Idéseminarium
David Alfter
Lars Borin, Elena Volodina
"Learner modeling is very important"
as Aristotle didn't say
Touching other people's work
- User modeling
- L2 Assessment
- Recommender Systems
- Crowdsourcing
User modeling
UM is ubiquitous
Learner modeling as subset of user modeling
Most learner modeling in programming
What about language learners?
L2 Assessment
Static vs. dynamic
In language learning, assessment as knowledge at point in time
What about continuous assessment?
Recommender Systems
What to do next?
What to do next?
Based on user model
What to do next?
Based on user model
or crowd model
What to do next?
Based on user model
or crowd model
"People who liked this product also liked this product"
Applicability to language learning?
Crowdsourcing
Collection of data through the masses
What crowd?
WHY
HOW
WHAT
Learner modeling
Why
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
How
Collect data from learners using the platform
Profile: static
Learner model: dynamic
- Track learner activity on platform
- Continuous assessment
- Stealth assessment
Roadmap
- 2016: Requirements for exercises
-
2017:
- Lärka exercises set up
- Diagnostic test
- 2017 1/2: Data collection and evaluation
- 2018: Data description/evaluation #1
- Mittseminarium: Theoretical user model
- 2019: User modeling in place
- 2019-2020: Study #2
-
2020-2021:
- Wrap up
- Writing
What has been done
- Ildikó Pilán, David Alfter, Elena Volodina. Coursebook texts as a helping hand for classifying linguistic complexity in language learners' writings. Proceedings of the workshop on Computational Linguistics for Linguistic Complexity (CL4LC), COLING 2016, Osaka, Japan.
- David Alfter, Elena Volodina. Modeling Individual Learner Knowledge in a Computer Assisted Language Learning System. Proceedings of SLTC 2016, Umeå, Sweden.
- David Alfter, Yuri Bizzoni, Anders Agebjörn, Elena Volodina, Ildikó Pilán. From Distributions to Labels: A Lexical Proficiency Analysis using Learner Corpora. Proceedings of the joint workshop on NLP for Computer Assisted Language Learning and NLP for Language Acquisition at SLTC 2016. Linköping Electronic Conference Proceedings 130: 1–7. Umeå, Sweden.
- Elena Volodina, Ildikó Pilán, David Alfter. Classification of Swedish learner essays by CEFR levels. Proceedings of EuroCALL 2016. Limassol, Cyprus.
- David Alfter, Elena Volodina. Learning the Learner: User Modeling in Intelligent Computer Assisted Language Learning Systems. CEUR Workshop proceedings. UMAP 2016, Halifax, Canada.
- David Alfter, Elena Volodina. Adaptive diagnostic test.
- David Alfter, Sandra Morales. Teachers' voices: A reflective approach to explore the use of an online platform for language teaching
- David Alfter. Evaluating sentence rearrangment and sentence composition tasks using Partial Tree Kernels
2017 - upcoming
What is being done
Diagnostic test
Implementation of
- exercises
- login
- logging
What profile data?
In collaboration with SweLL project
Profile
Education background
Language background
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
What will be done
- Data collection
- Data analysis
- Learner modeling
What to model?
Phonology
RecSys?
Vocabulary
Difficult
Vocabulary
Focus on...
Grammar
Needs in-depth error analysis
To infinity and beyond
- Morphology
- Grammar
Error analysis
Error groups
Closed tasks
More specific, less rich
Open tasks
Less specific, richer
Feedback?
Thank you for your attention!
Please clap and don't ask tough questions
Idéseminarium
By daalft
Idéseminarium
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