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A Novel E-learning Approach to Add More Cognition to Semantic Web
Abstract
E-Learning approach using semantic web provides relevant and meaningful information to the learner but human mind designs its own cognitive structure of the information which is fuzzy and uncertain. When knowledge structure of any domain is large and well connected then it is very easy to learn and acquire semantically connected knowledge. An E-Learning approach is designed where the semantic web is made more meaningful by adding human conceptual representation and reasoning mechanism to learn based upon the knowledge, profile and experience of learner.
... But changing conditions have directed researchers to develop more advanced software solutions. Nowadays, running Artificial Intelligence is a popular research interest for improving using features and functions of educational software systems and providing more effective and efficient teaching-learning activities [11][12][13][14][15][16][17][18][19][20][21][22][23]. By using Artificial Intelligence based factors, it is possible to design and develop more effective, efficient, and flexible software systems. ...
Self-learning process is an important factor that enables learners to improve their own educational experiences when they are away of face-to-face interactions with the teacher. A well-designed self-learning activity process supports both learners and teachers to achieve educational objectives rapidly. Because of this, there has always been a remarkable trend on developing alternative self-learning approaches. In this context, this study is based on two essential objectives. Firstly, it aims to introduce an intelligent software system, which optimizes and improves computer engineering students’ self-learning processes. Secondly, it aims to improve computer engineering students’ self-learning during the courses. As general, the software system introduced here evaluates students’ intelligence levels according to the Theory of Multiple Intelligences and supports their learning via accurately chosen materials provided over the software interface. The evaluation mechanism of the system is based on a hybrid Artificial Intelligence approach formed by an Artificial Neural Network, and an optimization algorithm called as Vortex Optimization Algorithm (VOA). The system is usable for especially technical courses taught at computer engineering departments of universities and makes it easier to teach abstract subjects. For having idea about success of the system, it has been tested with students and positive results on optimizing and improving self-learning have been obtained. Additionally, also a technical evaluation has been done previously, in order to see if the VOA is a good choice to be used in the system. It can be said that the whole obtained results encourage the authors to continue to future works. © 2017 Wiley Periodicals, Inc. Comput Appl Eng Educ 25:142–156, 2017; View this article online at wileyonlinelibrary.com/journal/cae; DOI 10.1002/cae.21787.
... INTRODUCTION Bulut Ozek et al., 2013; Conati et al., 2015; Graesser et al., 2005; Hernández‐ Del‐ Olmo, & Gaudioso, 2013; Hooshyar et al., 2015; Nespit et al., 2014; Jain et al., 2014; Walia et al., 2015; Wang et al., 2015; Wiggins et al., 2015Table 1When we examine the Table 2 ...
Nowadays, artificial intelligence supported e-learning scenarios are widely employed by educational institutions in order to ensure better teaching and learning experiences along educational activities.
In the context of performed scientific studies, positive results often encourage such institutions to apply their intelligent e-learning systems on different types of courses and report advantages of artificial intelligent in especially education field. It seems that the future of education will generally depend on important, multidisciplinary research areas like artificial intelligence. At this point, this study aims to report obtained findings regarding to usage of an artificial intelligence based e-learning software in English language courses. In this sense, the e-learning software has been used for one term in three different countries: Turkey, Italy, and Romania. As an international perspective for their intelligent, e-learning software and the approach, the authors are satisfied with the positive results.
With the development of information communication technologies, innovations, recent advances and there up gradation, their applications in learning are expanding. Innovations and creativity can deliver powerful learning results. In this paper, a neural network model with multilayer perceptron and back propagation method for the evaluation of database subject has been proposed. The proposed model has reported good results during the testing phase. This paper confers an intelligent model for the performance evaluation of IT and computer science students. The evaluation of student performance is significant to both; educational institutions and students as well for several purposes. The overall accuracy of the model is 87.5% when tested for the proposed Neural Network model.
Objective of this chapter is to introduce an Augmented Reality based intelligent mobile application (M-Learning application) to support courses of Computer Education. In the study, it was aimed to provide an alternative way of improving M-Learning experiences by employing both Augmented Reality and Artificial Intelligence based approaches in a common environment. Briefly, the application is able to use an intelligent mechanism evaluating students' several dynamic learning parameters to match them with the most appropriate course materials provided over the system. So, each student can encounter with appropriate course materials matching with their states over the application system. The related course materials include both AR based ones and standard ones as uploaded by teachers. An evaluation based flow has been run in the study by using the developed application through courses of Computer Education and the obtained results have shown that the application is effective and successful enough at improving students' learning experiences and achieving a good Open Computer Education.
In this paper, we present a conceptual framework for using the Semantic Web to get student data from other educational institutions, enabling the educational institutions to communicate and exchange student data. Educational institutions could then use this information to adjust the students' profiles and modify their learning paths. Semantic Web will create a more personalized dynamic course for each student, according to his/her ability, educational level, and experience.
Objective of this chapter is to introduce an Augmented Reality based intelligent mobile application (M-Learning application) to support courses of Computer Education. In the study, it was aimed to provide an alternative way of improving M-Learning experiences by employing both Augmented Reality and Artificial Intelligence based approaches in a common environment. Briefly, the application is able to use an intelligent mechanism evaluating students' several dynamic learning parameters to match them with the most appropriate course materials provided over the system. So, each student can encounter with appropriate course materials matching with their states over the application system. The related course materials include both AR based ones and standard ones as uploaded by teachers. An evaluation based flow has been run in the study by using the developed application through courses of Computer Education and the obtained results have shown that the application is effective and successful enough at improving students' learning experiences and achieving a good Open Computer Education.
ELearning (Electronic Learning) and m-learning (Mobile Learning) systems are online learning platforms. In our research we are modeling them as a weighted directed graph where each node represents a course unit. A directed graph represents an accurate picture of course descriptions for online courses through the computer-based implementation of various educational systems. The Learning Path Graph (LPG) represents and describes the structure of domain knowledge, including the learning goals, and all other available learning paths. In this paper, we propose an adaptive m-learning system architecture and a conceptual framework that uses the Semantic Web to obtain the students' data from other educational institutions. This process will enable the educational institutions to communicate and exchange students' data, and then use this information to adjust the students' profiles and modify their learning paths. The Semantic Web will create a more personalized dynamic course for individual students according to their ability, educational level, and experience.
E-Learning is efficient, task relevant and just-in-time learning grown from the learning requirements of the new, dynamically changing, distributed business world. The term Semantic Web encompasses efforts to build a new WWW architecture that enhances content with formal semantics, which enables better possibilities for navigating through the cyberspace and accessing its contents.Semantic Web is a product of Web 2.0 (second generation of web) that is supported with automated semantic agents for processing user data to help the user on ease of use and personalization of services. The paper describes a framework for the implementation of an e-learning system based on the Semantic Web, using desktop c# application, data visualization tools, HTML web page parser, RDF generating tools, and SPARQL RDF query language. Hopefully we have elucidated the enormous potential of making web content machine-understandable.
This paper describes a framework for the implementation of an e-learning system based on the Semantic Web, using software agents and Java Web Services.
In the last decade, the evolution of educational technologies has forced an extraordinary interest in new methods for delivering learning content to learners. Today, distance education represents an effective way for supporting and sometimes substituting the traditional formative processes, thanks to the technological improvements achieved in the field in recent years. However, the role of technology has often been overestimated. The amount of information students can obtain from the Internet is huge, and as a result, they can easily be confused. Teachers can also be disconcerted by this vast quantity of content and are often unable to suggest the correct content to their students. In the open scientific literature, it is widely recognized that an important factor for success in delivering learning content is related to the capability for customizing the learning process for the specific needs of a given learner. This task is still far from having been fully accomplished, and there is a real interest in investigating new approaches and tools to adapt the formative process to specific individual needs. In this scenario, the introduction of ontology formalism can improve the quality of the formative process, allowing the introduction of new and effective services. Ontologies can lead to important improvements in the definition of a course's knowledge domain, in the generation of an adapted learning path, and in the assessment phase. This paper provides an initial discussion of the role of ontologies in the context of e-learning. The improvements related to the introduction of ontologies formalism in the e-learning field are discussed, and a novel algorithm for ontology building through the use of Bayesian networks is shown. Finally, the application of this algorithm in the assessment process and some experimental results are illustrated.
One of the key deficiencies of the Semantic Web is its lack of cognitive plausibility. We argue that by accounting for people's reasoning mechanisms and cognitive representations, the usefulness of information coming from the Semantic Web will be enhanced. More specifically, the utilization and integration of conceptual spaces is proposed as a knowledge representation that affords two important human cognitive mechanisms, i.e., semantic similarity and concept combination. Formal conceptual space algebra serves as the basis for the Conceptual Space Markup Language (CSML), which facilitates the engineering of ontologies using a geometric framework. We demonstrate the usefulness of the approach through a concrete example and suggest directions for future work, especially the need for combining geometric representations and reasoning mechanisms with existing Semantic Web structures.
eLearning is efficient, task relevant and just-in-time learning grown from the learningrequirements of the new, dynamically changing, distributed business world. The term "Semantic Web" encompasses efforts to build a new WWW architecture that enhances content with formal semantics, which enables better possibilities for navigating through the cyberspace and accessing its contents. As such, the Semantic Web represents a promising technology for realizing eLearning requirements.
Introduction
The 12-lead Electrocardiogram (ECG) presents a plethora of information and demands extensive knowledge and a high cognitive workload to interpret. Whilst the ECG is an important clinical tool, it is frequently incorrectly interpreted. Even expert clinicians are known to impulsively provide a diagnosis based on their first impression and often miss co-abnormalities. Given it is widely reported that there is a lack of competency in ECG interpretation, it is imperative to optimise the interpretation process. Predominantly the ECG interpretation process remains a paper based approach and whilst computer algorithms are used to assist interpreters by providing printed computerised diagnoses, there are a lack of interactive human-computer interfaces to guide and assist the interpreter.
Methods
An interactive computing system was developed to guide the decision making process of a clinician when interpreting the ECG. The system decomposes the interpretation process into a series of interactive sub-tasks and encourages the clinician to systematically interpret the ECG. We have named this model ‘Interactive Progressive based Interpretation’ (IPI) as the user cannot ‘progress’ unless they complete each sub-task. Using this model, the ECG is segmented into five parts and presented over five user interfaces (1: Rhythm interpretation, 2: Interpretation of the P-wave morphology, 3: Limb lead interpretation, 4: QRS morphology interpretation with chest lead and rhythm strip presentation and 5: Final review of 12-lead ECG). The IPI model was implemented using emerging web technologies (i.e. HTML5, CSS3, AJAX, PHP and MySQL). It was hypothesised that this system would reduce the number of interpretation errors and increase diagnostic accuracy in ECG interpreters. To test this, we compared the diagnostic accuracy of clinicians when they used the standard approach (control cohort) with clinicians who interpreted the same ECGs using the IPI approach (IPI cohort).
Results
For the control cohort, the (mean; standard deviation; confidence interval) of the ECG interpretation accuracy was (45.45%; SD = 18.1%; CI = 42.07, 48.83). The mean ECG interpretation accuracy rate for the IPI cohort was 58.85% (SD = 42.4%; CI = 49.12, 68.58), which indicates a positive mean difference of 13.4%. (CI = 4.45, 22.35) An N − 1 Chi-square test of independence indicated a 92% chance that the IPI cohort will have a higher accuracy rate. Interpreter self-rated confidence also increased between cohorts from a mean of 4.9/10 in the control cohort to 6.8/10 in the IPI cohort (p = 0.06). Whilst the IPI cohort had greater diagnostic accuracy, the duration of ECG interpretation was six times longer when compared to the control cohort.
Conclusions
We have developed a system that segments and presents the ECG across five graphical user interfaces. Results indicate that this approach improves diagnostic accuracy but with the expense of time, which is a valuable resource in medical practice.
In the present study, a semantic perspective on e-learning theory is advanced and a modelling approach is used. This modelling approach towards the new learning theory is based on the four SECI phases of knowledge conversion: Socialisation, Externalisation, Combination and Internalisation, introduced by Nonaka in 1994, and involving two levels of knowledge—tacit and explicit. In the Socialisation phase, the teacher–student interaction activates the exploratory learning behaviour. This phase is emotionally and socially loaded. The Externalisation phase is partly emotional but a cognitive dimension is also needed, which requires creativity. This phase works optimally if it is collaborative in nature. In the Combination phase, an ability for hypothetical–deductive thinking is needed for the modelling approach. During the Internalisation phase, the learning process requires that students engage in seeking to understand and explain natural phenomena, which further demands testing of the theoretical concepts.
E-learning is the use of technology to enable to learn anywhere and anytime. The e-learning is fast, relevant and just-in-time learning grows from the learning requirement. In WWW for more improvement in e-learning the concept of the semantic web is used for developing e-learning.
Semantic web is mesh information linked up in such away to easily process able by the machine on global scale. The Semantic web relies heavily on the formal ontology that structure underling data from the purposes of comprehensive and transportable machine understanding. The success of the semantic web strongly depends upon the ontology.
This paper presents an approach for developing ontology based e-learning system for an Indian university.
Therapy decision making and support in medicine deals with uncertainty and needs to take into account the patient's clinical parameters, the context of illness and the medical knowledge of the physician and guidelines to recommend a treatment therapy. This research study is focused on the formalization of medical knowledge using a cognitive process, called Fuzzy Cognitive Maps (FCMs) and semantic web approach. The FCM technique is capable of dealing with situations including uncertain descriptions using similar procedure such as human reasoning does. Thus, it was selected for the case of modeling and knowledge integration of clinical practice guidelines. The semantic web tools were established to implement the FCM approach. The knowledge base was constructed from the clinical guidelines as the form of if-then fuzzy rules. These fuzzy rules were transferred to FCM modeling technique and, through the semantic web tools, the whole formalization was accomplished. The problem of urinary tract infection (UTI) in adult community was examined for the proposed approach. Forty-seven clinical concepts and eight therapy concepts were identified for the antibiotic treatment therapy problem of UTIs. A preliminary pilot-evaluation study with 55 patient cases showed interesting findings; 91% of the antibiotic treatments proposed by the implemented approach were in fully agreement with the guidelines and physicians' opinions. The results have shown that the suggested approach formalizes medical knowledge efficiently and gives a front-end decision on antibiotics' suggestion for cystitis. Concluding, modeling medical knowledge/therapeutic guidelines using cognitive methods and web semantic tools is both reliable and useful.
Recent developments of e-learning specifications such as Learning Object Metadata (LOM), Sharable Content Object Reference Model (SCORM), Learning Design and other pedagogy research in semantic e-learning have shown a trend of applying innovative computational techniques, especially Semantic Web technologies, to promote existing content-focused learning services to semantic-aware and personalised learning services. To facilitate this transforming process, this paper presents a novel context-aware semantic e- learning approach to integrate content provision, learning process and learner personality in an integrated semantic e-learning framework. As the basis of the computational framework, a scalable and extensible generic context model is proposed to structure the semantics of contextual relations and concepts in various contexts, such as learning content description, learning model, knowledge object representation and learner personality. Corresponding technical and pedagogical developments of this framework also consider compatibility issues with existing technologies (eg, XML/Resource Description Framework) and specifications (eg, IEEE LOM) in order to achieve the best interoperability.