Abstract
This study examines links between natural language processing and its application in math education. Specifically, the study examines language production and math success in an on-line, blended learning math program. Unlike previous studies that have relied on correlational analyses between linguistic knowledge tests and standardized math tests or compared math success between proficient and non-proficient speakers of English, this study examines the linguistic features of students’ language production while e-mailing a virtual pedagogical agent. In addition, the study examines a number of non-linguistic features such as grade and objective met within the program. The findings indicate that linguistic features related to the use of standardized language use explain around 8% of math success. These linguistic features outperform non-linguistic features.
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LeFevre, J.A., Fast, L., Skwarchuk, S.L., Smith-Chant, B.L., Bisanz, J., Kamawar, D., Penner-Wilger, M.: Pathways to math: longitudinal predictors of performance. Child Dev. 81(6), 1753–1767 (2010). doi:10.1111/j.1467-8624.2010.01508.x
Vukovic, R.K., Lesaux, N.K.: The relationship between linguistic skills and arithmetic knowledge. Learn. Individ. Diff. 23, 87–91 (2013). doi:10.1016/j.lindif.2012.10.007
Martiniello, M.: Language and the performance of English-language learners in math word problems. Harv. Educ. Rev. 78(2), 333–368 (2008)
Adams, T.L.: Reading math: more than words can say. Read. Teach. 56(8), 786–795 (2003)
MacGregor, M., Price, E.: An exploration of aspects of language proficiency and algebra learning. J. Res. Math. Educ. 30, 449–467 (1999). doi:10.2307/749709
Alt, M., Arizmendi, G.D., Beal, C.R.: The relationship between math and language: academic implications for children with specific language impairment and English language learners. Lang. Speech Hear. Serv. Sch. 45(3), 220–233 (2014). doi:10.1044/2014_LSHSS-13-0003
Hampden-Thompson, G., Mulligan, G., Kinukawa, A., Halle, T.: Math Achievement of Language-Minority Students During the Elementary Years. U.S. Department of Education, National Center for Education Statistics, Washington, DC (2008)
Martiniello, M.: Linguistic complexity, schematic representations, and differential item functioning for English language learners in math tests. Educ. Assess. 14(3–4), 160–179 (2009). doi:10.1080/10627190903422906
Crossley, S.A., Liu, R., McNamara, D.: Predicting math performance using natural language processing tools. In: Proceedings of the 7th International Learning Analytics and Knowledge (LAK) Conference. ACM, New York (2017)
Hernandez, F.: The relationship between reading and math achievement of middle school students as measured by the Texas assessment of knowledge and skills. Doctoral dissertation (2013)
Cummins, J.: Linguistic interdependence and the educational development of bilingual children. Rev. Educ. Res. 49(2), 222–251 (1979)
Ardasheva, Y., Tretter, T.R., Kinny, M.: English language learners and academic achievement: revisiting the threshold hypothesis. Lang. Learn. 62(3), 769–812 (2012). doi:10.1111/j.1467-9922.2011.00652.x
Mosqueda, E., Maldonado, S.I.: The effects of English language proficiency and curricular pathways: Latina/os’ math achievement in secondary schools. Equity Excell. Educ. 46(2), 202–219 (2013). doi:10.1080/10665684.2013.780647
Wang, J., Goldschmidt, P.: Opportunity to learn, language proficiency, and immigrant status effects on math achievement. J. Educ. Res. 93(2), 101–111 (1999). doi:10.1080/00220679909597634
Chen, F., Chalhoub-Deville, M.: Differential and long-term language impact on math. Lang. Test. 33(4), 577–605 (2015)
Khachatryan, G.A., Romashov, A.V., Khachatryan, A.R., Gaudino, S.J., Khachatryan, J.M., Guarian, K.R., Yufa, N.V.: Reasoning Mind Genie 2: an intelligent tutoring system as a vehicle for international transfer of instructional methods in mathematics. Int. J. Artif. Intell. Educ. 24(3), 333–382 (2014). doi:10.1007/s40593-014-0019-7
Kyle, K., Crossley, S.A.: Automatically assessing lexical sophistication: indices, tools, findings, and application. TESOL Q. 49(4), 757–786 (2015). doi:10.1002/tesq.194
Crossley, S.A., Kyle, K., McNamara, D.S.: The tool for the automatic analysis of text cohesion (TAACO): automatic assessment of local, global, and text cohesion. Behav. Res. Methods 48(4), 1227–1237 (2016)
Crossley, S.A., Kyle, K., McNamara, D.S.: Sentiment analysis and social cognition engine (SEANCE): an automatic tool for sentiment, social cognition, and social order analysis. Behav. Res. Methods (in press). doi:10.3758/s13428-016-0743-z
Thorndike, E.L., Lorge, I.: The Teacher’s Wordbook of 30,000 Words. Teachers College: Bureau of Publications: Columbia University, New York (1944)
KuÄŤera, H., Francis, N.: Computational Analysis of Present-Day American English. Brown University Press, Providence (1967)
Brown, G.D.: A frequency count of 190,000 words in the London-Lund Corpus of English conversation. Behav. Res. Methods Instr. Comput. 16(6), 502–532 (1984). doi:10.3758/BF03200836
Brysbaert, M., New, B.: Subtlexus: American word frequencies (2009). http://subtlexus.lexique.org
The British National Corpus, Version 3 (BNC XML Edition) 2007 Distributed by Oxford University Computing Services on Behalf of the BNC Consortium. http://www.natcorp.ox.ac.uk/
Davies, M.: The 385+ million word Corpus of Contemporary American English (1990–2008+): design, architecture, and linguistic insights. Int. J. Corpus Linguist. 14, 159–190 (2009). doi:10.1075/ijcl.14.2.02dav
Coltheart, M.: The MRC psycholinguistic database. Q. J. Exp. Psychol. 33(4), 497–505 (1981)
Kuperman, V., Stadthagen-Gonzalez, H., Brysbaert, M.: Age-of-acquisition ratings for 30,000 English words. Behav. Res. Methods 44(4), 978–990 (2012). doi:10.3758/s13428-012-0210-4
Brysbaert, M., Warriner, A.B., Kuperman, V.: Concreteness ratings for 40 thousand generally known English word lemmas. Behav. Res. Methods 46(3), 904–911 (2014). doi:10.3758/s13428-013-0403-5
Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995). doi:10.1145/219717.219748
Kiss, G.R., Armstrong, C., Milroy, R., Piper, J.: An associative thesaurus of English and its computer analysis. In: Aitken, A.J., Bailey, R.W., Hamilton-Smith, N. (eds.) The Computer and Literary Studies, pp. 153–165. Edinburgh University Press, Edinburgh (1973)
Nelson, D.L., McEvoy, C. L., Schreiber, T.A.: The University of South Florida word association, rhyme, and word fragment norms (1998). http://www.usf.edu/FreeAssociation/
Balota, D.A., Yap, M.J., Cortese, M.J., Hutchison, K.I., Kessler, B., Loftis, B., et al.: The English lexicon project. Behav. Res. Methods 39, 445–459 (2007)
Bird, S., Klein, E., Loper, E.: Natural Language Processing with Python. O’Reilly Media, Inc., Sebastopol (2009)
R Core Team: R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2014). http://www.R-project.org/
Grömping, U.: Relative importance for linear regression in R: the package relaimpo. J. Stat. Softw. 1(1) (2006)
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This research was supported in part by NSF 1623730. Opinions, conclusions, or recommendations do not necessarily reflect the views of the NSF.
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Crossley, S., Kostyuk, V. (2017). Letting the Genie Out of the Lamp: Using Natural Language Processing Tools to Predict Math Performance. In: Gracia, J., Bond, F., McCrae, J., Buitelaar, P., Chiarcos, C., Hellmann, S. (eds) Language, Data, and Knowledge. LDK 2017. Lecture Notes in Computer Science(), vol 10318. Springer, Cham. https://doi.org/10.1007/978-3-319-59888-8_28
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