D2.4. Building a Personal Learning Environment with Language-Technology-based Widgets: Services v2 - integrated thread

Hoisl, B., Haley, D., Wild, F., Anastasiou, L., Buelow, K., Koblische, R., Burek, G., Loiseau, M., Markus, T., Rebedea, T., Drachsler, H., Kometter, H., Westerhout, E., & Posea, V. (2010). D2.4. Building a Personal Learning Environment with Language-Technology-based Widgets: Services v2 - integrated thread. LTfLL-project.This deliverable reports on the results achieved by the LTfLL work packages in their efforts toward interoperability of the LTfLL tools and services. There are two aspects: one is the pedagogical utility of achieving interoperability; the other aspect involves the technical features. The technical basis of the interoperability is to use Wookie widgets in Elgg and is thoroughly described here. Finally, the deliverable provides details and screen shots of each widget for each LTfLL service embedded in the Elgg environment.The work on this publication has been sponsored by the LTfLL STREP that is funded by the European Commission's 7th Framework Programme. Contract 212578 [http://www.ltfll-project.org

D7.4 Validation 4

Armitt, G., Stoyanov, S., Hensgens, J., Smithies, A., Braidman, I., Mauerhofer, C., Osenova, P., Simov, K., Berlanga, A. J., Van Bruggen, J., Greller, W., Rebedea, T., Posea, V., Trausan-Matu, S., Dupre, D., Salem, H., Dessus, P., Loiseau, M., Westerhout, E., Monachesi, P., Koblische, R., Hoisl, B., Haley, D., & Wild, F. (2011). D7.4 Validation 4. LTfLL-project.This deliverable describes the objectives, approach, planning and results of the third pilot round, in which both individual and threaded services underwent validation. The two goals of this round were to provide input to the LTfLL exploitation plan and roadmap (deliverable 2.5). 531 participants (316 learners) took part in the pilots, which used LTfLL services based on five different languages. The average timespan of the pilots was three weeks and involved learners, tutors, teaching managers, the LTfLL team and Technology Enhanced Learning experts. The validation approach was based on Prototypical Validation Topics derived from the Round 2 validation topics, which refocused the validation topics on exploitation and allowed conclusions to be drawn across all services. Results demonstrated the areas of strength and weakness of each service, informing the selling points and barriers to adoption within the exploitation strategy, as well as suggesting possible further contexts of use. All services were noted to have high relevance in addressing burning issues for organizations, but further improvements to accuracy from a user viewpoint are required. Results on future enhancements to improve likelihood of adoption contribute to the roadmap. Results also provide an indication of each service's current readiness for adoption and provided insights into transferability issues. The overall conclusion is that some LTfLL services are more ready than others for adoption now, with some being currently more suited to sustainability in research settings.The work on this publication has been sponsored by the LTfLL STREP that is funded by the European Commission's 7th Framework Programme. Contract 212578 [http://www.ltfll-project.org

Extraction of definitions using grammarenhanced machine learning

In this paper we compare different approaches to extract definitions of four types using a combination of a rule-based grammar and machine learning. We collected a Dutch text corpus containing 549 definitions and applied a grammar on it. Machine learning was then applied to improve the results obtained with the grammar. Two machine learning experiments were carried out. In the first experiment, a standard classifier and a classifier designed specifically to deal with imbalanced datasets are compared. The algorithm designed specifically to deal with imbalanced datasets for most types outperforms the standard classifier. In the second experiment we show that classification results improve when information on definition structure is included.

Extraction of Dutch definitory contexts for eLearning purposes

The aim of the Language Technology for eLearning project is to facilitate the retrieval, management and distribution of learning material within a Learning Management System by exploiting Natural Language Processing techniques as well as semantic knowledge. One of the functionalities provided by the project is the possibility to create glossaries semiautomatically. Glossaries are derived from the learning objects in order to capture the exact definition which the author of these documents uses. A rule-based approach is employed to identify the relevant lexical and linguistic patterns which underlie the definition. In this paper, we discuss the grammar developed to identify the definitory contexts in the Dutch learning objects and we present the results of the quantitative evaluation

Creating glossaries using pattern-based and machine learning techniques

One of the aims of the Language Technology for eLearning project is to show that Natural Language Processing techniques can be employed to enhance the learning process. To this end, one of the functionalities that has been developed is a pattern-based glossary candidate detector which is capable of extracting definitions in eight languages. In order to improve the results obtained with the patternbased approach, machine learning techniques are applied on the Dutch results to filter out incorrectly extracted definitions. In this paper, we discuss the machine learning techniques used and we present the results of the quantitative evaluation. We also discuss the integration of the tool into the Learning Management System ILIAS

The impact of a mathematics game programming project on student motivation in grade 8

International audienceIn this paper, we describe the impact of a mathematics game programming project on the intrinsic motivation of eighth grade students (n = 8). We investigate which aspects of the project contributed to student motivation based on a taxonomy that distinguishes individual (e.g., challenge) and interpersonal motivation aspects (e.g., recognition). We also employ these aspects to compare the programming project to regular mathematics education. The findings reveal that students appreciated the project because it was challenging and they had freedom of choice. All group members had a positive attitude towards learning. In regular mathematics classes, they experienced a lack of challenge while the freedom of choice was minimal