Learning Management Systems (LMS) have played a significant role in education. The purpose of this study is to investigate the acceptance level of LMS amongst students of two Universities in Tehran, Payamnoor and Farhangian. The total number of participants was 200. This study was directed based on a quantitative research method and data collection from a questionnaire which was then interpreted according to accurate statistical procedures through SPSS software. Results show that most students, regardless their gender, age, and department were satisfied with the usage of Payamnoor and Farhangian LMSs. However, a student’s grades seem to play a significant role regarding his or hers level of satisfaction from the LMS.
Nowadays, Information and Communication Technology (ICT) provides an opportunity to discover new knowledge and create a desirable learning environment. That is why the influence of ICT on education is irrefutable. Technology has changed the learning styles: the way people prefer to learn and improve the quality of their learning. Physical and online classes can be held concurrently so that lecturers and students can interact via learning management systems. A Learning Management System (LMS) is an application software that plays a significant role in educational technology. Such software can be designed to augment and facilitate instructional activities including registration and management of education courses, analyzing skill gaps, reporting, and delivery of electronic courses concurrently. Since all information and corresponding data are recorded and monitored in the LMS, it can provide an accurate insight into student's online behavior. In general, measuring student performance is an important part of the education system. The fields of learning analytics and educational data mining both emphasize the analysis of educational data in order to improve teaching and learning styles as well as to predict student performance. In the current study, we use data from the Moodle LMS from a collection of courses from a single institution to identify weak/strong students during the course. The result has to be interpretable and understandable as the aim is to give this information to lecturers, who may use the information to improve their course and identify students who may need special attention.
There has recently been an increasing interest in Learning Management Systems (LMSs). It is currently unclear, however, exactly how these systems are perceived by their users. This article analyzes data on user acceptance for two LMSs (Blackboard and Canvas). The respective data are collected using a questionnaire modeled after the Technology Acceptance Model (TAM); it relates several variables that influence system acceptability, allowing for a detailed analysis of the system acceptance. We present analyses at two levels of the questionnaire data: questions and constructs (taken from TAM) as well as on different analysis levels using targeted methods. First, we investigate the differences between the above LMSs using statistical tests (t-test). Second, we provide results at the question level using descriptive indices, such as the mean and the Gini heterogeneity index, and apply methods for ordinal data using the Cumulative Link Mixed Model (CLMM). Next, we apply the same approach at the TAM construct level plus descriptive network analysis (degree centrality and bipartite motifs) to explore the variability of users’ answers and the degree of users’ satisfaction considering the extracted patterns. In the context of TAM, the statistical model is able to analyze LMS acceptance on the question level. As we are also very much interested in identifying LMS acceptance at the construct level, in this article, we provide both statistical analysis as well as network analysis to explore the connection between questionnaire data and relational data. A network analysis approach is particularly useful when analyzing LMS acceptance on the construct level, as this can take the structure of the users’ answers across questions per construct into account. Taken together, these results suggest a higher rate of user acceptance among Canvas users compared to Blackboard both for the question and construct level. Likewise, the descriptive network modeling for Canvas indicates a slightly higher concordance between Canvas users than Blackboard at the construct level.
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