Using Multi-Attribute Decision-Making Approach to Evaluate Learning Management Systems

Using Multi-Attribute Decision-Making Approach to Evaluate Learning Management Systems

Alaa M. Momani
DOI: 10.4018/IJWLTT.20210701.oa7
Article PDF Download
Open access articles are freely available for download

Abstract

E-learning is one of the fastest growing areas of the high technology development, especially in the academic environments. However, the instructor is a very important factor in the learning process, but the advantages of e-learning change the role which the instructor plays in this process. E-learning gives an opportunity to anyone to learn in a rapid and customised way. Nowadays, many learning management systems (LMSs) available in the marketplace offer electronic teaching and learning tools. Choosing the most appropriate LMS that fits the needs and requirements of instructor and the learner is one of the most confusing and difficult decisions to any educational institution. Accordingly, the need to a computer-based tool for getting help in taking such a decision is rising on. This paper offers a solution to this problem. It provides a description about a web-based decision support system named Easy Way to Evaluate LMS (EW-LMS). It has been developed by adopting multi-attribute decision-making algorithm in order to select the best LMS depending on the user needs.
Article Preview
Top

1. Introduction

No one denies the important role of technologies in growing up any working environment; the learning environment is not an exception. It is a suitable modernity way for both instructors and learners to be communicated in a virtual environment by ignoring the boundaries of distance and time. Electronic learning (e-learning) involves the use of multimedia interaction in order to get the best form of online communications. Goh, Hong, and Gunawan (2014) mentioned that the tools of online technologies have been widely used in education in order to facilitate some of co-learning among learners and lecturers. Learning Management System (LMS) was defined by Adzharuddin and Ling (2013) as an online portal that connects lecturers and students out of the classroom. It provides an avenue for classroom materials and activities to be shared easily rather than the traditional classrooms that would take too much time spent in delivering these materials. Undoubtedly, the knowledge in general can help in improving the study habits and be successful in any educational setting, regardless of what type of learner you are (Alishahedani, Sarosi, and Taylor 2019). Whereas, the interaction and delivery methods used in online classes are dramatically different from traditional classes (Hass and Joseph 2018; Manoharan 2008).

E-learning, as one of the most important fields in information technology, has a lot of development operations (Dolenc and Aberšek 2015; Kardan, Aziz, and Shahpasand 2015). These developments offer a huge number of LMSs which ask to find some serious solutions in order to evaluate them where this research aims to fill this gap (Khan, Shahzad, and Altaf 2019; Smolka 2017). Because of the huge amount of LMSs offered and because of this rapid and massive development in e-learning technologies over the world (Momani 2010a), in addition to the lack in serious solutions that offers professional tools to select the suitable LMS for educational institutions, there is an insisting need to define or develop some computer-based tools to evaluate the quality, efficiency, performance, and the suitability of this development and these LMSs. Obviously, the operation to assess any information management system is one of the decision-making processes. It is worth to know that the Decision Support Systems (DSS) are a part of the artificial intelligence field in computer science (Markova et al. 2019). In order to evaluate any such information system, firstly, it is important to find the major features of this kind of systems and the aim from using it, then, the administrator can easily assess if it meets the requirements needed from it (Momani 2008).

Complete Article List

Search this Journal:
Reset
Volume 19: 1 Issue (2024)
Volume 18: 2 Issues (2023)
Volume 17: 8 Issues (2022)
Volume 16: 6 Issues (2021)
Volume 15: 4 Issues (2020)
Volume 14: 4 Issues (2019)
Volume 13: 4 Issues (2018)
Volume 12: 4 Issues (2017)
Volume 11: 4 Issues (2016)
Volume 10: 4 Issues (2015)
Volume 9: 4 Issues (2014)
Volume 8: 4 Issues (2013)
Volume 7: 4 Issues (2012)
Volume 6: 4 Issues (2011)
Volume 5: 4 Issues (2010)
Volume 4: 4 Issues (2009)
Volume 3: 4 Issues (2008)
Volume 2: 4 Issues (2007)
Volume 1: 4 Issues (2006)
View Complete Journal Contents Listing