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.
In this paper we extrapolate the information about Bible's characters and places, and their interrelationships, by using text mining network-based approach. We study the narrative structure of the WEB version of 5 books: the Gospel of Matthew, Mark, Luke, John and Acts of the Apostles. The main focus is the protagonists' names interrelationships in an analytical way, namely using various network-based methods and descriptors. This corpus is processed for creating a network: we download the names of people and places from Wikipedia's list of biblical names, then we look for their co-occurrences in each verse and, at the end of this process, we get N co-occurred names. The strength of the link between two names is defined as the sum of the times that these occur together in all the verses, in this way we obtain 5 adjacency matrices (one per book) of N by N couples of names. After this pre-processing phase, for each of the 5 analysed books we calculate the main network centrality measures (classical degree, weighted degree, betweenness and closeness), the network vulnerability and we run the Community Detection algorithm to highlight the role of Messiah inside the overall networks and his groups (communities). We have found that the proposed approach is suitable for highlighting the structures of the names co-occurrences. The found frameworks' structures are useful for interpreting the characters' plots under a structural point of view.
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