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Using online knowledge communities (OKCs) as informal learning environments poses the question how likely these will integrate newcomers as peripheral participants. Previous research has identified surface characteristics of the OKC dialog as integrativity predictors. Yet, little is known about the role of dialogic textual complexity. This contribution proposes a comprehensive approach based on previously validated textual complexity indexes and applies it to predict OKC integrativity. The dialog analysis of N = 14 blogger communities with a total of 1937 participants identified three main components of textual complexity: dialog participation, structure and cohesion. From these, dialog cohesion was higher in integrative OKCs, thus significantly predicting OKC integrativity. This result adds to previous OKC research by uncovering the depth of OKC discourse. For educational practice, the study suggests a way of empowering learners by automatically assessing the integrativity of OKCs in which they may attempt to participate and access community knowledge. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

In search of methods that improve the efficiency of teaching and training in organizations, several authors point out that mass customization (MC) is a principle that covers individual needs of knowledge and skills and, at the same time, limits the development costs of customized training to those of mass training. MC is proven and established in the economic sector, and shows high potential for continuing education, too. The paper explores this potential and proposes a multidisciplinary, pragmatic approach to teaching and training in organizations. The first section of the paper formulates four design principles of MC deduced from an examination of economics literature. The second section presents amit™, a frame for mass customized training, designed according to the principles presented in the first section. The evaluation results encourage the further development and use of mass customized training in continuing education, and offer suggestions for future research. (http://www.tandfonline.com/doi/abs/10.1080/0158037X.2010.517996)

The massive student participation in Computer Supported Collaborative Learning (CSCL) sessions from online classrooms requires intense tutor engagement to track and evaluate individual student participation. In this study, we investigate how the time evolution of messages predicts students' participation using two models – a linear regression and a Random Forest model. A corpus of 10 chats involving 47 students was scored by 4 human experts and used to evaluate our models. Our analysis shows that students' pauses length between consecutive messages within a discussion is the strongest participation predictor accounting for R2 ¼ :796 variance in the human estimations while using a Random Forest model. Our results provide an extended basis for the automated assessment of student participation in collaborative online discussions. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

With half of the world already connected to the Internet, we are facing a growing amount of information available online, that is expected to increase exponentially in the following years. Educational environments are transitioning from closed structures to open, collaborative environments, using technology to build virtual classrooms. In this paper we present a customized crawler dedicated to alternative knowledge building environments used for potential community inquiry, that is unique in its power to combine data extraction and indexing capabilities that facilitate discourse-driven community network analysis integrated into the ReaderBench framework. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

Nistor, N., Dascalu, M., Stavarache, L.L., Tarnai, C., & Trausan-Matu, S. (2015). Predicting Newcomer Integration in Online Knowledge Communities by Automated Dialog Analysis. In Y. Li, M. Chang, M. Kravcik, E. Popescu, R. Huang, Kinshuk & N.-S. Chen (Eds.), State-of-the-Art and Future Directions of Smart Learning (Vol. Lecture Notes in Educational Technology, pp. 13–17). Berlin, Germany: Springer-Verlag Singapur ; Using online knowledge communities (OKCs) from the Internet as informal learning environments poses the question how likely these communities will be to integrate learners as new members. Such prediction is the purpose of the current study. Based on the approaches of voices interanimation and polyphony, a natural language processing tool was employed for dialog analysis in integrative versus non-integrative blog-based OKCs. Four dialog dimensions were identified: participants' long-term persistence in the discourse, the community response to their participation, their communicative centrality, and their communicative peripherality. Hierarchical clusters built upon these dimensions reflect socio-cognitive structures including central, regular, and peripheral OKC members. While the socio-cognitive structures did not make a significant difference, integrative OKCs display significantly stronger peripherality, community response, and centrality as compared to non-integrative OKCs. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

Nistor, N., Dascalu, M., Stavarache, L.L., Serafin, Y., & Trausan-Matu, S. (2015). Informal Learning in Online Knowledge Communities: Predicting Community Response to Visitor Inquiries. In G. Conole, T. Klobucar, C. Rensing, J. Konert & É. Lavoué (Eds.), 10th European Conf. on Technology Enhanced Learning (pp. 447–452). Toledo, Spain: Springer. ; Informal learning in online knowledge communities (OKCs) comprises visitor inquiries on specific topics. Learning can occur only if the OKC adequately respond. This study aims to predict OKC response, using a social learning analytics approach based on computational linguistics and Bakhtin's theory of dialogism. Observing the blog topic (cooking vs. politics & economics) and the visitor inquiry format (off-topic vs. on-topic), a field experiment with a 2 × 2 factorial design was conducted on a sample of N = 68 blogger communities with a total of 25,303 members. For the entire sample, the community response was influenced only by the inquiry format. In a separate examination of experimental groups, only for one examined topic (cooking) this remained true, while for the other (politics & economics) the community response only depended on the previously established dialog quality. The findings suggest identification criteria for responsive communities, which can support OKC integration in learning environments. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.

Whether interested in personal work, in learning about trending topics, or in finding the structure of a specific domain, individuals' work of staying up-to-date has become more and more difficult due to the increasing information overflow. ln our previous work our focus has been to create a semantic annotation model accompanied by dedicated views to explore the semantic similarities between scientific articles. This paper focuses on applying our approach on a dataset of 519 project proposal abstracts, with the intention to bring value to the current indexation methodologies that rely primarily on co- citations and keyword matching. Our experiment uses various Social Network Analysis metrics to compare the rankings generated by two complementary models relying on semantic similarity and co-authorship networks. The two models are statistically different based on representative project associations, are significantly correlated in terms of project rankings by eccentricity and closeness centrality, and the semantic similarity network is denser. ; This study is part of the RAGE project. The RAGE project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 644187. This publication reflects only the author's view. The European Commission is not responsible for any use that may be made of the information it contains.