Entwicklung und Implementierung eines modellgestützten Datenrepositories : das KBS Hyperbook System

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MACE: Joint Deliverable "Functional prototype for metadata tools and concepts"

This deliverable describes concepts and functional prototypes developed in MACE. Its goal is to describe the prototypes for metadata enrichment developed in the MACE project so far. As a joint deliverable, it is a collection of the following deliverables listed in the Description of Work: - D3.2 Functional Prototype for usage metadata - D4.3 Functional Prototype for contextual metadata - D5.2 Functional Prototype for competence and process metadata - D6.3 Functional Prototype for content and domain metadata For each deliverable, a separate chapter is included so that references to the planned deliverables can be derived easily. In addition, this deliverable is strongly connected to Joint Deliverable JD5: "MACE toolset and infrastructure, prototype", also due in M15

MACE: Deliverable D7.6 - Report on user interface design and community experiments

This deliverable presents the progress of the user interface design and community building experiments within the MACE project. In Chapter 2 we generally present the interface of the MACE portal, which is a platform to discover and enrich architectural resources and, at the same time, to support the community formed around architectural topics. Besides the advanced search, the portal provides various visual tools for metadata based search and browsing, tailored to architectural needs (see Chapter 3). Different metadata widgets are used to visualize and access multiple dimensions of each resource, as presented in Chapter 4. These widgets not only establish meaningful cross–connections between resources, but also invite to add and edit metadata effortlessly. In order to generate a critical mass of metadata and ensure sustainability of projects’ outcomes, supporting community and fostering end user contributions are critical. In Chapter 5, we present the components deploied in this direction as well as an analytical framework for incentive mechanisms. Within the dissemination strategy, the MACE project has got a unique chance to raise its public awareness at La Biennale of architecture in Venice, 2008. In this context we designed an interactive installation, demonstrating, in an exhibition setting, the benefits of resource interconnection via metadata (see Chapter 6). Chapter 7 presents our preliminary conclusions and an overview of planned future activities

An Approach for the Personalization of Exercises Based on Contextualized Attention Metadata and Semantic Web technologies

Proocedings of: 10th IEEE International Conference on Advanced Learning Technologies (ICALT 2010). Sousse, Tunisia, 5-7 July 2010.The generation of Contextualized Attention Metadata (CAM) allows to retrieve information about the different actions that users execute over different resources in a specific context. This paper presents how CAM is used within a learning system to personalize help provided to students while working on online exercises. We outline our approach and present two application examples within this framework for the personalization of exercises with hints.Work partially funded by the Learn3 project TIN2008-05163/TSI within the Spanish “Plan Nacional de I+D+I”, and the Madrid regional community project eMadrid S2009/TIC-1650. This research was partially supported by the European Commission within the Role IP (Grant agreement no.:231396).Publicad

MACE: Deliverable JD8 - Report on training

The objective of the MACE project is to interlink repositories to provide simplified access to digital, architectural learning resources. Gaps resulting from autonomous design, implementation, funding, and maintenance are bridged by implementing conceptual tools (ontologies, glossaries, and standards), interfaces and metadata agglomeration. Consequently, MACE will create innovative e-learning tools that help both expert users and laypeople to find, tag, acquire, use, and discuss contents from many architectural repositories that previously had limited accessibility. Even though the MACE software is designed to be as self explanatory as possible, it offers a variety of services and tools. In this context, this document reports on how users of MACE are trained to use MACE tools and to integrate with MACE tools

Dataset-driven research for improving recommender systems for learning

Verbert, K., Drachsler, H., Manouselis, N., Wolpers, M., Vuorikari, R., & Duval, E. (2011). Dataset-driven research for improving recommender systems for learning. In Ph. Long, & G. Siemens (Eds.), Proceedings of 1st International Conference Learning Analytics & Knowledge (pp. 44-53). February, 27-March, 1, 2011, Banff, Alberta, Canada. http://dl.acm.org/citation.cfm?id=2090122&CFID=77368864&CFTOKEN=72282583In the world of recommender systems, it is a common practice to use public available datasets from different application environments (e.g. MovieLens, Book-Crossing, or EachMovie) in order to evaluate recommendation algorithms. These datasets are used as benchmarks to develop new recommendation algorithms and to compare them to other algorithms in given settings. In this paper, we explore datasets that capture learner interactions with tools and resources. We use the datasets to evaluate and compare the performance of different recommendation algorithms for Technology Enhanced Learning (TEL). We present an experimental comparison of the accuracy of several collaborative filtering algorithms applied to these TEL datasets and elaborate on implicit relevance data, such as downloads and tags, that can be used to augment explicit relevance evidence in order to improve the performance of recommendation algorithms.dataTEL, STELLAR, AlterEgo, VOA3

Getting a grasp on tag collections by visualising tag clusters based on higher-order co-occurrences

Tagging learning resources in repositories or web portals offers a way to meaningfully describe these resources. The more tags there are, however, the more difficult it is to find one's way around the repository, especially when they are user-generated free-text tags. This paper therefore presents a visualisation of tag clusters based on higher-order co-occurrences that allows users of such repositories a plain but simple way of exploring them in an intuitive manner

Peeking into the black box: visualising learning activities

Learning analytics has emerged as the discipline that fosters the learning process based on monitored data. As learning is a complex process that is not limited to a single environment, it benefits from a holistic approach where events in different contexts and settings are observed and combined. This work proposes an approach to increase this coverage. Detailed information is obtained by combining logs from a LMS and events recorded with a virtual machine given to the students. A set of visualisations is then derived from the collected events showing previously hidden aspects of an experience that can be shown to the teaching staff for their consideration. The visualisations presented focus on different learning outcomes, such as self learning, use of industrial tools, time management, information retrieval, collaboration, etc. Depending on the information to convey, different types of visualisations are considered, ranging from graphs to starbusts and from scatter plots to heatmaps.Work partially funded by the projects: Adaptation of learning scenarios in the .LRN platform based on Contextualized Attention Metadata (CAM) (DE2009-0051), Learn3 (\Plan Nacional de I+D+I" TIN2008-05163/TSI), EEE (\Plan Nacional de I+D+I" TIN 2011-28308-C03-01), and Emadrid: Investigación y desarrollo de tecnologías para el e-learning en la Comunidad de Madrid (S2009/TIC-1650).Publicad