http://dl.gi.de/handle/20.500.12116/5007 2026-07-23T09:10:22Z 2026-07-23T09:10:22Z Burgos, Daniel http://dl.gi.de/handle/20.500.12116/5094 2017-11-15T15:01:01Z 2010-01-01T00:00:00Z

What is wrong with the IMS Learning Design specification? Constraints And Recommendations Burgos, Daniel Hartmann, Melanie; Herder, Eelco; Krause, Daniel; Nauerz, Andreas The work presented in this paper summarizes the research performed in order to implement a set of Units of Learning (UoLs) focused on adaptive learning processes, using the specification IMS Learning Design (IMS-LD). Through the implementation and analysis of four learning scenarios, and one additional application case, we identify a number of constraints on the use of IMS-LD to support adaptive learning. Indeed, our work in this paper shows how IMS-LD expresses adaptation. In addition, our research presents a number of elements and features that should be improved and-or modified to achieve a better support of adaptation for learning processes. Furthermore, we point out to interoperability and authoring issues too. Finally, we use the work carried out to suggest extensions and modifications of IMS-LD with the final aim of better supporting the implementation of adaptive learning processes.

2010-01-01T00:00:00Z Godoy, Daniela http://dl.gi.de/handle/20.500.12116/5092 2017-11-15T15:01:01Z 2010-01-01T00:00:00Z

On the Role of Social Tags in Filtering Interesting Resources from Folksonomies Godoy, Daniela Hartmann, Melanie; Herder, Eelco; Krause, Daniel; Nauerz, Andreas Social tagging systems allow users to easily create, organize and share collections of resources (e.g. Web pages, research papers, photos, etc.) in a collaborative fashion. The rise in popularity of these systems in recent years go along with an rapid increase in the amount of data contained in their underlying folksonomies, thereby hindering the user task of discovering interesting resources. In this paper the problem of filtering resources from social tagging systems according to individual user interests using purely tagging data is studied. One-class classification is evaluated as a means to learn how to identify relevant information based on positive examples exclusively, since it is assumed that users expressed their interest in resources by annotating them while there is not an straightforward method to collect non-interesting information. The results of using social tags for personal classification are compared with those achieved with traditional information sources about the user interests such as the textual content of Web documents. Finding interesting resources based on social tags is an important benefit of exploiting the collective knowledge generated by tagging activities. Experimental evaluation showed that tag-based classification outperformed classifiers learned using the full-text of documents as well as other content-related sources.

2010-01-01T00:00:00Z Kawase, Ricardo Papadakis, George Herder, Eelco http://dl.gi.de/handle/20.500.12116/5090 2017-11-15T15:01:01Z 2010-01-01T00:00:00Z

How Predictable Are You? A Comparison of Prediction Algorithms for Web Page Revisitation Kawase, Ricardo; Papadakis, George; Herder, Eelco Hartmann, Melanie; Herder, Eelco; Krause, Daniel; Nauerz, Andreas Users return to Web pages for various reasons. Apart from pages visited due to backtracking, users typically monitor a number of favorite pages, while dealing with tasks that reoccur on an infrequent basis. In this paper, we introduce a novel method for predicting the next revisited page in a certain user context that, unlike existing methods, doesn’t rely on machine learning algorithms. We evaluate it over a large data set comprising the navigational activity of 25 users over a period of 6 months. The outcomes suggest a significant improvement over methods typically used in this context, thus paving the way for exploring new means of improving user’s navigational support.

2010-01-01T00:00:00Z Doost, Ahmad Salim Melis, Erica http://dl.gi.de/handle/20.500.12116/5093 2017-11-15T15:01:01Z 2010-01-01T00:00:00Z

Student Model Adjustment Through Random-Restart Hill Climbing Doost, Ahmad Salim; Melis, Erica Hartmann, Melanie; Herder, Eelco; Krause, Daniel; Nauerz, Andreas ACTIVEMATH is a web-based intelligent tutoring system (ITS) for studying mathematics. Its course generator, which assembles content to personalized books, strongly depends on the underlying student model. Therefore, a student model is important to make an ITS adaptive. The more accurate it is, the better could be the adaptation. Here we present which parameters can be optimized and how they can be optimized in an efficient and affordable manner. This methodology can be generalized beyond ACTIVEMATH’s student model. We also present our results for the optimization based on two sets of log data. Our optimization method is based on random-restart hill climbing and it considerably improved the student model’s accuracy.

2010-01-01T00:00:00Z