Social Learning Network Research Articles

Ali Baba and the Thief, Convention Emergence in Games

In this paper we propose a model that supports the emergence of conventions via multiagent learning in social networks. In our model, individual agents repeatedly interact with their neighbours in a game called Ali Baba and the Thief. An agent learns its strategy to play the game using the learning rule imitate-the-best. We show that some conventions prescribing peaceful behaviours can emerge after repeated interactions among agents inhabited in some social networks. Our experiments suggest that there are critical points of convention emergence in Ali Baba and the Thief. When the quotient of the amount of robbery and the initial utility is smaller than the critical point, the probability of convention emergence is high. The probability drops dramatically as long as the quotient is larger than the critical point.

Two models of social learning in networks, with concepts drawn from economics and physics

In this paper, we consider the status of economics as a science, especially as compared to the most successful of sciences, physics, and formulate this in terms of the Popperian notion of criticisability (an extension of his earlier falsifiability demarcation criterion between science and other studies). We then discuss how methods having some similarity to models prevalent in physics might help elucidate dynamic analyses of heuristic learning in economics, though each of the two felds has a unique conceptual framework.

Information Needs and Utilization among Social Science Undergraduates in Nigerian Universities

Information is a means through which knowledge about a phenomenon derived from observation, study experience or instruction and collection in varying ways. However, failure rates continue to increase in some universities in Nigeria, and information dissemination is not improving. This study, therefore, examines the information needs and utilisation of undergraduates in the Departments of Accountancy and Business Administration within three selected Nigerian Universities based on ownership. The study adopts qualitative research approach using interview method to collect data in the federal, state and privately owned universities. The research adopted snowball sampling method as it was difficult to determine the specific sample frame due to non-availability of intake records. The study therefore recruited ninety respondents for an interview from the three universities covering various disciplines in social sciences including Business Administration, Accounting, Economics and Finance. The study was guided by Wilson’s 1991 information behaviour model using it major variables of information needs, seeking, awareness, utilisation and outcome. Findings revealed that students require relevant information that will assist them to succeed in their academic career. It also found that information needs of students vary depending on their course of study. Students, however, utilise information for various purposes including academic work, social networking, community development and lifelong learning experience.

Investigating the educational value of social learning networks: a quantitative analysis

Purpose The emergence of Education 2.0 enabled technology-enhanced learning, necessitating new pedagogical approaches, while e-learning has evolved into an instrumental pedagogy of collaboration through affordances of social media. Social learning networks and ubiquitous learning enabled individual and group learning through social engagement and social distribution of knowledge. Nevertheless, these developments have not been supported with extensive studies focusing on quantifying the impact of technology-enhanced learning on students’ progress and achievement. The purpose of this paper is to investigate how a quantitative overview of Facebook’s influence on students’ progress can be incorporated in a proposed e-moderation model of teaching and learning. Design/methodology/approach The approach is based on extending Salmon’s (2003) e-moderation model, which provides an emphasis on the theoretical perspectives that support socially situated learning environments of social networks such as Facebook. The findings revealed that students’ grades were positively influenced by the complementary use of Facebook on their courses of study. Findings The use of a social learning network also triggered a significant increase in student participation in learning activities delivered over Facebook. Research limitations/implications The main research limitations were due to the fact that a single social network was chosen for conducting the experiments. Furthermore, the investigation was narrowed down to a selected range of sessions offered to college and university students as part of their course. Originality/value The paper’s contribution is twofold, as it offers an original set of guidelines for conducting social learning network experiments and provides valuable quantifiable findings on the educational value of such networks.

Bridging the Gap between LMS and Social Network Learning Analytics in Online Learning

Despite the great potential of social network analysis (SNA) methods and visualizations for learning analytics in computer-supported collaborative learning (CSCL), these approaches have not been fully explored due to two important barriers: the scarcity and limited functionality of built-in tools in Learning Management Systems (LMS), and the difficulty to import educational data from formal virtual learning environments into social network analysis programs. This study aims to cover that gap by introducing GraphFES, an application and web service for extraction of interaction data from Moodle message boards and generation of the corresponding social graphs for later analysis using Gephi, a general purpose SNA software. In addition, this paper briefly illustrates the potential of the combination of the three systems (Moodle, GraphFES and Gephi) for social learning analytics using real data from a computer-supported collaborative learning course with strong focus on teamwork and intensive use of forums.

The convergence of Social Networking, Mobile and Virtual Learning Environments

The convergence of Social Networking, Mobile and Virtual Learning Environments

Visualizations of Online Course Interactions for Social Network Learning Analytics

Social network learning analytics aims to extract useful information to improve the learning process, but the variety of learning management systems makes this task burdensome and difficult to manage. This study shows how Gephi, a general-purpose, open-source social network analysis application, can be used by instructors and institutions to extract and visualize relevant information that is commonly hidden or difficult to observe for course coordinators and teachers. The empirical case study uses data from one cross-curricular course with 656 students at the Open University of Catalonia (UOC) and showcases the use of Gephi as a social network learning analytics tool. The study further discusses the potential of social network learning analytics to improve online instruction by visualization of educational data.

Linkages between Social-learning Networks and Farm Sustainability for Smallholder Shrimp Farmers in Sri Lanka

Linkages between Social-learning Networks and Farm Sustainability for Smallholder Shrimp Farmers in Sri Lanka

Use of smartphones by art and design students for accessing library services and learning

Purpose – The purpose of this paper is to explore art and design students’ use of smartphones for accessing library services and learning at the Hong Kong Design Institute (HKDI). Design/methodology/approach – A questionnaire survey involving 51 HKDI students was conducted to examine the students’ utilization of apps and the internet on mobile devices to find information for the purpose of academic learning, social networking, and collaborative learning. Findings – Survey results showed that while the HKDI students were all smartphone owners and active users of such mobile communication devices, only a minority of them “frequently” use these mobile devices for formal learning purposes. They demonstrated a keen preference to use search engines, social communications, and other diverse use of smartphones. Except for research and image/audio-visual needs, the rest of their needs and usage behaviour is similar to mainstream university students. Practical implications – The results suggest opportunities for the libraries to develop services and facilities that could better fulfil students’ information needs, and to improve the network coverage outside the library. Originality/value – This is probably the first study of its kind to explore art and design students’ use of smartphones for learning needs. In particular, the recent capability of smartphones and mobile internet speed are comparable with desktops, it is vital to re-examine the much changed environment and user needs.

Preferences, Homophily, and Social Learning

We study a sequential model of Bayesian social learning in networks in which agents have heterogeneous preferences, and neighbors tend to have similar preferences—a phenomenon known as homophily. We find that the density of network connections determines the impact of preference diversity and homophily on learning. When connections are sparse, diverse preferences are harmful to learning, and homophily may lead to substantial improvements. In contrast, in a dense network, preference diversity is beneficial. Intuitively, diverse ties introduce more independence between observations while providing less information individually. Homophilous connections individually carry more useful information, but multiple observations become redundant.

Social presence and impression management

This theoretical paper focuses on the concept of impression management to explain how networked learners’ presentations of self as part of the establishment, cultivation and use of productive social learning networks. The arguments in this paper connect social presence, which has long been considered an important element of online learning, with social capital, which has been used more recently to describe the way learners use social networks to support their learning. These arguments are situated at the intersection of social connectivity, goal-directed learning activity and learner experience. The broad area of interest is how learners’ goals and intentions affect their activity and behaviour in networked learning environments. More specifically, we are concerned with how learners’ goals related to study and learning affect their intentional activity toward forming and using the social connections that constitute learning networks. In this context, impression management is used to describe and explain how learners intentionally construct presentations of self as part of cultivating their social presence in networked learning and how that presentation of self leads to the establishment and development of connections (or ties) with others and the development of a network of productive connections which support learning. We argue that the constructions of self are not arbitrary; rather, they are seen as intentional efforts by learners to present themselves in ways which are not only socially acceptable, but maximise the extent to which they are views as attractive partners for social learning activity. Learners read the social environment and act intentionally in response to the social context to present themselves in ways that further their goals of productive networked learning activity. By expressing themselves in particular ways and forming impressions of others, individuals are able to establish, cultivate and use social connections and then build social networks constituted of those connections. Those social networks provide learners with the connections they need to generate social capital, that is, to find and use the value (or resources) through connections or relationships that constitute a network. The paper concludes with a the identification of several questions for educators to consider as part of informing their practice of networked learning and supporting goal-directed, technology-mediated social processes and the cultivation of productive learning networks.

Social networking, knowledge sharing, and student learning: The case of university students

The recent development of social networking brought new opportunities not only for socializing and marketing but also for knowledge sharing and learning. As the social computing technology is relatively new, a review of literature revealed lack of research on the impact of ways of using social networking sites (SNS) on knowledge sharing and learning among tertiary students. This paper aims to empirically examine the various categories of SNS use including: chatting and online discussion, creating knowledge and information content, file sharing, and enjoyment and entertainment by tertiary students at a University in Saudi Arabia. It will investigate the impact of these categories of SNS use on knowledge sharing and learning performance. We adopted a cross sectional survey questionnaire for data collection, and received 308 valid responses from a sample of students that include graduates and undergraduates. The results show that there are significant positive relationships between both chatting and online discussion and file sharing and knowledge sharing, and entertainment and enjoyment with student learning. The paper concludes with a discussion of the limitations and implications of this study, along with directions for further research.

The Effectiveness of E-Learning Implementation Using Social Learning Network Schoology on Motivation & Learning Achievement

The advancement of technology and the openness of the internet access in many places in Indonesia, making the shift in styles of learning and acquiring knowledge. Significant growth on such access also has changed the way everyone connected, communicate with each other and develop a social community. Unlike previous generations, college students in today’s era found many learning resources, both from his tutor at the College as well as from fellow students, also from other learning sources outside his College. The fact of this openness requires a new approach for a lecturer in the College to adopt a way of delivering his teaching, so the learning objectives can be achieved. One such implementation is implementing e-learning using the Social Learning Network. The purpose of this study is to see to what extent the effectiveness of the e-learning implementation on motivation and learning achievements of students. The research results showed that the learning opportunities has the greatest role in the motivation of learning, followed by collaborative learning. A high learning motivation using Social Learning Network, Schoology became a highly influential variable on learning achievements of Students in STMIK Primakara Bali

Fusing Social Networks with Deep Learning for Volunteerism Tendency Prediction

Social networks contain a wealth of useful information. In this paper, we study a challenging task for integrating users' information from multiple heterogeneous social networks to gain a comprehensive understanding of users' interests and behaviors. Although much effort has been dedicated to study this problem, most existing approaches adopt linear or shallow models to fuse information from multiple sources. Such approaches cannot properly capture the complex nature of and relationships among different social networks. Adopting deep learning approaches to learning a joint representation can better capture the complexity, but this neglects measuring the level of confidence in each source and the consistency among different sources. In this paper, we present a framework for multiple social network learning, whose core is a novel model that fuses social networks using deep learning with source confidence and consistency regularization. To evaluate the model, we apply it to predict individuals' tendency to volunteerism. With extensive experimental evaluations, we demonstrate the effectiveness of our model, which outperforms several state-of-the-art approaches in terms of precision, recall and F1-score.

Community-Based Question Answering via Heterogeneous Social Network Learning

Community-based question answering (cQA) sites have accumulated vast amount of questions and corresponding crowdsourced answers over time. How to efficiently share the underlying information and knowledge from reliable (usually highly-reputable) answerers has become an increasingly popular research topic. A major challenge in cQA tasks is the accurate matching of high-quality answers w.r.t given questions. Many of traditional approaches likely recommend corresponding answers merely depending on the content similarity between questions and answers, therefore suffer from the sparsity bottleneck of cQA data. In this paper, we propose a novel framework which encodes not only the contents of question-answer(Q-A) but also the social interaction cues in the community to boost the cQA tasks. More specifically, our framework collaboratively utilizes the rich interaction among questions, answers and answerers to learn the relative quality rank of different answers w.r.t a same question. Moreover, the information in heterogeneous social networks is comprehensively employed to enhance the quality of question-answering (QA) matching by our deep random walk learning framework. Extensive experiments on a large-scale dataset from a real world cQA site show that leveraging the heterogeneous social information indeed achieves better performance than other state-of-the-art cQA methods.

Assessment of Learning in Digital Interactive Social Networks: A Learning Analytics Approach

This paper summarizes initial field-test results from data analytics used in the work of the Assessment and Teaching of 21st Century Skills (ATC21S) project, on the “ICT Literacy – Learning in digital networks” learning progression. This project, sponsored by Cisco, Intel and Microsoft, aims to help educators around the world enable students with the skills to succeed in future career and college goals. The paper begins with describing some expansions to a common definition of learning analytics, then includes a review of the literature on ICT literacy, including the specific development that led to the ATC21S effort. This is followed by a description of the development of a “learning progression” for this project, as well as the logic behind the instrument construction and data analytics, along with examples of each. Data were collected in a demonstration digital environment in four countries: Australia, Finland, Singapore and the U.S. The results indicate that the new constructs developed by the project, and the novel item forms and analytics that were employed, are indeed capable of being employed in a large-scale digital environment. The paper concludes with a discussion of the next steps for this effort.

Going beyond your personal learning network, using recommendations and trust through a multimedia question-answering service for decision-support: A case study in the healthcare

Social learning networks enable the sharing, transfer and enhancement of knowledge in the workplace that builds the ground to exchange informal learning practices. In this work, three healthcare networks are studied in order to understand how to enable the building, maintaining and activation of new contacts at work and the exchange of knowledge between them. By paying close attention to the needs of the practitioners, we aimed to understand how personal and social learning could be supported by technological services exploiting social networks and the respective traces reflected in the semantics. This paper presents a case study reporting on the results of two co-design sessions and elicits requirements showing the importance of scaffolding strategies in personal and shared learning networks. Besides, the significance of these strategies to aggregate trust among peers when sharing resources and decision-support when exchanging questions and answers. The outcome is a set of design criteria to be used for further technical development for a social semantic question and answer tool. We conclude with the lessons learned and future work.

THE LAWS OF INFORMATION DYNAMICS AND BEHAVIOR IN DISTANCE EDUCATION SYSTEMS

Learning on the Internet, the behavior of young people learning in social networks (social networks) occurs under the laws, as a result of the interchange of information resources, discuss ideas, participation in social groups (communities), attraction to your account, use the services of social networks etc. it is Important to have a model of such behavior. In work methods of system analysis, Informatics and probability theory are studied such a model and formulated a number of laws of infodynamics which can regulate the overall information processes.

The Role of Social Learning Networks in Mobile Assisted Language Learning: Edmodo as a Case Study

The Role of Social Learning Networks in Mobile Assisted Language Learning: Edmodo as a Case Study

The coevolution of beliefs and networks

Social psychologists have shown that people experience cognitive dissonance when two or more of their cognitions diverge, and that they actively manage the dissonance. With this in mind, we develop a model of social learning in networks to understand the coevolution of beliefs and networks. We focus on beliefs concerning an objective phenomenon. Initial beliefs are based on noisy, private and unbiased information. Because the information is noisy, initial beliefs differ, creating dissonance. In our model, behavior is motivated by a desire to minimize this dissonance. In many circumstances this behavior adversely affects the efficiency of social learning, such that in equilibrium the mean aggregate belief is biased and there is significant variation of beliefs across the population. The parameterizations of our model that result in the most inefficient learning produce a fractionalized network structure in which there are a number of distinct groups: within any group all beliefs are identical; beliefs differ from group to group, sometimes greatly; there is no intergroup interaction. Since dissonance minimizing behavior is apparently a deeply rooted feature of humans, we are led to ask: What policies could improve the situation? Our results suggest that policies that improve the availability of objective information and/or increase the size of networks enhance efficiency of social learning. On the other hand, anything that makes changing networks more attractive as a dissonance minimizing strategy has the opposite effect.