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In the past few years we have participated in several EU funded projects, aimed to create the educational content and auxiliary ICT tools to support the development of some essential soft skills of the students: the creativity, and the ability to write syntheses of the ideas extracted from various sources. In this context, we produced an easy to use web application for the assessment of creativity based on a new scale, and we also proposed a methodology to evaluate the quality of text summaries written by our students starting from a given source. Through a serendipitous coincidence, this research occurred while we were in search of a means to assess the risk in education, and noticed that the students' anxiety about the future (AAF) might be a good measure of this risk. This paper is an attempt to make sense of the data collected using the newly created tools, by exploring the possible correlations between creativity, summarization skills, and AAF from the perspective of the students' ability to cope with the uncertainties of the future. [ABSTRACT FROM AUTHOR]

Providing adaptivity based on learning styles can support learners and make learning easier for them. However, for providing proper adaptivity, the learning styles of learners need to be known first. While most systems, which consider learning styles, use questionnaires in order to identify learning styles, we propose an automatic student modelling approach, which analyses the actual behaviour and actions of students during they are learning in an online course in order to infer students' learning styles. Such an automatic approach has the advantage that students do not have any additional effort for providing information about their learning styles. Additionally, an automatic approach can be more accurate by excluding extraordinary behaviour of students and adapting in the case that the learning styles changed over time. In this paper, we present an automatic student modelling approach for learning management system, which aims at identifying learning style preferences within the four dimensions of the Felder-Silverman learning style model (FSLSM). The approach is based on patterns derived from literature and a simple rule-based method for calculating learning styles from the students' behaviour. The proposed approach is evaluated by a study with 75 students, comparing the results of the learning style questionnaire with the results obtained by the proposed automatic student modelling approach. As a result, the approach is appropriate for identifying all learning style preferences within the active/reflective dimension of FSLSM and some learning style preferences within the sensing/intuitive and visual/verbal dimension. For the sequential/global dimension, results of learning style preferences show only moderate precision. [ABSTRACT FROM AUTHOR]

The study presented in this paper sought to explore several dimensions to online learning. Identifying the dimensions to online learning entails important basic issues which are of great relevance to educators today. The primary question is "what are the factors that contribute to the success/failure of online learning?" In order to answer this question we need to identify the important variables that (1) measure the learning outcome and (2) can help us understand the learning experience of students using specific learning tools. In this study, the dimensions we explored are student's attitude, affect, motivation and perception of an Online Learning Tool usage. A survey utilizing validated items from previous relevant research work was conducted to help us determine these variables. An exploratory factor analysis (EFA) was used for a basis of our analysis. [ABSTRACT FROM AUTHOR]