Social networks, as the most important communication tools, have had a profound impact on social aspects of community user interactions and they are used widely in various fields, such as education. Student interaction through different communication networks can affect individual learning and leads to improved academic performance. In this study, a combined approach of social network analysis and educational data mining (decision tree method) was used to study the impact of communication networks, behavior networks and the combination of these two networks on students' academic performance considering the role of factors such as computer self-efficacy, age, gender and university. The results of this study, which included 139 students, indicate gender is highly prioritised in all three models. Moreover, according to the results all three models had enough confidence level that among them communication networks with higher confidence, accuracy and precision had significant impacts on the prediction of academic performance.
Implementation of business intelligence systems (BIS) is very complex and requires a lot of resources and time. Business intelligence (BI) is a difficult concept and has a multi-tier architecture. The metadata causes the complexity of the BI. That is why a BI readiness model is required. The frequency of BI maturity models, such as the data warehousing (TDW), has been provided, but there are few frameworks for measuring the readiness. Moreover, readiness frameworks often provide a general model for all organizations. Hence, the objective of this study was to examine whether the factors affecting the organizational readiness for BI implementation in all organizations are identical. For this purpose, based on a comprehensive literature review, four factors of culture, people, strategy, and management were extracted as the most important factors affecting the readiness and implementation of BI, and they were studied in three educational, commerce, and IT organizations. Based on the findings, different factors affect various organizations, and using a general model should not be advised.
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