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Cars2003

Course: TLC 2, Fall 2009
School: U. Houston
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Language Measuring and Vocabulary in K-3 David Francis, Ph.D. University of Houston Texas Institute for Measurement, Evaluation, and Statistics The Reading Pillar Skilled Reading Speed and ease of reading with comprehension Fluency Comprehension Word Recognition Conceptual Knowledge/vocabulary Strategic processing of text Decoding using alphabetic principle Print Awareness & Letter Knowledge Motivation to Read Oral Language including Phonological Awareness Emergent Reading Decoding using other cues Sight Recognition Components of Reading First Phonemic Awareness Phonics Fluency Vocabulary Comprehension Background on Vocabulary Vocabulary knowledge (knowledge of the meanings of words) forms the basis for all reading comprehension If one fails to grasp the meanings of individual words in text, one will struggle to grasp the meaning of the text as a whole Word knowledge goes beyond vocabulary knowledge and enables the growth of vocabulary When individuals have generative knowledge about words, new words are acquired more easily Knowledge about the world of words begins early and is more a function of experience than of direct teaching Cumulative Vocabulary words 13 higherSES children (professional) 23 middle/lowerSES children (working class) 6 welfare children Age of child in months Age of child in months Hart & Risley, 1995 Estimated cumulative words addressed to child Language Experience Professional Working-class Welfare Age of child in months Hart & Risley, 1995 How many words do children know? There have been various attempts to estimate the size of childrens vocabularies and the size of the corpus of words in Printed School English One estimate puts the size of this corpus at about 88,000 different words (Nagy & Anderson, 1984) of which a typical high school student might know about half upon graduation Zeno et al (1995) place the corpus of words in printed English at roughly 150,000 unique words. How quickly do children acquire words? People have also looked at vocabulary growth Grade 1 High school Growth Growth = 6,000 words = 45,000 words = 39,000 words in 12 years 3,000 words per year Low-end estimates of word growth are about 1,000 words per year Others have found kids gaining from 1,000 to 5,000 words per year. How are new words learned? With good instruction, optimistic estimates place the number of words that can be taught directly at about 6-10 words per week. At this rate, a child would acquire roughly 400 words in a typical instructional year. Clearly, many words must be learned incidentally through experience and exposure to language Reading provides a great source of exposure to new words How are new words learned incidentally? Reading provides a natural context for acquiring new word meanings Because context and background knowledge offer clues to new words meanings, reading widely can substantially improve childrens reading achievement through changes in vocabulary knowledge. Cunningham and Stanovich have shown that reading volume has positive effects on vocabulary development (1991; 1997) Differences between Print and Speech Words used in print may be quite rare in spoken language display, dominance, literal, maneuver are not found in two large indices of spoken language but have high frequency in printed text (Cunningham & Stanovich, 1998) Rare words (i.e., words that are ranked below 10,000 in printed language) also convey much of the important information in print e.g., occurrence has a rank of 86,000 (Cunningham & Stanovich, 1998) Decontextualized Language is at the Core of Literacy Instruction Word Meanings Decontextualized language: Minimizes contextual cues & shared assumptions by explicitly encoding referents for pronouns, actions, and locations Also called literate or academic language because it allows literate people to discuss literary products Text Practicing Letter Sounds Table 4 Representation of Oral and Written Vocabulary in Program (Types) A B C1 C2 D E LWV Levels 2 4 6 8 10 12 13 16 Total Freq. 889 609 104 47 18 25 9 9 1710 Mean % (51.99) (35.61) (6.08) (2.75) (1.05) (1.46) (.53) (.53) Freq. 897 575 107 35 24 41 11 17 1707 % (52.55) (33.68) (6.27) (2.05) (1.41) (2.40) (.64) (1.00) Freq. 891 592 113 33 16 23 14 8 1690 % (52.72) (35.03) (6.69) (1.95) (.95) (1.36) (.83) (.47) Freq. 1101 785 197 64 28 45 15 14 2249 % (48.96) (34.90) (8.76) (2.85) (1.24) (2.00) (.67) (.62) Freq. 196 102 2 1 1 2 % (64.47) (33.55) (.66) (.33) (.33) (.66) Freq. 586 375 53 17 8 10 6 2 1057 % (55.44) (35.48) (5.01) (1.61) (.76) (.95) (.57) (.19) 304 SD (10.06) Mean 61.42 SD (9.12) SD (10.29) Mean 52.64 SD (10.83) Mean 53.78 SD (9.72) Mean 51.91 Mean 55.38 SD (10.10) SF1 53.24 Note. LWV = Living Word Vocabulary (Dale & ORourke, 1981). SFI = Standard Frequency Index Zeno et al., 1995). Relation of Frequency in Corpus to Grade 1 Frequency in Zeno et al. (1995) Some rare (G1 Basal) and not-so-rare (elementary literature) Words WORD craft due elk exhausted fifth fins flung gathering generally greatly hooks hops horned household illness jersey kingdom layer leash least lights LWV Level 6 6 6 6 8 8 6 6 6 8 12 12 6 6 6 6 6 6 8 6 13 Basal f/100 .001684892 .002813969 .002813969 .002813969 .002813969 .002813969 .002813969 .002813969 .002813969 .002813969 .002813969 .002813969 .002813969 .002813969 .001684892 .002813969 .002813969 .002813969 .001684892 .002813969 .002813969 Lit. f/1,000,000 4.952 11.638 7.429 7.429 23.029 5.448 13.371 16.343 11.886 12.133 5.200 5.200 5.200 10.648 5.695 10.648 20.800 25.257 11.390 139.904 97.314 Representation of Opportunity Words Across Basals Number of Programs LWV Level 1 2 3 4 Total 6 8 10 12 13 16 Total 87 14 11 22 3 4 141 33 12 2 2 1 1 51 9 4 3 5 0 0 21 0 2 0 0 1 0 3 129 32 16 29 5 5 216 Opportunity Words in Grade 1 Basals ad amuse arch attract backwards blues blur boar boast bony breed bronze burrow career cement chops chowder clam clippers clumsy cocoon con conservation construction contented craft creak creamy create crib device display doe dose driftwood elk establish exhausted fangs fearless fig flapped fled foil frisky furthermore gallery galley garlic genius gigantic glossary gown granite grief haze holly horned illness item jumper kicks leapt lent listener llama marine mercury meter mi mobile mold outdoor overcome packet perch phrase poetry poisonous porcupine potter pox prey prickly pueblo pulp radar relate relay resist rhinoceros rover rum sculpture seller shack shaken shrug slimy sow sped spoiled squad squire sturdy survive swap swoop tattered thankful ties towering turquoise twinkle wag walrus wee whaling whew whoa wraps wrestle yelp zoom brilliant celebrated coral draws dune elegant fins gerbil gruff hermit heron huff lance polar ramp reed reef returns ribbons rushes scurry si stated stirring thud timid typical vacuum vegetation yourselves alas bog brute cam cove dialogue flora guinea hangs lulu ping squid stripes taps blasted boa buster chi flahing foal fro gracious handles hatching heather hooks hops mantis mats maze rio senora sneaking stacks swish tad taro taut twinkling amazon splitting plankton ticking determination gust framework minded veterinarian promises hemisphere slanted magnificent rhythm chameleon digs The Challenge of Measuring Vocabulary There are many ways of knowing a word - Recognition vocab > Productive vocab - Literate vocab > oral vocab (if literate) - Reading vocab > writing vocab (if literate) What is a word? Root plus inflectional variant? (edit/edits/edited/editing). Count lexical entries? Levels of Word Knowledge (Dale & ORourke, 1986; Stahl, 2003) I never saw it before Ive heard of it but I dont know what it means I recognize it in contextit has something to do with I know it. Reading First requires Valid and reliable assessment for: - screening - diagnosis - progress monitoring - outcome Screening Does the students vocabulary level suggest the need for further evaluation of primary, oral language? Types of Assessments Proposed for RF Picture vocabulary (expressive and receptive) Semantic Fluency Confrontation Naming Fluency Reading and oral vocabulary Comparison of English and Spanish on four Standardized Assessments r = -.336 r = .305 r = -.084 r = -.135 Comparison of English and Spanish Narratives - Retell r = .392 r = .437 r = .410 r = .513 Comparison of English and Spanish Narratives - Unique r = .447 r = .499 r = .440 r = .537 Diagnosis What instruction does this student need? Administer baseline assessment from curriculum corpus to determine the proper instructional format (whole class vs. small group) for different sets of words Progress Monitoring Are students learning the lexical entries taught? Phonological properties Morphological extensions Written form (i.e., spelling) Multiple meanings Progress Monitoring PM assessment in vocabulary is ideally suited to curriculum based measurement PM assessments aid the teacher in evaluating classroom instruction more regularly than end of year outcome assessments Outcome 1. Have students learned the words taught? (vocabulary outcome) 2. Do these word meanings transfer to new contexts? (near transfer) 3. Has vocabulary size been increased such that reading comprehension on a standardized test increases (far transfer)? Outcome Assessment Needs to measure the extent to which students have mastered the vocabulary targeted for the current year Can measure word consciousness, metalinguistic knowledge, and the ability to work with text to learn new words Should assess depth of meaning Should include vocabulary learned in RLA, but also content area vocabulary in core subjects Conclusions Publishers need to provide teachers with cumulative vocabulary lists These need to be made available electronically to textbook adopters and should include information on: Frequency in text and lesson number Separate entry for each definition used Derivational forms Printed word frequency in other relevant corpora Conclusions Instruction needs to target oral language development from pre-school through high school Printed word frequency and age of acquisition are useful tools for guiding selection of lexical entries to be taught Assessment of vocabulary for the purpose of Reading First should focus on the link between assessment and instruction Narrative Performance and the Measurement of Vocabulary in Bilingual Children Jon F. Miller University of Wisconsin Madison Aquiles Iglesias Temple University Narrative Procedure Narratives are collected in both languages, and in two conditions Retell and Unique In both conditions, Frog stories from Mercer Mayer are used. Examiner sits across from the child so that the examiner cannot view the pictures as the child tells the story Optimum task for story retell, auditory model with visual support, pictures available during retell. Narrative Procedure Child Produces a story in two conditions Retell condition: Child and examiner review the book while the examiner tells the story using a standard script Child retells same story following the examiner model All Children retell the same story (FWAY) in both languages Unique story condition: Child is given a second story, pages through the story with the examiner (no oral model presented) Child tells the second story The specific story is assigned randomly from a set of 5 stories in the same Frog series. The same story is used in both languages. Testing in the two languages is about two weeks apart. Bilingual Language & Literacy Project Language Sample Data Variables Variable NCW WPM NTW NTWE NTWS NDW NDWE NDWS MLUW Language Measure No. Complete Words Words/Minute No. Total Words No. Total Words-English No. Total Word-Spanish No. Different Words No. Diff. Words-English No. Diff. Words-Spanish MLU in Words Description NCW provides an index of transcript length in terms of words. All words are counted, including words in mazes (filled pauses, false starts, repetitions, and reformulations) and words produced in the non-target language. WPM is a measure of speaking rate and has been used as a measure of language proficiency. The more proficient the child is in the language, the higher the WPM. NTW counts the total number of words that contribute to telling the story. Words in mazes and parenthetical remarks are not included in this count. Neither are words in incomplete or unintelligible utterances. NTW is broken down into English words (NTWE) and Spanish words (NTWS) for each language sample. NDW counts the different words within the set of total words (NTW) and is broken down into English words (NDWE) and Spanish words (NDWS). NDW is a wellestablished measure of vocabulary diversity and is highly associated with advancing age. MLUW measures the length of the utterances in words. The more proficient the child is, the longer the utterances s/he produces. This is a general measure of syntactic complexity and correlates very highly with age. RUBRIC is a narrative structure analysis consisting of seven components: introduction, character development, mental states, referencing, conflict resolution, cohesion and conclusion. Each component is given a score (0-5). RUBRIC is the total score (0-35). The higher the score, the better the story. SI is a measure of the syntactic complexity of the child's utterances, calculating the ratio of number of clauses to number of utterances. The higher the SI value, the more subordinating clauses used. 1 1, 2 Notes 1, 2 1 RUBRIC Story Structure Rubric SI Subordination Index 1. Calculations are based only on the complete and intelligible utterances. Utterances that contain unintelligible segments or have been abandoned or interrupted are not included. 2. A high number for NDW and NTW in the non-target language is an indicator of deficiency in the target language, e.g., as many as 30% of the words used to tell the story in English are Spanish words and this number decreases with advancing grade. The reverse, however, is not necessarily true. A low number for NDW and NTW in the non-target language may not indicate a proficiency in the target language as some children do not choose to code switch. Narrative Structure Measure Seven categories rated independently on a 0 5 rating scale Introduction Character development Mental states Referencing Conflict resolution Cohesion Conclusion Reliability: r = .85 1.0 (3 raters, 20 transcripts) Subjects with English and Spanish Narratives in Retell and Unique Conditions LANGUAGE ENGLISH ENGLISH ENGLISH ENGLISH SPANISH SPANISH SPANISH SPANISH GRADE 1 2 3 K 1 2 3 K Retell 386 377 193 303 397 349 166 346 Unique 417 439 192 306 451 421 173 380 Differences in MLU-W Between English and Spanish Retell Conditions Type 3 Tests of Fixed Effects Effect Grade Language Grade X Language Num Den DF DF F Value Pr > F 3 1 3 823 514 514 65.25 <.0001 113.90 <.0001 1.73 0.1608 Least Squares Means Effect Grade Grade Grade Grade Language Language Grade X Language Grade X Language Grade X Language Grade X Language Grade X Language Grade X Language Grade X Language Grade X Language 1 1 2 2 3 3 K K IISG___ COND_LANG 1 2 3 K ENGLISH SPANISH ENGLISH SPANISH ENGLISH SPANISH ENGLISH SPANISH ENGLISH SPANISH Estimate 6.2271 7.0257 6.6881 5.8405 6.6938 6.1968 6.5042 5.9501 6.8908 6.4853 7.2152 6.8362 6.1652 5.5157 Standard Error DF t Value Pr > |t| 0.05570 823 0.05617 823 0.06528 823 0.06650 823 0.04022 514 0.03653 514 0.07671 514 0.06441 514 0.06963 514 0.06811 514 0.07502 514 0.08588 514 0.09758 514 0.07203 514 111.79 119.07 107.63 87.82 166.43 169.63 84.79 92.38 98.96 95.21 96.17 79.60 63.18 76.57 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Differences in MLU as a Function of Story in the UNIQUE Condition Type 3 Tests of Fixed Effects Effect Grade Language Story(Language) Grade X Language Grade X Story(Language) Num Den DF DF F Value Pr > F 3 1 8 3 24 813 475 475 475 475 49.21 <.0001 112.14 <.0001 3.13 0.0018 2.88 0.0356 1.08 0.3626 Least Squares Means Effect Grade Grade Grade Grade Language Language Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) IISG___ COND_LANG Story 1 2 3 K ENGLISH SPANISH ENGLISH ENGLISH ENGLISH ENGLISH ENGLISH SPANISH SPANISH SPANISH SPANISH SPANISH BDFF BDAF FGTD FOHO OFTM BDFF BDAF FGTD FOHO OFTM Estimate 6.1587 6.5954 6.9392 5.9467 6.6706 6.1494 6.5744 6.5535 6.7627 6.5282 6.9340 6.0141 6.0765 6.2827 5.9963 6.3774 Standard Error DF t Value Pr > |t| 0.06345 813 0.05721 813 0.06305 813 0.06472 813 0.03939 475 0.03990 475 0.08684 475 0.08537 475 0.08735 475 0.09335 475 0.08700 475 0.08873 475 0.08734 475 0.09142 475 0.09090 475 0.08737 475 97.07 115.28 110.06 91.88 169.35 154.11 75.70 76.76 77.42 69.93 79.70 67.78 69.57 68.72 65.97 72.99 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Differences in MLU-W Between Unique and Retell Conditions in English and Spanish Kindergarten Condition English - Retell English - Unique Spanish - Retell Spanish - Unique ER EU SR SU 1.0000 0.6886 0.3374 0.2172 0.6886 1.0000 0.2675 0.3074 0.3374 0.2675 1.0000 0.4853 0.2172 0.3074 0.4853 1.0000 Grade 1 Condition English - Retell English - Unique Spanish - Retell3 Spanish - Unique4 ER EU SR SU 1.0000 0.6397 0.3077 0.3474 0.6397 1.0000 0.2105 0.2932 0.3077 0.2105 1.0000 0.4115 0.3474 0.2932 0.4115 1.0000 Grade 2 Condition English - Retell English - Unique Spanish - Retell3 ER EU SR SU 1.0000 0.6003 0.3938 0.2515 0.6003 1.0000 0.3453 0.2636 0.3938 0.3453 1.0000 0.5560 Spanish - Unique4 0.2515 0.2636 0.5560 1.0000 Grade 3 Condition English - Retell English - Unique Spanish Retell Spanish - Unique ER EU SR SU 1.0000 0.6746 0.3525 0.2802 0.6746 1.0000 0.2998 0.3843 0.3525 0.2998 1.0000 0.5467 0.2802 0.3843 0.5467 1.0000 Tests of Effects of Grade, Condition, and Language on MLU-W Type 3 Tests of Fixed Effects Effect WS Effects1 Grade Grade X WS Effects1 Num Den DF DF F Value Pr > F 3 1826 3 831 50.46 <.0001 69.88 <.0001 2.43 0.0096 9 1826 Estimates Effect Condition ME Language ME Condition X Language Standard Estimate Error 0.07838 0.9943 -0.00030 DF t Value Pr > |t| 1.45 12.27 -0.01 0.1463 <.0001 0.9951 0.05393 1826 0.08106 1826 0.04948 1826 Contrasts Effect Grade X Condition Grade X Language Grade X Lang X Cond G-3 vs Othr X Cond K,1,2 differences X Cond Num Den DF DF F Value Pr > F 3 3 3 1 2 831 831 831 831 831 60.04 <.0001 82.92 <.0001 61.01 <.0001 111.13 <.0001 45.31 <.0001 Least Squares Means MLU-W Grade 1 1 1 1 2 2 2 2 3 3 3 3 K K K K Cond ENGLISH-RETELL ENGLISH-UNIQUE SPANISH-RETELL SPANISH-UNIQUE ENGLISH-RETELL ENGLISH-UNIQUE SPANISH-RETELL SPANISH-UNIQUE ENGLISH-RETELL ENGLISH-UNIQUE SPANISH-RETELL SPANISH-UNIQUE ENGLISH-RETELL ENGLISH-UNIQUE SPANISH-RETELL SPANISH-UNIQUE Standard Estimate Error 6.5194 6.4714 5.9517 5.8310 6.8897 6.7295 6.4877 6.4427 7.2082 7.1884 6.8364 6.7030 6.1691 6.2409 5.5225 5.6643 DF t Value 85.98 88.57 92.79 64.48 99.05 97.25 96.12 83.60 96.19 91.18 80.39 88.83 63.53 70.63 77.26 72.59 Pr > |t| <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.07582 1826 0.07306 1826 0.06414 1826 0.09043 1826 0.06956 1826 0.06920 1826 0.06749 1826 0.07707 1826 0.07494 1826 0.07883 1826 0.08504 1826 0.07546 1826 0.09711 1826 0.08837 1826 0.07148 1826 0.07803 1826 Effects of Story on Number of Different Words in the UNIQUE Condition Type 3 Tests of Fixed Effects Effect Grade Language Story(Language) Grade X Language Grade X Story(Language) Num Den DF DF F Value Pr > F 3 1 8 3 24 813 475 475 475 475 55.92 <.0001 14.71 0.0001 12.63 <.0001 0.39 0.7593 0.45 0.9895 Least Squares Means Effect Grade Grade Grade Grade Language Language Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) Story(Language) IISG___ COND_LANG Story 1 2 3 K ENGLISH SPANISH ENGLISH ENGLISH ENGLISH ENGLISH ENGLISH SPANISH SPANISH SPANISH SPANISH SPANISH BDFF BDAF FGTD FOHO OFTM BDFF BDAF FGTD FOHO OFTM Estimate 72.3868 79.5070 85.7571 61.9169 72.8284 76.9555 68.7055 62.7983 81.5639 78.8562 72.2178 72.8540 66.0010 82.3692 84.4135 79.1398 Standard Error DF t Value Pr > |t| 1.3557 813 1.3429 813 1.4188 813 1.3376 813 0.9062 475 0.8295 475 1.9972 475 1.9701 475 2.0086 475 2.1437 475 2.0005 475 1.8465 475 1.8214 475 1.8916 475 1.8918 475 1.8138 475 53.40 59.20 60.44 46.29 80.37 92.78 34.40 31.88 40.61 36.78 36.10 39.46 36.24 43.54 44.62 43.63 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 Correlations Between Measures from Retell and Unique Conditions for English mluw_er ndw_er ntw_er ncw_er wpm_er rubric_er mluw_eu MLUW ndw_eu NDW ntw_eu NTW ncw_eu NCW wpm_eu WPM rubric_eu RUBRIC si_eu SI 0.68682 0.40087 0.43902 0.40296 <.0001 <.0001 <.0001 <.0001 656 656 656 656 0.45853 0.76778 0.74294 0.67119 <.0001 <.0001 <.0001 <.0001 656 656 656 656 0.44522 0.64015 0.73701 0.70515 <.0001 <.0001 <.0001 <.0001 656 656 656 656 0.37840 0.54724 0.67018 0.70146 <.0001 <.0001 <.0001 <.0001 656 656 656 656 0.46868 0.52766 0.49527 0.43301 <.0001 <.0001 <.0001 <.0001 656 656 656 656 0.50429 0.61103 0.61198 0.53174 <.0001 <.0001 <.0001 <.0001 656 656 656 656 0.46206 0.45045 0.42033 0.36042 <.0001 <.0001 <.0001 <.0001 656 656 656 656 0.35781 <.0001 656 0.52590 <.0001 656 0.40945 <.0001 656 0.35111 <.0001 656 0.86580 <.0001 656 0.41759 <.0001 656 0.34913 <.0001 656 si_er 0.39947 0.40246 <.0001 <.0001 656 656 0.61823 0.42970 <.0001 <.0001 656 656 0.54157 0.34955 <.0001 <.0001 656 656 0.45297 0.29152 <.0001 <.0001 656 656 0.43987 0.40223 <.0001 <.0001 656 656 0.67350 0.38973 <.0001 <.0001 656 656 0.39036 0.45080 <.0001 <.0001 656 656 Correlations Between Measures for Retell and Unique Conditions for Spanish mluw_sr ndw_sr ntw_sr ncw_sr wpm_sr rubric_sr mluw_su MLUW ndw_su NDW ntw_su NTW ncw_su NCW wpm_su WPM rubric_su RUBRIC si_su SI 0.57522 0.32133 0.41878 0.34864 <.0001 <.0001 <.0001 <.0001 637 637 637 637 0.38437 0.67831 0.67018 0.63394 <.0001 <.0001 <.0001 <.0001 637 637 637 637 0.41966 0.59262 0.70379 0.64180 <.0001 <.0001 <.0001 <.0001 637 637 637 637 0.33167 0.55704 0.63648 0.66559 <.0001 <.0001 <.0001 <.0001 637 637 637 637 0.30974 0.38577 0.34474 0.35725 <.0001 <.0001 <.0001 <.0001 637 637 637 637 0.29121 0.49872 0.43934 0.46234 <.0001 <.0001 <.0001 <.0001 637 637 637 637 0.29096 0.35304 0.31315 0.33155 <.0001 <.0001 <.0001 <.0001 637 637 637 637 0.16261 <.0001 636 0.31570 <.0001 636 0.23948 <.0001 636 0.24197 <.0001 636 0.56097 <.0001 636 0.23884 <.0001 636 0.23378 <.0001 636 si_sr 0.20251 0.30550 <.0001 <.0001 637 637 0.46051 0.39086 <.0001 <.0001 637 637 0.38562 0.31708 <.0001 <.0001 637 637 0.36682 0.32126 <.0001 <.0001 637 637 0.28745 0.32431 <.0001 <.0001 637 637 0.49148 0.33588 <.0001 <.0001 637 637 0.25403 0.38787 <.0001 <.0001 637 637 Correlations Between English and Spanish Narrative Measures in the Retell Condition mluw_sr ndw_sr ntw_sr ncw_sr wpm_sr rubric_sr mluw_er MLUW ndw_er NDW ntw_er NTW ncw_er NCW wpm_er WPM rubric_er RUBRIC si_er SI 0.44056 0.18332 0.21792 0.27164 <.0001 <.0001 <.0001 <.0001 518 518 518 518 0.19432 0.37353 0.34737 0.42281 <.0001 <.0001 <.0001 <.0001 518 518 518 518 0.27458 0.42404 0.45429 0.51308 <.0001 <.0001 <.0001 <.0001 518 518 518 518 0.29127 0.45827 0.49967 0.56337 <.0001 <.0001 <.0001 <.0001 518 518 518 518 0.12431 0.09585 0.10530 0.16780 0.0046 0.0292 0.0165 0.0001 518 518 518 518 0.27036 0.26292 0.27628 0.31755 <.0001 <.0001 <.0001 <.0001 518 518 518 518 0.23981 0.18519 0.18460 0.24962 <.0001 <.0001 <.0001 <.0001 518 518 518 518 0.10861 0.0134 518 0.09969 0.0233 518 0.12239 0.0053 518 ...

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Imaging Facial Physiology for the Deception DetectionP. Tsiamyrtzis1 J. Dowdall1 D. Shastri1 I. Pavlidis1 M.G. Frank2(1) Department of Computer Science, Univ. of Houston, Houston, TX 77204 tsiamyrt@stat.umn.edu,djshastr@bayou.uh.edu, jbdowdal, ipav

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Physiology-Based Face Recognition: A Novel ApproachPradeep BuddharajuObjectiveMotivation Face recognition technology is touchless, highly automated, and most natural since it coincides with the mode of recognition that we as humans employ on our

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Imaging Breathing Rate in the CO2 Absorption BandJin Fei, Zhen Zhu, Ioannis Pavlidis Department of Computer Science, University of Houston, Houston, TX 77204I. IntroductionObjective Measure human breathing rate through thermal imaging in real tim

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Breathing Air Flow Pattern Analysis in Thermal ImagingJin Fei, Ioannis Pavlidis Department of Computer Science, University of Houston, Houston, TX 77204I. IntroductionObjective Breathing air flow pattern analysis and its individuality in thermal

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Computer 101Technology Assistance Division SWTC has joined with the Sheriff's Association of Texas to go on-site at locations around Texas to train law enforcement personnel in basic computer skills and the elements of the Microsoft Office Suite in

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May/June 2008THE PORTOF HOUSTONENSURiNg a SaFE & SEcURE PORTA bi-monthly publication.Contents26 Secure ToursTrips aboard Sam Houston safe, secure, funMay/June 2008COVER STORYFEATURES10 Beyond the Fenceline: Increased Safety and Secu

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Selected Provisions in Homeland Security Act of 2002 (P.L. 107-296, enacted 11/25/02) of Particular Interest to UniversitiesThe Act authorizes a transfer of activities from 22 agencies to the new Department of Homeland Security, including the Immig

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Technology Assistance Division Project SummaryMiddle Rio Grand Development Council ProjectThe vast Rio Grande region of Texas presents a number of public safety challenges. Dispersed resources and diverse constituencies make effective and timely co

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RDT&E Division Project SummaryRFID-based Property and Evidence Management SystemRFID (Radio Frequency Identification) technology has been applied to many fields with the main purpose of locating and tracking objects or people through Ultrahigh Fr

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Testbed Division Project SummaryAutomated Contraflow Traffic Management for Urban and Coastal Area EvacuationThe Advanced Concepts Business Unit of SAIC contacted SWTC after Hurricane Rita because it has received internal funding to further devel

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Appendix AProject PlanTexas Internet Grid for Research and Education (TIGRE)Rice University, Texas A & M University, Texas Tech University, University of Houston, and The University of Texas at AustinRevision 1.2 March 3, 200621. Introduc

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University of Houston HiPCAT Institutional UpdateFebruary, 2005 Resources & Services TLC2 successfully completed the letter of credit and contract to procure two dark fiber rings for several institutions in Houston and LEARN and most likely also NL

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Winners year 2008 1st Place: Kevin Shen, Bellaire High School (Houston, TX). 2nd Place: Qianning Zhang, Bellaire High School (Houston, TX) 3rd Place: Sachin Subramanian, Bellaire High School (Houston, TX) Winners year 2007 1st Place: Sailesh Prabhu f

arsenic.txt

U. Houston - TLC - 2

25.942778,97.518889,026.035,97.786111,126.050278,97.710833,126.064167,97.760833,126.081667,97.836111,126.091111,97.955833,026.093056,97.617778,026.095278,98.201389,126.125833,97.938889,026.148889,97.910278,026.155833,97.961667,026.164167,9

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U. Houston - TLC - 2

TLC2 OverviewLennart Johnsson Director Cullen Prof of Computer Science, Mathematics, and Electrical and Computer EngineeringTLC2 Missionto foster and support collaborative multidisciplinary research, education and training in Computational Scienc

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TIGRE StatusSeptember, 2006TIGRE Overview Integrate resources of Texas institutions to enhance research and educational capabilities Foster academic, private, and government partnerships State of Texas funded Texas Internet Grid for Research a

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