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Automated user-centered task selection and input modification

Automated user-centered task selection and input modification. Rintse van der Werf Geke Hootsen Anne Vermeer MASLA project Tilburg University. Outline. Background Research Discussion and future research. User-centered learning. Approaches in educational research Authentic

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Automated user-centered task selection and input modification

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  1. Automated user-centered task selection and input modification Rintse van der Werf Geke Hootsen Anne Vermeer MASLA project Tilburg University

  2. Outline • Background • Research • Discussion and future research

  3. User-centered learning • Approaches in educational research • Authentic • User initiated • Motivating • Individual needs

  4. MASLA project • Models of Adaptive Second Language Acquisition • Combination of Computer Science and Second Language Acquisition • Goal: building a model for personalized digital language learning web based applications • How can learning materials automatically be adapted to fit the characteristics and preferences of the language learner? • Criterion is learning effect.

  5. Requirements for adaptivity • Annotated learning material • domain model • Knowledge about learner characteristics • user model • User model + domain model -> adaptation model (rules) (Dexter model, 1990; AHAM model (De Bra, 2000))

  6. L2 - proficiencies Graphical User Interface Learner backgrounds Learning styles Curriculum Learning contents MASLA Framework

  7. Task: Vocabulary learning through reading • Incidental vocabulary learning (side effect of reading for comprehension) • ZOPD (Vygotsky, 1962); Comprehensible input (Krashen, 1987) • Assessing learner proficiency • Assessing text difficulty based on frequency information from corpora => combined in text coverage

  8. Text Coverage easier more difficult

  9. Interpreting text coverage • Hazenberg, 1994; Laufer, 1989; Vermeer, 1998 • Lemma Coverage: • 85%: Global understanding • 90%: Good understanding • 95%: Almost complete understanding

  10. Effective Instruction • Comprehensible but challenging • Lemma coverage 85% - 92% • Support from input modification • Dictionary/glossary (see Hulstijn et al., 1996; Plass et al., 1998; Watanabe, 1997) • User initiated “focus on form”

  11. Text Coverage Top criterion Bottom criterion

  12. Summary of research background • Web based tool for automatic adaptive selection of the appropriate text for a specific user. • Automated analysis of text difficulty. • User proficiency calculation from score on vocabulary test. • User gets text that is comprehensible but challenging and has input modification for unknown words to support for understanding the text.

  13. Research questions • A. Adaptive selection of texts leads to: • A learning effect for all users • No difference between learners with different proficiency levels • B. Using input modification: • There is a relation between noticing and retention • (There is no difference in this relation for different proficiency levels)

  14. Method (1) • Subjects (N=32) • Reading Texts (16) • 4 clusters • Input modification

  15. Almost complete comprehension Global comprehension Text coverage for selected texts

  16. Almost complete comprehension Global comprehension Mean text coverage per cluster

  17. Method (1) • Subjects (N=32) • Reading Texts (16) • 4 clusters • Input modification

  18. Method (2) • Data collection: • User logging and tracking • Testing material • Vocabulary proficiency test • Text specific vocabulary tests • Comprehension questions • Procedure

  19. Learning gains Learning gains Learning gains Learning gains Procedure

  20. Results (1) • A mean learning effect occurred for all clusters • 5% learning gains • No significant difference between groups • both pre and posttest scores • learning gains

  21. Results (2) • Correlation between noticing and retention • Mean Φ correlation for subjects: .28 • Mean Φ correlation for items: .50 • in general, the use of the dictionary was limited • No significant difference between proficiency groups • In lookup behavior • In correlation

  22. Conclusion • Automated assessment of texts based on corpora information is a useful indication of text (task?) difficulty. • Adaptive selection of texts based on vocabulary proficiency works. • Open, web based learning environment provides flexibility in the curriculum and opportunities for individualized tasks.

  23. Discussion and future work • Increase learning gains • More adaptivity in text selection • Increase exposure to target words • Based on observed behavior • Increase usability of input modification • Individualize annotation • Based on observed behavior • More focus on form • Use different corpus for text coverage • Now children’s corpus, future Celex/CGN • Unknown lemmas • Multiword expressions

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