scholarly journals An ISO 9126 Based Quality Model for the e-Learning Systems

2016 ◽  
Vol 6 (5) ◽  
pp. 370-375 ◽  
Author(s):  
Rachida Djouab ◽  
◽  
Moncef Bari ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 4071 ◽  
Author(s):  
Abdul Hafeez Muhammad ◽  
Ansar Siddique ◽  
Ahmed E. Youssef ◽  
Kashif Saleem ◽  
Basit Shahzad ◽  
...  

The rapid growth of Information and Communication Technologies (ICT)—specifically, the Internet—has given emergence to e-learning. Resultantly, web-based e-learning systems are being increasingly developed to enhance the learning process. However, the utilization of such systems is low, mainly owing to poor quality content and overall design problems. To improve usage, it is imperative to identify the factors with the most significant impact on the quality of these systems so that the e-learning industry keeps these factors in consideration while developing e-learning systems. This study focused on the identification and prioritization of factors related to the design quality of e-learning systems through a hierarchical quality model. Thus, firstly, an extensive literature review was conducted to identify the factors that most affect the quality of web-based e-learning systems. Secondly, among the identified factors, only those with the most significant effect were considered. To identify the most important quality criteria, a survey was conducted. An instrument was deployed among 157 subjects, including e-learning designers, developers, students, teachers, and educational administrators. Finally, a second instrument was distributed among 51 participants to make a pairwise comparison among the criteria and rank them according to their relative importance. The identified and prioritized factors were classified into four main categories. Among these four factors, content was identified as the most important factor, whereas design was found to be the least important factor.


2018 ◽  
Vol 12 ◽  
pp. 85-98
Author(s):  
Bojan Kostadinov ◽  
Mile Jovanov ◽  
Emil STANKOV

Data collection and machine learning are changing the world. Whether it is medicine, sports or education, companies and institutions are investing a lot of time and money in systems that gather, process and analyse data. Likewise, to improve competitiveness, a lot of countries are making changes to their educational policy by supporting STEM disciplines. Therefore, it’s important to put effort into using various data sources to help students succeed in STEM. In this paper, we present a platform that can analyse student’s activity on various contest and e-learning systems, combine and process the data, and then present it in various ways that are easy to understand. This in turn enables teachers and organizers to recognize talented and hardworking students, identify issues, and/or motivate students to practice and work on areas where they’re weaker.


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