E-learning Domain Research Articles

Improving Learner’s Cognitive Learning Skills in Course based Learning Environment

Objectives: The existing metadata standards in e-learning domain have been subject to much criticism for its lack of explicit category of metadata to allow cognitive-based description of learning resources. In order to overcome the deficiency of existing metadata standards, this research work proposed the content model for course based e-learning environment. Methods/Statistical Analysis: A course-based e-learning approach is employed to assist the students in constructing their own knowledge, by taking cognitive constraints into consideration. We are proposing the semantic based framework to describe the educational material for the course based learning environment. Findings: The researcher examined the required background knowledge for cognitive thinking, the differences between knowledge and cognition and then proposed the semantic based structuring of course contents for providing adaptable and intelligent learning environment. Application/Improvements: The proposed course content model influences the thinking skills, cognitive behaviour of learner and to overcome the deficiency of existing metadata standards.

ICT enabled classroom effectiveness scale development and validation: A case of multi-campus university

The research work aims at developing a valid and reliable scale for ICT (Information and communication technology) enabled classroom effectiveness from student’s perspective in a multi-campus university setting. A standard methodology for scale development is used for developing and validating the scale which comprises of exploratory and confirmatory factor analysis. The sample population was the students from a premier multi-campus university. The results revealed ICT enabled classroom effectiveness as a multi-dimensional construct comprising of four factors namely class design and infrastructure; scheduling and coordination; technical support staff; and resource availability. Although a plethora of literature is available in the domain of e-learning, none of them have considered the aspects of ICT enabled classroom effectiveness specifically in an Indian multi-campus university. The limitation of the study lies in terms of sample size and generalizability. Emphasizing the identified factors will give a cutting edge advantage for the universities by enhancing the effectiveness and efficiency of ICT enabled classroom teaching.

A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining

In recent years, there has been significant growth in the use of online learning resources by learners. However, due to information overload, many learners are experiencing difficulties in retrieving useful and relevant learning resources that meet their learning needs. Although existing recommender systems have recorded significant success in e-commerce domain, they still experience drawbacks in making accurate recommendations of learning resources in e-learning domain due to differences in learner characteristics such as learning style, knowledge level as well as learners’ sequential learning patterns. Most of the existing recommendation techniques do not consider differences in learner characteristics. This problem can be alleviated through incorporation of additional information about the learner into the recommendation process. Furthermore, many recommendation techniques experience cold-start and rating sparsity problems. In this paper, we propose a hybrid knowledge-based recommender system based on ontology and sequential pattern mining (SPM) for recommendation of e-learning resources to learners. In the proposed recommendation approach, ontology is used to model and represent the domain knowledge about the learner and learning resources whereas SPM algorithm discovers the learners’ sequential learning patterns. Our approach involves four steps: (1) creating ontology to represent knowledge about the learner and learning resources, (2) computing ratings similarity based on ontology domain knowledge and making predictions for the target learner, (3) generation of top N learning items by the collaborative filtering recommendation engine, and (4) application of SPM algorithm to the top N learning items to generate the final recommendations for the target learner. A number of experiments were carried out to evaluate the proposed hybrid recommender system and results show improved performance. Furthermore, the proposed hybrid approach can alleviate both the cold-start and data sparsity problems by making use of ontological domain knowledge and learner’s sequential access pattern respectively before the initial data to work on is available in the recommender system.

Mapeamento Sistemático na Literatura Acadêmico-Científica sobre Abordagens para Formação de Grupos em E-Learning

This work presents the results of a systematic mapping on scientific papers that discuss the group formation for collaborative learning. The aim is to investigate researches associated with approaches and strategies to form group, in the context of E-Learning domains. Our mapping shows that the majority of publications are focused on analyzing the context of students interactions, along collaborative E-learning activities, and use the acquired knowledge to improve the algorithms for groups optimization. Group types were specified for further experiment in homogeneous grouping, heterogeneous grouping and complementary for programming skills from mathematical modeling and implementation of algorithm grouping. The results of this work produce theoretical fundaments to support the investigation of new group formation approaches, which are based on group profiles and associated with criteria and preferences that were not explored yet. Furthermore, the results enable a more precise analysis of interactions and impact of collaboration on the efficiency of e-learning students.

Knowledge-based recommendation: a review of ontology-based recommender systems for e-learning

Recommender systems in e-learning domain play an important role in assisting the learners to find useful and relevant learning materials that meet their learning needs. Personalized intelligent agents and recommender systems have been widely accepted as solutions towards overcoming information retrieval challenges by learners arising from information overload. Use of ontology for knowledge representation in knowledge-based recommender systems for e-learning has become an interesting research area. In knowledge-based recommendation for e-learning resources, ontology is used to represent knowledge about the learner and learning resources. Although a number of review studies have been carried out in the area of recommender systems, there are still gaps and deficiencies in the comprehensive literature review and survey in the specific area of ontology-based recommendation for e-learning. In this paper, we present a review of literature on ontology-based recommenders for e-learning. First, we analyze and classify the journal papers that were published from 2005 to 2014 in the field of ontology-based recommendation for e-learning. Secondly, we categorize the different recommendation techniques used by ontology-based e-learning recommenders. Thirdly, we categorize the knowledge representation technique, ontology type and ontology representation language used by ontology-based recommender systems, as well as types of learning resources recommended by e-learning recommenders. Lastly, we discuss the future trends of this recommendation approach in the context of e-learning. This study shows that use of ontology for knowledge representation in e-learning recommender systems can improve the quality of recommendations. It was also evident that hybridization of knowledge-based recommendation with other recommendation techniques can enhance the effectiveness of e-learning recommenders.

An Intelligent Service-Based Layered Architecture for eLearning and eAssessment

The rapid advancement in ICT (Information & Communication Technology) is causing a paradigm shift in eLearning domain. Traditional eLearning systems suffer from certain shortcomings like tight coupling of system components, lack of personalization, flexibility, and scalability and performance issues. This study aims at addressing these challenges through an MAS (Multi Agent System) based multi-layer architecture supported by web services. The foremost objective of this study is to enhance learning process efficiency by provision of flexibility features for learning and assessment processes. Proposed architecture consists of two sub-system namely eLearning and eAssesssment. This architecture comprises of five distinct layers for each sub-system, with active agents responsible for miscellaneous tasks including content handling, updating, resource optimization, load handling and provision of customized environments for learners and instructors. Our proposed architecture aims at establishment of a facilitation level to learners as well as instructors for convenient acquisition and dissemination of knowledge. Personalization features like customized environments, personalized content retrieval and recommendations, adaptive assessment and reduced response time, are believed to significantly enhance learning and tutoring experience. In essence characteristics like intelligence, personalization, interactivity, usability, laidback accessibility and security, signify aptness of proposed architecture for improving conventional learning and assessment processes. Finally we have evaluated our proposed architecture by means of analytical comparison and survey considering certain quality attributes.

Sentiment Analysis to Evaluate Teaching Performance

The aim of this work is to review a specific learning analytics method - sentiment analysis - in the field of Higher Education, showing how it is employed to monitor student satisfaction on different platforms, and to propose an architecture of Sentiment Analysis for Higher Education purposes, which trace and unify what emerges from the literature review. First, a literature review is carried out, which proves the widespread and increasing interest of the communities, of both scholars and practitioners, in the use of sentiment analysis in the field of Higher Education. The analysis, focused on three different e-learning domains, identifies weaknesses and gaps, and in particular the lack of a unifying approach which is able to deal with the different domains. Secondly, a prototype architecture – LADEL (Learning Analytics Dashboard for E-Learning) - is introduced, which is able to deal with the different e-learning domains. Some preliminary experiments are carried out, highlighting some limitations and open issues, as stimulus to continue the development of the platform.

Intuitionistic Group Decision Making to Identify the Status of Student's Knowledge Acquisition in E-Learning Systems

Learner's performance evaluation in an E-learning environment is a multi-criteria decision problem and important to personalize the sequence of learning concepts according to their knowledge level. Crisp responses, leads uncertainty in the evaluation process due to successful guesses or choosing a more probable answer. Analysis of learner's response to a complex/subjective questions needs more effort. Moreover, due to uncertainty and imprecise nature of learner, traditional methods are inadequate to assess, how much time he has spent on studying the learning contents and also the number of backtracking he followed. This paper proposes an intuitionistic fuzzy multicriteria decision making that investigates the use of an intuitionistic fuzzy technique for order preference by similarity to ideal solution (TOPSIS method) for the evaluation of student in an E-learning environment. Synthetic data are created for the criteria's that affect the student evaluation in E-learning domain and thereafter results are evaluated.

Information Sharing, Community Development, and Deindividuation in the eLearning Domain

In a study of the information behaviors of graduate students enrolled in an online Masters of Library and Information Science (MLIS) program, it was determined that learners engage in threaded discussions not only for cognitive purposes but for affective reasons as well. The information sharing among students was particularly prolific during a session in which medical ailments and information were discussed. Data were collected from an asynchronous class in a graduate LIS program, and were examined through learner/context analysis and textual analysis. This study used syllabi, course construction, specific assignments and requirements, and other details that contribute to the totality of the learning environment. Specific attention was given to the threaded discussions assigned in the class. This data provided insight into the students’ activities and learning during 15 weeks, and enhanced the overall context for the small world that develops within an online learning community.
 Students connected with their peers and instructor through copious exchanges of information during which a concerted and consistent effort was made to connect with one another by using personal names, engaging in humor and joke-telling, using emoticons, and expressing support and empathy.

Distributed learning process: principles of design and implementation

At the present stage, broad information and communication technologies (ICT) usage in educational practices is one of the leading trends of global education system development. This trend has led to the instructional interaction models transformation. Scientists have developed the theory of distributed cognition (Salomon, G., Hutchins, E.), and distributed education and training (Fiore, S. M., Salas, E., Oblinger, D. G., Barone, C. A., Hawkins, B. L.). Educational process is based on two separated in time and space sub-processes of learning and teaching which are aimed at the organization of fl exible interactions between learners, teachers and educational content located in different non-centralized places. The purpose of this design research is to fi nd a solution for the problem of formalizing distributed learning process design and realization that is signifi cant in instructional design. The solution to this problem should take into account specifi cs of distributed interactions between team members, which becomes collective subject of distributed cognition in distributed learning process. This makes it necessary to design roles and functions of the individual team members performing distributed educational activities. Personal educational objectives should be determined by decomposition of team objectives into functional roles of its members with considering personal and learning needs and interests of students. Theoretical and empirical methods used in the study: theoretical analysis of philosophical, psychological, and pedagogical literature on the issue, analysis of international standards in the e-learning domain; exploration on practical usage of distributed learning in academic and corporate sectors; generalization, abstraction, cognitive modelling, ontology engineering methods. Result of the research is methodology for design and implementation of distributed learning process based on the competency approach. Methodology proposed by authors determines specifi cs of educational objectives, learning content and distributed learning lifecycle design, approaches to implementing learning scenarios, as well as the functions and roles of students and teachers in a distributed interaction. Main advantage of the author’s methodology is that it allows to use various paradigms (behaviorism, cognitivism, constructivism) in instructional design. It is applicable to all levels of distributed learning: traditional face-to-face learning with ICT support, blended learning, distance learning in virtual learning environments. Conclusion. Methodology presented by the authors has been tested in the professional teachers training programs for IT-teachers to be at the Novokuznetsk Institute (Branch) of Kemerovo State University in the curriculum of disciplines ‘Educational process management informatization’, ‘Innovative methods and technologies for e-learning’ and ‘Distance learning organization’.

A Study about Using E-learning Platform (Moodle) in University Teaching Process

The paper present a study about using e-learning platform in university teaching process. The study aims to present the relevant results of a questionnaire administered to a group of students from of different faculties of University POLITEHNICA of Bucharest and identified the way in which Moodle platform could increase the learning motivation and how much are the interested students to develop learning activities in the virtual space. Following this empirical research, a series of conclusions could be drawn and synthetized, as concerns the need of training and information the e-learning domain.

An Ontology Based Framework for Intelligent Web Based e-Learning

E-Learning is a fast, just-in-time, and non-linear learning process, which is now widely applied in distributed and dynamic environments such as the World Wide Web. Ontology plays an important role in capturing and disseminating the real world knowledge for effective human computer interactions. However, engineering of domain ontologies is very labor intensive and time consuming. Some machine learning methods have been explored for automatic or semi-automatic discovery of domain ontologies. Nevertheless, both the accuracy and the computational efficiency of these methods need to be improved. While constructing large scale ontology for real-world applications such as e-learning, the ability to monitor the progress of students' learning performance is a critical issue. In this paper, a system is proposed for analyzing students' knowledge level obtained using Kolb's classification based on the students level of understanding and their learning style using cluster analysis. This system uses fuzzy logic and clustering algorithms to arrange their documents according to the level of their performance. Moreover, a new domain ontology discovery method is proposed uses contextual information of the knowledge sources from the e-Learning domain. This proposed system constructs ontology to provide an effective assistance in e-Learning. The proposed ontology discovery method has been empirically tested in an e-Learning environment for teaching the subject Database Management Systems. The salient contributions of this paper are the use of Jaccard Similarity measure and K-Means clustering algorithm for clustering of learners and the use of ontology for concept understanding and learning style identification. This helps in adaptive e-learning by providing suitable suggestions for decision making and it uses decision rules for providing intelligent e-Learning.

The effectiveness of internet-based e-learning on clinician behavior and patient outcomes: a systematic review protocol.

Review question/objective The objective of this systematic review is to identify, appraise and synthesize the best available evidence for the effectiveness of internet-based e-learning programs on health care professional behavior and patient outcomes. Inclusion criteria Types of participants This review will consider studies whose participants were Health care professionals in any health care context. For the purpose of this review, a Health care professionals will be any individual who requires a degree qualification to practice in their respective field Types of intervention(s) This review will consider studies that evaluated an internet-based e-learning program. For the purposes of this review internet-based e-learning programs are defined as any asynchronous educational intervention that is mediated electronically via the internet. Types of outcomes This review will consider studies that include the following outcome measures: • Impact on clinician behavior measured using objectively administered evaluation criteria • Impact on patient outcomes measured using objectively administered evaluation criteria For the purposes of this review, impact on clinician behavior is defined as the degree to which the intervention influenced their ability to perform the skill for which the intervention was designed. For the purposes of this review, impact on patient outcomes is defined as the degree to which patients’ health care outcomes were affected (either positively or negatively) as a result of the intervention.

A Review of the Critical Success Factors of Implementing e-Learning in Higher Education

E-Learning in higher education has changed the way of how teaching and learning are conducted, and this has also created new learning opportunities for both on and off campus education. To implement e-learning in higher education institutions successfully, many factors need to be examined. The scope of the subject of e-learning discussed in this paper is limited to internet-based learning, which includes networked learning, distance learning, and online learning. The main purpose of this paper is to review past research in the domain of e-learning in order to identify the Critical Success Factors (CSF) that can affect the successful implementation of e-learning in higher education. This paper will summarize and present eight important CSFs commonly found which are educational technology, computing experience, attitude, social influence, course development, language, teaching and learning styles and demography of the students.

Knowledge and intelligent computing methods in e-learning

E-learning is the use of technology that enables people to learn at anytime from anywhere. Various single knowledge-based methods (KBM) such as rule-base reasoning (RBR) and case-base reasoning (CBR); and intelligent computing methods (ICM) such as genetic algorithm (GA), particle swarm optimisation (PSO), artificial neural network (ANN), multi-agent systems (MAS), ant colony optimisation (ACO), fuzzy logic (FL) etc. Integrated KBM-ICM methods such as GA-CBR, ANN-RBR, GA-Ontology and ANN-Mining have been used in various e-learning contexts such as: the learning path generation, adaptive course sequencing and personalisation of recommended learning object etc. We have made a study of different individual KBM and ICM methods; and integrated KBS-ICM methods applicable to e-learning domain right from the mid 1990s to 2014. The study is presented in a tabular form, showing the KBM-ICM methods, e-learning problems to be addressed, specific features and the implementation in the e-learning domain. From the results, it is observed that a single KBM is not deployed to solve any e-learning problem. A single ICM and integrated KBM-ICM methods are used to solve various e-learning problems. The study and its presentation in the context help the novice researchers to resume their work in the area of e-learning systems.

Ontology based Semantic Web Technologies in E-learning Environment using Protégé

The rapid advanced development of mobile phone usage has been increased very sharply. A semantic web is major concept about creation of Meta data. Most of the web page oriented application like HTML and XML uses the concept of Meta data. This paper proposes class concept hierarchy method for implementing and retrieving information about e-learning domain through PROTEGE tools and also shows RDFS and OWL based samples method implementation for e-learning domains in these tools. It produced the result like number of course material prepared by tutor, total number of student registration and number of exam, quiz, assignment conducted by particular tutor using SPARQL query language.

Ontology based Semantic Web Technologies in E-learning Environment using Protégé

The rapid advanced development of mobile phone usage has been increased very sharply. A semantic web is major concept about creation of Meta data. Most of the web page oriented application like HTML and XML uses the concept of Meta data. This paper proposes class concept hierarchy method for implementing and retrieving information about e-learning domain through PROTEGE tools and also shows RDFS and OWL based samples method implementation for e-learning domains in these tools. It produced the result like number of course material prepared by tutor, total number of student registration and number of exam, quiz, assignment conducted by particular tutor using SPARQL query language.

Integration Solutions for Video Data in Educational Platforms

Interdisciplinarity will become more and more not just a way of restructuring content, but also a new method of organizing the teaching process. The need of reusing video data or parts of it in the context or content of diverse courses is often imposed. Successful management of large video data implies using the results obtained on queries based on semantics. This paper aims to present the mode in which video data are modeled in e-Learning platforms trough semantic annotation of the content, using the MPEG-7 standard and specific ontologies of the e-Learning domain and as far as retrieval and reuse of results in specific applications for creating interactive courses.

Comparative study of mining algorithms for adaptive e-learning environment

Many algorithms have been introduced in the area of sequential pattern mining over the past few years. In this paper, we try to investigate some of these algorithms and make a performance comparison. The main objective of this paper is a survey based on the recently published research papers to perform the comparison of various frequent pattern mining algorithms in the realm of adaptive e-learning. This study gives the comparative advantages and drawbacks of these algorithms. For this, we use a real case study of an Indian e-learning site and select the most suitable algorithm for generating frequent usage patterns (of topics referred by various learners). This would be useful in recommending the next navigation path to the new learner(s) in an adaptive e-learning domain.

Graph-based semantic annotation for enriching educational content with linked data

In this paper, a new approach to semantic annotation with linked data in the field of document enrichment is presented. This application has been developed in the domain of Education and contrary to traditional semantic annotation, which relates each relevant term of the document with an instance of the ontology, in our approach relevant terms are connected to a (sub)graph of the ontology. Specifically, each relevant term is related to an instance which is expanded to a predefined depth limit, so the term is finally annotated with a (sub)graph. During the expansion process, instances unrelated with the document topics are ruled out, so only relevant and contextualized information is finally included. As result of this process, the document is annotated with a set of interconnected (sub)graphs, and students may access and navigate through these contents to deepen the topics described in the document. This approach has several benefits. From the document enrichment perspective, a set of (sub)graphs, provides a better description, moreover considering the semantic nature of linked data. From the computational perspective, this approach is also more suitable, particularly in the domain of Education. Filtering linked data is computationally expensive, and thus this process cannot be performed in real time. Our approach has been validated in the e-Learning domain and compared with similar approaches that also annotate with linked data.