Burek, Gaston, Armitt, Gillian, Gerdemann, Dale, Hoisl, Bernhard, Koblischke, Robert, Mauerhofer, Christoph, Osenova, Petya, Simov, Kiril. 2010. A Language Usage-based Service for Providing Formative Feedback and Learner Positioning. 17th International Conference of the Association for Learning Technology (ALT-C), Nottingham, Großbritannien, 07.09.-09.09.

RIS

@MISC{Burek2010,
title = {A Language Usage-based Service for Providing Formative Feedback and Learner Positioning},
author = {Gaston Burek and Gillian Armitt and Dale Gerdemann and Bernhard Hoisl and Robert Koblischke and Christoph Mauerhofer and Petya Osenova and Kiril Simov},
year = {2010},
address = {Nottingham},
url = {http://altc2010.alt.ac.uk/attachments/0001/1636/burek_final_poster.pdf},
language = {EN},
misc = {17th International Conference of the Association for Learning Technology (ALT-C)},
abstract = {Self-directed learners can benefit from personalised 'feedback on demand' during their learning but this is often not practical owing to tutors' time constraints. Tutors can benefit from computerised support with positioning learners and providing individualised feedback at the right level. Language technologies (phrase extraction and Latent Semantic Analysis (LSA)) analysing speech genres (Bakhtin 1986) offer an opportunity to address these issues. Experts in a domain develop a speech genre, with characteristic phrases. Becoming an expert involves the adoption of the community's speech genre. Thus, learner knowledge can be evaluated by means of textual distance based on characteristic phrases. Through the EU-funded LTfLL project, bespoke on-line software was designed as Service Usage Models in collaboration with a commercial training partner. Ontology and lexical resources are used to identify concepts covered by learners. Positioning is achieved through combining linguistic patterns (phrases) extraction and LSA (Burek and Gerdemann 2009) to compare learner texts with a corpus of expert texts built from IT learning materials and high quality learner texts. The software indirectly measures learners' degree of expertise by textual distance to the relevant speech genre. The poster illustrates the feedback and positioning information provided by the user interface and how the language technology-based comparison of learner and expert texts is achieved to compare speech genres.},
}