Structural equation model (SEM)-neural network (NN) model for predicting quality determinants of e-learning management systems

2017 ◽  
Vol 36 (10) ◽  
pp. 1053-1066 ◽  
Author(s):  
Sujeet Kumar Sharma ◽  
Avinash Gaur ◽  
Venkataramanaiah Saddikuti ◽  
Ashish Rastogi
Keyword(s):  
Neural Network ◽  
Structural Equation Model ◽  
Structural Equation ◽  
Learning Management Systems ◽  
Equation Model ◽  
Management Systems ◽  
E Learning ◽  
Learning Management

An Implementation of the UTAUT Model for Understanding Students' Perceptions of Learning Management Systems

2019 ◽  
Vol 17 (3) ◽  
pp. 1-24 ◽  
Author(s):  
Ahmed Alshehri ◽  
M J Rutter ◽  
Sally Smith
Keyword(s):  
Structural Equation ◽  
Learning Management Systems ◽  
Course Delivery ◽  
Management Systems ◽  
Learning Technology ◽  
Academic Institutions ◽  
Use Of Technology ◽  
E Learning ◽  
Blackboard System ◽  
Learning Management

The rapid growth of e-learning around the globe is inspiring various academic institutions to adopt it. Uptake is motivated by convincing benefits such as flexibility, accessibility and the management of course delivery. In fact, academic institutions place great emphasis on e-learning and are investing significantly in information technology infrastructures. However, in spite of this effort and investment, it seems that instructors and students do not always fully benefit from the learning technology and more often learning management systems (LMSs) remain underutilized. Thus, this study adopts the Unified Theory of Acceptance and Use of Technology (UTAUT) to study how people accept and use the Blackboard system. The data were analysed using Structural Equation Modelling (SEM) techniques to test the hypothesized research model. The empirical results found that technical support is fundamental in determining the acceptance and use of e-learning systems. The findings of the study may help to provide insights into a better approach to promote e-learning acceptance.

MENGUKUR TINGKAT KEPUASAN PENGGUNA E-LEARNING (UIN RADEN FATAH PALEMBANG)

2020 ◽  
Vol 5 (01) ◽  
pp. 79-86
Author(s):  
Muhamad Son Muarie ◽  
Fathiyah Nopriani
Keyword(s):  
Structural Equation Model ◽  
Structural Equation ◽  
Equation Model ◽  
E Learning

E-learning adalah mediator yang menghubungkan sumber daya informasi dan layanan pengguna yang dapat diakses dari mana saja dan kapan saja. Penelitian ini bertujuan untuk mengetahui seberapa besar pengaruh komponen Kepuasan Komputasi Pengguna Akhir (konten, akurasi, bentuk, kemudahan penggunaan, dan ketepatan waktu) terhadap kepuasan penggunaan   E-learning di Universitas Islam Negeri Raden Fatah Palembang. Mengetahui tingkat kepuasan pengguna (siswa) dengan penerapan teknologi pendidikan, yaitu E-learning. Mengetahui faktor apa yang mempengaruhi kepuasan menggunakan E-learning. Sebagai dasar pertimbangan untuk meningkatkan atau meningkatkan kualitas layanan teknologi pendidikan, yaitu e-learning di Universitas Islam Negeri Raden Fatah di Palembang. Populasi dan sampel penelitian diambil di Universitas Islam Negeri Raden Fatah Palembang, yang telah menggunakan E-learning sebagai mediator informasi antara dosen dan mahasiswa. SEM (Structural Equation Model) adalah metode analisis multivariat yang dapat digunakan untuk menggambarkan hubungan simultan antara variabel linier antara indikator dan variabel yang tidak dapat diukur secara langsung (variabel laten).

scholarly journals Recommendation System Based On Association Rules For Distributed E-Learning Management Systems

2015 ◽  
Vol 67 (1) ◽  
pp. 99-104 ◽  
Author(s):  
Gabroveanu Mihai
Keyword(s):  
Association Rules ◽  
Management System ◽  
Recommendation System ◽  
Learning Management System ◽  
Distributed Learning ◽  
Learning Management Systems ◽  
Management Systems ◽  
User Data ◽  
E Learning ◽  
Learning Management

Abstract Traditional Learning Management Systems are installed on a single server where learning materials and user data are kept. To increase its performance, the Learning Management System can be installed on multiple servers; learning materials and user data could be distributed across these servers obtaining a Distributed Learning Management System. In this paper is proposed the prototype of a recommendation system based on association rules for Distributed Learning Management System. Information from LMS databases is analyzed using distributed data mining algorithms in order to extract the association rules. Then the extracted rules are used as inference rules to provide personalized recommendations. The quality of provided recommendations is improved because the rules used to make the inferences are more accurate, since these rules aggregate knowledge from all e-Learning systems included in Distributed Learning Management System.

scholarly journals Towards Model-Based User Interface Development of e-Learning Management Systems

2012 ◽  
Vol 47 (1) ◽  
pp. 59-71
Author(s):  
Josefina Guerrero-García ◽  
Juan Manuel González-Calleros ◽  
Jaime Muñoz-Arteaga ◽  
Miguel Ángel León-Chávez ◽  
Carlos Reyes-García
Keyword(s):  
User Interface ◽  
Learning Management Systems ◽  
Management Systems ◽  
Model Based ◽  
E Learning ◽  
Learning Management

scholarly journals From Cognitive Landscapes to Digital Hyperscapes

2003 ◽  
Vol 4 (2) ◽  
Author(s):  
Jose Bidarra ◽  
Ana Dias
Keyword(s):  
Learning Styles ◽  
Learning Management Systems ◽  
Learning Objects ◽  
Management Systems ◽  
Digital Information ◽  
Cognitive Profiles ◽  
E Learning ◽  
Learning Management ◽  
Key Terms ◽  
Massive Information

<P> The widespread diffusion of e-Learning in organizations has encouraged the discovery of more effective ways for conveying digital information to learners, for instance, via the commonly called Learning Management Systems (LMS). A problem that we have identified is that cognitive variables and pedagogical processes are rarely taken into consideration and sometimes are confused with the mere use by learners of “diversified” hypermedia resources. Within the context of widespread dissemination of multimedia content that has followed the emergence of massive information resources, we discuss the need for more powerful and effective learner-centered tools capable of handling all kinds of design configurations and learning objects. </p> <P class=abstract><B>Key Terms: </B>cognitive profiles, learning styles, mind mapping, multimedia and hypermedia content, hyperscapes, e-Learning, learning objects, Learning Management Systems (LMS).</P>

Major Trends, Issues, and Challenges with Learning Management Systems

2014 ◽  
pp. 318-331 ◽  
Author(s):  
Betul Özkan Czerkawski ◽  
Dawn Panagiota Gonzales
Keyword(s):  
Higher Education ◽  
Online Instruction ◽  
Management System ◽  
Teaching And Learning ◽  
Learning Management Systems ◽  
Management Systems ◽  
Profound Impact ◽  
Blended Courses ◽  
E Learning ◽  
Learning Management

A Learning Management System (LMS) offers a set of tools for e-learning delivery and management. For institutions offering online or blended courses, an LMS has a profound impact on teaching and learning because it is the main technology used in higher education e-learning courses. This chapter discusses major trends, issues, and challenges with the LMS in the context of online instruction for higher education. The chapter ends with a discussion of new trends with LMSs.

A New Method for Measuring Text Similarity in Learning Management Systems Using WordNet

2014 ◽  
Vol 9 (2) ◽  
pp. 1-13
Author(s):  
Bassel Alkhatib ◽  
Ammar Alnahhas ◽  
Firas Albadawi
Keyword(s):  
Learning Management Systems ◽  
Learning System ◽  
New Method ◽  
Management Systems ◽  
Similarity Learning ◽  
Text Similarity ◽  
Learning Content ◽  
E Learning ◽  
Learning Management ◽  
Automatic Collection

As text sources are getting broader, measuring text similarity is becoming more compelling. Automatic text classification, search engines and auto answering systems are samples of applications that rely on text similarity. Learning management systems (LMS) are becoming more important since electronic media is getting more publicly available. As LMS continuously needs content enrichment and the web is getting richer, automatic collection of learning materials becomes an innovative idea. Intelligent agents can be used with a similarity measurement method to implement the automatic collection process. This paper presents a new method for measuring text similarity using the well-known WordNet Ontology. The proposed method assumes that a text is similar to another if it represents a more specific semantic. This is more suitable for LMS content enrichment as learning content can usually be expanded by a more specific one. This paper shows how the hierarchy of WordNet can be taken advantage of to determine the importance of a word. It is also shown how similarity method within an e-learning system is exploited to achieve two goals. The first one is the enrichment of the e-learning content, and the second is the detection of semantically similar questions in e-learning questions banks.

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