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Negotiated learner modelling to maintain today's learner models.

Susan Bull1

  • 1Institute of Education, University College London, London, UK.

Research and Practice in Technology Enhanced Learning
|January 8, 2019
PubMed
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Negotiated open learner models enhance learning by allowing users to interact with and refine their data, promoting reflection and accuracy in educational technology. This approach improves the learning process through user engagement.

Area of Science:

  • Educational Technology
  • Artificial Intelligence in Education
  • Learning Analytics

Background:

  • Modern technology-enabled learning environments generate substantial data.
  • Open learner models (OLMs) are used to promote metacognitive activities and learner model accuracy.
  • Existing OLMs allow direct user interaction for data refinement.

Purpose of the Study:

  • To propose negotiated open learner models (NOLMs).
  • To maintain accuracy of learner models with multiple data sources.
  • To prompt learner reflection through model discussion.

Main Methods:

  • Investigating the use of negotiated open learner models.
  • Facilitating user interaction and discussion of learner models.
  • Integrating multiple data sources into learner models.
Keywords:
Intelligent tutoring systemsMultiple learning resourcesNegotiated learner modelsOpen learner modelsVisualisation

Related Experiment Videos

Main Results:

  • Negotiated open learner models can maintain accuracy.
  • User engagement in model discussion prompts reflection.
  • NOLMs offer a method for refining multi-source learner data.

Conclusions:

  • Negotiated open learner models are effective tools.
  • This approach enhances both learner model accuracy and metacognition.
  • NOLMs represent a promising advancement in educational technology.