Newcomer Integration in Online Knowledge Communities: Exploring the Role of Dialogic Textual Complexity

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.

Designing Massive Open Online Learning Processes: The importance of the social element

This is a pre-print for personal use only. Please refer to the Springer website for the official, published version http://www.springer.com/978-3-662-52923-2

ReaderBench goes Online: A Comprehension-Centered Framework for Educational Purposes

In this paper we introduce the online version of our ReaderBench framework, which includes multi-lingual comprehension-centered web services designed to address a wide range of individual and collaborative learning scenarios, as follows. First, students can be engaged in reading a course material, then eliciting their understanding of it; the reading strategies component provides an in-depth perspective of comprehension processes. Second, students can write an essay or a summary; the automated essay grading component provides them access to more than 200 textual complexity indices covering lexical, syntax, semantics and discourse structure measurements. Third, students can start discussing in a chat or a forum; the Computer Supported Collaborative Learning (CSCL) component provides indepth conversation analysis in terms of evaluating each member’s involvement in the CSCL environments. Eventually, the sentiment analysis, as well as the semantic models and topic mining components enable a clearer perspective in terms of learner’s points of view and of underlying interests.

Predicting Student Performance and Differences in Learning Styles based on Textual Complexity Indices applied on Blog and Microblog Posts

Social media tools are increasingly popular in Computer Supported Collaborative Learning and the analysis of students' contributions on these tools is an emerging research direction. Previous studies have mainly focused on examining quantitative behavior indicators on social media tools. In contrast, the approach proposed in this paper relies on the actual content analysis of each student's contributions in a learning environment. More specifically, in this study, textual complexity analysis is applied to investigate how student's writing style on social media tools can be used to predict their academic performance and their learning style. Multiple textual complexity indices are used for analyzing the blog and microblog posts of 27 students engaged in a project-based learning activity. The preliminary results of this pilot study are encouraging, with several indexes predictive of student grades and/or learning styles.