Browsing by Subject "Social Networks"

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    Proposing a New System Architecture for Next Generation Learning Environment
    (2016) Aboualizadehbehbahani, Maziar; King, Brian; Jafari, Ali; Wu, Huanmei
    The emergence of information exchange and act of offering features through external interfaces is a vast but immensely valuable challenge, and essential elements of learning environments cannot be excluded. Nowadays, there are a lot of different service providers working in the learning systems market and each of them has their own advantages. On that premise, in today's world even large learning management systems are trying to cooperate with each other in order to be best. For instance, Instructure is a substantial company and can easily employ a dedicated team tasked with the development of a video conferencing functionality, but it chooses to use an open source alternative instead: The BigBlueButton. Unfortunately, different learning system manufacturers are using different technologies for various reasons, making integration that much harder. Standards in learning environments have come to resolve problems regarding exchanging information, providing and consuming functionalities externally and simultaneously minimizing the amount of effort needed to integrate systems. In addition to defining and simplifying these standards, careful consideration is essential when designing new, comprehensive and useful systems, as well as adding interoperability to existing systems, all which subsequently took part in this research. In this research I have reviewed most of the standards and protocols for integration in learning environments and proposed a revised approach for app stores in learning environments. Finally, as a case study, a learning tool has been developed to avail essential functionalities of a social educational learning management system integrated with other learning management systems. This tool supports the dominant and most popular standards for interoperability and can be added to learning management systems within seconds.
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    Recommendation Systems in Social Networks
    (2023-05) Mohammad Jafari, Behafarid; King, Brian; Luo, Xiao; Jafari, Ali; Zhang, Qingxue
    The dramatic improvement in information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. CourseNet working is a next-generation LMS adopting machine learning to add personalization, gamifi cation, and more dynamics to the system. This work tries to come up with two recommender systems that can help improve CourseNetworking services. The first one is a social recommender system helping CourseNetworking to track user interests and give more relevant recommendations. Recently, graph neural network (GNN) techniques have been employed in social recommender systems due to their high success in graph representation learning, including social network graphs. Despite the rapid advances in recommender systems performance, dealing with the dynamic property of the social network data is one of the key challenges that is remained to be addressed. In this research, a novel method is presented that provides social recommendations by incorporating the dynamic property of social network data in a heterogeneous graph by supplementing the graph with time span nodes that are used to define users long-term and short-term preferences over time. The second service that is proposed to add to Rumi services is a hashtag recommendation system that can help users label their posts quickly resulting in improved searchability of content. In recent years, several hashtag recommendation methods are proposed and de veloped to speed up processing of the texts and quickly find out the critical phrases. The methods use different approaches and techniques to obtain critical information from a large amount of data. This work investigates the efficiency of unsupervised keyword extraction methods for hashtag recommendation and recommends the one with the best performance to use in a hashtag recommender system.