Automatic Recognition of Learner Groups in Exploratory Learning Environments

Abstract. In this paper, we present the application of unsupervised learning techniques to automatically recognize behaviors that may be detrimental to learning during interaction with an Exploratory Learning Environment (ELE). First, we describe how we use the k-means clustering algorithm for off-line identification of learner groups with distinguishing interaction patterns who also show similar learning improvements with an ELE. We then discuss how a k-means on-line classifier, trained with the learner groups detected off-line, can be used for adaptive support in ELEs. We aim to show the value of a data-based approach for recognizing learners as an alternative to knowledge-based approaches that tend to be complex and time-consuming even for domain experts, especially in highly unstructured ELEs.

User-adaptive explanatory program visualization: Evaluation and insights from eye movements

User-adaptive visualization and explanatory visualization have been suggested to increase educational effectiveness of program visualization. This paper presents an attempt to assess the value of these two approaches. The results of a controlled experiment indicate that explanatory visualization allows students to substantially increase the understanding of a new programming topic. Furthermore, an educational application that features explanatory visualization and employs a user model to track users' progress allows students to interact with a larger amount of material than an application which does not follow users' activity. However, no support for the difference in short-term knowledge gain between the two applications is found. Nevertheless, students admit that they prefer the version that estimates and visualizes their progress and adapts the learning content to their level of understanding. They also use the application's estimation to pace their work. The differences in eye movement patterns between the applications employing adaptive and non-adaptive explanatory visualizations are investigated as well. Gaze-based measures show that adaptive visualization captivates attention more than its non-personalized counterpart and is more interesting to students. Natural language explanations also accumulate a big portion of students' attention. Furthermore, the results indicate that working memory span can mediate the perception of adaptation. It is possible that user-adaptation in an educational context provides a different service to people with different mental processing capabilities. © Springer Science+Business Media B.V. 2010

Contributing student pedagogy

<p>A Contributing Student Pedagogy (CSP) is a pedagogy that encourages students to contribute to the learning of others and to value the contributions of others. CSP in formal education is anticipatory of learning processes found in industry and research, in which the roles and responsibilities of 'teacher' and 'student' are fluid. Preparing students for this shift is one motivation for use of CSP. Further, CSP approaches are linked to constructivist and community theories of learning, and provide opportunities to engage students more deeply in subject material.</p> <p>In this paper we advance the concept of CSP and relate it to the particular needs of computer science. We present a number of characteristics of this approach, and use case studies from the available literature to illustrate these characteristics in practice. We discuss enabling technologies, provide guidance to instructors who would like to incorporate this approach in their teaching, and suggest some future directions for the study and evaluation of this technique. We conclude with an extensive bibliography of related research and case studies which exhibit elements of CSP.</p&gt

Solar Weather Event Modelling and Prediction

Key drivers of solar weather and mid-term solar weather are reviewed by considering a selection of relevant physics- and statistics-based scientific models as well as aselection of related prediction models, in order to provide an updated operational scenario for space weather applications. The characteristics and outcomes of the considered scientific and prediction models indicate that they only partially cope with the complex nature of solar activity for the lack of a detailed knowledge of the underlying physics. This is indicated by the fact that, on one hand, scientific models based on chaos theory and non-linear dynamics reproduce better the observed features, and, on the other hand, that prediction models based on statistics and artificial neural networks perform better. To date, the solar weather prediction success at most time and spatial scales is far from being satisfactory, but the forthcoming ground- and space-based high-resolution observations can add fundamental tiles to the modelling and predicting frameworks as well as the application of advanced mathematical approaches in the analysis of diachronic solar observations, that are a must to provide comprehensive and homogeneous data sets.peerReviewe