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.

Education and self-regulation of learning for gifted pupils: Systemic design and development.

Gifted pupils differ from their age-mates with respect to development potential, actual competencies, self-regulatory capabilities, and learning styles in one or more domains of competence. The question is how to design and develop education that fits and further supports such characteristics and competencies of gifted pupils. Analysis of various types of educational interventions for gifted pupils reflects positive cognitive or intellectual effects and differentiated social comparison or group-related effects on these pupils. Systemic preventive combination of such interventions could make these more effective and sustainable. The systemic design is characterised by three conditional dimensions: differentiation of learning materials and procedures, integration by and use of ICT support, and strategies to improve development and learning. The relationships to diagnostic, instructional, managerial, and systemic learning aspects are expressed in guidelines to develop or transform education. The guidelines imply the facilitation of learning arrangements that provide flexible self-regulation for gifted pupils. A three-year pilot in Dutch nursery and primary school is conducted to develop and implement the design in collaboration with teachers. The results constitute prototypes of structured competence domains and supportive software. These support the screening of entry characteristics of all four-year old pupils and assignment of adequate play and learning processes and activities throughout the school career. Gifted and other pupils are supported to work at their actual achievement or competency levels since their start in nursery school, in self-regulated learning arrangements either in or out of class. Each pupil can choose other pupils to collaborate with in small groups, at self-chosen tasks or activities, while being coached by the teacher. Formative evaluation of the school development process shows that the systemic prevention guidelines seem to improve learning and social progress of gifted pupils, including their self-regulation. Further development and implementation steps are discussed.

Predicting Academic Performance Based on Students' Blog and Microblog Posts

This study investigates the degree to which textual complexity indices applied on students’ online contributions, corroborated with a longitudinal analysis performed on their weekly posts, predict academic performance. The source of student writing consists of blog and microblog posts, created in the context of a project-based learning scenario run on our eMUSE platform. Data is collected from six student cohorts, from six consecutive installments of the Web Applications Design course, comprising of 343 students. A significant model was obtained by relying on the textual complexity and longitudinal analysis indices, applied on the English contributions of 148 students that were actively involved in the undertaken projects.

The Booth: Bringing Out the Super Hero in You

Demo paper about the booth

Enhancing public speaking skills - an evaluation of the Presentation Trainer in the wild

Full paper presented at EC-TEL 2016

Software Components for Serious Game Development

The presentation explains the approach of the RAGE project. It presents three examples of RAGE software components and how these can be easily reused for applied game development.

Nurturing communities of inquiry: A formative study of the DojoIBL platform

Full paper presented at EC-TEL 2016

DojoIBL: Nurturing Communities of Inquiry

Demo paper presented at EC-TEL 2016

DojoIBL

Available under the GNU Lesser General Public License (LGPL3)

Software Components for Serious Game Development

The large upfront investments required for game development pose a severe barrier for the wider uptake of serious games in education and training. Also, there is a lack of well-established methods and tools that support game developers at preserving and enhancing the games’ pedagogical effectiveness. The RAGE project, which is a Horizon 2020 funded research project on serious games, addresses these issues by making available reusable software components that aim to support the pedagogical qualities of serious games. In order to easily deploy and integrate these game components in a multitude of game engines, platforms and programming languages, RAGE has developed and validated a hybrid component-based software architecture that preserves component portability and interoperability. While a first set of software components is being developed, this paper presents selected examples to explain the overall system’s concept and its practical benefits. First, the Emotion Detection component uses the learners’ webcams for capturing their emotional states from facial expressions. Second, the Performance Statistics component is an add-on for learning analytics data processing, which allows instructors to track and inspect learners’ progress without bothering about the required statistics computations. Third, a set of language processing components accommodate the analysis of textual inputs of learners, facilitating comprehension assessment and prediction. Fourth, the Shared Data Storage component provides a technical solution for data storage - e.g. for player data or game world data - across multiple software components. The presented components are exemplary for the anticipated RAGE library, which will include up to forty reusable software components for serious gaming, addressing diverse pedagogical dimensions.