Mojisola Anjorin

CROKODIL: a platform for collaborative resource-based learning

On-the-job learning is primarily a personal knowledge acquisition process accomplished increasingly based on resources found on the Web. These days, collaboratively learning from and with others on the Web is taking on a very prominent position in this learning process. CROKODIL aims to provide support for collaborative learning based on web resources. In this paper, we introduce our learning scenario and an evaluation of our target group. We describe our pedagogical concepts, and present the results of an evaluation of these concepts. CROKODIL supports the semantic tagging of resources as well as the collaborative use of these resources and their information. Social networking functionalities are integrated in the platform to encourage and support collaborative learning. We also present some extensions to the base functionality of the platform, such as resource recommendations and interfaces for the integration in existing learning management systems.

Exploiting semantic information for graph-based recommendations of learning resources

Recommender systems in e-learning have different goals as compared to those in other domains. This brings about new requirements such as the need for techniques that recommend learning resources beyond their similarity. It is therefore an ongoing challenge to develop recommender systems considering the particularities of e-learning scenarios like CROKODIL. CROKODIL is a platform supporting the collaborative acquisition and management of learning resources. It supports collaborative semantic tagging thereby forming a folksonomy. Research shows that additional semantic information in extended folksonomies can be used to enhance graph-based recommendations. In this paper, CROKODILs folksonomy is analysed, focusing on its hierarchical activity structure. Activities help learners structure their tasks and learning goals. AScore and AInheritScore are proposed approaches for recommending learning resources by exploiting the additional semantic information gained from activity structures. Results show that this additional semantic information is beneficial for recommending learning resources in an application scenario like CROKODIL.

Location based learning content authoring and content access in the docendo platform

Learning does not only take place in a conventional classroom setting but also during everyday activities such as field trips. Such context sensitive learning is not new and can be observed in many different areas. The increasing availability of mobile devices and network access opens up new possibilities for providing context aware support for such learning scenarios. Related work focuses mainly on supporting the learner by delivering context aware learning content and by appropriately adapting the learning media to the limited viewing capabilities of their mobile devices. The learning content provided is dependent on the context of the learner e.g. the learners current location. In this paper, we argue the need for providing context aware support for authors of learning content. We present an extension of docendo, an open learning content authoring and management platform, to support teachers while creating location based learning material for field trips. An important new feature is the automatic mapping of the location of the author to the learning resources collected by the author.

FReSET: an evaluation framework for folksonomy-based recommender systems

FReSET is a new recommender systems evaluation framework aiming to support research on folksonomy-based recommender systems. It provides interfaces for the implementation of folksonomy-based recommender systems and supports the consistent and reproducible offline evaluations on historical data. Unlike other recommender systems framework projects, the emphasis here is on providing a flexible framework allowing users to implement their own folksonomy-based recommender algorithms and pre-processing filtering methods rather than just providing a collection of collaborative filtering implementations. FReSET includes a graphical interface for result visualization and different cross-validation implementations to complement the basic functionality.

Context determines content: an approach to resource recommendation in folksonomies

By means of tagging in social bookmarking applications, so called folksonomies emerge collaboratively. Folksonomies have shown to contain information that is beneficial for resource recommendation. However, as folksonomies are not designed to support recommendation tasks, there are drawbacks of the various recommendation techniques. Graph-based recommendation in folksonomies for example suffers from the problem of concept drift. Vector space based recommendation approaches in folksonomies suffer from sparseness of available data. In this paper, we propose the flexible framework VSScore which incorporates context-specific information into the recommendation process to tackle these issues. Additionally, as an alternative to the evaluation methodology LeavePostOut we propose an adaptation LeaveRTOut for resource recommendation in folksonomies. In a subset of resource recommendation tasks evaluated, the proposed recommendation framework VSScore performs significantly more effective than the baseline algorithm FolkRank.

Ranking resources in folksonomies by exploiting semantic information

Organizing and sharing resources are the main aims of social bookmarking applications. By tagging resources, folksonomies emerge collaboratively. Information in folksonomies is valuable for ranking resources in social bookmarking applications as well as on the Web. Folksonomies are therefore important for knowledge management. There are however limitations to ranking in folksonomies, as they are not designed for search. As new Web 2.0 applications emerge providing semantic information, it becomes essential to incorporate this information for improved ranking strategies. Hence, in this work, the algorithms AspectScore and InteliScore are proposed. Both algorithms aim to overcome limitations and drawbacks of graph-based ranking algorithms in folksonomies by incorporating semantic information. Furthermore, a method that leverages semantic information to disambiguate tags is proposed as well as an evaluation methodology for ranking resources in folksonomies.

CROKODIL: a platform supporting the collaborative management of web resources for learning purposes

CROKODIL is an ongoing project at the Darmstadt University of Technology. The aim of the project is to implement a platform for collaborative knowledge acquisition based on web resources. In this paper, we analyze according to a social search model, how CROKODIL provides support for all stages of the search process which is an important and integrated part in todays learning process.

Optimizing collaborative learning processes by using recommendation systems

In this paper, we present the work of the research group e-learning at the Darmstadt University of Technology by describing a paradigmatic research cooperation between the departments of philosophy and computer science. Here, the perspective of Parallelkommunikation from the philosophy of technology is used to increase the transparency of recommendation systems used in certain learning scenarios. This is achieved by providing explanations to allow a better understanding of the reasons behind recommendations. With this enhanced understanding, users can give more relevant feedback, thereby actively influencing the ranking of resources.