115,022 research outputs found
Trace-based Collaborative Learning System
The users of any educational software may leave traces which concern all their activities. In collaborative learning context, these traces are very voluminous and very heterogeneous. They are the results of various interactions between the actors themselves, and between the actors and the system. Hence, first, they must be collected and filtered. Then, these traces must be analyzed to help or support these actors (the tutor in his task of monitoring learners and the teacher-author in his task of creating educational courses).
It is this context that defines our research work, which is focused on implementing a collaborative learning system based on traces called SYCATA (SYstème pour la Collecte et l’Analyse des Traces d’Apprentissage collaboratif). SYCATA collects all traces of actors’ activities (especially learners) and groups them into five categories. It offers a multitude of forms (graphical, numerical or mixed) to show these traces to the tutors and the authors. Some traces may be viewed by learners to promote their pedagogical activities and raise their awareness. SYCATA was implemented and experimented with a sample of university students where good results have been obtained
The relationship of (perceived) epistemic cognition to interaction with resources on the internet
Information seeking and processing are key literacy practices. However, they are activities that students, across a range of ages, struggle with. These information seeking processes can be viewed through the lens of epistemic cognition: beliefs regarding the source, justification, complexity, and certainty of knowledge. In the research reported in this article we build on established research in this area, which has typically used self-report psychometric and behavior data, and information seeking tasks involving closed-document sets. We take a novel approach in applying established self-report measures to a large-scale, naturalistic, study environment, pointing to the potential of analysis of dialogue, web-navigation – including sites visited – and other trace data, to support more traditional self-report mechanisms. Our analysis suggests that prior work demonstrating relationships between self-report indicators is not paralleled in investigation of the hypothesized relationships between self-report and trace-indicators. However, there are clear epistemic features of this trace data. The article thus demonstrates the potential of behavioral learning analytic data in understanding how epistemic cognition is brought to bear in rich information seeking and processing tasks
Collaborative Filtering in a Non-Uniform World: Learning with the Weighted Trace Norm
We show that matrix completion with trace-norm regularization can be
significantly hurt when entries of the matrix are sampled non-uniformly. We
introduce a weighted version of the trace-norm regularizer that works well also
with non-uniform sampling. Our experimental results demonstrate that the
weighted trace-norm regularization indeed yields significant gains on the
(highly non-uniformly sampled) Netflix dataset.Comment: 9 page
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
We present a general approach for collaborative filtering (CF) using spectral
regularization to learn linear operators from "users" to the "objects" they
rate. Recent low-rank type matrix completion approaches to CF are shown to be
special cases. However, unlike existing regularization based CF methods, our
approach can be used to also incorporate information such as attributes of the
users or the objects -- a limitation of existing regularization based CF
methods. We then provide novel representer theorems that we use to develop new
estimation methods. We provide learning algorithms based on low-rank
decompositions, and test them on a standard CF dataset. The experiments
indicate the advantages of generalizing the existing regularization based CF
methods to incorporate related information about users and objects. Finally, we
show that certain multi-task learning methods can be also seen as special cases
of our proposed approach
A recommender system for process discovery
Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances.Peer ReviewedPostprint (author’s final draft
On The Effect of Hyperedge Weights On Hypergraph Learning
Hypergraph is a powerful representation in several computer vision, machine
learning and pattern recognition problems. In the last decade, many researchers
have been keen to develop different hypergraph models. In contrast, no much
attention has been paid to the design of hyperedge weights. However, many
studies on pairwise graphs show that the choice of edge weight can
significantly influence the performances of such graph algorithms. We argue
that this also applies to hypegraphs. In this paper, we empirically discuss the
influence of hyperedge weight on hypegraph learning via proposing three novel
hyperedge weights from the perspectives of geometry, multivariate statistical
analysis and linear regression. Extensive experiments on ORL, COIL20, JAFFE,
Sheffield, Scene15 and Caltech256 databases verify our hypothesis. Similar to
graph learning, several representative hyperedge weighting schemes can be
concluded by our experimental studies. Moreover, the experiments also
demonstrate that the combinations of such weighting schemes and conventional
hypergraph models can get very promising classification and clustering
performances in comparison with some recent state-of-the-art algorithms
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