1. 1 Are all questions created equal?: Factors that influence cloze question difficulty. Brooke Soden Hensler Carnegie Mellon University (starting graduate school at Florida Center for Reading Research this Fall) Joseph E. Beck Carnegie Mellon University Funding: National Science Foundation Society for the Scientific Study of Reading – July 2006

2. 2 Why Look at Multiple Choice Cloze Questions? Multiple Choice Cloze are widely used assessments of comprehension Problem: outcome measure is typically binary (little information about student). Goal: use multiple choice cloze questions to… More accurately assess students Track student reading development Better understand what makes cloze questions hard

3. 3 Project LISTEN’s Computer Reading Tutor (Mostow & Aist, 2001) Automated Students use throughout year Accompanying paper standardized test scores (pre & post)

4. 4 Student is reading a story aloud to the Reading Tutor…

5. 5 A question appears… *Reading Tutor reads both Question and Response Choices. (Mostow, et al., 2004)

6. 6 Student resumes reading story aloud to the Reading Tutor…

7. 7 Reading Tutor Advantages Well-specified & unbiased question construction (randomly generated) Questions automatically administered, scored, & recorded Longitudinal collection over school year Large N (students & questions)

8. 8 How many Q’s from Whom? Data Description 81,175 Questions 1042 Students 11 = Median number of questions answered (Many students infrequent users of tutor) 2001-02 & 2002-03 School years Diverse population in Pittsburgh area

9. 9 Research Questions Is a particular part of speech (e.g., nouns, verbs, etc.) more difficult for students? If nouns are learned first (Gentner, 1982; Golinkoff, et al., 2000), might students be more proficient at answering noun questions? Which factors influence question difficulty? How can we better assess students using multiple choice cloze questions? Vocabulary researchers have given partial credit for correct part of speech (e.g., Schwanenflugel, et al., 1997)

10. 10 Approach Build logistic regression model to predict individual question performance Terms in model: student identity, part of speech of answer, properties of question (e.g., question length) Advantages of modeling approach Simultaneously estimates impact of question properties and student proficiency on question performance Makes use of all ~80k questions

11. 11 Effect of Parts of Speech NounsVerbs Adverbs Adjectives (p < 0.001) <<< (p < 0.05)

12. 12 Effect of Parts of Speech NounsVerbs Adverbs Adjectives (p < 0.001) easier harder <<< (p < 0.001)(p < 0.05)

13. 13 Impact of other Part of Speech terms Difficulty Significance Most Common p < 0.01 Part of Speech # of Choices  p < 0.001 with Answer’s POS “Sally had to _______ her lips when she heard the news.” (cloud, purse, holds, magnificent) “Henry read his _______ under the tree.” (cup, dog, book, hair)

14. 14 Difficulty Significance Most Common p < 0.01 Part of Speech # of Choices  p < 0.001 with Answer’s POS “Henry read his _______ under the tree.” (cup, dog, book, hair) “Sally had to _______ her lips when she heard the news.” (lamp, purse, beautiful, magnificent) Impact of other Part of Speech terms  less common POS = harder  more common POS = easier

15. 15 Difficulty Significance Most Common p < 0.01 Part of Speech # of Choices  p < 0.001 with Answer’s POS “Henry read his _______ under the tree.” (cup, dog, book, hair) “Sally had to _______ her lips when she heard the news.” (lamp, purse, beautiful, magnificent) Impact of other Part of Speech terms  fewer choices with correct POS  more choices with correct POS = harder = easier (verb) (noun)

16. 16 Impact of other terms Difficulty Significance Question  p < 0.001 Length Deletion p < 0.001 Location “We can _______ the stars in the sky despite the bright city lights around us.” (at, with, most, see) “They rode their _______.” (farmer, bikes, play, blue)

17. 17 Impact of other terms Difficulty Significance Question  p < 0.001 Length Deletion p < 0.001 Location “We can _______ the stars in the sky despite the bright city lights around us.” (at, with, most, see) “They rode their _______.” (farmer, bikes, play, blue)  longer = harder  shorter = easier

18. 18 Impact of other terms Difficulty Significance Question  p < 0.001 Length Deletion p < 0.001 Location “We can _______ the stars in the sky despite the bright city lights around us.” (at, with, most, see) “They rode their _______.” (farmer, bikes, play, blue)  blank earlier = harder  blank later = easier

19. 19 Using model to assess student reading comprehension Model estimates Beta parameter for each student Represents how well student did at answering cloze questions (controlling for difficulty factors) Should correlate with external comprehension measure Compare Beta vs. percent correct for predicting WRMT comprehension composite* Student Beta: r =.644, p <.001 Percent correct: r =.507, p <.001 Reliability of difference in correlations, p <.01 Also provides check on validity of regression model *N = 465, 1 extreme outlier was eliminated from analyses.

20. 20 Conclusions Length of question, location of deleted word, and part of speech of correct answer affect question difficulty. Logistic regression is a strong choice for analyzing cloze data. Multiple-choice cloze questions can assess a student at a more accurate level than current practice.

21. 21 Questions? Nominated for Best Paper Award: Soden Hensler, B., Beck, J. E. (2006). Better student assessing by finding difficulty factors in a fully automated comprehension measure. Intelligent Tutoring Systems. Brooke Soden Hensler bsodenhensler@gmail.com Joseph E. Beck joseph.beck@gmail.com Project LISTEN & The Reading Tutor http://www.cs.cmu.edu/~listen/

22. 22 References Gentner, D. (1981). Some interesting differences between verbs and nouns. Cognition and Brain Theory, 4(2). Golinkoff, R.M., Hirsh-Pasek, K., Bloom, L., Smith, L. B., Woodward, A. L., Akhtar, N., Tomasello, M., & Hollich, G. (2000). Becoming a word learner: A debate on lexical acquisition. New York: Oxford University Press. Mostow, J. & Aist, G. (2001). Evaluating tutors that listen: An overview of Project LISTEN. In K. Forbus & P. Feltovich (Eds.), Smart Machines in Education (169 - 234) Menlo Park, CA: MIT/AAAI Press. Mostow, J., Beck, J. E., Bey, J., Cuneo, A., Sison, J., Tobin, B. & Valeri, J. (2004). Using automated questions to assess reading comprehension, vocabulary, and effects of tutorial interventions. Technology, Instruction, Cognition and Learning, 2, p. 97-134 Schwanenflugel, P.J., Stahl, S. A., & McFalls, E. L. (1997). Partial word knowledge and vocabulary growth during reading comprehension. Journal of Literacy Research, 29(4).

23. 23 Additional Slides x

24. 24 Terms in Model FactorsDescription of Term Part of Speech Simplified part of speech classification of the correct answer as Noun, Verb, Adjective, Adverb, or Function Word. Most Common Part of Speech Whether or not the correct answer’s POS is the most common POS the word could take on. POS ConfusabilityThe number of POS the correct answer can take on. Level of Difficulty 4 Levels of Difficulty based on frequency in English or special annotation. Student IdentityUnique Identification for each student. Covariates Question Length Number of characters of the cloze question and the corresponding response choices. Deletion Location Proportion of the sentence that is before the blank (location of word deletion). # Choices with Answer's POS Probability that the student could have answered the question using only part of speech information.

25. 25 Developmental Trends in Learning Parts of Speech

26. 26 Developmental Trends in Learning Parts of Speech p <.001 p =.71 p =.99 p =.52p =.64

27. 27 Syntactic Awareness p =.48 p =.73 p =.01 p =.02 p <.001

28. 28 Effect of Part of Speech *Interpretation: positive Beta means student is more likely to answer question correctly Part of Speech Noun<Verb<Adjective<Adverb< Function Words Beta0.39 0.29 0.19 0.12 (comparison point) Significance p <.001 ---