The relationship between personal cognition, social context, and knowledge sharing in global communities of practice

The purpose of this study was to identify the structural pathways of personal cognition and social context as they influence knowledge sharing behaviors in communities of practice. Based on the existing literature, ten hypotheses and a conceptual model built on the basis of the social cognitive theory were developed regarding the interrelationships of the five constructs: self-efficacy for knowledge sharing, outcome expectations, sense of community, leadership of a community, and knowledge sharing. The data were collected through an online questionnaire from the employees who have participated in communities of practice in a Fortune 100 corporation. A total of 438 usable questionnaires were collected. Overall, three analyses were conducted in order to prove the given hypotheses: (a) hypothesized measurement model fit, (b) relational and influential associations among the constructs, and (c) structural equation model analysis (SEM). In addition, open-ended responses were analyzed. The results presented that (a) hypothesized measurement models were valid and reliable, (b) personal cognitive factors, self-efficacy and outcome expectations for knowledge sharing, were found to be significant predictors of community members’ sense of community and knowledge sharing behaviors, (c) sense of community had the most significant impact on the knowledge sharing, (d) as the perceived social context, sense of community mediated the effects of personal cognition on knowledge sharing behaviors, and (e) personal cognition and social context jointly contributed to knowledge sharing. In brief, all of the hypotheses were positively supported. A conclusive summary is provided along with contributive discussion. Implications and contributions to HRD researchers and practitioners are discussed, and recommendations are provided.

Automated Discovery of Social Networks in Online Learning Communities

As a way to gain greater insights into the operation of online communities, this dissertation applies automated text mining techniques to text-based communication to identify, describe and evaluate underlying social networks among online community members. The main thrust of the study is to automate the discovery of social ties that form between community members, using only the digital footprints left behind in their online forum postings. Currently, one of the most common but time consuming methods for discovering social ties between people is to ask questions about their perceived social ties. However, such a survey is difficult to collect due to the high investment in time associated with data collection and the sensitive nature of the types of questions that may be asked. To overcome these limitations, the dissertation presents a new, content-based method for automated discovery of social networks from threaded discussions, referred to as ‘name network’. As a case study, the proposed automated method is evaluated in the context of online learning communities. The results suggest that the proposed ‘name network’ method for collecting social network data is a viable alternative to costly and time-consuming collection of users’ data using surveys. The study also demonstrates how social networks produced by the ‘name network’ method can be used to study online classes and to look for evidence of collaborative learning in online learning communities. For example, educators can use name networks as a real time diagnostic tool to identify students who might need additional help or students who may provide such help to others. Future research will evaluate the usefulness of the ‘name network’ method in other types of online communities.

Contextual text mining

With the dramatic growth of text information, there is an increasing need for powerful text mining systems that can automatically discover useful knowledge from text. Text is generally associated with all kinds of contextual information. Those contexts can be explicit, such as the time and the location where a blog article is written, and the author(s) of a biomedical publication, or implicit, such as the positive or negative sentiment that an author had when she wrote a product review; there may also be complex context such as the social network of the authors. Many applications require analysis of topic patterns over different contexts. For instance, analysis of search logs in the context of the user can reveal how we can improve the quality of a search engine by optimizing the search results according to particular users; analysis of customer reviews in the context of positive and negative sentiments can help the user summarize public opinions about a product; analysis of blogs or scientific publications in the context of a social network can facilitate discovery of more meaningful topical communities. Since context information significantly affects the choices of topics and language made by authors, in general, it is very important to incorporate it into analyzing and mining text data. In general, modeling the context in text, discovering contextual patterns of language units and topics from text, a general task which we refer to as Contextual Text Mining, has widespread applications in text mining. In this thesis, we provide a novel and systematic study of contextual text mining, which is a new paradigm of text mining treating context information as the ``first-class citizen.'' We formally define the problem of contextual text mining and its basic tasks, and propose a general framework for contextual text mining based on generative modeling of text. This conceptual framework provides general guidance on text mining problems with context information and can be instantiated into many real tasks, including the general problem of contextual topic analysis. We formally present a functional framework for contextual topic analysis, with a general contextual topic model and its various versions, which can effectively solve the text mining problems in a lot of real world applications. We further introduce general components of contextual topic analysis, by adding priors to contextual topic models to incorporate prior knowledge, regularizing contextual topic models with dependency structure of context, and postprocessing contextual patterns to extract refined patterns. The refinements on the general contextual topic model naturally lead to a variety of probabilistic models which incorporate different types of context and various assumptions and constraints. These special versions of the contextual topic model are proved effective in a variety of real applications involving topics and explicit contexts, implicit contexts, and complex contexts. We then introduce a postprocessing procedure for contextual patterns, by generating meaningful labels for multinomial context models. This method provides a general way to interpret text mining results for real users. By applying contextual text mining in the ``context'' of other text information management tasks, including ad hoc text retrieval and web search, we further prove the effectiveness of contextual text mining techniques in a quantitative way with large scale datasets. The framework of contextual text mining not only unifies many explorations of text analysis with context information, but also opens up many new possibilities for future research directions in text mining.

Nietzsche's orphans: music and the search for unity in revolutionary Russia, 1905-1921

At the dawn of the twentieth century, Imperial Russia was in the throes of immense social, political and cultural upheaval. The effects of rapid industrialization, rising capitalism and urbanization, as well as the trauma wrought by revolution and war, reverberated through all levels of society and every cultural sphere. In the aftermath of the 1905 revolution, amid a growing sense of panic over the chaos and divisions emerging in modern life, a portion of Russian educated society (obshchestvennost’) looked to the transformative and unifying power of music as a means of salvation from the personal, social and intellectual divisions of the contemporary world. Transcending professional divisions, these “orphans of Nietzsche” comprised a distinct aesthetic group within educated Russian society. While lacking a common political, religious or national outlook, these philosophers, poets, musicians and other educated members of the upper and middle strata were bound together by their shared image of music’s unifying power, itself built upon a synthesis of Russian and European ideas. They yearned for a “musical Orpheus,” a composer capable of restoring wholeness to society through his music. My dissertation is a study in what I call “musical metaphysics,” an examination of the creation, development, crisis and ultimate failure of this Orphic worldview. To begin, I examine the institutional foundations of musical life in late Imperial Russia, as well as the explosion of cultural life in the aftermath of the 1905 Revolution, a vibrant social context which nourished the formation of musical metaphysics. From here, I assess the intellectual basis upon which musical metaphysics rested: central concepts (music, life-transformation, theurgy, unity, genius, nation), as well as the philosophical heritage of Nietzsche and the Christian thinkers Vladimir Solov’ev, Aleksei Khomiakov, Ivan Kireevskii and Lev Tolstoi. Nietzsche’s orphans’ struggle to reconcile an amoral view of reality with a deeply felt sense of religious purpose gave rise to neo-Slavophile interpretations of history, in which the Russian nation (narod) was singled out as the savior of humanity from the materialism of modern life. This nationalizing tendency existed uneasily within the framework of the multi-ethnic empire. From broad social and cultural trends, I turn to detailed analysis of three of Moscow’s most admired contemporary composers, whose individual creative voices intersected with broader social concerns. The music of Aleksandr Scriabin (1871-1915) was associated with images of universal historical progress. Nikolai Medtner (1879-1951) embodied an “Imperial” worldview, in which musical style was imbued with an eternal significance which transcended the divisions of nation. The compositions of Sergei Rachmaninoff (1873-1943) were seen as the expression of a Russian “national” voice. Heightened nationalist sentiment and the impact of the Great War spelled the doom of this musical worldview. Music became an increasingly nationalized sphere within which earlier, Imperial definitions of belonging grew ever more problematic. As the Germanic heritage upon which their vision was partially based came under attack, Nietzsche’s orphans found themselves ever more divided and alienated from society as a whole. Music’s inability to physically transform the world ultimately came to symbolize the failure of Russia’s educated strata to effectively deal with the pressures of a modernizing society. In the aftermath of the 1917 revolutions, music was transformed from a symbol of active, unifying power into a space of memory, a means of commemorating, reinterpreting, and idealizing the lost world of Imperial Russia itself.