2,448
Views
1
CrossRef citations to date
0
Altmetric
Articles

Spoken propositional idea density, a measure to help second language English speaking students: A multicentre cohort study

ORCID Icon, , , ORCID Icon, ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon show all

References

  • Aluísio S, Cunha A, Scarton C. 2016. Evaluating progression of Alzheimer’s disease by regression and classification methods in a narrative language test in Portuguese. Paper presented at the 12th International Conference on Computational Processing of the Portuguese Language, July 13–15, Tomar, Portugal.
  • Bloomfield A, Wayland SC, Rhoades E, Blodgett A, Linck J, Ross S. 2010. What makes listening difficult? Factors affecting second language listening comprehension. College Park (MD): University of Maryland.
  • Bloomfield A, Wayland S, Blodgett A, Linck J. 2011. Factors related to passage length: implications for second language listening comprehension. Paper presented at the 33rd Annual Meeting of the Cognitive Science Society; July 20–23; Boston, MA.
  • Bradbury NA. 2016. Attention span during lectures: 8 seconds, 10 minutes, or more? Adv Physiol Educ. 40(4):509–513.
  • Bradley AP. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognit. 30(7):1145–1159.
  • Brown C, Snodgrass T, Kemper SJ, Herman R, Covington MA. 2008. Automatic measurement of propositional idea density from part-of-speech tagging. Behav Res Methods. 40(2):540–545.
  • Chaudron C, Loschky L, Cook J. 1994. Second language listening comprehension and lecture note-taking. In: Flowerdew J, editor. Academic listening: Research perspectives. Cambridge (UK): Cambridge University Press; p. 75–92.
  • Cross SS, Harrison RF, Kennedy RL. 1995. Introduction to neural networks. The Lancet. 346(8982):1075–1079.
  • de Pauli STZ, Kleina M, Bonat WH. 2020. Comparing artificial neural network architectures for Brazilian stock market prediction. Ann Data Sci. 7(4):613–628.
  • DeFrancesco C, Perkins K. 2012. An analysis of the proposition density, sentence and clause types, and non-finite verbal usage in two college textbooks. In: Plakhotnik MS, Nielsen SM, Pane DM, editors. Proceedings of the 11th annual college of education & GSN research conference. Miami: Florida International University; p. 20–25.
  • Farias ST, Chand V, Bonnici L, Baynes K, Harvey D, Mungas D, Simon C, Reed B. 2012. Idea density measured in late life predicts subsequent cognitive trajectories: implications for the measurement of cognitive reserve. J Gerontol Series B Psychol Sci Social Sci. 67(6):677–686.
  • Kemper S, Jackson JD, Cheung H, Anagnopoulos CA. 1993. Enhancing older adults’ reading comprehension. Discourse Proc. 16(4):405–428.
  • Khan I, Kulkarni A. 2013. Knowledge extraction from survey data using neural networks. Procedia Comput Sci. 20:433–438.
  • Kintsch W, Keenan J. 1973. Reading rate and retention as a function of the number of propositions in the base structure of sentences. Cogn Psychol. 5(3):257–274.
  • Kintsch W, Van Dijk TA. 1978. Toward a model of text comprehension and production. Psychol Rev. 85(5):363–394.
  • Kintsch W, Kozminsky E, Streby WJ, Mckoon G, Keenan JM. 1975. Comprehension and recall of text as a function of content variables. J Verbal Learn Verbal Behav. 14(2):196–214.
  • Lau E, Sun L, Yang Q. 2019. Modelling, prediction and classification of student academic performance using artificial neural networks. SN Appl Sci. 1(9):1–10.
  • Lunn AM, Manfrin A. 2021. Pedagogic Interest Group: a novel and proven collaborative, adhocracy research group structure. MedEdPublish. 10:182.
  • Miller JR, Kintsch W. 1980. Readability and recall of short prose passages: a theoretical analysis. J Exper Psychol. 6:335.
  • Riley KP, Snowdon DA, Desrosiers MF, Markesbery WR. 2005. Early life linguistic ability, late life cognitive function, and neuropathology: findings from the Nun Study. Neurobiol Aging. 26(3):341–347.
  • Rupp AA, Garcia P, Jamieson J. 2001. Combining multiple regression and CART to understand difficulty in second language reading and listening comprehension test items. Int J Testing. 1(3):185–216.
  • Simon H. 1999. Neural networks: a comprehensive foundation. Upper Saddle River, NJ: Prentice Hall.
  • Sirts K, Piguet O, Johnson M. 2017. Idea density for predicting Alzheimer’s disease from transcribed speech. arXiv preprint arXiv:1706.04473.
  • Snowdon DA, Kemper SJ, Mortimer JA, Greiner LH, Wekstein DR, Markesbery WR. 1996. Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life: findings from the Nun Study. JAMA. 275(7):528–532.
  • Srividya M, Mohanavalli S, Bhalaji N. 2018. Behavioral modeling for mental health using machine learning algorithms. J Med Syst. 42(5):88–12.
  • Stine EA, Hindman J. 1994. Age differences in reading time allocation for propositionally dense sentences. Aging Cogn. 1:2–16.
  • Stine EL, Wingfield A, Poon LW. 1986. How much and how fast: rapid processing of spoken language in later adulthood. Psychol Aging. 1(4):303–311.
  • Tang Z-H, Liu J, Zeng F, Li Z, Yu X, Zhou L. 2013. Comparison of prediction model for cardiovascular autonomic dysfunction using artificial neural network and logistic regression analysis. PLoS One. 8(8):e70571.
  • Tauroza S, Allison D. 1990. Speech rates in British English. Appl Ling. 11(1):90–105.
  • Thomas G, Kenny LC, Baker PN, Tuytten R. 2017. A novel method for interrogating receiver operating characteristic curves for assessing prognostic tests. Diagn Progn Res. 1(1):1–9.
  • Tokan F, Türker N, Yıldırım T. 2006. ROC analysis as a useful tool for performance evaluation of artificial neural networks. Paper presented at the 16th International Conference on Artificial Neural Networks; September 10–14; Athens, Greece.
  • Woolf K. 2020. Differential attainment in medical education and training. BMJ. 368:m339.