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Are all questions created equal?: Factors that influence cloze question difficulty.

This study explores the factors influencing the difficulty of multiple-choice cloze questions and aims to improve the assessment of students' reading comprehension. The research examines the impact of parts of speech, question length, and question location on question performance. The findings provide valuable insights for educators and researchers in the field of reading assessment.

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Are all questions created equal?: Factors that influence cloze question difficulty.

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  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. BeckCarnegie Mellon University Society for the Scientific Study of Reading – July 2006 Funding: National Science Foundation

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

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

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

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

  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. 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. 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. 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. Effect of Parts of Speech < < < Nouns Verbs Adjectives Adverbs (p < 0.001) (p < 0.05) (p < 0.001)

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

  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. 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 “Henry read his _______ under the tree.” (cup, dog,book, hair) “Sally had to _______ her lips when she heard the news.” (lamp,purse, beautiful, magnificent)  more common POS = easier  less common POS = harder

  15. 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 “Henry read his _______ under the tree.” (cup, dog, book, hair) “Sally had to _______ her lips when she heard the news.” (lamp, purse, beautiful, magnificent) (noun)  more choices with correct POS = harder (verb) • fewer choices with correct POS = easier

  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. 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. 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. 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. 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. 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. 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. Additional Slides x

  24. Terms in Model

  25. Developmental Trends in Learning Parts of Speech

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

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

  28. Effect of Part of Speech*Interpretation: positive Beta means student is more likely to answer question correctly

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