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.

Designing networked learning for innovation: A dimension perspective

In her presentation, Emmy discusses a case study where a teacher group is followed in its innovative attempt to start up a new curriculum. Searching for guidelines to facilitate knowledge-creating teacher groups, the ‘Dimensions of Social Learning (DSL) Framework’ is applied as a starting point to assess the group's social configuration. To supplement the DSL Framework towards knowledge creation, social capital and value creation theories were studied. This has resulted in an extended framework that can bring the knowledge creation of teacher groups into view.