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Robust learning of vocabulary in classrooms and in CALL

Robust learning of vocabulary in classrooms and in CALL. Alan Juffs, Lois Wilson University of Pittsburgh Maxine Eskenazi Michael Heilman CMU AAAL, ,Washington DC, March 31, 2008. Funding. This research is supported by the United States of America National Science Foundation

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Robust learning of vocabulary in classrooms and in CALL

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  1. Robust learning of vocabulary in classrooms and in CALL Alan Juffs, Lois Wilson University of Pittsburgh Maxine Eskenazi Michael Heilman CMU AAAL, ,Washington DC, March 31, 2008 PSLC ELI CMU Pitt

  2. Funding This research is supported by the United States of America National Science Foundation Grant: SBE-0354420 English Language Institute (ELI) University of Pittsburgh Language Technologies Institute (LTI) Carnegie Mellon University PSLC ELI

  3. Pittsburgh Science of Learning Center • Exploring learning in math, science and second language learning • Attempting a general theory of robust learning; robust learning should: • Be long-term • Transfer to new contexts • Accelerate future learning • VanLehn (2006, p. 5) • The English Language Institute at the University of Pittsburgh is one of seven site partners with the PSLC that provide in vivo research contexts PSLC ELI

  4. The problem:Vocabulary and reading in an Intensive English Program (IEP) • Goal of IEP reading: prepare for academic reading • Assume Nation: 1000/2000 lists • http://www.lextutor.ca • http://www.vuw.ac.nz/lals/staff/paul-nation/nation.aspx (Nation, 2007, webcast) • Target vocabulary: Coxhead word list • http://www.vuw.ac.nz/lals/research/awl/info.html PSLC ELI

  5. Reading Curriculum and vocabulary(Nation, 2001; 2007) • Issue: how best to develop fluency and refinement in these AWL items and enhance long-term retention • Students’ books inadequate • Students don’t do extensive reading out of class • Class has heterogeneous L1 • Students need to get from 2000 level frequency to 4-5000 frequency for academic readiness in one year PSLC ELI

  6. REAP - LTI at CMU • REAP (REAder Specific Practice; Brown and Eskenazi, 2004; Collins-Thompson and Callan, 2004) <http://cs.reap.cmu.edu>. • i) a search engine that finds text passages satisfying very specific lexical constraints: • Filters: the system is set to filter for AWL words in texts of L1 English grades 6-8 level reading. • Data base of 50,000 documents (after filtering from 10 million) • Each document about 1000 words • Total words: 50,000,000 C.f Cambridge and Nottingham Spoken corpus of 5,000,000 (McCarthy, 2006) • ii) selecting materials from an open-corpus (the Web), thus satisfying a wide range of student interests and classroom needs, and • iii) the potential to model an individual’s degree of acquisition and fluency for each word in a constantly-expanding lexicon so as to provide student-specific practice and remediation PSLC ELI

  7. Solution to the problem? • REAP = researchers/teachers think of this as a way for individuals acquiring the AWL word list in a rich context through extensive reading that will also reinforce known words. • But issues of focus on the task • Juffs et al. (submitted) found that students transformed the CALL tasks, either skipping reading or focusing too much on individual words PSLC ELI

  8. What we know about vocabulary learning from recent ‘lab’ studies • Hulstijn and Laufer (2001) • Advanced ESL in Holland and Israel • A) M-choice- B) fill in the blank C) writing 10 words floor effects in delayed post-test, but superior effects for writing practice and fill in the blank • Folse (2006) • IEP context in the USA • Gains small for all conditions • Fill in the blank ‘most efficient’ compared to writing • Barcroft (2004, 2006) • Beginning Spanish L2 • Writing distracts from learning forms in first exposure PSLC ELI

  9. Research Questions • What does vocabulary learning look in ‘in vivo’ as opposed to a very tightly controlled study? (action research) • How does in class vocabulary instruction differ from the CALL in the reading course in intermediate ESL? • How do the learning outcomes differ? • If there are differences, what might the source of those differences be? • Can deeper processing through writing be ‘skipped’ by using a CALL program? PSLC ELI

  10. Procedure with Software • Take a pre-test to establish individual focus word list of 100 Coxhead words. • Read texts selected to provide examples of the words they don’t know. While reading, may choose to get help by clicking (or not) on words and looking at definitions. • ‘Reading check’ question after reading the passage • Vocabulary check question. • If target word wrong, another passage with that word in presented right away; otherwise word tested later • All words seen at least 5 times, in the text, in the vocabulary questions and reading check. • 40 minutes per week in class with the REAP system. • End of term post-test cloze and production tests. PSLC ELI

  11. Example text: highlights PSLC ELI

  12. Procedure in Class • Words chosen by curriculum supervisor on the basis of her focus/assessment • Take a pre-test to establish individual focus word list of 58 AWL words. • All students given the same list to study. Quizzes during the term. • Look-ups in class, homework exercises, student board work • In class instruction based on pair/ group work and interaction and written practice • 40-50 minutes a week of in class and homework. • Final post-test in cloze format. • Final post-test of production PSLC ELI

  13. Participants and MTELP PSLC ELI

  14. Results • Pre-test • Post-tests • Average gain • Analysis of long-term transfer in writing in the database. • By BNC • By AWL • http://www.lextutor.ca (T. Cobb) PSLC ELI

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  16. Fall 07: Words used in writing • Focus words seen in REAP: 10 uses • BNC-1,000 [ fams 2 : types 2 : tokens 2 ] assume produce • BNC-2,000 [ fams 1 : types 1 : tokens 1 ] distinction • BNC-3,000 [ fams 1 : types 1 : tokens 1 ] conceive • BNC-4,000 [ fams 1 : types 1 : tokens 1 ] abandon • BNC-5,000 [ fams 1 : types 1 : tokens 1 ] derive • BNC-6,000 [ fams 2 : types 2 : tokens 2 ] cite (x3) prohibit PSLC ELI

  17. Fall 07 Words used from class: 840 • BNC-1,000 [ fams 14 : types 15 : tokens 15 ] approach basis client effect effective functional key notice point present process regarding specific tend • BNC-2,000 [ fams 18 : types 19 : tokens 19 ] accessible alternative analysis analyze challenge consistent engage essential focus gather image interpret monitor option promote recruit reserve status ultimately • BNC-3,000 [ fams 6 : types 6 : tokens 6 ] peak prominent randomly speculate suspend unique • BNC-4,000 [ fams 5 : types 5 : tokens 5 ] conduct objective profile rigid trend • BNC-5,000 [ fams 4 : types 4 : tokens 4 ] cultivate emerge resemble ritual • BNC-6,000 [ fams 1 : types 1 : tokens 1 ] thrive • BNC-7,000 [ fams 1 : types 1 : tokens 1 ] parameter • BNC-9,000 [ fams 1 : types 1 : tokens 1 ] overstate • BNC-11,000 [ fams 1 : types 1 : tokens 1 ] adept (x11) • BNC-14,000 [ fams 1 : types 1 : tokens 1 ] admonish (x15) PSLC ELI

  18. Fall 07 AWL Words • Sublist 1 • analysis 2 analyze 3 approach 5 consistent 3 functional 17 interpret 4 process 48 specific 37 • Sublist 2 conduct 3 focus 49 • Sublist 3 alternative 21 • Sublist 4 accessible 1 emerge 2 option 21 parameter 11 promote 5 status 37 • Sublist 5 challenge 21 image 11 monitor 27 objective 11 trend 8 • Sublist 7 ultimately 1 unique 12 • Sublist 8 randomly 2 • Sublist 9 rigid 20 suspend 15 PSLC ELI

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  20. Relationship between proficiency and learning • Matthew Effect: • Stanovich (1986) • Parable of the Talents • Matthew 25:29 • For everyone who has will be given more, and he will have in abundance. Whoever does not have, even what he has will be taken from him. PSLC ELI

  21. MTELP Fall class • Predicts in class • Pretest: r= 0.52, p ≤.01 • Post-test: r= 0.59, p ≤ .01 • Production: r= 0.58, p ≤.01 • Predicts REAP • Cloze: r= 0.53, p ≤.01 • Production: r= 0.59, p ≤.01 • Not related to look ups or amount read PSLC ELI

  22. MTELP Spring class • Predicts in class • Pretest: r= 0.63, p ≤.05 • Post-test: r= 0.36, p ≤ .06, ns • Production: r= 0.44, p ≤.02 • Predicts REAP • Cloze: r= 0.58, p ≤.001 • Not related to look ups or amount read PSLC ELI

  23. Partial Correlations • Controls for MTELP score • Fall class • Class and Reap Production, r=0.61, p ≤.01 • Class and REAP cloze, r= 0.42, p ≤ .05 • Independent of overall proficiency, the CALL and classroom learning is similar: students who work hard in class, and work with the computer,achieve similar gains. • The pre-test on in class vocabulary does NOT correlate with gains in REAP or in class when MTELP is controlled for. PSLC ELI

  24. Discussion • Hulstijn and Laufer (2001) • Involvement load hypothesis • Need: zero--external- internal [cognitive?] • 0- - Teacher -- CALL • Search: zero- provided - lookup • CALL - teacher/class -- Self/CALL • Evaluation • Non-linguistic - CALL multiple choice • Fill in blank- in class practice • Free production - in class homework and practice PSLC ELI

  25. Conclusions • Students who have more going in, learn more on the whole. • Key importance of 1000 and 2000 level words • Written output practice, combined with involvement in class, may produce more learning than CALL programs that provide exposure only even if students are ‘internally motivated’ by self-selecting lists • Writing may take more time, but that may be what it takes for robust learning of new vocabulary PSLC ELI

  26. Conclusions • Folse (2006) • Writing is time consuming compared to fill-in-the blank • Writing may be time consuming, but for robust learning written production is key. • Barcroft (2006) • Initial exposure, writing may compete for resources • For subsequent learning, writing is key PSLC ELI

  27. Conclusions • Support general point by Allum (2002) • Students like computers but prefer classroom activities when the same materials are used. • Juffs et al: students need training on how to use software. PSLC ELI

  28. Conclusions • Supports recommendations for on-line interactivity • Zapata and Sagarra (2007) • Recommend on line vocabulary work book and feedback • Need to select important vocabulary carefully and focus on it • Externally provided vocabulary lists have face validity PSLC ELI

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  30. References • Allum, P. (2002). CALL and the classroom: the case for comparative research. ReCALL, 14, 146-166. • Barcroft, J. (2004). Effects of sentence writing in second language lexical acquisition. Second Language Research, 20, 303-334. • Barcroft, J. (2006). Negative Effects of forced output on vocabulary learning. Second Language Research, 22, 487-497. • Folse, K. S. (2006). The effect of type of written exercise on L2 vocabulary retention. TESOL Quarterly, 40, 273-293. • Hulstijn, J., & Laufer, B. (2001). Some empirical evidence for the involvement load hypothesis in vocabulary acquisition. Language Learning, 51, 539-558. • Juffs, A., Friedline, B. F., Eskenazi, M., Wilson, L., & Heilman, M. (in review). Activity theory and computer-assisted learning of English vocabulary. Applied Linguistics. • Stanowicz, K. E. (1986). Matthew effects in reading: some consequences of individual differences in the acquisition of literacy. Reading Research Quarterly, 21, 360-407. PSLC ELI

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