tested at a polytechnic in Nigeria. The document provides background on e-learning and argues that an evolutionary approach will increase the efficiency, flexibility, quality and reusability of the developed e-learning system for Nigerian higher education.
11.towards a model of e learning in nigerian higher institutionsAlexander Decker
This document summarizes a study on developing an e-learning model for higher education institutions in Nigeria. It presents an evolutionary software modelling approach to developing the e-learning platform in increments, getting user feedback at each stage. The paper reviews existing e-learning models and frameworks. It then describes the modules that would make up the proposed e-learning model, including login, profile, forum, chat and search pages. The goal is to design an interactive interface to support collaborative online learning and address limitations of traditional education systems in Nigeria. Future work will involve developing a prototype and integrating it into other higher education institutions.
If i upload it, will they come using lazy user theory to explain student use ...Alexander Decker
This document summarizes a study that examined student use of optional online learning resources based on the theory of lazy user behavior. The study surveyed 55 undergraduate business students about their perceptions and use of different online services like groupware and online courses. The results indicated that students' decisions about using administrative online services were strongly influenced by minimizing effort, but effort minimization was less of a factor for more learning-focused services. The findings provide insights into how students allocate effort across different online learning options.
Assessing the Readiness to Adopt E-Learning among Industrial Training Institu...IOSRJBM
E-learning is using computer and Internet to learn part of or full course whether it is in school, college or in any educational training. The use of e-learning has gained momentum in recent years contrasting to the under valuations done in the previous years. This study was undertaken to assess the readiness of the Industrial Training Institute (ITI) students to adopt e-learning platform. The constructs attitude, perceived ease of use, perceived benefits and behavioural intention from Technology Adoption Model are studied to attain the objective. It is a descriptive research and the population used for this study is Industrial Training Institutes (ITI) in Tamil Nadu. The data was collected from sample of 267 students. The findings of the research infer that the ITI students are willing to adopt the e-learning platform.
Effectiveness of computer supported cooperative learningGambari Isiaka
This study investigated the effectiveness of different computer-supported cooperative learning strategies (STAD, Jigsaw II, and TAI) on senior secondary students' physics performance in Nigeria. 167 students from 4 classes participated. Students were assigned to learn about equilibrium of forces and simple harmonic motion using either an independent computer-assisted instruction or one of the cooperative strategies supported by a computer program. Pre- and post-tests were used to measure performance. The study found that the cooperative learning strategies enhanced performance more than independent instruction. Academic ability also influenced performance, but gender did not. This provides support for using computer-assisted instruction within cooperative learning settings to improve physics learning.
020. students’ attitude and behavioural intention on adoption of internet for...Gambari Isiaka
This document summarizes a study that examined students' attitudes and behavioral intentions regarding adopting the internet for learning. The study surveyed 200 undergraduate students at Al-Hikmah University in Nigeria. The findings showed that perceived usefulness was the strongest determinant for adopting the internet for learning. Students' attitudes were also found to significantly influence their adoption of the internet. However, facilitating conditions did not significantly impact adoption. The study aims to better understand factors influencing internet adoption for education among Nigerian students.
Dr. Chuck Holt and Dr. Amy Burkman, NATIONAL FORUM OF EDUCATIONAL ADMINISTRAT...William Kritsonis
Dr. Chuck Holt and Dr. Amy Burkman, NATIONAL FORUM OF EDUCATIONAL ADMINISTRATION AND SUPERVISION JOURNAL, 30(3) 2013
Dr. David E. Herrington, Invited Guest Editor, NFEAS JOURNAL, 30(3) 2013
Dr. William Allan Kritsonis, Editor-in-Chief (Since 1982)
Implementing e-learning in far western region of Nepal by Gajendra Sharma* and Mahesh Prashad Bhatta in Crimson Publishers: Electronics and Telecommunications
The rapid developments of internet and communication technologies have materially altered many characteristics and concepts of the learning environment. E-learning has started to make way into developing countries and is believed to have huge potential for governments struggling to meet a growing demand for education while facing shortage of expert teachers, shortage of update textbooks and limited teaching materials. The objective of this study is to determine the major challenges of implementing e-learning systems in far western region of Nepal. The results of this study will serve as a basic for improving higher education in developing countries. There are many commercial or free e-learning systems available on the market. Most of these e-learning systems provide lot of functionality and modules. Some courses are completely based on e-learning resources instead of traditional learning model. E-learning system also offers graphs and charts of student’s results. This system is based on linear workflow. That means students can see new learning resources and tests only after previous was done. Students can also create their own learning plan by defining dates. System is able to export this plan into general calendar format or remind students via e-mail.
https://crimsonpublishers.com/cojec/fulltext/COJEC.000514.php
For More open access journals in Crimson Publishers
please click on link: https://crimsonpublishers.com
For More Articles on Electronics and Telecommunications
Please click on: https://crimsonpublishers.com/cojec
Based on the experience of using the “Moodle”, the application of new ontology-based intelligent information technologies is proposed. In the article, proposed is a new e-learning information technology based on an ontology driven learning engine, which is matched with modern pedagogical technologies. With the help of proposed engine and developed question database we have conducted an experiment, where students were tested. The developed ontology driven system of e-learning facilitates the creation of favorable conditions for the development of personal qualities and creation of a holistic understanding of the subject area among students throughout the educational process.
This document discusses the relationship between information and communication technology (ICT) and e-learning, with a focus on how data mining can be used in the context of e-learning. It first provides background on e-learning and how ICT has enhanced e-learning through technologies like web 2.0. It then discusses how educational data mining uses data collected by e-learning systems and tools to gain insights about students, learning, and how to improve practices. Specific techniques like analyzing keystroke data and data at different levels can provide valuable information. The document concludes that data mining techniques applied by education experts can help address open challenges in e-learning systems and help transform education in India.
This document compares traditional and virtual approaches to higher education in Iran. It discusses key differences between traditional universities, which rely on in-person instruction, and virtual universities, which utilize electronic and online learning. Some of the main differences covered include educational technologies used, independence of learning, sources of course content, flexibility of schedules, and types of student-teacher interaction. The document also examines the current state of virtual education in Iran, noting debates around infrastructure and definitions. It predicts that within 10 years, most Iranian universities will significantly incorporate virtual education approaches.
This document discusses a study on student learning through e-learning modules offered by corporations in partnership with colleges in Mumbai, India. The study examines factors influencing student enrollment in e-learning courses and assesses the effectiveness of these courses. A survey was administered to 100 students enrolled in an e-learning module with over 225 courses. The data was analyzed using statistical tests to understand enrollment rates, course completion rates, and knowledge acquisition. The study aims to evaluate how well e-learning delivers content and retains students, and to identify challenges faced by both students and colleges in these programs.
Between theory and practice the importance of ict in higher educationMaria Loizou
This document discusses the importance of using information and communication technologies (ICT) in higher education. It argues that ICT provides opportunities for motivating learning and collaborative work among students. Specifically, the document shares the authors' experience using new technologies in university teaching activities. The use of ICT is seen as necessary for universities to adapt to the European Higher Education Area. Key points discussed include different paradigms of online teaching and learning, the role of virtual campuses and personal learning environments, and the rise of social networks for collaborative learning.
This document summarizes research on the development, implementation, and use of e-portfolios in educational contexts. The researchers conducted a mixed-methods study with master's students and graduates to examine how an e-portfolio program influenced the transfer of e-portfolio skills to K-12 classrooms. Survey results showed graduates believed e-portfolios were valuable but faced challenges in transferring skills due to inconsistent technology support. Analysis of student e-portfolios revealed themes of using Web 2.0 tools, reflective processes, and assessment. Graduates reported applying e-portfolio skills to support student learning and peer collaboration.
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.
This document discusses classifying user preferences of web learning systems using a neural network with genetic algorithm optimization. It begins with an abstract describing using cognitive attributes from user questionnaires to train classifiers to identify areas for improving a web learning system's layout. A multilayer perceptron neural network was proposed to classify user preferences, and genetic algorithm was used to optimize the neural network parameters to improve performance. 182 students were given questionnaires assessing their cognitive responses to known and unknown subjects on a learning website to collect training data for the proposed genetically optimized neural network classifier.
This document summarizes a literature review that analyzed research predicting student performance and dropout rates using machine learning techniques. The review identified 78 relevant papers published between 2009-2021. These papers mostly used student data from universities and MOOC platforms to test machine learning classifiers for predicting at-risk students and dropout likelihood. The review found that machine learning methods effectively predicted student performance and helped universities develop intervention strategies to improve student outcomes.
A Systematic Literature Review Of Student Performance Prediction Using Machi...Angie Miller
This document summarizes a systematic literature review of research predicting student performance using machine learning techniques. The review examined studies from 2009 to 2021 that identified students at risk of dropping out. It found that various machine learning methods were used to understand challenges and predict performance. Most studies used data from university databases and online learning platforms. Machine learning was shown to effectively predict student risk levels and dropout rates, helping improve student outcomes.
The document discusses using Learning Factor Analysis (LFA), an educational data mining technique, to model student knowledge based on student-tutor interaction log data. LFA uses a multiple logistic regression model with difficulty factors defined by subject experts to quantify skills. A combinatorial search method called A* search is used to select the best-fitting model. The document illustrates applying LFA to data from an online math tutor, identifying 5 skills and presenting the results of the logistic regression modeling, including fit statistics and learning rates for skills. Learning curves are used to visualize student performance over time.
A Study on Learning Factor Analysis – An Educational Data Mining Technique fo...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.
Student View on Web-Based Intelligent Tutoring Systems about Success and Rete...ijmpict
Purpose of this research is to determine the students' point of view about web based intelligent tutoring system's (WBITS) availability, effects on the success and contribution to learning about work, energy and conservation of energy topics. The system will be evaluated on student's angle of view. Intelligence tutoring system that used on the research is used only online by 21 Elementary School Math Teacher candidate for 4 weeks on Physics I course. Public opinion poll that developed by the researchers have used as a data gathering tool. Data gathered in this research has analyzed by descriptive statistical method. Participant students have underlined that web based intelligent tutoring systems are effective on physics courses. Mathematics teacher candidates have expressed their opinion that it is helpful to use the WBITS because it is not depending on time and place, it has capability to serve lots of events and problem solving possibility and it is helping to increase the education performance.
Design a personalized e-learning system based on item response theory and art...eraser Juan José Calderón
Design a personalized e-learning system based on item response theory and artificial neural network approach. Ahmad Baylari, Gh.A. Montazer*IT Engineering Department, School of Engineering, Tarbiat Modares University, Tehran, Iran
A design of a multi-agent recommendation system using ontologies and rule-bas...IJECEIAES
Learners attend their courses in remote or hybrid systems find it difficult to follow one size fits all courses. These difficulties have increased with the pandemic, lockdown, and the stress they cause. Hence, the role of adaptive systems to recommend personalized learning resources according to the learner's profile. The purpose of this paper is to design a system for recommending learning objects according learner's condition, including his mental state, his COVID-19 history, as well as his social situation and ability to connect to the e-learning system on a regular basis. In this article, we present an architecture of a recommendation system for personalized learning objects based on ontologies and on rule-based reasoning, and we will also describe the inference rules required for the adaptation of the educational content to the needs of the learners, taking into account the learner’s health and mental state, as well as his social situation. The system designed, and validated using the unified modeling language (UML). It additionally allows teachers to have a holistic view of learners’ progress and situations.
Using Ontology in Electronic Evaluation for Personalization of eLearning Systemsinfopapers
I. Pah, F. Stoica, L. F. Cacovean, E. M. Popa, Using Ontology in Electronic Evaluation for Personalization of eLearning Systems, Proceedings of the 8th WSEAS International Conference on APPLIED INFORMATICS and COMMUNICATIONS (AIC’08), Rhodes, Greece, August 20-22, ISSN: 1790-5109, ISBN: 978-960-6766-94-7, pp. 332-337, 2008
Semantic web technologies have been attracting interest in many domains. E-learning is not an exception which also involves with many activities or tasks such as instructional design, content development, authoring, delivery, assessment, feedback and etc. which can be sequenced and composed as workflow. Web based E-learning services should be focused in this aspect to fulfill variant e-learners’ requirements. This paper focuses on the Adaptive instructional design framework in which three significant facets are considered 1) Knowledge extraction from user’s behavior, interactions and actions and convert them into semantics 2) Detection of learners style from the semantics defined in the knowledge base and 3) Composition of the workflow for the variant learners to satisfy their requirements dynamically. In this paper we have proposed SEALMS –Semantically Enhanced Adaptive Learning Management System a theoretical framework tracks the learners profile and composes the services for learners using OWL-S. Modules of SEALMS include intelligent agents which perform a kind of reasoning and deriving results from the input fed, finally personalized workflow has been recommended for the elearner.SEALMS is also a cyclic model where the feedback can be taken and reviving process can be initiated from the start to obtain the better results.
Discovering and building the knowledge base of Information Management through...Sheila Webber
This paper was presented as part of a symposium at the Society for Research in Higher Education (SRHE) conference in December 2009.
Webber, S. (2009) “Discovering and building the knowledge base of Information Management through different roles and spaces”. Paper presented at: Challenging higher education: knowledge, policy and practice: Society for Research in Higher Education conference 2009, 8-10 December 2009.
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.
A Survey on E-Learning System with Data MiningIIRindia
E-learning process has been widely used in university campus and educational institutions are playing vital role to enhance the skill set of students. Modern E-learning done by many electronic devices, such as smartphones, Tabs, and so on, on existing E-learning tools is insufficient to achieve the purpose of online training of education. This paper presents a survey of online e-Learning authoring tools for creating and integrating reusable e-learning tool for generation and enhancing existing learning resources with them. The work concentrates on evaluation of the existing e-learning tools a, and authoring tools that have shown good performance in the past for online learners. This survey work takes more than 20 online tools that deal with the educational sector mechanism, for the purpose of observations, and the outcome were analyzed. The findings of this paper are the main reason for developing a new tool, and it shows that educators can enhance existing learning resources by adding assessment resources, if suitable authoring tools are provided. Finally, the different factors that assure the reusability of the created new e-learning tool has been analysed in this paper.E-learning environment is a guide for both students and tutorial management system. The useful on the e-learning system for apart from students and distance learning students. The purpose of using e-learning environment for online education system, developed in data mining for more number of clustering servers and resource chain has been good.
Language Translation for E-learning SystemsIRJET Journal
The document discusses machine translation techniques for e-learning systems to allow educational materials to be translated into multiple languages. It describes different types of machine translation, including MT for watchers, revisers, translators, and authors. It also outlines various machine translation approaches like knowledge-based MT, statistical MT, and example-based MT. The goal is to use machine translation to generate study materials in languages students can understand, in order to improve learning outcomes and enable students to achieve online learning goals.
OLAP based Scaffolding to support Personalized Synchronous e-Learning IJMIT JOURNAL
The advent of asynchronous web based learning systems has helped the learner in a self paced,
personalized and flexible learning style. It can be even more useful with a supportive synchronous tutorial
(question-answer) session. The challenge is to provide sufficient information to the instructor about the
learner’s experience in that particular course at run time. Online analytical processing (OLAP) is a very
useful technique in producing such run time information in the form of reports. In this paper we have
designed an automated scaffolding technique to hold this vital information about the learner which we have
obtained by OLAP techniques on the log data of the LMS users. We have also proposed an overall
architecture of the scaffolding where this information can be easily accessed and used by the instructor in
the synchronous tutorial session to make the system more adaptive.
Adaptive Learning Management System Using Semantic Web Technologies ijsc
Ontologies and semantic web services are the basics of next generation semantic web. This upcoming technologies are useful in many fields such as bioinformatics, business collaboration, Data integration and etc. E-learning is also the field in which semantic web technologies can be used to provide dynamism in learning methodologies. E-learning includes set of tasks which may be instructional design, content development, authoring, delivery, assessment, feedback and etc. that can be sequenced and composed as workflow. Web based Learning Management Systems should concentrate on how to satisfy the e-learners requirements. In this paper we have suggested the theoretical framework ALMS-Adaptive Learning management System which focuses on three aspects 1) Extracting the knowledge from the use's interaction, behaviour and actions and translate them into semantics which are represented as Ontologies 2) Find the Learner style from the knowledge base and 3)deriving and composing the workflow depending upon the learner style. The intelligent agents are used in each module of the framework to perform reasoning and finally the personalized workflow for the e-learner has been recommended.
ADAPTIVE LEARNING MANAGEMENT SYSTEM USING SEMANTIC WEB TECHNOLOGIESijsc
Ontologies and semantic web services are the basics of next generation semantic web. This upcoming
technologies are useful in many fields such as bioinformatics, business collaboration, Data integration and
etc. E-learning is also the field in which semantic web technologies can be used to provide dynamism in
learning methodologies. E-learning includes set of tasks which may be instructional design, content
development, authoring, delivery, assessment, feedback and etc. that can be sequenced and composed as
workflow. Web based Learning Management Systems should concentrate on how to satisfy the e-learners
requirements. In this paper we have suggested the theoretical framework ALMS-Adaptive Learning
management System which focuses on three aspects 1) Extracting the knowledge from the use's interaction,
behaviour and actions and translate them into semantics which are represented as Ontologies 2) Find the
Learner style from the knowledge base and 3)deriving and composing the workflow depending upon the
learner style. The intelligent agents are used in each module of the framework to perform reasoning and
finally the personalized workflow for the e-learner has been recommended.
Solving The Problem of Adaptive E-Learning By Using Social NetworksEswar Publications
This paper propose an enhanced E-Learning Social Network Exploiting Approach focused around chart model and clustering algorithm, which can consequently gathering dispersed e-learners with comparative premiums and make fitting suggestions, which can at last upgrade the collective learning among comparable e-learners. Through closeness
revelation, trust weights overhaul and potential companions change, the algorithm actualized a programmed adjusted trust association with progressively upgraded fulfillments.
A novel approach of multimedia instruction applications in engineering educat...Karla Long
This document summarizes a research article that investigated the use of multimedia instruction applications in engineering education. It conducted a systematic literature review of studies from 2004 to 2014 on this topic. The review found that the engineering curriculum, educational resources, and students' learning characteristics were often incompatible, posing major challenges for teaching and learning engineering courses. The study concluded that multimedia instruction can enhance students' understanding of engineering concepts through visualization and indirectly support higher-order learning. It suggested future research on mobile multimedia instruction and student-generated multimedia approaches to improve engineering education.
Similar to Understanding the role of individual learner in adaptive and personalized e-learning system (20)
Square transposition: an approach to the transposition process in block cipherjournalBEEI
The transposition process is needed in cryptography to create a diffusion effect on data encryption standard (DES) and advanced encryption standard (AES) algorithms as standard information security algorithms by the National Institute of Standards and Technology. The problem with DES and AES algorithms is that their transposition index values form patterns and do not form random values. This condition will certainly make it easier for a cryptanalyst to look for a relationship between ciphertexts because some processes are predictable. This research designs a transposition algorithm called square transposition. Each process uses square 8 × 8 as a place to insert and retrieve 64-bits. The determination of the pairing of the input scheme and the retrieval scheme that have unequal flow is an important factor in producing a good transposition. The square transposition can generate random and non-pattern indices so that transposition can be done better than DES and AES.
Hyper-parameter optimization of convolutional neural network based on particl...journalBEEI
The document proposes using a particle swarm optimization (PSO) algorithm to optimize the hyperparameters of a convolutional neural network (CNN) for image classification. The PSO algorithm is used to find optimal values for CNN hyperparameters like the number and size of convolutional filters. In experiments on the MNIST handwritten digit dataset, the optimized CNN achieved a testing error rate of 0.87%, which is competitive with state-of-the-art models. The proposed approach finds optimized CNN architectures automatically without requiring manual design or encoding strategies during training.
Supervised machine learning based liver disease prediction approach with LASS...journalBEEI
In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.
A secure and energy saving protocol for wireless sensor networksjournalBEEI
The research domain for wireless sensor networks (WSN) has been extensively conducted due to innovative technologies and research directions that have come up addressing the usability of WSN under various schemes. This domain permits dependable tracking of a diversity of environments for both military and civil applications. The key management mechanism is a primary protocol for keeping the privacy and confidentiality of the data transmitted among different sensor nodes in WSNs. Since node's size is small; they are intrinsically limited by inadequate resources such as battery life-time and memory capacity. The proposed secure and energy saving protocol (SESP) for wireless sensor networks) has a significant impact on the overall network life-time and energy dissipation. To encrypt sent messsages, the SESP uses the public-key cryptography’s concept. It depends on sensor nodes' identities (IDs) to prevent the messages repeated; making security goals- authentication, confidentiality, integrity, availability, and freshness to be achieved. Finally, simulation results show that the proposed approach produced better energy consumption and network life-time compared to LEACH protocol; sensors are dead after 900 rounds in the proposed SESP protocol. While, in the low-energy adaptive clustering hierarchy (LEACH) scheme, the sensors are dead after 750 rounds.
Plant leaf identification system using convolutional neural networkjournalBEEI
This paper proposes a leaf identification system using convolutional neural network (CNN). This proposed system can identify five types of local Malaysia leaf which were acacia, papaya, cherry, mango and rambutan. By using CNN from deep learning, the network is trained from the database that acquired from leaf images captured by mobile phone for image classification. ResNet-50 was the architecture has been used for neural networks image classification and training the network for leaf identification. The recognition of photographs leaves requested several numbers of steps, starting with image pre-processing, feature extraction, plant identification, matching and testing, and finally extracting the results achieved in MATLAB. Testing sets of the system consists of 3 types of images which were white background, and noise added and random background images. Finally, interfaces for the leaf identification system have developed as the end software product using MATLAB app designer. As a result, the accuracy achieved for each training sets on five leaf classes are recorded above 98%, thus recognition process was successfully implemented.
Customized moodle-based learning management system for socially disadvantaged...journalBEEI
This study aims to develop Moodle-based LMS with customized learning content and modified user interface to facilitate pedagogical processes during covid-19 pandemic and investigate how teachers of socially disadvantaged schools perceived usability and technology acceptance. Co-design process was conducted with two activities: 1) need assessment phase using an online survey and interview session with the teachers and 2) the development phase of the LMS. The system was evaluated by 30 teachers from socially disadvantaged schools for relevance to their distance learning activities. We employed computer software usability questionnaire (CSUQ) to measure perceived usability and the technology acceptance model (TAM) with insertion of 3 original variables (i.e., perceived usefulness, perceived ease of use, and intention to use) and 5 external variables (i.e., attitude toward the system, perceived interaction, self-efficacy, user interface design, and course design). The average CSUQ rating exceeded 5.0 of 7 point-scale, indicated that teachers agreed that the information quality, interaction quality, and user interface quality were clear and easy to understand. TAM results concluded that the LMS design was judged to be usable, interactive, and well-developed. Teachers reported an effective user interface that allows effective teaching operations and lead to the system adoption in immediate time.
Prototype mobile contactless transaction system in traditional markets to sup...journalBEEI
1) Researchers developed a prototype contactless transaction system using QR codes and digital payments to support physical distancing during the COVID-19 pandemic in traditional markets.
2) The system allows sellers and buyers in traditional markets to conduct fast, secure transactions via smartphones without direct cash exchange. Buyers scan sellers' QR codes to view product details and make e-wallet payments.
3) Testing showed the system's functions worked properly and users found it easy to use and useful for supporting contactless transactions and digital transformation of traditional markets. However, further development is needed to increase trust in digital payments for users unfamiliar with the technology.
Wireless HART stack using multiprocessor technique with laxity algorithmjournalBEEI
The use of a real-time operating system is required for the demarcation of industrial wireless sensor network (IWSN) stacks (RTOS). In the industrial world, a vast number of sensors are utilised to gather various types of data. The data gathered by the sensors cannot be prioritised ahead of time. Because all of the information is equally essential. As a result, a protocol stack is employed to guarantee that data is acquired and processed fairly. In IWSN, the protocol stack is implemented using RTOS. The data collected from IWSN sensor nodes is processed using non-preemptive scheduling and the protocol stack, and then sent in parallel to the IWSN's central controller. The real-time operating system (RTOS) is a process that occurs between hardware and software. Packets must be sent at a certain time. It's possible that some packets may collide during transmission. We're going to undertake this project to get around this collision. As a prototype, this project is divided into two parts. The first uses RTOS and the LPC2148 as a master node, while the second serves as a standard data collection node to which sensors are attached. Any controller may be used in the second part, depending on the situation. Wireless HART allows two nodes to communicate with each other.
Implementation of double-layer loaded on octagon microstrip yagi antennajournalBEEI
This document describes the implementation of a double-layer structure on an octagon microstrip yagi antenna (OMYA) to improve its performance at 5.8 GHz. The double-layer consists of two double positive (DPS) substrates placed above the OMYA. Simulation and experimental results show that the double-layer configuration increases the gain of the OMYA by 2.5 dB compared to without the double-layer. The measured bandwidth of the OMYA with double-layer is 14.6%, indicating the double-layer can increase both the gain and bandwidth of the OMYA.
The calculation of the field of an antenna located near the human headjournalBEEI
In this work, a numerical calculation was carried out in one of the universal programs for automatic electro-dynamic design. The calculation is aimed at obtaining numerical values for specific absorbed power (SAR). It is the SAR value that can be used to determine the effect of the antenna of a wireless device on biological objects; the dipole parameters will be selected for GSM1800. Investigation of the influence of distance to a cell phone on radiation shows that absorbed in the head of a person the effect of electromagnetic radiation on the brain decreases by three times this is a very important result the SAR value has decreased by almost three times it is acceptable results.
Exact secure outage probability performance of uplinkdownlink multiple access...journalBEEI
In this paper, we study uplink-downlink non-orthogonal multiple access (NOMA) systems by considering the secure performance at the physical layer. In the considered system model, the base station acts a relay to allow two users at the left side communicate with two users at the right side. By considering imperfect channel state information (CSI), the secure performance need be studied since an eavesdropper wants to overhear signals processed at the downlink. To provide secure performance metric, we derive exact expressions of secrecy outage probability (SOP) and and evaluating the impacts of main parameters on SOP metric. The important finding is that we can achieve the higher secrecy performance at high signal to noise ratio (SNR). Moreover, the numerical results demonstrate that the SOP tends to a constant at high SNR. Finally, our results show that the power allocation factors, target rates are main factors affecting to the secrecy performance of considered uplink-downlink NOMA systems.
Design of a dual-band antenna for energy harvesting applicationjournalBEEI
This report presents an investigation on how to improve the current dual-band antenna to enhance the better result of the antenna parameters for energy harvesting application. Besides that, to develop a new design and validate the antenna frequencies that will operate at 2.4 GHz and 5.4 GHz. At 5.4 GHz, more data can be transmitted compare to 2.4 GHz. However, 2.4 GHz has long distance of radiation, so it can be used when far away from the antenna module compare to 5 GHz that has short distance in radiation. The development of this project includes the scope of designing and testing of antenna using computer simulation technology (CST) 2018 software and vector network analyzer (VNA) equipment. In the process of designing, fundamental parameters of antenna are being measured and validated, in purpose to identify the better antenna performance.
Transforming data-centric eXtensible markup language into relational database...journalBEEI
eXtensible markup language (XML) appeared internationally as the format for data representation over the web. Yet, most organizations are still utilising relational databases as their database solutions. As such, it is crucial to provide seamless integration via effective transformation between these database infrastructures. In this paper, we propose XML-REG to bridge these two technologies based on node-based and path-based approaches. The node-based approach is good to annotate each positional node uniquely, while the path-based approach provides summarised path information to join the nodes. On top of that, a new range labelling is also proposed to annotate nodes uniquely by ensuring the structural relationships are maintained between nodes. If a new node is to be added to the document, re-labelling is not required as the new label will be assigned to the node via the new proposed labelling scheme. Experimental evaluations indicated that the performance of XML-REG exceeded XMap, XRecursive, XAncestor and Mini-XML concerning storing time, query retrieval time and scalability. This research produces a core framework for XML to relational databases (RDB) mapping, which could be adopted in various industries.
Key performance requirement of future next wireless networks (6G)journalBEEI
The document provides an overview of the key performance indicators (KPIs) for 6G wireless networks compared to 5G networks. Some of the major KPIs discussed for 6G include: achieving data rates of up to 1 Tbps and individual user data rates up to 100 Gbps; reducing latency below 10 milliseconds; supporting up to 10 million connected devices per square kilometer; improving spectral efficiency by up to 100 times through technologies like terahertz communications and smart surfaces; and achieving an energy efficiency of 1 pico-joule per bit transmitted through techniques like wireless power transmission and energy harvesting. The document outlines how 6G aims to integrate terrestrial, aerial and maritime communications into a single network to provide ubiquitous connectivity with higher
Noise resistance territorial intensity-based optical flow using inverse confi...journalBEEI
This paper presents the use of the inverse confidential technique on bilateral function with the territorial intensity-based optical flow to prove the effectiveness in noise resistance environment. In general, the image’s motion vector is coded by the technique called optical flow where the sequences of the image are used to determine the motion vector. But, the accuracy rate of the motion vector is reduced when the source of image sequences is interfered by noises. This work proved that the inverse confidential technique on bilateral function can increase the percentage of accuracy in the motion vector determination by the territorial intensity-based optical flow under the noisy environment. We performed the testing with several kinds of non-Gaussian noises at several patterns of standard image sequences by analyzing the result of the motion vector in a form of the error vector magnitude (EVM) and compared it with several noise resistance techniques in territorial intensity-based optical flow method.
Modeling climate phenomenon with software grids analysis and display system i...journalBEEI
This study aims to model climate change based on rainfall, air temperature, pressure, humidity and wind with grADS software and create a global warming module. This research uses 3D model, define, design, and develop. The results of the modeling of the five climate elements consist of the annual average temperature in Indonesia in 2009-2015 which is between 29oC to 30.1oC, the horizontal distribution of the annual average pressure in Indonesia in 2009-2018 is between 800 mBar to 1000 mBar, the horizontal distribution the average annual humidity in Indonesia in 2009 and 2011 ranged between 27-57, in 2012-2015, 2017 and 2018 it ranged between 30-60, during the East Monsoon, the wind circulation moved from northern Indonesia to the southern region Indonesia. During the west monsoon, the wind circulation moves from the southern part of Indonesia to the northern part of Indonesia. The global warming module for SMA/MA produced is feasible to use, this is in accordance with the value given by the validate of 69 which is in the appropriate category and the response of teachers and students through a 91% questionnaire.
An approach of re-organizing input dataset to enhance the quality of emotion ...journalBEEI
The purpose of this paper is to propose an approach of re-organizing input data to recognize emotion based on short signal segments and increase the quality of emotional recognition using physiological signals. MIT's long physiological signal set was divided into two new datasets, with shorter and overlapped segments. Three different classification methods (support vector machine, random forest, and multilayer perceptron) were implemented to identify eight emotional states based on statistical features of each segment in these two datasets. By re-organizing the input dataset, the quality of recognition results was enhanced. The random forest shows the best classification result among three implemented classification methods, with an accuracy of 97.72% for eight emotional states, on the overlapped dataset. This approach shows that, by re-organizing the input dataset, the high accuracy of recognition results can be achieved without the use of EEG and ECG signals.
Parking detection system using background subtraction and HSV color segmentationjournalBEEI
Manual system vehicle parking makes finding vacant parking lots difficult, so it has to check directly to the vacant space. If many people do parking, then the time needed for it is very much or requires many people to handle it. This research develops a real-time parking system to detect parking. The system is designed using the HSV color segmentation method in determining the background image. In addition, the detection process uses the background subtraction method. Applying these two methods requires image preprocessing using several methods such as grayscaling, blurring (low-pass filter). In addition, it is followed by a thresholding and filtering process to get the best image in the detection process. In the process, there is a determination of the ROI to determine the focus area of the object identified as empty parking. The parking detection process produces the best average accuracy of 95.76%. The minimum threshold value of 255 pixels is 0.4. This value is the best value from 33 test data in several criteria, such as the time of capture, composition and color of the vehicle, the shape of the shadow of the object’s environment, and the intensity of light. This parking detection system can be implemented in real-time to determine the position of an empty place.
Quality of service performances of video and voice transmission in universal ...journalBEEI
The universal mobile telecommunications system (UMTS) has distinct benefits in that it supports a wide range of quality of service (QoS) criteria that users require in order to fulfill their requirements. The transmission of video and audio in real-time applications places a high demand on the cellular network, therefore QoS is a major problem in these applications. The ability to provide QoS in the UMTS backbone network necessitates an active QoS mechanism in order to maintain the necessary level of convenience on UMTS networks. For UMTS networks, investigation models for end-to-end QoS, total transmitted and received data, packet loss, and throughput providing techniques are run and assessed and the simulation results are examined. According to the results, appropriate QoS adaption allows for specific voice and video transmission. Finally, by analyzing existing QoS parameters, the QoS performance of 4G/UMTS networks may be improved.
A multi-task learning based hybrid prediction algorithm for privacy preservin...journalBEEI
There is ever increasing need to use computer vision devices to capture videos as part of many real-world applications. However, invading privacy of people is the cause of concern. There is need for protecting privacy of people while videos are used purposefully based on objective functions. One such use case is human activity recognition without disclosing human identity. In this paper, we proposed a multi-task learning based hybrid prediction algorithm (MTL-HPA) towards realising privacy preserving human activity recognition framework (PPHARF). It serves the purpose by recognizing human activities from videos while preserving identity of humans present in the multimedia object. Face of any person in the video is anonymized to preserve privacy while the actions of the person are exposed to get them extracted. Without losing utility of human activity recognition, anonymization is achieved. Humans and face detection methods file to reveal identity of the persons in video. We experimentally confirm with joint-annotated human motion data base (JHMDB) and daily action localization in YouTube (DALY) datasets that the framework recognises human activities and ensures non-disclosure of privacy information. Our approach is better than many traditional anonymization techniques such as noise adding, blurring, and masking.
A brief introduction to quadcopter (drone) working. It provides an overview of flight stability, dynamics, general control system block diagram, and the electronic hardware.
OCS Training Institute is pleased to co-operate with
a Global provider of Rig Inspection/Audits,
Commission-ing, Compliance & Acceptance as well as
& Engineering for Offshore Drilling Rigs, to deliver
Drilling Rig Inspec-tion Workshops (RIW) which
teaches the inspection & maintenance procedures
required to ensure equipment integrity. Candidates
learn to implement the relevant standards &
understand industry requirements so that they can
verify the condition of a rig’s equipment & improve
safety, thus reducing the number of accidents and
protecting the asset.
20CDE09- INFORMATION DESIGN
UNIT I INCEPTION OF INFORMATION DESIGN
Introduction and Definition
History of Information Design
Need of Information Design
Types of Information Design
Identifying audience
Defining the audience and their needs
Inclusivity and Visual impairment
Case study.
A brand new catalog for the 2024 edition of IWISS. We have enriched our product range and have more innovations in electrician tools, plumbing tools, wire rope tools and banding tools. Let's explore together!
Unblocking The Main Thread - Solving ANRs and Frozen FramesSinan KOZAK
In the realm of Android development, the main thread is our stage, but too often, it becomes a battleground where performance issues arise, leading to ANRS, frozen frames, and sluggish Uls. As we strive for excellence in user experience, understanding and optimizing the main thread becomes essential to prevent these common perforrmance bottlenecks. We have strategies and best practices for keeping the main thread uncluttered. We'll examine the root causes of performance issues and techniques for monitoring and improving main thread health as wel as app performance. In this talk, participants will walk away with practical knowledge on enhancing app performance by mastering the main thread. We'll share proven approaches to eliminate real-life ANRS and frozen frames to build apps that deliver butter smooth experience.
Understanding Cybersecurity Breaches: Causes, Consequences, and PreventionBert Blevins
Cybersecurity breaches are a growing threat in today’s interconnected digital landscape, affecting individuals, businesses, and governments alike. These breaches compromise sensitive information and erode trust in online services and systems. Understanding the causes, consequences, and prevention strategies of cybersecurity breaches is crucial to protect against these pervasive risks.
Cybersecurity breaches refer to unauthorized access, manipulation, or destruction of digital information or systems. They can occur through various means such as malware, phishing attacks, insider threats, and vulnerabilities in software or hardware. Once a breach happens, cybercriminals can exploit the compromised data for financial gain, espionage, or sabotage. Causes of breaches include software and hardware vulnerabilities, phishing attacks, insider threats, weak passwords, and a lack of security awareness.
The consequences of cybersecurity breaches are severe. Financial loss is a significant impact, as organizations face theft of funds, legal fees, and repair costs. Breaches also damage reputations, leading to a loss of trust among customers, partners, and stakeholders. Regulatory penalties are another consequence, with hefty fines imposed for non-compliance with data protection regulations. Intellectual property theft undermines innovation and competitiveness, while disruptions of critical services like healthcare and utilities impact public safety and well-being.
Software Engineering and Project Management - Introduction to Project ManagementPrakhyath Rai
Introduction to Project Management: Introduction, Project and Importance of Project Management, Contract Management, Activities Covered by Software Project Management, Plans, Methods and Methodologies, some ways of categorizing Software Projects, Stakeholders, Setting Objectives, Business Case, Project Success and Failure, Management and Management Control, Project Management life cycle, Traditional versus Modern Project Management Practices.
Encontro anual da comunidade Splunk, onde discutimos todas as novidades apresentadas na conferência anual da Spunk, a .conf24 realizada em junho deste ano em Las Vegas.
Neste vídeo, trago os pontos chave do encontro, como:
- AI Assistant para uso junto com a SPL
- SPL2 para uso em Data Pipelines
- Ingest Processor
- Enterprise Security 8.0 (Maior atualização deste seu release)
- Federated Analytics
- Integração com Cisco XDR e Cisto Talos
- E muito mais.
Deixo ainda, alguns links com relatórios e conteúdo interessantes que podem ajudar no esclarecimento dos produtos e funções.
https://www.splunk.com/en_us/campaigns/the-hidden-costs-of-downtime.html
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-leading-observability-practice.pdf
https://www.splunk.com/en_us/pdfs/gated/ebooks/building-a-modern-security-program.pdf
Nosso grupo oficial da Splunk:
https://usergroups.splunk.com/sao-paulo-splunk-user-group/
Understanding the role of individual learner in adaptive and personalized e-learning system
1. Bulletin of Electrical Engineering and Informatics
Vol. 10, No. 6, December 2021, pp. 3313~3324
ISSN: 2302-9285, DOI: 10.11591/eei.v10i6.3192 3313
Journal homepage: http://beei.org
Understanding the role of individual learner in adaptive and
personalized e-learning system
Alva Hendi Muhammad, Dhani Ariatmanto
Magister of Informatics Engineering, Universitas Amikom Yogyakarta, Indonesia
Article Info ABSTRACT
Article history:
Received Jul 22, 2021
Revised Sep 24, 2021
Accepted Oct 27, 2021
Dynamic learning environment has emerged as a powerful platform in a
modern e-learning system. The learning situation that constantly changing has
forced the learning platform to adapt and personalize its learning resources for
students. Evidence suggested that adaptation and personalization of e-learning
systems (APLS) can be achieved by utilizing learner modeling, domain
modeling, and instructional modeling. In the literature of APLS, questions
have been raised about the role of individual characteristics that are relevant
for adaptation. With several options, a new problem has been raised where the
attributes of students in APLS often overlap and are not related between
studies. Therefore, this study proposed a list of learner model attributes in
dynamic learning to support adaptation and personalization. The study was
conducted by exploring concepts from the literature selected based on the best
criteria. Then, we described the results of important concepts in student
modeling and provided definitions and examples of data values that
researchers have used. Besides, we also discussed the implementation of the
selected learner model in providing adaptation in dynamic learning.
Keywords:
Adaptation and personalization
Adaptive learning system
Dynamic learning
Learner model attributes
Student modeling
This is an open access article under the CC BY-SA license.
Corresponding Author:
Alva Hendi Muhammad
Magister of Informatics Engineering
Universitas Amikom Yogyakarta
Jl. Ring Road Utara, Condongcatur, Depok, Sleman, 55281 Indonesia
Email: alva@amikom.ac.id
1. INTRODUCTION
The coronavirus disease-19 (covid-19) pandemic has forced traditional classroom learning to be
replaced by online learning thru e-learning platforms. E-learning has been the subject of much systematic
investigation, and its form has a long history of change. The first generation of e-learning (1960-1980)
introduced by Patrick Suppes and Donald Bitzer was in the form of computer instruction, known as computer
assisted instruction (CAI) or computer assisted learning (CAL). The second generation of e-learning (1990-
2005) was heavily influenced by personal computer (PCs), multimedia compact disc, read-only-memory
(CD-ROMs), and the internet. This generation emerged web-based e-learning. The third generation of
e-learning (2006-2015) was influenced by smartphones and online video services. This form of e-learning
was known as a mobile learning and ubiquitous learning system. To date, e-learning has entered the fourth
generation, which is characterized by the open and massive number of students through massive open online
courses (MOOC). The use of e-learning that continues to expand has been the common learning solution
during the pandemic.
Existing research recognizes that among varied forms of e-learning that exist today, the same
characteristic lies behind the e-learning system that was generically designed to facilitate all user characters
(one-size-fits-all). Criticism of this traditional form of e-learning was mainly of generalizing the student’s
2. ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 6, December 2021 : 3313 – 3324
3314
background, understanding, motivation, and basic skills [1]-[3]. Thus, dynamic learning was introduced that
aims at achieving learning goals by adapting and personalizing the needs of students and the availability of
learning materials. The mechanism behind dynamic learning was providing services or learning material
suitable for students’ needs when using e-learning. Research in the field of dynamic learning has been carried
out extensively for many years, but the main problem that continues to be investigated in the literature is
related to the model that can be reused in various e-learning systems [3], [4]. The model in this context
covering adaptation model, user model, and domain knowledge model [5], [6]. The main issue addressed in
this paper is modelling the user, student or learner, in a dynamic learning environment according to the
situation and background learning framework [7]-[9].
A common condition in dynamic learning is the activity and progress of the student that constantly
change over time and place. Therefore, this paper set out to identify attributes of learning that derived from
interaction among pedagogy, technology, and knowledge transfer with the actors involved (student).
Modeling students in dynamic learning has a vital role in transforming student characteristics while using e-
learning in the form of ‘user profile’. The user profiles will have similar attributes between e-learning users.
However, they will have different values between students depending on the context of learning. Combining
the values and the attributes of the student model will form a learner profile of e-learning users. In simple
terms, the learner modeling process is shown in Figure 1. Through this mechanism, the learner profiles will
contain information that represents the characteristics of students at a specific moment of time. In the actual
application of dynamic learning, the value data from student can be provided directly by the user or
automatically obtained by the system from the user’s behavior [1], [10].
Figure 1. Learner model in e-learning
An early study of student modeling in e-learning was introduced by [11], that defines the following list
of questions: “Who is being modeled?”, “What is the characteristics of the student?”, “How to model students?”,
and “Why student information is needed?”. Furthermore, [11] succeeded in identifying student models in web-
based adaptive learning systems that consist of user knowledge, interests, goals, background, individual
characteristics, and context. The more comprehensive attributes of learner modeling were proposed in [12], [13]
by considering human factors in determining the criteria. The attributes proposed include (1) personal user
characteristics, such as name, age, gender, cognitive, personality, and learning style; (2) knowledge and skills, in
the form of experience, knowledge, and psycho-motor skills; and (3) system-related user characteristics data,
including objectives and requirements, preferences, interaction styles, and motivation. Several authors have also
conducted similar research and contributed additional attributes of the learner model [14]-[16].
In fact, these many attributes have caused a new issue with the lack of attribute naming standards.
Besides, the attributes that keep changing over time have often caused an impractical implementation of
dynamic learning [2]. Considering the fact that e-learning continues to evolve and the environment is
gradually changing, this study aims to make a classification of student modeling for dynamic learning.
Specifically, this paper contributes to the literature in two ways. First, we propose the definitions of
individual attributes of the learner model with reference to the current literature (2010-2020). Second, we
intend to clarify the differences in students’ attributes that exist in the current literature for APLS.
2. RESEARCH METHOD
In order to understand the role of learner model attributes in APLS, some research questions were
posed to address the review:
− What are the main attributes that have been proposed to model the individual learner profile?
− What are the strategies proposed in the existing literature to utilize the known attributes?
− What are the current challenges and limitations for well-known attributes in APLS?
The domain of the study is limited to the individual attributes in an adaptive and personalized
learning system. The research questions above are not addressed the accuracy or performance comparison of
3. Bulletin of Electr Eng & Inf ISSN: 2302-9285
Understanding the role of individual learner in adaptive and personalized … (Alva Hendi Muhammad)
3315
the existing attributes. However, we discuss how the existing literature utilizes each attributes to solve the
issues raised in APLS.
This study adopted the meta-modeling creation process [17] for designing the learner model. The
method contains the process of modeling various models (meta-modeling) to standardize and reuse the
available models. The improvement of the method used in this research includes adding model collection,
eliminating the use of relationships between concepts, and adding validation at the last stage. Furthermore,
the method used in this study consisted of six modeling stages is being as:
− Step 1. Identification source of knowledge and collects learner models
The first step of the modeling is identifying the source of knowledge and collects learner models. This step
aims to find a similar model that already exists in the literature in the domain of dynamic learning. The sources of
knowledge were limited to web of science and scopus database. The articles were searched by the following four
phrases: (dynamic OR adapt* OR personal*); (e-learning OR hypermedia OR “web-based learning” OR “intelligent
tutoring”); (“learner model” OR “student model” OR “user model”); and (evaluate* OR empiric* OR experiment*).
Then, the articles were filtered from the year 2010-2020, using English, and only from journals. The example of the
source collection from Scopus was illustrated in Figure 2. After manually reviewing the model by considering the
popularity of the citation number, 56 articles were selected from both databases. These 56 models contain designs and
concepts of learner models in dynamic, adaptive, and personalized learning environments.
Figure 2. The article results from scopus database
− Step 2: Classification of the learner model
The aim of this step is to classify the learner model according to the defined criteria. In this paper,
the criteria of the learner model in dynamic learning are the situation and background of the student [9]. The
situation focuses on handling the condition when learning occurs, while the background focuses on defining
the state of the study. These two classifications were selected since they represent a general taxonomy and
not specific to a particular model. However, these classifications can be applied well to many models. The
classification also worked well with dynamic learning by allowing direct mapping of the learner’s attribute
with a learning activity for adaptation purposes. From the 56 candidate models, 40 of them have been in
accordance with the defined criteria, that is the completeness and coverage as suggested in [9]. Finally, the
unused 16 models will be stored and used as comparisons in the final validation process.
− Step 3: Extraction of the concepts that relate to the learner model
The extraction process in this step is conducted by exploring the concepts (text mining) that have a
high relationship with the learner model. The extracted concepts from the article must represent ‘something’
or ‘object’ from the students. For instance, “name, address, and gender” are the concept that might represent
students. The example that illustrates the concept extraction process is shown in Table 1.
Table 1. Example of the extracted concepts from articles
No. Reference Candidate Concepts Total
1. [18] Personal Data, Username, Country, Organization, Language, Performance Data, Time of Last
Session, Detail Level, Session Number, Programming Language, Teaching History, Concept
History, Test History, Unit History, Learning Style, Initial Skill Level, Experience Level, Actual
Skill Level, Duration, Number of Passes, Degree of Mastery, Knowledge Level, Instructional Plan,
Concept Title, Difficulty Factor
25
2. [19] Experience, Background, Goals, Knowledge Level, Preference, Interest, Interaction Style,
Attitude, Learning Experience, Personality, Cognitive Style, Learning Performance, Time Taken,
Correct Answer, Trait
15
... ... ... ...
36. [20] Personal Information, Contact, Demographic, Biographic, Qualification, Certification,
Performance, Preference, Format Presentation, Language, Media Type, Interest, Learning Goal,
Background Knowledge, Learning Style, Learning Activity, Prior Knowledge, Portfolio, Security,
Relation, Competency, Knowledge, Skill, Ability
24
4. ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 6, December 2021 : 3313 – 3324
3316
− Step 4: Selection of the general concepts from the model
This step aims to improve the results of the concepts, as previously shown in Table 1. The process
includes selecting the most common (generality) and frequent concept names with more than three
occurrence frequencies. For example, Table 2 shows the process of naming the concept. The word
‘background’ appears four times in various models, followed by ‘background knowledge’, and ‘domain
background’. Thus, the background will be selected as the name used as a general concept for background
knowledge and background domain.
Table 2. Example of the naming process from selected models
General Concept Concepts Frequency Generality
Background Background 4 1
Background Knowledge 2 1
Background Domain 1 0
Cognitive Style Cognitive 1 1
Cognitive Ability 1 1
Cognitive Capacity 1 0
Cognitive Skills 1 1
Cognitive Processing 1 0
Cognitive Purpose Indicator 1 0
Cognitive State 2 1
Cognitive Style 4 1
Cognitive Task 1 0
Level of Cognitive 1 1
Visual and Cognitive 1 0
− Step 5: Validation of the proposed concept
The validation aims to measure the concepts’ quality and accuracy in terms of generalization,
correctness, and completeness. Validation was conducted by comparing the frequency of occurrence of the
data presented in Table 3 with the validation set suggested in the first step. Then, the degree of confidence
(DoC) was measured. DoC value is a real number that expresses the reliability of the probability [17]. The
formula of DoC is being as:
𝐷𝑜𝐶 =
𝐹𝑟𝑒𝑞𝑢𝑒𝑛𝑐𝑦 𝑜𝑓 𝐶𝑜𝑛𝑐𝑒𝑝𝑡
𝑇𝑜𝑡𝑎𝑙 𝑀𝑜𝑑𝑒𝑙 𝑜𝑓 𝑉𝑆 1
𝑥100%
The final results of DoC and the proposed concept is presented in Table 3. The validation in Table 4
aims to determine the final concept used as the learner model. The lower DoC value means the concept has a
minor relationship with the suggested learner model. However, not all concepts in the very mild category are
eliminated. Some concepts are still in use after comparing the occurrence rate with the 16 models from the
validation set.
Table 3. Degree of confidence for the proposed concept
DoC Classification Learner Model Concepts
70-100% (VeryStrong) Knowledge Level, Prior Knowledge, Preference
50-69% (Strong) Learning Style, Background, Skills, Presentation, Time
30-49% (Moderate) Cognitive Style, Personality, Domain Concept, Learning Activity, Difficulty Level, Goal, Experience,
Course, Interactivity
11-29% (Mild) Competency, Learning Performance, Device, Emotion, Interest, Learning Motivation, Objective
0-10% (Very Mild) Qualification, Instructional Plan, Bandwidth, Location, Age (), Gender (), Language (), Personal
Information (), Content (), Topic ()
()=Delete the concept
Table 4. Final concept of the proposed learner model
Category Concepts
Situation Bandwidth, Course, Device, Difficulty Level, Domain Concept, Emotion, Goal, Instructional Plan, Interactivity,
Interest, Learning Activity, Learning Motivation, Location, Preference, Presentation, Time
Background Background, Cognitive Style, Competency, Experience, Knowledge Level, Learning Style, Learning
Performance, Objective, Personality, Prior Knowledge, Qualification, Skill
5. Bulletin of Electr Eng & Inf ISSN: 2302-9285
Understanding the role of individual learner in adaptive and personalized … (Alva Hendi Muhammad)
3317
3. RESULTS
This section presents the findings of learner model attributes and then highlights the definition and
specific data values for each attribute. The following sub-section then discuss the illustration of its value
during the implementation. An example of using the learner model under a dynamic learning environment
will also be presented.
The learning model suggested in this study follows the framework of situated learning as presented
in [9]. We realize the adaptivity and personality in dynamic learning can be easily achieved by considering
the situation and background of the learner simultaneously. Thus, the attributes of the learner should fall
under these two categories: situation or background. The situation is any circumstances surrounding the
student, while the background focuses on shaping the state of the learning history. The classification of the
concept based on its categories is presented in Table 4. The lists of the concept were organized
alphabetically. Even though some concepts might have a different degree of importance when implementing
in the domain. However, to determine the degree of importance will need more information related to the
implemented domain. Without understanding the implemented domain, it would be impractical to compare
the importance of the concepts.
3.1. Learning attributes related to situation
Included in the category of situation is the concept that depends on the system. Therefore, this
category encompasses all information related to the interaction between the user, system, device,
infrastructure, and the environment. The description of each concept and example of the data instances based
on literature are presented is being as:
− Bandwidth. Bandwidth provides information related to the network or internet connectivity in a specific amount
of time. Some of the internet connectivity elements include: RFID, IrDA, Bluetooth, WiFi, WiMax, UMTS, 4G,
or Satellite [1]. Moreover, bandwidth elements may include static and dynamic properties. The static properties
may be a range of elements to represent the bandwidth values, such as low, medium, high, or defines as the
minimum and maximum [13]; while the dynamic properties display the value of the actual speed or capacity of
the network. Knowing the network connection gives insight into the e-learning system, to provide contents
adaptation related to media presentation or size of resources that can be retrieved into student’s device.
− Device. Device contains information related to the type of equipment that is used for learning. The
information that is attached to a device contains technical elements, such as screen size, connectivity,
central processing unit, interface, input/output, memory, and storage capacity, and battery lifetime. From
these characteristics, we could refer to the hardware that the learner uses, such as PC, laptop, PDA,
Smartphone, or Tablet [21]. The device information is important to determine the media presentation or
learning resource that would be adaptable to the student [22].
− Difficulty Level. Difficulty level indicates the difference in the task that is hard to accomplish or understood
by students. The difficulty level is relevant to determine the learners’ abilities because inappropriate content
can result in disorientation during learning. In particular, the dynamic parameter of difficulty level in
different stages of learning to deliver appropriate learning content. In contrast, some studies used the static
parameter to define the difficulty level. For instance, Ouf et al. [20] defined three values of high, fair, and
low, as the difficulty parameter. Idris et al. [15] proposed a three-step difficulty level, which consists of
Beginner, Intermediate, and Advanced. Another static parameter as suggested by [22] defined difficulty
level by using the scale from 0 (easy) to 1 (difficult).
− Domain Concept. A domain concept consists of a topic of the domain. The domain concept can also be
associated with learning objects in e-learning. The relationship among domain concepts of the course is
known as the learning path. Information related to domain concepts is crucial for adaptation. This allows
the system to generate and predict the content for the student based on the information in the domain
concept. The information in the domain concept is also needed for determining the adaptation strategy,
which is generally defined in the instructional model or adaptation model.
− Emotion. Emotion is described as a diffuse affective state that consists of a subjective feeling change
without apparent cause. Different from mood, which tends to appear when things are not going well
according to their expectation, emotion tends to come from a known cause [23]. For example, learners
exhibit a happy emotion when they understand a topic or pass the test. Nevertheless, emotion is often
combined and used interchangeably with mood to represent the affective states of the student [7].
− Goal. Goal is the description of an objective that the learner tries to achieve. The goal is simply the answer
to the question: “What does the student want to achieve?” The goal can be modelled with possible learner
goals or tasks that the system can recognize. Typically, a learner has to select one of the pre-defined goals.
Cena et al. in [7], the goal’s information is stored to indicate the particular topic in the system that the
learner wants to visit. Some systems can capture goals from users and models as a probabilistic overlay
[24]. Lamia and Tayeb [25] suggested two classifications of goal, that is the long-term goal and short-term
6. ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 6, December 2021 : 3313 – 3324
3318
goal. The long-term goal is the lifelong study plan and is typically permanent in the course. In contrast, the
short-term goal provides the learner the opportunity to solve a certain problem, such as passing an
assignment or doing a simulation. Goals can be modelled through navigation or monitoring dashboards.
Learning goals are usually set at the beginning of the course, so that the system can provide information
related to the goals that the learner wants to achieve. Then, the system might provide the needed resources
to achieve the goals or remind the learners of their expected learning level.
− Instructional Plan. The instructional plan refers to a specific kind of strategy used to provide an effective learning
object design to reach a more successful learning outcome. The instructional plan focuses on the current learning
states, needs, and learning outcomes of students [18]. Also, the instructional plan is a developing functional
learning system based on a systematic approach that meets the requirements of a specific target group.
− Interactivity. Interactivity level is described as the degree to which the student can influence the learning
resource. Interactivity level promotes opportunities to interact with learning resources in different ways.
Moreover, interactivity level indicates the degree to which the learning resource can respond to the actions
and input of the user [26]. It has a value space range from very low to very high. The learning activity in
very low interactivity level relates to passive activities with less response from the user, such as reading an
essay or answering test questions [27]. Whereas the very high interactivity level value often relates to active
response from the user, such as a 3D simulation environment that needs the user to do a series of steps.
− Interest. Interest is an indication of a learner being more attracted to a particular learning topic than others.
This allows adaptive systems to make lessons that match the learner’s interest. To represent user interest,
some authors suggest the keywords-level model and concept-level model [28]. The keyword-level model
uses a weighted vector of keywords to filter and retrieve the relevant learning materials to the keyword. In
contrast to this approach, a concept-level model uses a weighted overlay of the concept-level domain model
to represent user interests.
− Learning Activity. Learning activity is defined as any activity that the learner does while progressing through a
unit of instruction in the course. Several types of learning activities exist according to cognitive or learning
style. For instance, Felder-Silverman exemplifies learning activities like case studies, brainstorming,
discussion, problem solving, experiments, observing, mind maps, group discussion, case studies, reading,
storytelling, interviews, listening to lectures, questionnaires, and consulting references [20].
− Learning Motivation. Learning motivation is defined as the desire or the willingness to make an effort
towards a specific learning goal. Learning motivation is often classified as a system-related characteristic
because it intensely depends on the learning goal [29]. The learning goal itself is system-dependent and can
be affected by the presentation of learning content [26].
− Location. Location is the representation of the place or position where the learner can be located. The
location could be located automatically using position measurement from GPS or cellular ID [13].
However, most of them are usually selected manually by users according to the type of settings like a
classroom, library, laboratory, home, outdoor, train, or bus [30]. Some works allow learners to create initial
settings which consist of context attributes, such as location, time, and date [21].
− Preference. Preferences are the learning features that relate to likes and dislikes regarding interfaces,
services, or resources. Preferences such as font type, size, and other parameters are associated with the
interface [25]. The variables relate to device communication, hardware, and software which are associated
with the service [29]. Furthermore, the preferred type of courses or instructional model can be associated
with resources. Different methods have been proposed to gather learning preferences from the system. Ouf
et al. [20] suggest using questionnaires for collecting learner preferences. This method provides real-time
information without any need for further processing.
− Moreover, this method is similar to Lamia and Tayeb [25], which provides checklists for users to select preferred
interface elements. After the preferences are determined, the system applies adaptation in the new contexts. The
preference can also be defined as an ordered list of values, which indicate their likes and dislikes.
− Presentation. Presentation is described as the technical datatype or media type of the learning resource that
is intended for presentation to the user. The information on the presentation can be used to identify the
hardware and software needed to access the resources. The type of presentation may take several
multimedia forms, such as text, picture, video, audio, application, animation, or hypertext [22].
− Time. In adaptive e-learning, time could represent the plan of activity, duration spent on studying, or
indicate the date of learning [18]. Similar to location, time context is often used in conjunction with another
context, either schedule, place, or motivation [7].
3.2. Learning attributes related to background
The background encompasses information regarding the general user, individual traits, experience,
and learning history. Information related to the general user could be captured directly from the user’s
7. Bulletin of Electr Eng & Inf ISSN: 2302-9285
Understanding the role of individual learner in adaptive and personalized … (Alva Hendi Muhammad)
3319
feedback. This mechanism differs from individual traits. Individual traits, including cognitive style, learning
style, and personality, usually require separate testing and interpretation of the results. The description of the
concept in this category are is being as:
− Background. In broad terms, background refers to the previous knowledge of a student in some
particular areas, usually outside the domain of the concept but still relevant to the body of knowledge.
For example, the background for students in the field of Computer Science or Information Technology
might contain information related to knowledge in programming, system and development, networking,
and operating systems [31]. The other fields will contain different information of the background [32].
− Cognitive Style. The cognitive style is often described as the way students think, observe, and remember
information. Cognitive style, along with learning style, has become the essential concepts in adaptive
learning. It encompasses the working memory, control and speed of processing, and visual attention
[11], [33]. Researchers have identified that the cognitive processing of the human is often related to age,
exercise, and experience [5], [16]. According to the wechsler adult intelligent scale (WAIS), the
cognitive style could be classified into six broad types: information, comprehension, vocabulary,
similarities, object assembly, and picture arrangement [34].
− Competency. Competency shows the qualification of a student in a field. Although this is not compulsory
for the user model in dynamic learning, competency can be included as the learner’s portfolio.
Competency should represent that a student possesses the skills and knowledge required in a particular
field [23]. A student with technologically related certification, for example, demonstrates the
competency and knowledge that can be used to personalize content learning.
− Experience. Experience is the representation of the prior record associated with the learner’s previous
practice outside the core domain of the system. Some authors [6], [35] define experience as the user’s
general knowledge about a discipline. However, we also need to include the knowledge inside the
discipline in the learning history. Thus, experience represents a range of backgrounds that might be used
in modeling adaptive learning, including job, profession, responsibilities, or working experiences in
related areas [6].
− Knowledge Level. Knowledge level represents an element record that reflects the expertise level of
knowledge about a topic given. Knowledge level could represent the acquired knowledge or required
knowledge. The acquired knowledge is the knowledge that the student acquires at a given level. This is
also given to the student after they pass a chapter or a test. The required knowledge describes the
necessary knowledge that students should have before studying a material. The knowledge level is
essential in adaptive learning. A system can adapt the level of students and decide what the next stage of
the learning process might be by accessing the knowledge of the learners. This way of adaptation is
promoted by [15], [35], [36]. Knowledge level can also be reflected using performance level indicators,
automatic system score, or question items level, such as well learned, learned, poor, and very poor [22].
− Learning Performance. Learning performance aims to measure the achievements and effectiveness of
different kinds of learning activities. Learning performance can be determined by several methods. First
is using learning goal or task accomplishment rates [37]. Second, the general measuring score of pre or
post-test after completing a lesson. Then, academic performance by grading the result of pre and post-
test. An effective assessment is a vital way to measure learners’ performance and accomplishment of the
learning outcomes. That is what learners are expected to know, understand, and do to be successful in a
domain concept [38].
− Learning Style. Learning style is the way a student learns or prefers to learn. In fact, every student has
different methods or strategies which they prefer when studying. Many studies have been done to
investigate the specific learning style of students to improve the learning process. The selection of the
appropriate learning style was derived from the educational and psychological fields. In e-learning
environments, the common learning styles adopted for adaptive learning are Riding’s Cognitive Style
analysis [11], Felder/Silverman index of learning styles [39], [40], Myers-Briggs type indicator [41], and
Kolb’s model [42]. Some studies have proposed their own model as in [43], [44].
− Objective. The objective defines a classification of educational objectives that learners use to formulate
their intentions. An objective in a subject can be measured by a degree of control that learners seek to
achieve [45]. Many objectives of the study were derived from Bloom’s taxonomy. According to Bloom’s
taxonomy, the objective of study can be classified into six categories: knowledge, understanding,
application, analysis, synthesis, and evaluation.
− Personality. Personality reflects a record that stores the relevant learning attitude of a learner. According
to Kim et al. [19], a learner’s experience in e-learning will be affected significantly if the instruction
style matches the learner’s personality. That is why the modelling of personality is often closely tied to
the preference for learning resource presentation. Some studies proposed different strategies of
personality types, such as keirsey’s temperament theory (KTS), five-factor model, and MBTI [14], [41].
8. ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 6, December 2021 : 3313 – 3324
3320
KTS suggests four personality types that are highly relevant to learning: Rational (intuitive thinking),
Idealist (intuitive feeling), Artisan (sensory perception), and Guardian (sensory judgment). The five-
factor model also has different effects on learning. This personality consists of five dimensions:
openness, conscientiousness, extraversion, agreeableness, and neuroticism. The MBTI model provides
personality preferences along four bipolar psychological dimensions: introvert-extravert, sensing-
intuition, thinking-feeling, and judging-perceiving [46].
− Prior Knowledge. Prior knowledge is a way to represent the accumulation of knowledge and learning
that each learner brings to a particular course. Prior knowledge aims to follow every student’s activity
during the learning process. It is a form of reflection of the student’s performance or success/failure rate
[40]. Prior knowledge data can be obtained from the result of the test, history of last visited learning
resources, or time spent on completing a chapter [22], [35].
− Qualification. The qualification indicates the level of education of a learner. In IMS, education is used as
a context to describe the environment where learning activities take places, such as University, School,
Training, and others [7]. The qualification is used not only to represent the learning resources but also to
represent the attributes of learners. Qualification can also be used to generate and personalize content
that is matched with the education context [47].
− Skill. Skill represents the ability of a learner to perform well in a particular area. The skill of learner is
often determined to personalize learning content based on the difficulty level. Skill might be used to
represent a general ability in learning or just in a specific domain. For example, [18] uses skill to
measure the degree of mastery in a specific programming language. The values range from bad (no skill)
to expert (high skill). On the other hand, skill can also reflect the learner’s general ability, such as
mathematical skill, analytical, problem solving, reasoning, and logical thinking [47].
4. DISCUSSION
This sub-section analysis the difference of the findings with the existing learner models [3], [6]. A
practical illustration of adaptation and personalization in dynamic learning will then be presented. We
describe the application in mobile, ubiquitous, and pervasive learning [1], [48] and then show the proposed
learner model identifying the context for adaptation.
4.1. General framework for APLS
The previous results have shown that a student might have many attributes in e-learning. Hence, an
intelligence system is needed for processing learning materials based on learning context. To date, several
intelligence learning tools and systems have been built with various modifications and improvements. The
designs are generally similar to the framework shown in Figure 3 (adapted from [9]), which is divided into
two domains. The first domain is context identification that handles the situation when learning occurs and
defines the learner’s state. The second domain is the adaptation and personalization process. It is the core of
the system that will summarize the information and provide recommendations related to the available
learning objects. Several methods currently exist for the assessment and recommendation of the learning
object. The previous studies by [49], [50] provided an excellent review on this theme.
Figure 3. General APLS framework
Learning object
recommendation
Bandwidth, Device,
Difficulty Level, Domain
Concept, Emotion, Goal,
Instructional Plan,
Interactivity, Interest,
Learning Activity, Learning
Motivation, Location,
Preference, Presentation,
Time
Context Identification Adaptation and Personalization Process
Context
Learning Object
Repository
Adaptation Engine
Data Information Knowledge Decision
Background, Cognitive
Style, Competency,
Experience, Knowledge
Level, Learning Style,
Learning Performance,
Objective, Personality,
Prior Knowledge,
Qualification, Skill
Assessment
(summarize the facts)
Situation
(who/where)
Background
(summary of learner
history)
Recommendations
(prediction for the next
step)
9. Bulletin of Electr Eng & Inf ISSN: 2302-9285
Understanding the role of individual learner in adaptive and personalized … (Alva Hendi Muhammad)
3321
4.2. Model comparison
Comparison against other models can be used to identify the coverage of the concepts. First, we
compare our model with the work of [3]. The source of literature in their model was from Science citation
index expanded and social science citation index. Thus, the method for searching focuses on all individual
characteristics, including learning styles, cognitive styles, background knowledge, age, and gender. The
refinement was conducted by selection by titles, abstracts, and full texts. They also considered the citation
number for each variable. Their final results successfully identified 17 attributes of the learner for adaptive
learning, including age, gender, cognitive abilities such as processing speed, working memory, spatial ability,
metacognitive abilities, personality, anxiety, emotional and affective states, cognitive styles, learning styles,
experience, background knowledge, motivation, expectations, and preferences. The comparison with our
model had shown that most of the suggested concepts supported, except age and gender. We already
identified both concepts before, but they were dropped during validation since it less popular for adaptation
in other models.
In addition, we compare our findings with the study of [6]. The study focused on identifying
personal traits that can be identified for adaptive learning. The source of knowledge in the study was obtained
from 78 sources related to personal traits, specifically in cognition, affective, behavior, or combination
between domains. Their final results have identified the most factors suitable for adaptive learning, including
learning style, cognitive style, emotions, cognitive abilities, knowledge level, and personality type. Since
their research focuses on personal trait, our model’s comparison was valid when comparing attributes in the
background. Our model has identified all the suggested attributes for adaptive learning environments, from
learning style to personality type.
4.3. Model application
In what follows, we illustrate our findings of the learner model for mobile, ubiquitous, and pervasive
learning. Accordingly, the learner model determines the context during learning in two aspects: situation and
background. Therefore, we first propose a set of values related to the situation. The data source of dimension
could be identified directly by the system or manually supplied by the user. From the application description
[1], the aim of the learning in this scenario is to assist the student during learning intermediate English. In
this subject, student must individually and collaboratively present a project related to starting a new business.
The context considered as the situation in the presented scenario is digital property, physical property,
location, environment, availability, illumination level, and noise level. In addition, the scenario also considers
the student’s background, namely interest, need, preference, and contribution. Using our learner model, we
identify the situation each student provided to the system is being as:
− Digital property and physical property are the software and hardware technical capabilities. These can be
represented as device, bandwidth, and presentation.
− Location and environment are the descriptions of where the learners stand. Our model uses the similar
name, i.e., location.
− Availability is the available time for learning. This can be represented as time.
− Illumination level and noise level where the learner is located. We identified this as interactivity.
Furthermore, the example of the background used by each student in this scenario are:
− Interest and contribution relate to learner’s attention and interaction on learning skills to improve. This
can be represented as interest, learning activity, goal, and objective.
− Need is a request for language support. This can be represented as difficulty level, knowledge level, and
instructional plan.
− Preference is the inclination to select a service. We use a similar concept name to define preference.
Finally, the data context instantiated from learners is assessed to allow learning material
recommendation. The recommendation is selected from the list of resources. These resources might in the
form of learning activities, learning materials, learning objects, or services. In the scenario supported above,
the resources are various learning activities that students should complete.
5. CONCLUSION
This paper has explored the different dimensions of learner modeling in dynamic learning, including
transforming mechanisms to adapt and personalize learning. The impact of adaptive and personalized
learning in formal education should be viewed as an opportunity to improve the learning experience. The
process of producing a learner model included synthesizing a new learner model from existing models and
validating the proposed model with several models in the existing domain. The result of the synthesis process
is a generic learner model for the APLS. We also provided classification and dimension based on an
assessment of the situation and background model. Focusing more on the proposed model, we presented all
10. ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 6, December 2021 : 3313 – 3324
3322
definitions and related data values for each proposed attribute. With the addition of the definition and data
values, it is expected that this learner model would assist the researcher in reusing our model for
implementing dynamic learning. Furthermore, we demonstrated the implementation of the learner model by
assisting dynamic learning in mobile and ubiquitous contexts. This is an important contribution in developing
a learner model that supports a dynamic learning environment and facilitating adaptation and personalization.
REFERENCES
[1] S. Gómez, P. Zervas, D. G. Sampson and R. Fabregat, “Context-aware adaptive and personalized mobile learning
delivery supported by UoLmP,” Journal of King Saud University (Computer and Information Sciences), vol. 26,
no. 1, Supplement, pp. 47-61, 2014, doi: 10.1016/j.jksuci.2013.10.008.
[2] H. Xie, H. C. Chu, G. J. Hwang and C. C. Wang, “Trends and development in technology-enhanced
adaptive/personalized learning: A systematic review of journal publications from 2007 to 2017,” Computers &
Education, vol. 140, p. 103599, 2019, doi: 10.1016/j.compedu.2019.103599.
[3] J. Nakic, A. Granic and V. Glavinic, ‘‘Anatomy of student models in adaptive learning systems: A systematic
literature review of individual differences from 2001 to 2013,’’ Journal of Educational Computing Research, vol.
51, no. 4, pp. 459-489, January 2015, doi: 10.2190/EC.51.4.e.
[4] S. Y. Chen and J. H. Wang, “Individual differences and personalized learning: a review and appraisal,” Universal
Access in the Information Society, no. 0123456789, pp. 1-17, 2020, doi: 10.1007/s10209-020-00753-4.
[5] H. Yago, J. Clemente and D. Rodriguez, “Competence-based recommender systems: a systematic literature
review,” Behaviour & Information Technology, vol. 37, no. 10-11, pp. 958-977, 2018, doi:
10.1080/0144929X.2018.1496276.
[6] N. B. A. Normadhi, L. Shuib, H. N. Md Nasir, A. Bimba, N. Idris and V. Balakrishnan, “Identification of personal
traits in adaptive learning environment: Systematic literature review,” Computers & Education, vol. 130, pp. 168-
190, 2019, doi: 10.1016/j.compedu.2018.11.005.
[7] F. Cena, S. Likavec and A. Rapp, “Real World User Model: Evolution of User Modeling Triggered by Advances in
Wearable and Ubiquitous Computing: State of the Art and Future Directions,” Information Systems Frontiers, vol.
21, no. 5, pp. 1085-1110, 2019, doi: 10.1007/s10796-017-9818-3.
[8] S. S. Khanal, P. W. C. Prasad, A. Alsadoon and A. Maag, “A systematic review: machine learning based
recommendation systems for E-learning,” Education and Information Technologies, vol. 25, no. 4, pp. 2635-2664,
2020, doi: 10.1007/s10639-019-10063-9.
[9] A. Muhammad, J. Shen, G. Beydoun and D. Xu, “SBAR: A Framework to Support Learning Path Adaptation in
Mobile Learning BT-Frontier Computing,” in International Conference on Frontier Computing, vol. 422, pp. 655-
665, 2018, doi: 10.1007/978-981-10-3187-8_62.
[10] Y. M. Huang and P. S. Chiu, “The effectiveness of a meaningful learning-based evaluation model for context-
aware mobile learning,” British Journal of Educational Technology, vol. 46, no. 2, pp. 437-447, 2015, doi:
10.1111/bjet.12147.
[11] P. Germanakos and M. Belk, “User Modeling,” Human-Centred Web Adaptation and Personalization. Springer,
Cham, pp. 79-102, 2016, doi: 10.1007/978-3-319-28050-9_3.
[12] L. A. Cárdenas-Robledo and A. Peña-Ayala, “A holistic self-regulated learning model: A proposal and application
in ubiquitous-learning,” Expert Systems with Applications, vol. 123, pp. 299-314, 2019, doi:
10.1016/j.eswa.2019.01.007.
[13] L. A. Cárdenas-Robledo and A. Peña-Ayala, “Ubiquitous learning: A systematic review,” Telematics and
Informatics, vol. 35, no. 5, pp. 1097-1132, 2018, doi: 10.1016/j.tele.2018.01.009.
[14] V. Slavuj, A. Meštrović and B. Kovačić, “Adaptivity in educational systems for language learning: a review,”
Computer Assisted Language Learning, vol. 30, no. 1-2, pp. 64-90, 2017, doi: 10.1080/09588221.2016.1242502.
[15] N. Idris, S. Z. M. Hashim, R. Samsudin and N. B. H. Ahmad, “Intelligent learning model based on significant
weight of domain knowledge concept for adaptive E-learning,” International Journal on Advanced Science,
Engineering and Information Technology (IJASEIT), vol. 7, no. 4-2, pp. 1486-1491, 2017, doi: 10.18517/ijaseit.7.4-
2.3408.
[16] E. Mousavinasab, N. Zarifsanaiey, S. R. N. Kalhori, M. Rakhshan, L. Keikha and M. G. Saeedi, “Intelligent
tutoring systems: a systematic review of characteristics, applications and evaluation methods,” Interactive Learning
Environments, vol. 29, no. 1, pp. 142-163, 2021, doi: 10.1080/10494820.2018.1558257.
[17] A. Al-Dhaqm, S. Razak, S. H. Othman, A. Ngadi, M. N. Ahmed and A. A. Mohammed, ‘‘Development and
validation of a database forensic metamodel (DBFM),’’ PloS one, vol. 12, no. 2, p. e0170793, February 2017, doi:
10.1371/journal.pone.0170793.
[18] Z. Jeremić, J. Jovanović and D. Gašević, “Student modeling and assessment in intelligent tutoring of software
patterns,” Expert Systems with Applications, vol. 39, no. 1, pp. 210-222, January 2012, doi:
10.1016/j.eswa.2011.07.010.
[19] J. Kim, A. Lee and H. Ryu, “Personality and its effects on learning performance: Design guidelines for an adaptive
E-learning system based on a user model,” International Journal of Industrial Ergonomics, vol. 43, no. 5, pp. 450-
461, 2013, doi: 10.1016/j.ergon.2013.03.001.
[20] S. Ouf, M. Abd Ellatif, S. E. Salama and Y. Helmy, “A proposed paradigm for smart learning environment based
on semantic web,” Computers in Human Behavior, vol. 72, pp. 796-818, 2017, doi: 10.1016/j.chb.2016.08.030.
11. Bulletin of Electr Eng & Inf ISSN: 2302-9285
Understanding the role of individual learner in adaptive and personalized … (Alva Hendi Muhammad)
3323
[21] I. El Guabassi, Z. Bousalem, M. Al Achhab, I. Jellouli and B. E. El Mohajir, “Personalized adaptive content system
for context-Aware ubiquitous learning,” Procedia Computer Science, vol. 127, pp. 444-453, 2018, doi:
10.1016/j.procs.2018.01.142.
[22] B. Dogan and E. Dikbıyık, “OPCOMITS: Developing an adaptive and intelligent web based educational system
based on concept map model,” Computer Applications in Engineering Education, vol. 24, no. 5, pp. 676-691, 2016,
doi: 10.1002/cae.21740.
[23] N. Valle, P. Antonenko, D. Valle, K. Dawson, A. C. Huggins-Manley and B. Baiser, “The influence of task-value
scaffolding in a predictive learning analytics dashboard on learners’ statistics anxiety, motivation, and
performance,” Computers & Education, vol. 173, p. 104288, 2021, doi: 10.1016/j.compedu.2021.104288.
[24] S. Sosnovsky and P. Brusilovsky, “Evaluation of topic-based adaptation and student modeling in QuizGuide,” User
Modeling and User-Adapted Interaction, vol. 25, no. 4, pp. 371-424, 2015, doi: 10.1007/s11257-015-9164-4.
[25] M. Lamia and L. M. Tayeb, “Discovering learner styles in adaptive E-learning hypermedia systems,” Journal of
Universal Computer Science, vol. 19, no. 11, pp. 1522-1542, 2013, doi: 10.3217/jucs-019-11-1522.
[26] A. Pensabe-Rodriguez, E. Lopez-Dominguez, Y. Hernandez-Velazquez, S. Dominguez-Isidro and J. De-la-Calleja,
“Context-aware mobile learning system: Usability assessment based on a field study,” Telematics and Informatics,
vol. 48, p. 101346, 2020, doi: 10.1016/j.tele.2020.101346.
[27] S. V. Kolekar, R. M. Pai and M. P. M. M., “Rule based adaptive user interface for adaptive E-learning system,”
Education and Information Technologies, vol. 24, no. 1, pp. 613-641, 2019, doi: 10.1007/s10639-018-9788-1.
[28] P. Brusilovsky and E. Millán, “User models for adaptive hypermedia and adaptive educational systems,” The
adaptive web. Springer, Berlin, Heidelberg, vol. 4321 LNCS, pp. 3-53, 2007, doi: 10.1007/978-3-540-72079-9_1.
[29] C. Conati, O. Barral, V. Putnam and L. Rieger, “Toward personalized XAI: A case study in intelligent tutoring
systems,” Artificial Intelligence, vol. 298, p. 103503, 2021, doi: 10.1016/j.artint.2021.103503.
[30] H. Xie, D. Zou, R. Zhang, M. Wang and R. Kwan, “Personalized word learning for university students: a profile-
based method for E-learning systems,” Journal of Computing in Higher Education, vol. 31, no. 2, pp. 273-289,
2019, doi: 10.1007/s12528-019-09215-0.
[31] A. Peña-Ayala and L. Cárdenas, “A Revision of the Literature Concerned with Mobile, Ubiquitous and Pervasive
Learning: A Survey,” in Mobile, Ubiquitous, and Pervasive Learning: Fundaments, Applications, and Trends, Ed.
Cham: Springer International Publishing, vol. 406, pp. 55-100, 2016, doi: 10.1007/978-3-319-26518-6_3.
[32] O. B. Yedri, L. El Aachak and M. Bouhorma, “Assessment-driven Learning through Serious Games: Guidance and
Effective Outcomes,” International Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 5, p.
3304, October 2018, doi: 10.11591/ijece.v8i5.pp3304-3316.
[33] S. Asai, D. T. D. Phuong, F. Harada and H. Shimakawa, “Predicting cognitive load in acquisition of programming
abilities,’’ International Journal of Electrical and Computer Engineering (IJECE), vol. 9, no. 4, pp. 3262-3271,
August 2019, doi: 10.11591/ijece.v9i4.pp3262-3271.
[34] M. Lang, M. Matta, L. Parolin, C. Morrone and L. Pezzuti, “Cognitive profile of intellectually gifted adults:
Analyzing the Wechsler Adult Intelligence Scale,” Assessment, vol. 26, no. 5, pp. 929-943, 2019, doi:
10.1177/1073191117733547.
[35] F. Colace, M. De Santo, D. Di Stasi and M. Casillo, “An adaptive educational hypermedia system for supporting
students in their traditional learning process in computer engineering education,” The International journal of
engineering education, vol. 32, no. 4, pp. 1625-1636, 2016.
[36] A. E. Labib, J. H. Canós and M. C. Penadés, “On the way to learning style models integration: a Learner’s
Characteristics Ontology,” Computers in Human Behavior, vol. 73, pp. 433-445, 2017, doi:
10.1016/j.chb.2017.03.054.
[37] S. Sarwar, Z. U. Qayyum, R. García-Castro, M. Safyan and R. F. Munir, “Ontology based E-learning framework: A
personalized, adaptive and context aware model,” Multimedia Tools and Applications, vol. 78, no. 24, pp. 34745-
34771, 2019, doi: 10.1007/s11042-019-08125-8.
[38] Y. Lin, S. Feng, F. Lin, W. Zeng, Y. Liu and P. Wu, “Adaptive course recommendation in MOOCs,” Knowledge-
Based Systems, vol. 224, p. 107085, 2021, doi: 10.1016/j.knosys.2021.107085.
[39] M. M. El-Bishouty et al., “Use of Felder and Silverman learning style model for online course design,” Educational
Technology Research and Development, vol. 67, no. 1, pp. 161-177, 2019, doi: 10.1007/s11423-018-9634-6.
[40] B. Arsovic and N. Stefanovic, “E-learning based on the adaptive learning model: case study in Serbia,” Sādhanā-
Academia Proceeding Engineering Science, vol. 45, no. 1, pp. 1-13, 2020, doi: 10.1007/s12046-020-01499-8.
[41] M. Rastegarmoghadam and K. Ziarati, “Improved modeling of intelligent tutoring systems using ant colony
optimization,” Education and Information Technologies, vol. 22, no. 3, pp. 1067-1087, 2017, doi: 10.1007/s10639-
016-9472-2.
[42] J. Feldman, A. Monteserin and A. Amandi, “Automatic detection of learning styles: state of the art,” Artificial
Intelligence Review, vol. 44, no. 2, pp. 157-186, 2015, doi: 10.1007/s10462-014-9422-6.
[43] H. M. El-Bakry and A. A. Saleh, “Adaptive E-learning based on learner’s styles,” Bulletin Electrical Engineering
and Informatics, vol. 2, no. 4, pp. 240-251, December 2013, doi: 10.11591/eei.v2i4.189.
[44] Ö. Özyurt and H. Özyurt, “Learning style based individualized adaptive E-learning environments: Content analysis
of the articles published from 2005 to 2014,” Computers in Human Behavior, vol. 52, pp. 349-358, 2015, doi:
10.1016/j.chb.2015.06.020.
[45] J. El Bouhdidi, M. Ghailani and A. Fennan, “A probabilistic approach for the generation of learning sessions
tailored to the learning styles of learners,” International Journal of Emerging Technologies in Learning (IJET), vol.
8, no. 6, pp. 42-49, 2013, doi: 10.3991/ijet.v8i6.3084.
[46] S. Fatahi, “An experimental study on an adaptive E-learning environment based on learner’s personality and
12. ISSN: 2302-9285
Bulletin of Electr Eng & Inf, Vol. 10, No. 6, December 2021 : 3313 – 3324
3324
emotion,” Education and Information Technologies, vol. 24, no. 4, pp. 2225-2241, 2019, doi: 10.1007/s10639-019-
09868-5.
[47] C. Romero and S. Ventura, “Educational data mining and learning analytics: An updated survey,” WIREs Data
Mining and Knowledge Discovery, vol. 10, no. 3, pp. 1-21, January 2020, doi: 10.1002/widm.1355.
[48] U. Lucke and C. Rensing, “A survey on pervasive education,” Pervasive and Mobile Computing, vol. 14, pp. 3-16,
2014, doi: 10.1016/j.pmcj.2013.12.001.
[49] A. A. Kardan, M. Aziz and M. Shahpasand, “Adaptive systems: a content analysis on technical side for E-learning
environments,” Artificial intelligence review, vol. 44, no. 3, pp. 365-391, June 2015, doi: 10.1007/s10462-015-
9430-1.
[50] O. Zawacki-Richter, V. I. Marín, M. Bond and F. Gouverneur, “Systematic review of research on artificial
intelligence applications in higher education - where are the educators?,” International Journal of Educational
Technology in Higher Education, vol. 16, no. 1, pp. 1-27, 2019, doi: 10.1186/s41239-019-0171-0.
BIOGRAPHIES OF AUTHORS
Alva Hendi Muhammad is a lecturer of Informatics Engineering at Universitas Amikom
Yogyakarta in Indonesia. He also served as the Secretary of the Postgraduate Program in
Informatics Engineering (distance learning). Alva has a Ph.D in IT from the University of
Technology Sydney Australia. His research interests include AI for Engineering and
Educational Technology, distance learning, as well as decision and expert system modeling.
Dhani Ariatmanto is a full-time lecturer at the Faculty of Informatics, University of Amikom
Yogyakarta. He was born in Jakarta, Indonesia, in 1980. Dhani received a master’s degree in
Informatics Engineering from the University of Amikom Yogyakarta. He is currently pursuing
a doctoral program at Universiti Malaysia Pahang. His research interests include Image
Processing, Digital Watermarking, and Multimedia Application.