Learner modelling through interactional data collection

David Alfter


University of Gothenburg




Learner modeling


Language learning individual experience

Learner differences

  • Prior knowledge
  • Learning speed
  • Learning style
  • Language background

Online learning platforms rarely personalized

Fixed progression path

Personalized learning environments

Collection of available tools


Collect data from learners using the platform

Profile: static

Learner model: dynamic

  • Track learner activity on platform
  • Continuous assessment
  • Stealth assessment



Education background

Language background

Swell project


Collaboration with teachers

Learner model

Data collected during interaction with learning platform

General data

Not learner-specific

  • Current time
  • Frequency of use
  • Time since last visit
  • Screen size of device
  • IP address
  • Geolocation

Multiple choice vocabulary exercise

  • Time taken
  • History
  • Final answer

Variables to collect

Spelling exercise

Spelling exercise

  • Frequent (L1 specific) misspellings
  • Difficult sounds
  • Diagnostic and prognostic

Hypothesis testing

Evaluation of automatically graded vocabulary list

Grading of new vocabulary items

Adaptive diagnostic test

Different exercise types

  • Bundled gaps
  • Vocabulary knowledge
  • Sentence rearrangement
  • Sentence composition
  • Multiple choice

Teacher evaluation

To infinity and beyond

  • Morphology
  • Grammar

Error analysis

Error groups

Thank you for your attention!

Please clap and don't ask tough questions

Learner modelling through interactional data collection

By daalft

Learner modelling through interactional data collection

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