Stefan Trausan-Matu, Philippe Dessus (Eds.)


Natural Language Processing in
Support of Learning: Metrics,
Feedback and Connectivity


Second International Workshop - NLPSL 2010

Organized and supported by the LTfLL (Language Technologies for Lifelong
Learning) FP7, EU Research Project



Politehnica University of Bucharest
Bucharest, ROMANIA

14th September, 2010

2
Preface


In supporting Lifelong Learning (LLL) on the Social Web (Web2.0), Natural
Language Technologies (LT) increasingly play a central role due to the fact that text
is the leading medium of communication and collaboration. LT cover now a wide
range of topics, including advanced semantic resources and applications like
ontologies, knowledge extraction, text mining, Natural Language Processing (NLP)
and Latent Semantic Analysis (LSA). The peculiarities of Web2.0 impose also the
consideration of using LT for social software (social networks analysis) and
collaborative interactions on chats and forums. Pragmatics, discourse and
conversation analysis are very important analysis domains.
For LLL, providing feedback entails measuring differences among learners;
between learners and their desired characteristics (e.g., knowledge, competences,
motivation, self-regulation processes); or between learners and their looked-for
resources (e.g. web-links, articles, courses). Difference measuring often have been
performed by computing and analyzing 'distances' using several techniques like
factorial analysis, instance-based learning, clustering, and so on. Corpora on which
these measures are made are text-based artifacts, that is to say multiple forms of
pieces of evidence such as text materials (written by teachers), spoken utterances,
essays, summaries, forum or chat messages. Some of the metrics used are based on
shallow syntactical and morphological aspects of the interaction and production
artifacts (e.g., text length). Others are focused more on semantic and pragmatic
aspects. These measures are used for providing various kinds of feedback for
supporting learning and connections between learners. For instance, relations between
learners' utterances, knowledge, concept acquisition, emotional states, essay scores,
and even learners themselves have all been investigated with the help of computing
semantic distances.
The purpose of this workshop was to focus on using language technologies in
support of learning and teaching - by trying to identify what questions and problems
are solved, but also to raise and discuss how well the metrics and algorithms
developed assist in the provision of support and the construction of feedback for
learning. What are the most efficient ways? To what extent do they match distances
inferred by a teacher's assessments? The workshop addresses the problem of how
support can be provided and feedback be generated in order to help students learn and
teachers to assess their progress.
Several Natural Language Processing techniques like Latent Semantic Analysis
(LSA) or the use of semantic and pragmatic analysis of conversations have been
successfully deployed in various educational applications to enrich learning and
teaching with information technology. However, few research approaches considered
also in detail the problem of providing feedback.
The primary goal of the workshop was to bring together experts in the related
fields in order to share knowledge (i.e., approaches, models, issues, solutions)
acquired in the domain of using language technologies for learning, to present
applications in the domain, to present the achievements of the FP7 project LTfLL