Affordances offered through ubiquitous nature of Web 2.0 technologies and social media have progressively become universal constituents of our lives. Presently our students have seen the escalation in use of multimedia in their studies. With technological advances in telecommunication technologies, students have become accustomed to instant, global communications modes. Educational institutions have progressively adapted more innovative pedagogical approaches in their provision. Web 2.0 has fundamentally altered communication methods between people around the world. Access to information, dissemination, sharing and creation of new digitised content are powerful tools that ease social media adaptation in everyone’s life. Over the last decade multimedia authoring tools have become more useful for content generation. The price and expertise to use these authoring tools has decreased, therefore offering opportunity for educators to broaden their experimental horizons with these technologies. With the advent of Web 2.0, access to information, dissemination, sharing and creation of new digitised content are powerful tools that ease social media adaptation in student’s life. Universities have reported reforms in the use of Education 2.0, while Web 2.0 is finding its momentums in further education and schools. Since the advent of Web 2.0 many educational institutions have reported remarkable positive influences in students learning behaviours. Research studies have illustrated association between students improved communication and collaboration linked to improved motivation hence more on going academic performance. Sociallearningnetworks represent a more diverse mechanism than a content delivery platform. The potential to release both students and instructors creative talents, ease of content creation and collaboratively sharing teaching and learning resources has enabled educational institutions to explore the strategic benefits of sociallearningnetworks. Recent studies indicate that these digital elements when aligned with the best practices of multimedia design become powerful learning agents. This study is aimed at highlighting the importance of sociallearningnetworks in education from a qualitativeperspective. A series of recent studies at higher and further education has provided guidelines for the improved use of social media in e-learning. This paper’s findings will introduce qualitative verdicts for a framework adaptation of sociallearningnetworks in e-learning.
SocialNetworks have evolved from the pre-digital ages to the online internet scene. With the explosion of data prevalent in every aspect of social function in today’s social structure, more efficient and robust methods of discovering knowledge from data mining approaches are required to organize and infer useful social relationships for various real-world applications like recom- mendation, topic modeling, trust reciprocity, etc. In this paper, we have covered a wide scope of current research literature on various relational learning techniques employed in the Online Social Network context. The widely used methods covered in this survey are involved in inferring and identifying relationships co-referenced from structures and include but are not limited to: Detection, Prediction and Markovian Logic approaches from their related core social perspec- tives of Communities, Links and Networks. It is hoped that this survey will provide both a wider and deeper perspective on the various methods and techniques used to learn from relational structures so that new knowledge can be uncovered from highly complex and voluminous data of the Online SocialNetworks scene. In conclusion, relational structures are the building blocks of socialnetworks that relate to how actual relationships evolve in real-life scenarios.
programming and the effectiveness of such. Social-emotional learning is a vague and ambiguous term that is used as a comprehensive umbrella for multiple programs that are implemented in a school targeting students' emotional intelligence (Hoffman, 2009). Such programs captured under SEL include school based derived from public health, mental health and juvenile-justice viewpoints as well as programming rooted in moral and character development (Hoffman, 2009). One of the largest points of contention is the theoretical framework is which social-emotional learning is based. Emotional intelligence still is a questionable construct within the scientific community. Many programs that utilize emotional intelligence as its research base often do not delineate what components of EI are being used (Cherniss, Extein, Goleman, & Weissberg, 2006; Hoffman, 2009). According to Cherniss et al. (2006) "there has been some confusion between the underlying core abilities of EI and the many social and emotional 'competencies' that are built on those core abilities". (p. 240) Although social-emotional learning programs are considered homogenous, the fact is that many programs under the SEL umbrella target different attributes of EI, but are not explicit in doing so (Hoffman, 2009). Hoffman stated (2009) "the literature on SEL paints for some; a diverse, positive picture of how focusing on social and emotional competencies can benefit students and schools, whereas for others, it is rife with confusion and lack of empirical and evaluative rigor". (p. 537)
of the qualitative predictions we derive. A model with payoff-uncorrelated trembles would generate very different predictions which, as we show below, would not be consistent with the data. In such a model, if the sequence (A, A, B) is observed, then for small tremble rates, the only way to rationalize the deviating B choice is through a tremble, which means that the decision imparts no information about the private signal, since the tremble rate is independent of beliefs. Thus, cascades will form and persist in such a model (modulo occasional trembles) just as in the standard Nash equilibrium. Because the standard information cascade logic applies to this setting, no information is learned from observing long decision histories, so that posterior beliefs are constrained to an interval around the prior. For instance, when q = 6/9 and there is a tremble rate of ² = 0.05, terminal beliefs are 0.22 and 0.78. At these beliefs, subjects who get a signal contrary to the cascade have posteriors of 0.63 and 0.37, respectively, so that breaking the cascade with a contrary signal results in expected losses of 41%.
Trust management in peer-to-peer systems have been also widely studied [Kamvar et al., 2003; Zhou et al., 2008]. Spectral decomposition is used on the adjacency matrix of the network graph to estimate global reputation, which is also called the global group trust metric [Ziegler and Lausen, 2004]. Guha et al.  associate trust relations to matrix operations. For example, the commu- tativity of trust is associated to matrix transpose, while direct propagation is associated to matrix multiplication. Different from global group methods, our approach models trust from a personal perspective. Propagated trust takes into account personal bias. Besides, matrix approaches require several iterations to converge.
As the topic of ESN business models has received limited attention so far, this study opted for an explorative, qualitative approach. To explore the business models of ESN providers, a multiple case study method (Yin 2009) was conducted. More specifically, a multiple mini-case study strategy was executed where the depth and richness within one case is limited, but where the number of different cases is relatively large (George and Bennett 2005). This helps establish a board view across different ESN providers. Moreover, as high-level information on the customer-facing elements of the business model should be mostly publically available and gaining access to ESN providers is difficult, data collection focussed on primary information available on the ESN providers’ websites complemented with secondary information when required. This information was summarized and compared using the customer-facing elements of the Business model Canvas: (1) Customer Segments, (2) Value Propositions, (3) Channels, (4) Customer Relationships, and (5) Revenue Streams (Osterwalder and Pigneur 2010).
So far we have observed the way in which socialnetworks behave in the physical world, which includes the way they form, evolve and support the students through the learning journey. However, as learning has evolved from a practice taking place in the physical world to computer-supported learning systems that mediate interaction with the learning material, establishing a strong foundation for substituting the social part of learning has become crucial. To date, many efforts have attempted to maintain the interaction of students in the on-line perspective through the use of social software. NJIT’s virtual classroom  to certain extent is an evidence of success in this effort despite of many lim- itations, where student pursued their college degree while working full time anytime/anywhere interacting with their online peers, mentors and other students in the class while using social and advanced computer network. Recently a new generation of social software has the potential to de- liver more effective support to users’ social lives. This new generation of web-based software, known to web develop- ers as “Web 2.0 ”, has quickly gained widespread popularity, to the point that millions of users worldwide are creating content, tagging photographs, sharing videos, blogging, and making friends through the web every day. This perceived popularity has placed a new pressure upon universities com- peting within ‘information economies’ to acknowledge and apply social software effectively within education.
Following Arjoranta’s  suggestion of focusing definitions on Wittgensteinian family resemblances instead of a common core, we performed a survey among ex- perts on gamification in order identify key terms that are relevant for describing gamification and, by corollary, indicators of the presence of gamification. A catalogue of questions was derived from the survey results and applied to five popular LMS. The instrument consists of 38 items, each with a standardized part categorizing the response into yes, no, or maybe and an open part detailing the reasoning for the an- swer. “Maybe” responses were reserved for cases in which a system had potential for gamification, but relied on user input for it. The items are divided into five categories – experiential, mechanics, rewards, goals, and social (see Table 1 for example items). Four evaluators applied the instrument to each of the five LMS – one expert and three evaluators with basic training in gamification. Agreement on the standardized part of each item was calculated to validate the instrument. Following Capterra’s October 2014  ranking, we analyzed the following five LMS focused on K-12 or higher education in this research (in order of ranking): Moodle, Edmodo, Blackboard Learn, Schoology, and Canvas.
A number of the serious privacy cases already discussed, such as the Netflix scandal, have shown that anonymization of socialnetworks is much harder than it looks. Rich datasets have are often published for research purposes with only casual attempts to anonymize them. Research in de-anonymization has also seen an upswing [4, 87, 117, 120], leading to high-profile data releases being followed by high-profile privacy breaches. These developments have forced organizations to make some effort to better anonymize the released data. However, distorting data to achieve this contradicts the very purpose of a release, since it damages utility. So how hard can it be to re-identify users? In this chapter we present a generic and automated approach to re-identifying nodes in anonymized socialnetworks which enables novel anonymization techniques to be quickly evaluated. It uses a machine-learning model to match pairs of nodes in disparate anonymized subgraphs. Social network graphs in particular are high-dimensional and feature-rich data sets, and it is extremely hard to preserve their anonymity. Thus, any anonymization scheme has to be evaluated in detail, including those with a sound theoretical basis . As discussed in §§ 2.4 to 2.6, many techniques have been proposed to resist de-anonymization; however Dwork and Naor have shown  that preserving privacy of an individual whose data is released cannot be achieved in general. The resulting uncertainty makes mass data release a very tricky proposition specially from the perspective of data subjects.
Both providers expressed disappointment that they had not met their targets for reductions below the baseline, but were also clear that they regarded the
reductions that they had made as an achievement for the entrenched rough sleepers within the cohort. They also described how their targets for reductions below the baseline had been difficult to set and were a matter of judgement at the time rather than being based in evidence for this group, which was not available. ‘We’re delighted to have got this far. It’s a very tough target.’ (Thames Reach) The SIB has also provided valuable learning about how people use the streets and the magnet effects of the street for many of those for whom rough sleeping is entrenched. One issue raised in the first report and that has been ongoing during year two is a view from the providers that the baseline measure does not
The different degrees of actor inclusiveness across the networks seem to be influenced by the nature of resources circulating within each of the networks, or to reflect the value that the trainers attached to the resources. Thus, to some extent, the level of inclusiveness reflects the relative strategic importance of each network to the trainers. Communication exchanges normally take place serendipitously with a variety of people and involve mainly transmission of general information, some of which might not be very valuable or relevant to the trainers’ learning needs. Thus, the trainers might feel more free to exchange such resources. In contrast, advice is a special type of resource and is presumably exchanged among people who have closer or stronger relations. In other words, the trainers seem to be more selective in choosing their advice- exchange partners than they are in determining information exchange or collaboration associates. This is consistent with Tough’s (1971) finding, as noted by Cross (1981), where it is reported that locating competent help is one of the major problems in self-directed learning projects.
opinions, beliefs and activities and as well as sexual and racial belongings. Information and communication activities integrating mobile applications, blogging, twitting and photo/video sharing are key social networking activities (Boyd & Ellison, 2008), contributing extensively to adapt with rapid communication and information changes. Social Network Sites (SNS) are hence basically known to help individuals to stay connected with each other in a bounded virtual setting. Nowadays, in this digital age and with globalization, social network sites have become the mainstream communication medium, with networks such as Facebook, Twitter and LinkedIn gaining much supremacy, popularity and having a major impact globally (McCafferty, 2011). Their user-friendliness, accessibility, and fast information retrieval have added hugely to their acceptance amongst internet users (Pew Research Center, 2010). Social network has become a key part in people’s lives and its power and influence is not negligible, given its impacts over internet users. However, there is definitely a limit to all online platforms as the lifecycle of socialnetworks depends mostly upon the users (Falls & Deckens, 2012; eMarketer, 2012). When an online platform can no more innovate to provide better service to the subscribed users (Nielsen, 2011), causing them to spend lesser time or drop the use of that particular online platform, it is a sign of “saturation”. In other words, saturation of socialnetworks is the decrease or slowing rate of growth of its usage percentage. Social network saturation is a burning subject among many researchers, such as the Pew Research Center, the EDUCAUSE Learning Initiative, the IEEE Computer Society, eMarketer and Nielsen, with the goal to determine if saturation has occurred or will occur in the near future.
In examining how social relationships or connectedness develops online, Cho (2002) found that there are structural factors (e.g., a pre-existing friendship network) and psychological factors (e.g., individual communication styles) that influence the formation of collaborative learning and working networks. In their study of an online course, Harmon and Jones (2000) report the phenomenon of fast friendship (i.e., sharing suffering and working at a distance tend to push students to quickly form strong bonds) and being overwhelmed (the amount of time required and the feeling of being hopelessly behind others) among the online learners. These reactions may rise from the psychological and emotional pressures resulting from independent learning at a distance, especially when students sit before a computer screen alone most of the time. To cope with these problems, Lally and Barrett (1999) suggest that instructors provide more opportunities for socio-emotional discourse and networking among the learners. In fact, the lack of the social communication of an online course may hinder the maintenance of the group’s well-being, and the development of necessary support for the members may facilitate group decision making and problem solving (MacDonald & Gibson, 1998). Orey, Koenecke, and Crozier (2003) found that if a learning community has not developed in an online course, the students tend to receive help from family members, colleagues, or friends and build a supporting community offline.
based the questionnaire on the conceptual model as was developed in out literature review. The questions of the questionnaire were mostly of qualitative nature, using an answering scale with predetermined statements from which the respondents had to choose.
The target population for this research was defined as all people that are familiar with OSNs and that have an opinion in this matter. As the study was done in the Netherlands, all respondents are Dutch residents. Respondents were chosen by taking a non-probability sample, the sample was chosen by using a convenience sampling method. All the respondents were approached through personal and business networks of the authors. Given the explorative nature of the study, this sampling method is appropriate (Schreuder et al., 2001) and is not likely to damage the usefulness of the results from the study. Data collection was done partly through a self- administrated web survey and partly through personal collection.
In order to control for exogenous factors that potentially affect the interaction processes and learning outcomes, we also include five control variables. To account for potential technological difficulties, we include perceived ease of use as a control variable (Davis 1989). Ease of use is a critical factor that affects how users integrate any IT system into their daily routines (Gray and Durcikova 2005). We also control for an individual’s overall virtual learning experience as well as for cognitive ability (measured by the educational level) to account for differences in learning outcomes (Baldwin et al. 1997). In addition, we intend to control for self-rated expertise of the learning topic as well as for the learning group size as sustaining virtual interaction tends to be more difficult for larger groups (Ridings and Wasko 2010). In the next step of our research process (stage 2), we will refine our item pool and develop a measurement scale. To initially validate and refine the measurement instrument developed from the literature, we will conduct semi-structured interviews with experts from organizational training departments and human capital consultancies (Bock et al. 2005). The experts will be asked to go through the initial questionnaire and to provide feedback on clarity, completeness, and whether the construct definitions capture the essence of the respective phenomena. Subsequently, the final measurement scale is intended to be developed using established card-sorting and item-ranking procedures in order to reword and/or remove unclear items and to ensure content validity (Anderson and Gerbing 1991; Moore and Benbasat 1991). Towards this end, the measurement scale will be pre-tested with a selected sample of users of collaborative learningnetworks to ensure construct validity (Straub et al. 2004).
Rita not only spent time with Taiwanese students, she also socialised with students from different countries, such as Japan, Korea, China, Poland and Russia. She seemed to get along with them most of the time. She did not like certain students but it did not affect her social life too much at school. She went out with them, visited pubs, and had BBQs together, etc. From my observation at their school party, I could see Rita actively trying to talk to students of different nationalities. Because Rita did not like the behaviour and attitude of some students from one particular country, she did not want to socialise with students from that country. After Eric and Linda left, she became close to Sharon, my Korean participant. They started the course around the same time and were sometimes in the same class. Rita sometimes went to the café where Sharon worked to meet her, and sometimes Sharon went to the pub where Rita worked. In her last two interviews. Rita mentioned a lot about a Japanese student, Mari, who she met in school. She seemed to admire and appreciate that student. According to Rita, she learned a lot from her although in the beginning Rita’s confidence was undermined by that student’s good English. Rita went out with her several times and when the student’s boyfriend visited her, Rita also met him. Rita also said she liked to go to school because of this student. It seemed this Japanese student influenced Rita quite a bit.
S.S. had been supporting depressed people through social network services and face-to-face meetings. When talking with his clients, he tried to be easy to talk to (“I say ‘yahho-’ (‘hello’ in spoken language in Japanese) often.”), to share his painful experiences (“I also have experience of being bullied in school. You seem to be highly anxious, too. Are you okay?”), to rest enough when supporting others (“I do not talk to them [his clients] when I am not in good condition. If I do, then I am afraid I might accuse them. The frequency of interviews de- pends on my physical capacity and timing.”) and to have the client engage as much as he/she could do. However, he felt that he might not have the answers his clients were searching for due to a lack of knowledge and/or com- mon sense. Through his LRT participation, he gained confidence after receiving positive reactions from others at his age. Learning about self-caring techniques made him want to learn more about this field. Participation in the interviews also gave him the chance to reflect on his abilities and to confirm his competences.
This section provides a quantitative evaluation of OntoLearn s main algorithms. We believe that a quantitative evaluation is particularly important in complex learning systems, where errors can be produced at almost any stage. Even though some of these errors (e.g. subtle sense distinctions) may not have a percievable effect on the final ontology, as shown by the results of the qualitativeevaluation in Section 4.2, it is nevertheless important to gain insight on the actual system capabilities, as well as on the pararmeters and external circumstances that may positively or negatively influence the final performance.
worldview that centralizes the personal—personal goals, personal uniqueness, and personal control—and peripheralizes the social; collectivism refers to a worldview that stresses relatedness, belongingness, duty and harmony. Many studies contrast East Asian countries and the U.S. as two culturally distant entities, arguing that East Asians are generally more collectivistic or have an interdependent self-construal, and Americans, particularly European Americans, are generally more individualistic or have an independent self-construal (Hofstede, Hofstede, and Minkov 2010; Kitayama and Markus 2000; Markus and Kitayama 1991; Markus and Schwartz 2010; Triandis 1995). These studies also suggest that well-being is more often attained through the realization of positive social relationships in collectivistic Asian cultures, but more through personal achievement in individualistic European-American cultures. The notion implies that interpersonal networks may be larger and stronger among East Asians but smaller and weaker among Americans. However, this cultural contrast between the “East” and the “West” has also been challenged. A meta-analysis based on empirical evidence from 1980 to 1999 indicates that among East Asians, only Chinese are more collectivistic than European Americans, and Japanese and Koreans are no more collectivistic than European Americans (Oyserman, Coon, and Kemmelmeier 2002). The findings not only cast doubt on the East-West divide on the collectivism-individualism orientation, but also demonstrate that East Asia is a culturally diverse region.
The millimeter wave technology offers beam-based cell coverage. The architecture enables each cell can have one or more synchronization signal block beam. To cover the entire area, a grid formation of beam takes place. The UE compute and communicate all desirable beams to the cell site which is serving at present. The cell site is assisted by the Q-Learning algorithm to whether or handover the UE to neighboring cell site and also to which beam , based on Beam State Information.Machine learning has the ability to give less complex answers for complex issues by dissecting an immense volume of information in a brief span, learning for adjusting its usefulness to progressively evolving situations, and foreseeing not so distant future occasions with sensibly great exactness. The 5G correspondence systems are getting complex because of development of exceptionally enormous number of new associated gadgets and new sorts of administrations. In addition, the prerequisites of making virtual system cuts reasonable to give ideal administrations to assorted clients furthermore, applications are presenting difficulties to the productive the board of system assets, preparing data about a gigantic volume of traffic, remaining vigorous against all potential security dangers, and adaptively modification of organize usefulness for time-changing outstanding task at hand . A colossal volume of information of complex nature should be investigated to complete savvy choice for the structure, development, organization, activity, organization and the executives of a system cut with the goal that it can successfully fulfill the quality of service (QoS) prerequisites of the administration expected to be conveyed through it, regardless of time varying remaining tasks at hand and system conditions. It is troublesome for a human to make and work system cuts physically by preparing the huge volumes of information in a brief timeframe. Along these lines, it is being important to mechanize these assignments. Machine learning strategies are empowering agent for the mechanization of system cutting capacities. Machine learning has the capacity of detecting (e.g., oddity discovery), mining (e.g., administration grouping), expectation (e.g., gauging client or traffic pattern), and thinking (e.g., design of framework parameters for adjustment). The regulated learning procedures learn (or find) a work from preparing information, which include sets of info and wanted yields. The yield of the capacity can be ceaseless qualities (called relapse) or a class mark of the information esteems (called characterization). In the wake of preparing, the learning specialist or component predicts the estimation of capacity for