LTeL - Language Technology for eLearning -

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LTeL - Language Technology for eLearning -

• Paola Monachesi, Lothar Lemnitzer, Kiril Simov,
  Alex Killing, Diane Evans, Cristina Vertan

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LT4eL - Language Technology for eLearning -1-

• EU-IST-FP6 Project 2005 - 2008
• The LT4eL project uses multilingual language
  technology tools and semantic web techniques for
  improving the retrieval of learning material. The
  developed technology will facilitate personalized
  access to knowledge within learning management
  systems and support decentralisation and
  co-operation in content management.

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LT4eL - Language Technology for eLearning -2-

• Start date 1 December 2005
• Duration 30 months
• EU finacing 1.5 milion Euro
• Type project STREP IST-4
• Coordination Paola Monachesi (Utrecht
  university)
• Contact for information Paola.Monachesi_at_let.uu.nl

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LT4eL - Partners

• Utrecht University (UU), The Netherlands
• University of Hamburg (UHH), Germany
• University Al.I.Cuza of Iasi (UAIC), Romania
• University of Lisbon (FFCUL), Portugal
• Charles University Prague (CUP), Czech Republic
• Institute for Parallel Processing, Bulgarian
  Academy of Sciences (IPP-BAS), Bulgaria
• University of Tübingen (UTU), Germany
• Institute of Computer Science, Polish Academy of
  Sciences (ICS-PAS), Poland
• Zürich University of Applied Sciences Winterthur
  (ZHW), Switzerland
• University of Malta (UOM), Malta
• University of Cologne (UCO), Germany
• Open University (OU), United Kingdom

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LT4eL - Languages

• Bulgarian
• Czech
• Dutch
• German
• Maltese
• Polish
• Portugese
• Romanian
• English

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LT4eL -Aims

• Improve retrieval of learning material
• Facilitate construction of user specific courses
• Improve creation of personalized content
• Support decentralization of content management
• Allow for multilingual retrieval of content

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LT4eL- Objectives -1-

• Scientific and Technological Objectives
• Creation of an archive of learning objects and
  linguistic resources
• Integration of language technology resources in
  eLearning
• Integration of semantic Knowledge in eLearning
• Integration of functionalities in open source LMS
• Validation of enhanced LMS

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LT4eL- Objectives -2-

• Political objectives
• Support multilinguality
• Knowledge transfer
• Awareness raising
• Exploitation of resources
• Facilitate access to education

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LT4eL - Workpackages

• ? WP1 - Setting the scene - WP leader University
  AI. I. Cuza of Iasi
• ? WP2 - Semi-automatic metadata generation driven
  by Language Technology resources - WP leader
  University of T?ingen
• ? WP4 - Integration of the new functionalities in
  the ILIAS Learning Management System - WP leader
  University of Cologne
• ? WP3 - Enhancing eLearning with semantic
  knowledge - WP leader IPP, Bulgarian Academy of
  Science
• ? WP5 - Validation of new functionalities in the
  ILIAS Learning Management System - WP leader
  Open University (England)
• ? Multilinguality - Leader University Hamburg

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Documents SCORM
Pseudo-Struct.
Basic XML
CONVERTOR 2
Documents SCORM
Pseudo-Struct
Documents HTML
Glossary
CONVERTOR 1
Metadata (Keywords) Ling. Annot XML
BG
EN
Documents User (PDF, DOC, HTML, SCORM,XML)
REPOSITORY
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Collection of Learning materials

• collection of the learning material (uploads
  updates at http//consilr.info.uaic.ro/uploads_lt4
  el/ - passwd protected)
• ? IST domains for the LOs?
• 1. Use of computers in education, with
  sub-domains?
• 1.1 Teaching academic skills, with sub-domains?
  
• 1.1.1 Academic skills?
• 1.1.2 Relevant computer skills for the
  above tasks (MS Word, Excel, Power Point, LaTex,
  Web pages, XML)?
• 1.1.3 Basic computer skills (use of
  computer for beginners) (chats, e-mail, Intenet)?
  
• 1.2 e-Learning, e-Marketing?
• 1.3 The ITeach document (Leonardo project,
  http//i-teach.fmi.uni-sofia.bg/)
• 1.4 Impact of use of computers in society
• 1.5 Studies about use of computers in schools /
  high schools?
• 1.6 Impact of e-Learning on education?
• 2. Calimera documents (parallel corpus developped
  in the Calimera FP5 project, http//www.calimera.o
  rg/ )

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Collection of learning materials and linguistic
tools

• normalization of the learning material?
  commonly agreed DTD and convertors from html/txt
  to basic XML format? Inventarization and
  classification of existing tools
  (http//consilr.info.uaic.ro/uploads_lt4el/tools/a
  ll.php?) relevant to
• ? the integration of language technology
  resources in eLearning (WP2)
• the integration of semantic knowledge (WP3)?
• Inventarization and classification of existing
  language resources? corpora and frequencies
  lists? http//consilr.info.uaic.ro/uploads_lt4el/
  menu/all.php
• ? lexica http//www.let.uu.nl/lt4el/wiki/index.
  php/Lexica_Joint_Table

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Documents SCORM
Pseudo-Struct.
Basic XML
CONVERTOR 2
Documents SCORM
Pseudo-Struct
Documents HTML
Glossary
CONVERTOR 1
Metadata (Keywords) Ling. Annot XML
BG
EN
Documents User (PDF, DOC, HTML, SCORM,XML)
REPOSITORY
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WP2 Integration of language resources in
eLearning

• Aims of the Workpackage
• supporting authors in the generation of metadata
  for Los
• improving keyword-driven search for LOs
• supporting the development of glossaries for
  learning material

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Metadata

• metadata are essential to make LOs visible for
  larger groups of users
• authors are reluctant or not experienced enough
  to supply them
• NLP tools are supposed to help them in that task
• the project uses the LOM metadata schema as a
  blueprint

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Task 1 Identification of keywords

• Good keywords have a typical, non random
  distribution in and across Los
• Keywords tend to appear mor often at certain
  places in texts (headings etc.)
• Keywords are often highlighted / emphasised by
  authors

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Modelling Keywordiness

• Residual Inverse document frequency used to model
  inter text distribution of KW
• Term burstiness used to model intra text
  distribution of KW
• Knowledge of text structure used to identify
  salient regions (e,g, headings)
• Layout features of texts used to identify
  emphasised words and weight them higher

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Challenges

• Treating multi word keywords (suffix arrays will
  be used to identify n-gramsof arbitrary length)
• Assigning a combined weight which takes into
  account all the aforementioned factors
• Evaluation
• manually assigned keywords will be used to
  measure precision and recall of key word
  extractor against
• inter annotator agreement will be tested to get
  a upper bound for keyword assignment task

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Task 2 Identification of definitory contexts

• This task makes use of the linguistic annotation
  of Los
• The approach is empirical
• Identification of definitory contexts is language
  specific
• Workflow
• Definitory contexts will be searched and marked
  in LOs (manually)
• Local grammars will be drafted on the basis of
  these examples
• The linguistic annotation will be used for these
  grammars
• The grammars will be applied to new Los
  Integration
• The tools will be integrated as additional
  functions to the ILIAS LMS
• The tools will also be available for
  integration in other LMS
• We consider making the tools available as web
  services

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Documents SCORM
Pseudo-Struct.
Basic XML
CONVERTOR 2
Documents SCORM
Pseudo-Struct
Documents HTML
Glossary
CONVERTOR 1
Metadata (Keywords) Ling. Annot XML
BG
EN
Documents User (PDF, DOC, HTML, SCORM,XML)
REPOSITORY
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WP3ontology based cross-lingual retrieval

• Generic approach
• For each domain
• Using computers for beginners
• Impact of eLearning in Society
• we built a domain ontology
• For consistency reasons we consider also an upper
  ontology (DOLCE)
• Lexical material in all 9 languages is mapped on
  the ontology and on the upper ontology
• According to
• types of relations in the ontology and
• Uses cases
• Similarity (predefined ontological chunks)
• we define some search patterns for the user
  interface

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Domain Ontology

• First built starting with English documents
• Concepts are based on
• Extracted keywords in WP2 and
• Glossaries for the given domains
• Concepts have generic names with parts in English
  (for readability reasons) e.g C11_editors
• For each concept we provide labels with
  explanation of the concept in english and ideally
  in all other languages
• Types of relations
• Is_a
• Part_of
• Here we need some informations about what people
  are searching
• The ontology will be encoded in OWL- DL

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Mapping multilingual resources on the domain
ontology -1-

• Trivial for words having exact a correspondent
  in the ontology
• Problems appear when
• One word in a language sub-sums two or more
  concepts in the ontology
• One word in a language sub-sums two or more
  concepts in an ontology but only in relations
  with some other concepts
• One word has a much restrictive meaning not
  present in the ontology

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Mapping multilingual resources on the domain
ontology -2-

• Solution to 1
• Express the lexical items in OWL-DL expressions
  disjunction, conjunctions of classes (give
  example)
• Solution to 2
• Express the lexical items in OWL-DL using
  together with operations on classes also
  relations between the involved concepts
• Solution to 3
• Insert new concept in the ontology

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Ontology enrichment

• If one word cannot be mapped directly on the
  ontology look if a similar meaning can be
  retrieved in some other languages.
• If this seems to be not an isolated case insert
  the new concept in the ontology.
• In any case assign to each concept a label
  indicated the languages in which this concept is
  lexicalised
• The insertion of a new concept will be done with
  FACT or RACER

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Linking lexicon, domain ontology and upper
ontology

• Domain ontology concepts will be mapped on the
  upper ontology.
• This will ensure that all important properties of
  main classes are considered.
• Not relevant senses of some lexical items could
  be also mapped directly on the upper ontology

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Documents SCORM
Pseudo-Struct.
Basic XML
CONVERTOR 2
Documents SCORM
Pseudo-Struct
Documents HTML
Glossary
CONVERTOR 1
Metadata (Keywords) Ling. Annot XML
BG
EN
Documents User (PDF, DOC, HTML, SCORM,XML)
REPOSITORY
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WP4 Tasks

• Integration of LT4eL Tools for semi-automated
  metadata generation, definitory context
  extraction and ontology supported extended data
  retrieval into a learning management system
  (prototype based on ILIAS LMS)
• Developing and providing documentation for a
  standard-technology-based interface between the
  language technology tools and learning management
  systems

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WP4 Objective - Fostering Re-Use of LT-Tools
LMS 1(ILIAS)
LMS 2(e.g. Moodle)
LMS 3(e.g. ATutor)
LT-InterfaceXML-RPC /Web Service
LT-InterfaceXML-RPC /Web Service
LT-InterfaceXML-RPC /Web Service
Language Technology Tools
Language Technology Tools
Language Technology Tools

• Simple-as-possible, well-documented and
  standards-based interface

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WP4 Using LT-Tools in Learning Managements
Systems

• Possible Use Case Scenarios
• Author annotates learning object with keywords
• Author generates glossary for learning object
• Tutor searches for learning objects
• Learner searches for learning material in
  multiple languages
• Learner browses through learning material with
  ontology based information

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WP4 Example ILIAS-LT-Tools Use Case

• Scenario Keyword Generation
• 1. Author adds new learning object to the LMS
  (e.g. HTML file)
• 2. ILIAS displays a form including input fields
  for title, language and filename
• 3. Author enters title and language, selects a
  local .pdf file and hits Upload File
• 4. ILIASLTTools display the (LOM) metadata input
  form, including a list of auto-generated,
  suggested keywords
• 5. Author selects some of the suggested keywords,
  enters some new keywords and hits Save
• 6. ILIAS saves the metadata

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Documents SCORM
Pseudo-Struct.
Basic XML
CONVERTOR 2
Documents SCORM
Pseudo-Struct
Documents HTML
Glossary
CONVERTOR 1
Metadata (Keywords) Ling. Annot XML
BG
EN
Documents User (PDF, DOC, HTML, SCORM,XML)
REPOSITORY
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WP5 Validation of enhanced LMS.

• Challenge is to answer these questions
• How does this compare with what can already be
  done with existing systems?
• What added value is there?
• What is the educational / pedagogic value of
  these functionalities?
• Problem is to evaluate the functionality and
  separate from issues of usability or
  unfamiliarity with the LMS platform.How can we
  expect users to identify any benefit?

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How can we expect users to identify any benefit?

• Present them with tasks to complete using LMS
• With no project functionality
• With project functionality
• Partial
• Full
• Identify potential users
• Course Creators
• Content Authors or Providers
• Teachers
• Sudents
• studying in their own language
• studying in a second language

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Create outline User Scenarios

• We define scenarios, in this context, as
• a story focused on a user or group of users
  which provides information on
• the nature of the users,
• the goals they wish to achieve and
• the context in which the activities will take
  place.
• They are written in ordinary language, and are
  therefore understandable to various stakeholders,
  including users.
• They may also contain different degrees of detail.

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Example Outline Scenario for a student

• A student has just completed studying in English
  a topic on 'The use of computers in Schools'.
• They are interested in finding more information
  on the use of this topic within their subject
  domain.
• Their first language is German
• Suggested search approaches might be
• standard search as available within the LMS not
  using any of LT4eL functionality.
• add in the lexicon
• add in the multi-linguality
• add in the ontology
• Users will be given guidance / familiarisation
  activities in using each of the tools
  beforehand.?
• User Scenarios are under development for all the
  identified users.
• Each scenario will focus on one or more of the
  new functionalities dependent on the roles of a
  particular user.

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Possible Teachers /Course creators tasks

• Add new content to new course structure
• Search for existing content and add to course
  structure
• Add new content to existing course
• Add supplementary content (could be another
  language)
• Modify existing content
• Create new content and make available to the
  system.

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Feedback from Users

• Sessions will be used to gather some initial
  feedback using
• individual interviews
• group plenary
• questionnaires

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Project plan

• Preparatory work in place (May 06).
• Development functionalities complete (November
  2006).
• Integration functionalities in LMS complete (May
  2007)
• First cycle integration functionalities in LMS
  and their validationcomplete (November 2007)
• Second cycle integration functionalities in LMS
  and their validationcomplete (May 2008)