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Refined Micro-analysis of Fluency Gains in a Reading Tutor that Listens

Refined Micro-analysis of Fluency Gains in a Reading Tutor that Listens. Jack Mostow* and Joseph Beck Project LISTEN ( www.cs.cmu.edu/~listen ) Carnegie Mellon University * Consultant and Scientific Advisory Board Chair, Soliloquy Learning Society for the Scientific Study of Reading

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Refined Micro-analysis of Fluency Gains in a Reading Tutor that Listens

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  1. RefinedMicro-analysis of Fluency Gains in a Reading Tutor that Listens • Jack Mostow* and Joseph Beck • Project LISTEN (www.cs.cmu.edu/~listen) • Carnegie Mellon University * Consultant and Scientific Advisory Board Chair, Soliloquy Learning • Society for the Scientific Study of Reading • 13th Annual Meeting, July, 2006 • Funding: National Science Foundation, Heinz Endowments 1

  2. Research questions and approach • Guided oral reading builds fluency [NRP 00] • Typically repeated oral reading • … but how do its benefits vary? • How good is repeated vs. wide reading? • How good is massed vs. spaced practice? • How do the answers vary with student proficiency? • Approach: micro-analyze oral reading data • Massive: hundreds of children • Longitudinal: entire school year • Fine-grained: word by word 2

  3. Massive 650 students age 5-14 Mostly grades 1-4 Longitudinal 2003-2004 school year 55,000 sessions Fine-grained 6.9 million words “Heard” by recognizer Video at www.cs.cmu.edu/~listen Project LISTEN’s Reading Tutor: Rich source of guided oral reading data 3

  4. Reading speeds up with practice: example • Initial encounter of muttered: I’ll have to mop up all this (5630 ms) muttered Dennis to himself but how • 5 weeks later (different word pair in different sentence): Dennis (110 ms) muttered oh I forgot to ask him for the money • Word reading time = latency + production time  1/fluency • How does word reading time change in general? 4

  5. Do some types of encounters help more than others? Learning curve for mean reading time of first 20 encounters, excluding top 50 words 5

  6. Four types of word encounters • Predict reading time for 770,858type 1 encounters • from prior encounters of all 4 types. 6

  7. Predictor variables • Number of word encounters so far of each type • Wide vs. reread • Spaced vs. massed • Word difficulty • # of letters • # of past help requests (controls for difficulty for that student) • Student proficiency • WRMT Word Identification grade-equivalent score, e.g. 2.3 • Interpolated for each encounter from pre- and post-test scores 7

  8. Exponential model of word reading time • = L * # letters + (P * proficiency + constant A) * e - learning rate B *Exposure • Define weights for each type of encounter • rfor rereading vs. 1 for wide reading • mfor massed vs. 1for spaced • h for help requests • Exposure = weighted sum of # of word encounters so far • 1 * # of wide, spaced encounters • + r * # of reread, spaced encounters • + m * # of wide, massed encounters • + r * m * # of reread, massed encounters • + h * # of help requests • [Beck, J. Using learning decomposition to analyze student fluency development. ITS2006 Educational Data Mining Workshop, Taiwan.] 8

  9. Analysis • Use SPSS non-linear regression to fit parameters • Caveat: 770,858 trials are not independent • So be conservative: • Split 650 students into 10 groups • Fit r, m, … for each group • From the 10 estimates of each parameter, compute: • Mean ± standard error • Differs significantly from 1? 9

  10. Overall results • Wide reading beats rereading • r = .68 ± .13 • r < 1 (p = .007) • 2 new stories ≈ 3 old stories • Spaced beats massed practice • m = .67 ± .13 • m < 1 (p = .007) • 2 spaced encounters ≈ 3 massed encounters • Do these results vary by proficiency? 10

  11. When does wide reading beat rereading? Maybe only for high readers? Seeing a word again the same day May help low readers more than waiting (p = .058) Helps higher readers less than seeing it later (p < .01) Effects of proficiency 11

  12. Conclusion: type of practice matters! • Wide reading beats rereading • At least for higher readers • Advantage of spaced practice varies with proficiency • Low readers: seeing a word again the same day may help more • Higher readers: better to wait • Fluency growth is slow (learning curve is gradual) • So differences in practice quality are hard to detect • But possible by micro-analysis of massive, longitudinal, fine-grained data • Future work • Clarify interaction with proficiency • Refine model of fluency practice • Test correlational results experimentally • Thank you! Questions? • See papers & videos at www.cs.cmu.edu/~listen 12

  13. Predictive models of word reading in text 13

  14. Outcome variable • Combine reading time, errors, help requests • Cap reading time at 3 seconds (0.1% of data) • Treat error as 3 seconds • Treat help request as 3 seconds 14

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