One of the goals of the Knowledge Puzzle Project is to automatically generate a domain ontology from plain text documents and use this ontology as the domain model in computer-based education. This paper describes the generation procedure followed by TEXCOMON, the Knowledge Puzzle Ontology Learning Tool, to extract concept maps from texts. It also explains how these concept maps are exported into a domain ontology. Data sources and techniques deployed by TEXCOMON for ontology learning from texts are briefly described herein. Then, the paper focuses on evaluating the generated domain ontology and advocates the use of a threedimensional evaluation: structural, semantic, and comparative. Based on a set of metrics, structural evaluations consider ontologies as graphs. Semantic evaluations rely on human expert judgment, and finally, comparative evaluations are based on comparisons between the outputs of state-of-the-art tools and those of new tools such as TEXCOMON, using the very same set of documents in order to highlight the improvements of new techniques. Comparative evaluations performed in this study use the same corpus to contrast results from TEXCOMON with those of one of the most advanced tools for ontology generation from text. Results generated by such experiments show that TEXCOMON yields superior performance, especially regarding conceptual relation learning.

This article presents results from an investigation of the association between student academic performance and social ties. Based on social capital and networked learning research, we hypothesized that (a) students' social capital accumulated through their course progression is positively associated with their academic performance and (b) students with more social capital have significantly higher academic performance (operationalized as grade point average). Both hypotheses were supported by results of an empirical study that analyzed 10 years of student course enrolment records (N = 505) in a master's degree program offered through distance education at a Canadian university. These results are consistent with previous studies that looked at social networks built through student interaction in classrooms or computermediated communication environments. The significance of this research lies in the simplicity of the method used to establish student social networks from existing course registration records readily available through an institution's student information system. Direct implications of this research are that (a) study plans for students should consider investment in building new social ties in each course during degree programs and (b) readily available data about cross-class networks can be used in software systems supporting study planning.

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Creating meaning from a wide variety of available information and being able to choose what to learn are highly relevant skills for learning in a connectivist setting. In this work, various approaches have been utilized to gain insights into learning processes occurring within a network of learners and understand the factors that shape learners' interests and the topics to which learners devote a significant attention. This study combines different methods to develop a scalable analytic approach for a comprehensive analysis of learners' discourse in a connectivist massive open online course (cMOOC). By linking techniques for semantic annotation and graph analysis with a qualitative analysis of learner-generated discourse, we examined how social media platforms (blogs, Twitter, and Facebook) and course recommendations influence content creation and topics discussed within a cMOOC. Our findings indicate that learners tend to focus on several prominent topics that emerge very quickly in the course. They maintain that focus, with some exceptions, throughout the course, regardless of readings suggested by the instructor. Moreover, the topics discussed across different social media differ, which can likely be attributed to the affordances of different media. Finally, our results indicate a relatively low level of cohesion in the topics discussed which might be an indicator of a diversity of the conceptual coverage discussed by the course participants.

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