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Lexical Quality of ESL Learners: Effects of Focused Training on Encoding

Lexical Quality of ESL Learners: Effects of Focused Training on Encoding. Susan Dunlap, Benjamin Friedline, Alan Juffs, & Charles A. Perfetti University of Pittsburgh Jeanine Sun Washington University in St. Louis. Background. ESL encoding task (of RSAs)

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Lexical Quality of ESL Learners: Effects of Focused Training on Encoding

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  1. Lexical Quality of ESL Learners:Effects of Focused Training on Encoding Susan Dunlap, Benjamin Friedline, Alan Juffs, & Charles A. Perfetti University of Pittsburgh Jeanine Sun Washington University in St. Louis

  2. Background • ESL encoding task (of RSAs) • Arab L1 seem to make more spelling errors than Korean, Chinese, and Spanish L1 • Differences cannot necessarily be accounted for by L1 writing system, L1 orthographic depth, L2 vocabulary knowledge, or L2 fluency

  3. Previous Research • Arab L1 have more problems with prelexical word identification; Japanese L1 have more problems with online word integration (Fender, 2003)

  4. Previous Research • Reading skill better than L1 as a predictor of L2 spelling accuracy in school-aged children (Wade-Woolley & Siegel, 1997)

  5. Theoretical Framework • Lexical Quality Hypothesis • (Perfetti & Hart, 2001) in L1 • orthography, phonology, meaning • plus don’t forget: syntax and morphology • L1 affects L2 learning of grammar, spelling, vocabulary, etc. • (MacWhinney, 2005)

  6. Connection to PSLC Framework • Robust Learning • Retention (of trained words) • Transfer (to new words) • Accelerated future learning (faster decrease in error rates across ESL years) • Assistance dilemma • Explicit vs. implicit instruction

  7. Hypotheses/Predictions • Intervention with focused encoding and meaning-based encoding task will increase quality of lexical representations • Retention • improved lexical quality (of trained words) • Transfer • improved lexical quality (of new/untrained words) • Accelerated future learning • faster decrease in error rates (steeper slope)

  8. Method • Two-phase approach • Phase 1: Knowledge Component Analysis • Phase 2: Focused Intervention

  9. Method • Phase 1 – Knowledge Component Analysis • in-depth coding of RSA transcription data • aka data mining

  10. Coding • Correct • AWL K1-5 (e.g., accumulation, techniques) • acceptable (e.g., blog, otolaryngology, falafel) • Typing (form) • capitalization (e.g., english) • punctuation (e.g., couldnt) • spacing (e.g., myfriend) • Errors • encoding errors

  11. Error Types • Consonant • Missing conect (spa4) • Extra fittness (kor3) • Substitution afternoom (kor4) • Vowel • Missing tuch (chi4) • Extra aabout (ara4) • Substitution becose (kor3) • Multiple C/V errors voleyboll (spa3) • Transpositions afetr (ara3), becuase (kor5) • Lexical/morphological • Plural, tense, affixes truthable (kor4); laught (tai3) • Garble cabegle (chi4); thr (ara4)

  12. Preliminary Findings

  13. Preliminary Findings

  14. Preliminary Findings

  15. Summary of Preliminary Findings • For all L1 groups, errors decrease from Level 3 to Level 5 • Arab L1 group makes more errors compared to other L1 groups, this difference persists through Level 5 • Arab L1 seem to be attempting more “advanced” words (fewer AWL1 words) • Vowel errors most prevalent for Arab L1 • Consonant errors most prevalent for Spanish L1

  16. Method • Phase 2 – Intervention • Fall 2008 • In vivo ESL LearnLab • Designed to focus attention to form-meaning mappings

  17. Implementation • Participants • Pilot in Fall 2008 (Level 5 students) • Data collection in Spring 2009, weeks 1-15 • ESL 3, 4, and 5 writing classes • Exercises • Required but not graded • Done in language lab (CL G-17) • Overseen by researcher on site for weekly scheduled lab times • Programmed in Revolution (or Flash?) • Separate from REAP-based vocabulary study

  18. Predicted Results • L1 x Level x Focus (whole word/sublexical) • Retention • improved lexical quality (of trained words) • Transfer • improved lexical quality (of new/untrained words) • Accelerated future learning • faster decrease in error rates (steeper slope)

  19. Acknowledgments • Sally J. Andrews, Michael Nugent, Claire Bradin Siskin • PSLC ESL LearnLab, funded by NSF award number SBE-0354420

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