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Lexical Quality of ESL Learners: Effects of Focused Training on Encoding Susan Dunlap, Benjamin Friedline, Alan Juffs, & Charles A. Perfetti University.

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Presentation on theme: "Lexical Quality of ESL Learners: Effects of Focused Training on Encoding Susan Dunlap, Benjamin Friedline, Alan Juffs, & Charles A. Perfetti University."— Presentation transcript:

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 –Missingconect (spa4) –Extrafittness (kor3) –Substitutionafternoom (kor4) Vowel –Missingtuch (chi4) –Extraaabout (ara4) –Substitutionbecose (kor3) Multiple C/V errorsvoleyboll (spa3) Transpositionsafetr (ara3), becuase (kor5) Lexical/morphological –Plural, tense, affixestruthable (kor4); laught (tai3) Garblecabegle (chi4); thr (ara4)

12 Preliminary Findings

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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

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23 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)

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


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