An Fuzzy Model of Emotion for Virtual Human in E-learning Software

2008 ◽  
Author(s):  
Zhen Liu
Keyword(s):  
Fuzzy Model ◽  
Virtual Human ◽  
E Learning ◽  
Learning Software

scholarly journals Customer Satisfaction in Career Enhancement with the Usage of E-Learning Software

2019 ◽  
Vol 8 (2S8) ◽  
pp. 466-468
Keyword(s):  
Customer Satisfaction ◽  
Human Needs ◽  
E Learning ◽  
Career Enhancement ◽  
Learning Software ◽  
Chennai City ◽  
Business Sectors ◽  
The Way

Showcasing has its source in the way that people are animals of necessities and needs. Since numerous items can fulfill a given need, item decision is guided by the ideas of significant worth, cost and fulfillment. These items are realistic in a few different ways; self-creation, pressure, asking and trade. Promoting emerges from this last way to deal with obtaining items. The entire of promoting depends on the procedure of trade. Advertising thoroughly means working with business sectors to realize trade to fulfill human needs and needs. Association lifts its quality on its promoting approaches, which are the most fundamental piece of development and improvement of the association. In this unique circumstance, E-Learning Companies in Chennai is in the phase of reinforcing up its promoting capacities, along these lines making a need to grasp the effect of its arrangements on their customers. The task targets finding the effect of promoting approaches of E-Learning Companies towards its clients in Chennai city, which would go about as a venturing stone for the organization to spread the action to the whole nation.

scholarly journals Stakeholder’s analysis in e-learning software process development

2015 ◽  
Vol 2 (5) ◽  
pp. e4 ◽  
Author(s):  
L.J. Romero ◽  
L.C. Ballejos ◽  
M.M. Gutiérrez ◽  
M.L. Caliusco
Keyword(s):  
Process Development ◽  
Software Process ◽  
E Learning ◽  
Learning Software

Technological Evaluation and Optimization of E-Learning Systems Components

2011 ◽  
pp. 150-173 ◽  
Author(s):  
Eugenijus Kurilovas ◽  
Valentina Dagiene
Keyword(s):  
Quality Evaluation ◽  
Evaluation Criteria ◽  
Learning Objects ◽  
Learning Systems ◽  
Main Research ◽  
Evaluation Models ◽  
E Learning ◽  
Multiple Criteria Evaluation ◽  
Technological Evaluation ◽  
Learning Software

The main research objective of the chapter is to provide an analysis of the technological quality evaluation models and make a proposal for a method suitable for the multiple criteria evaluation (decision making) and optimization of the components of e-learning systems (i.e. learning software), including Learning Objects, Learning Object Repositories, and Virtual Learning Environments. Both the learning software ‘internal quality’ and ‘quality in use’ technological evaluation criteria are analyzed in the chapter and are incorporated into comprehensive quality evaluation models. The learning software quality evaluation criteria are further investigated in terms of their optimal parameters, and an additive utility function based on experts’ judgements, including multicriteria evaluation, numerical ratings, and weights, is applied to optimize the learning software according to particular learners’ needs.

scholarly journals A review of learning theories and models underpinning technology-enhanced learning artefacts

2020 ◽  
Vol 17 (4) ◽  
pp. 341-354
Author(s):  
Rawad Hammad ◽  
Zaheer Khan ◽  
Fadi Safieddine ◽  
Allam Ahmed
Keyword(s):  
Hybrid Approach ◽  
Learning Theories ◽  
Bibliographic Databases ◽  
Technology Enhanced Learning ◽  
Content Type ◽  
Key Characteristics ◽  
Main Driver ◽  
E Learning ◽  
Learning Software ◽  
Enhanced Learning

PurposeVarious technology-enhanced learning software and tools exist where technology becomes the main driver for these developments at the expense of pedagogy. The literature reveals the missing balance between technology and pedagogy in the continuously evolving technology-enhanced learning domain. Consequently, e-learners struggle to realise the pedagogical value of such e-learning artefacts. This paper aims to understand the different pedagogical theories, models and frameworks underpinning current technology-enhanced learning artefacts to pave the way for designing more effective e-learning artefacts.Design/methodology/approachTo achieve this goal, a review is conducted to survey the most influential pedagogical theories, models and frameworks. To carry out this review, five major bibliographic databases have been searched, which has led to identifying a large number of articles. The authors selected 34 of them for further analysis based on their relevance to our research scope. The authors critically analysed the selected sources qualitatively to identify the most dominant learning theories, classify them and map them onto the key characteristics, criticism, approaches, models and e-learning artefacts.FindingsThe authors highlighted the significance of pedagogies underpinning e-learning artefacts. Furthermore, the authors presented the common and special aspects of each theory to support our claim, which is developing a hybrid pedagogical approach. Such a hybrid approach remains a necessity to effectively guide learners and allow them to achieve their learning outcomes using e-learning artefacts.Originality/valueThe authors found that different pedagogical approaches complement rather than compete with each other. This affirms our recommended approach to adopt a hybrid approach for learning to meet learners' requirements. The authors also found that a substantive consideration for context is inevitable to test our evolving understanding of pedagogy.

scholarly journals Toward Social Media Content Recommendation Integrated with Data Science and Machine Learning Approach for E-Learners

Symmetry ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 1798 ◽  
Author(s):  
Zeinab Shahbazi ◽  
Yung Cheol Byun
Keyword(s):  
Social Media ◽  
Computer Science ◽  
Data Science ◽  
Learning System ◽  
Local Context ◽  
Great Success ◽  
Learning Context ◽  
Wide Range ◽  
E Learning ◽  
Learning Software

Electronic Learning (e-learning) has made a great success and recently been estimated as a billion-dollar industry. The users of e-learning acquire knowledge of diversified content available in an application using innovative means. There is much e-learning software available—for example, LMS (Learning Management System) and Moodle. The functionalities of this software were reviewed and we recognized that learners have particular problems in getting relevant recommendations. For example, there might be essential discussions about a particular topic on social networks, such as Twitter, but that discussion is not linked up and recommended to the learners for getting the latest updates on technology-updated news related to their learning context. This has been set as the focus of the current project based on symmetry between user project specification. The developed project recommends relevant symmetric articles to e-learners from the social network of Twitter and the academic platform of DBLP. For recommendations, a Reinforcement learning model with optimization is employed, which utilizes the learners’ local context, learners’ profile available in the e-learning system, and the learners’ historical views. The recommendations by the system are relevant tweets, popular relevant Twitter users, and research papers from DBLP. For matching the local context, profile, and history with the tweet text, we recognized that terms in the e-learning system need to be expanded to cover a wide range of concepts. However, this diversification should not include such terms which are irrelevant. To expand terms of the local context, profile and history, the software used the dataset of Grow-bag, which builds concept graphs of large-scale Computer Science topics based on the co-occurrence scores of Computer Science terms. This application demonstrated the need and success of e-learning software that is linked with social media and sends recommendations for the content being learned by the e-Learners in the e-learning environment. However, the current application only focuses on the Computer Science domain. There is a need for generalizing such applications to other domains in the future.

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