Semantic Annotation for Semi-Automatic Positioning of the Learner

1
Semantic Annotation for Semi-Automatic
Positioning of the Learner

• Kiril Simov, Petya Osenova
• Linguistic Modelling Department, IPP,
• Bulgarian Academy of Sciences
• Supporting eLearning With Language Resources and
  Semantic Data
• LREC 2010
• 22st May, 2010, Valletta, Malta

2
Plan of the Talk

• Positioning Task (PT)
• Semantic Annotation
• Application to Positioning Task

3
Positioning Task

• Within Lifelong Learning settings the learner
  needs to find (with the help of a tutor) an
  optimal learning path
• Identification of the learning path is done by
  comparison of the learners competence to a
  target competence

4
Competence

• Characteristics that individuals have and use in
  appropriate, consistent ways in order to achieve
  desired performance. These characteristics
  include knowledge, skills, aspects of self-image,
  social motives, traits, thought patterns,
  mind-sets, and ways of thinking, feeling and
  acting.
• (Dubois, David D. and William J. Rothwell,
  Competency-Based Human Resource Management,
  Davies-Black Publishing, Palo Alto, 2004, p. 16.)

5
Positioning in a Learning Network

• A learning network connects actors, human as well
  as agents, institutions and learning resources.
  (Kalz et al. 2007)
• Positioning learners competence can be
  automatically compared to a set of concept
  evidences of the target competence within a
  learning network

6
Knowledge Rich Approach

• A learning network is a set of resources
  including tutors, experts, learning materials and
  learners, whose connections are mediated by
  ontologies
• Conceptual information within the ontology
  represent the target competence in a field of
  study
• The target competence of a learning path is
  represented via a curriculum
• Learners competence is represented in text form

7
Semantic Annotation for PT

• The semantic annotation explicates the conceptual
  part of the target and learners competence
• Grammar-based semantic annotation with concepts
• Discourse segmentation
• Lexical chains creation to support the concept
  annotation and
• Sentiment analysis for evaluation of the concept
  usage in the text

8
First Version of KRA

• Q Name some of the technical specifications of
  different kinds of monitors
• A Output device, monitor, display devices of a
  PC
• Common concepts CRT monitor, display, monitor
• Missing concepts contrast, frame rate, graphical
  elements, image, LCD monitor, picture, pixel,
  ratio, refresh rate, rendering, resolution,
  screen, size, VGA
• Additional concepts types, devices, Output
  device, PC, power, space

9
Conclusion

• We assume the positioning task to be performed on
  the basis of concept annotation of curriculum and
  learners profile (CV and tests)
• The result from the semantic annotation
  represents the target and the learners
  competence
• Comparison of the two competences facilitates the
  tutor to define an optimal learning path for the
  learner