Of two contrasting philosophies that underpin openness in education and what that entails

From the conclusion: The ultimate question is a normative one: Which way do we want that openness in education to go? That question concerns educational resources, open educational practices and what other forms the educational system may spawn. For ultimately, we as stakeholders, in the learning of our children and grandchildren, in the professional development and Bildung of ourselves, should get the educational systems that we want, including appropriate forms of openness therein. Every individual then should decide for herself or himself to what extent this requires education as a public good and to what extent education as a private good, that is, as a commodity subject to market forces. It should not come as a surprise that we side with the humanitarian elaboration of openness. Indeed, we feel that governments as guardians of the public space should actively get involved in promoting this kind of openness, indeed, much as Delors in 1996 advocated for education as a whole.

Extracting Patterns from Educational Traces via Clustering and Associated Quality Metrics

Clustering algorithms, pattern mining techniques and associated quality metrics emerged as reliable methods for modeling learners’ performance, comprehension and interaction in given educational scenarios. The specificity of available data such as missing values, extreme values or outliers, creates a challenge to extract significant user models from an educational perspective. In this paper we introduce a pattern detection mechanism with-in our data analytics tool based on k-means clustering and on SSE, silhouette, Dunn index and Xi-Beni index quality metrics. Experiments performed on a dataset obtained from our online e-learning platform show that the extracted interaction patterns were representative in classifying learners. Furthermore, the performed monitoring activities created a strong basis for generating automatic feedback to learners in terms of their course participation, while relying on their previous performance. In addition, our analysis introduces automatic triggers that highlight learners who will potentially fail the course, enabling tutors to take timely actions.

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.