scholarly journals Adoption and Use of Learning Management Systems in Education: The Role of Playfulness and Self-Management

2021 ◽  
Vol 13 (3) ◽  
pp. 1127
Author(s):  
Selen Balkaya ◽  
Ulas Akkucuk
Keyword(s):  
Behavioral Intention ◽  
Learning Management Systems ◽  
Individual Characteristics ◽  
Organizational Level ◽  
Management Systems ◽  
Equation Modeling ◽  
Factors Affecting ◽  
Learning Management ◽  
It Innovations ◽  
The Way

This article investigates the factors affecting primary and secondary education teachers’ behavioral intention to adopt learning management systems (LMSs). Information technology (IT) innovations have the power to change the way we work, educate, learn, and basically the way we live. The effect of IT innovations on education makes it critical to understand the current usage situation of LMSs and the factors affecting their adoption by teachers. The unified theory of acceptance and use of technology (UTAUT) was extended with factors from education and game-based learning literature. In order to see the effect of individual- and organizational-level characteristics, multi-group structural equation modeling (SEM) analysis was conducted and discrepancies in relationships were reported. Evaluation of users and non-users and teachers of different fields were also compared to each other. The findings of this study not only contribute to theory through the development and testing of a thorough model relating technology features and individual characteristics to behavioral intention to use, but also offer strong implications for practitioners who would like to increase LMS usage and create a more effective learning environment.

Critical Factors Influencing Instructors’ Acceptance and Use of Learning Management Systems

2012 ◽  
pp. 116-140 ◽  
Author(s):  
Kamla Ali Al-Busaidi ◽  
Hafedh Al-Shihi
Keyword(s):  
Distance Education ◽  
Computer Anxiety ◽  
Learning Management Systems ◽  
Individual Characteristics ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Management Systems ◽  
System Quality ◽  
Perceived Ease Of Use ◽  
Learning Management

Learning management systems (LMS) enable educational institutions to manage their educational resources, support their distance education, and supplement their traditional way of teaching. Although LMS survive via instructors’ and students’ use, the adoption of LMS is initiated by instructors’ acceptance and use. Consequently, this study examined the impacts of instructors’ individual characteristics, LMS’ characteristics, and organization’s characteristics on instructors’ acceptance and use of LMS as a supplementary tool and, consequently, on their continuous use intention and their pure use intention for distance education. The findings indicated that, first, instructors’ supplementary use of LMS is determined by perceived usefulness, training, management support, perceived ease of use, information quality, and computer anxiety. Second, instructors’ perceived usefulness of LMS is determined by system quality, perceived ease of use, and incentives policy. Third, instructors’ perceived ease of use is determined by computer anxiety, technology experience, training, system quality, and service quality. Furthermore, instructors’ continuous supplementary use intention is determined by their current supplementary use, perceived usefulness, and perceived ease of use, while instructors’ pure use intention is determined only by their perceived usefulness of LMS.

scholarly journals Critical success factors for improving learning management systems diffusion in KSA HEIs: An ISM approach

2021 ◽  
Author(s):  
Yousef Alduraywish ◽  
John Patsavellas ◽  
Konstantinos Salonitis
Keyword(s):  
Structural Equation ◽  
Success Factors ◽  
Learning Management Systems ◽  
Appropriate Technology ◽  
Management Systems ◽  
Equation Modeling ◽  
Future Research ◽  
Top Management ◽  
Learning Management ◽  
Significant Factors

AbstractHigher educational institutes (HEIs) are managing their resources by using learning management systems (LMS) which facilitate the learning processes. This paper aims to develop the relationships among success factors typically found in the technology, as well as the human and organisational aspects using an interpretive structural modelling (ISM) approach for LMS diffusion in HEIs in the Kingdom of Saudi Arabia (KSA). The success factors possessing a higher driving power in the ISM approach need to be prioritised as many other dependent variables are affected by them. Success factors emerging with high dependence contribute to facilitating the implementation of LMS. A key finding of the modelling is that clearly defined information technology (IT) policies along with appropriate technology infrastructure are significant factors for facilitating the technology aspect of LMS implementation. Additionally, the strengthening and standardisation of IT education resources, level of computer skills, proper training programmes for staff to deliver knowledge to users as well as a high level of human competencies are significant factors for facilitating the human aspect of LMS implementation. Moreover, the support of top management is a very significant factor for improving the organisational aspect of LMS. To ensure successful LMS implementation, KSA HEIs should focus on effective learning environments, facilitate education activities, top management involvement and increased interaction between pedagogy and technology. Understanding user characteristics and online needs is essential to ensure that barriers are overcome, ensuring successful and continued LMS implementation. Further, in this research, the relationship models among the identified success factors in terms of technology, human and organisational have not been statistically validated. However, it has been suggested that future research may be targeted to develop the initial model through ISM for success factors for improving LMS implementation and then testing it using Structural equation modeling (SEM).

scholarly journals From LMS to adaptive training systems

2019 ◽  
pp. 58-64 ◽  
Author(s):  
Y. B. Popova
Keyword(s):  
Learning Process ◽  
Adaptive Learning ◽  
Learning Management Systems ◽  
Individual Characteristics ◽  
Educational Process ◽  
Learning Systems ◽  
Learning System ◽  
Management Systems ◽  
Artificial Neural ◽  
Learning Management

The use of information technology and, in particular, learning management systems, increases the ability of both the teacher and the learner to achieve their goals in the educational process. Such systems provide educational content, help organize and monitor training, collect progress statistics, and can also take into account the individual characteristics of each user of the system. The purpose of this study is to determine the direction of development of modern learning systems and technologies for their implementation. The evolution of learning management systems, the transition to intelligent learning systems, the main stages of such systems were reviewed, the types of learning sequences were analyzed, the transformationinto adaptive learning systems was identified, and the scheme of the system and its mathematical model were presented. Expertise systems, the theory of fuzzy sets and fuzzy logic, cluster analysis, as well as genetic algorithms and artificial neural networks are defined as the mechanisms for implementing the learning systems. An artificial neural network in an adaptive learning system will allow you to create a unique training program that will build on existing knowledge and the level of perception of educational material by students. By formalizing the intellectual processes that both the teacher and the student carry out, it is possible to automate a certain part of the teacher’s functions, reduce the cost of manual labor, which will make it easier to monitor the learning process and also make the learning process more efficient.

Investigating the factors influencing continuance usage intention of Learning management systems by university instructors

2015 ◽  
Vol 11 (4) ◽  
pp. 491-509 ◽  
Author(s):  
Samar Mouakket ◽  
Anissa M. Bettayeb
Keyword(s):  
Learning Management Systems ◽  
Perceived Usefulness ◽  
Management Systems ◽  
Equation Modeling ◽  
Intention To Use ◽  
Continuance Intention ◽  
Content Type ◽  
University Instructors ◽  
Blackboard System ◽  
Learning Management

Purpose – There is a growing demand worldwide for the adoption of Learning management systems (LMS) by academic institutions to support e-Learning platform. Yet limited research has been conducted to investigate the factors affecting its usage, particularly by university instructors. To fill this research void, the expectation-confirmation model (ECM) was used as the core framework for analysis, while additional critical independent factors related to organizational, technological and individual characteristics were added to find a better model to understand university instructors’ continuance intention to use Blackboard system as a popular LMS. Design/methodology/approach – Sample data were gathered from 158 university instructors at a university in the United Arab Emirates (UAE) who volunteered to participate in this study. Structural equation modeling technique was used to verify the causal relationships between the constructs. Findings – Perceived usefulness (PU) affected satisfaction of Blackboard system. Both PU and satisfaction affected instructors’ continuance intentions to use Blackboard system. User-interface design affected both PU and satisfaction. Technical support influenced perceived usefulness. Training influenced perceived usefulness, but it had no influence on satisfaction. Computer self-efficacy had no influence on perceived usefulness. Originality/value – Based on the ECM, this study contributes significantly to the limited body of research on capturing the influence of organizational, technological and individual motivators to explain university instructors’ continuance intention to use LMS.

Dynamic Student Modelling of Learning Styles for Advanced Adaptivity in Learning Management Systems

2013 ◽  
Vol 4 (1) ◽  
pp. 85-100 ◽  
Author(s):  
Sabine Graf ◽  
Kinshuk
Keyword(s):  
Learning Styles ◽  
Cognitive Abilities ◽  
Learning Management Systems ◽  
Individual Characteristics ◽  
Management Systems ◽  
Student Model ◽  
Student Modelling ◽  
Learning Management ◽  
The Individual ◽  
Over Time

Learning management systems (LMSs) are commonly used in e-learning; however, they typically do not consider the individual differences of students, including their different background knowledge, cognitive abilities, motivation, and learning styles. A basic requirement for enabling such systems to consider students’ individual characteristics is to know these characteristics first. This paper focuses on the consideration of learning styles and introduces a dynamic student modelling approach that monitors students’ behaviour over time and uses these data to build an accurate student model by frequently refining the information in the student model as well as by responding to changes in students’ learning styles over time. The proposed approach is especially useful for LMSs, which are commonly used by educational institutions for whole programs of study and therefore can monitor students’ behaviour over time, in different courses. The paper demonstrates how this approach can be integrated in an adaptive mechanism that enables LMSs to automatically generate courses that fit students’ learning styles and discusses how dynamic student modelling can help in identifying students’ learning styles more accurately, which enables the LMS to provide more accurate adaptivity and therefore support students’ learning processes more effectively.

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