An open source library to parse and analyze online collaborative knowledge-building portals

With the success of collaborative knowledge-building portals, such as Wikipedia, Stack Overflow, Quora, and GitHub, a class of researchers is driven towards understanding the dynamics of knowledge building on these portals. Even though collaborative knowledge building portals are known to be better than expert-driven knowledge repositories, limited research has been performed to understand the knowledge building dynamics in the former. This is mainly due to two reasons; first, unavailability of the standard data representation format, second, lack of proper tools and libraries to analyze the knowledge building dynamics.We describe Knowledge Data Analysis and Processing Platform (KDAP), a programming toolkit that is easy to use and provides high-level operations for analysis of knowledge data. We propose Knowledge Markup Language (Knol-ML), a generic representation format for the data of collaborative knowledge building portals. KDAP can process the massive data of crowdsourced portals like Wikipedia and Stack Overflow efficiently. As a part of this toolkit, a data-dump of various collaborative knowledge building portals is published in Knol-ML format. The combination of Knol-ML and the proposed open-source library will help the knowledge building community to perform benchmark analysis.Link of the repository: Verma et al. (2020)Video Tutorial: Verma et al. (2020)Supplementary Material: Verma et al. (2020)

Scientific Associations as Communities of Practice for Fostering Collaborative Knowledge Building: Case Study of IAKM

Knowledge building is a social process that is driven by the willingness of people to share their expertise and create new knowledge. Scientific Communities of Practice (CoPs) are communities of professors and researchers whose aim is to foster scientific knowledge generation. In the KM literature, research concerning this kind of CoPs has been substantially neglected so far. The present research analyses the case study of the International Association for Knowledge Management (IAKM) seen as a scientific CoP where members are mostly academics with research interests in developing and promoting knowledge management. Based on a collection of quantitative and qualitative data about member collaborations and scientific production, the study investigates the structure of interactions and the collaborative processes of IAKM members and the specific mechanisms of knowledge building within this CoP, seen as a paradigmatic example of scientific community. Members were asked to respond to a survey regarding their collaborative activities carried out with other IAKM members in the period of 2011 – 2020. The descriptive analysis revealed the kind of collaborations, the distribution of interactions across the community, and the dynamic patterns over time. A follow-up social network analysis was used to provide deeper insight into the community structure and dynamics. The research found that a CoP can really be useful for progress in a scientific field because it can provide a platform for trust and mutual acquaintance that reduces barriers to collaboration and knowledge building across different universities, professional roles, countries, and cultures, which is increasingly important for the progress of science. Most importantly, IAKM exhibited a cohesive and active core membership with pivotal roles played by a number of active members, which contributed significantly to the growth of the Association and, in general, to the advancements in the field of KM through collaborative knowledge building.

Information Measures of Knowledge Contribution: A New Method to Measure Knowledge Contribution in Collaborative Knowledge Building: An Information Theory Perspective

The measurement of knowledge contribution in collaborative knowledge building is an important research topic in computer-supported collaborative learning. The information measures of knowledge contribution based on information theory are proposed in this study, which includes two measures: amount of information and information gain. Discourse data collected from a collaborative knowledge building activity were analyzed to validate these measures. The results showed that our information measures can complement the traditional behavioral. With the help of the two measures, community-level variation tendency and individual-level knowledge contribution characteristics could be analyzed in collaborative knowledge building activities. A log function was used to fit the community knowledge variation tendency to measure the convergence of knowledge building. Students were clustered into five types according to their behaviors and contributions in collaborative knowledge building. Both teachers and researchers can benefit from these two information measures by using them in practice.

An Open Source Toolkit to Parse and Analyze Online Crowdsourced Portals

With the success of crowdsourced portals, such as Wikipedia, Stack Overflow, Quora, and GitHub, a class of researchers is driven towards understanding the dynamics of knowledge building on these portals. Even though collaborative knowledge building portals are known to be better than expert-driven knowledge repositories, limited research has been performed to understand the knowledge building dynamics in the former. This is mainly due to two reasons; first, unavailability of the standard data representation format, second, lack of proper tools and libraries to analyze the knowledge building dynamics. We describe Knowledge Data Analysis and Processing Platform (KDAP), a programming toolkit that is easy to use and provides high-level operations for analysis of knowledge data. We propose Knowledge Markup Language (Knol-ML), a generic representation format for the data of collaborative knowledge building portals. KDAP can process the massive data of crowdsourced portals like Wikipedia and Stack Overflow efficiently. As a part of this toolkit, a data-dump of various collaborative knowledge building portals is published in Knol-ML format. The combination of Knol-ML and the proposed open-source library will help the knowledge building community to perform benchmark analysis.

An Approach for Assessing Large Online Communities in Informal Learning Environments

Online Learning Communities (OLC), supported by social web technologies, have proved to be beneficial for collaborative knowledge building, mainly in informal environments. There is an increasing interest in assessing online Social Learning (SL) in these communities. However, there is no agreement on how their performance can be measured. This paper presents an approach which combines structure and discourse analyses to assess large online communities used in SL. Its objective is to identify conditions and behavioral patterns associated to learning. The results point out a set of quantitative features which shows that participation and ongoing collaboration have a fundamental role for knowledge creation and sharing.