Abstract
One of the main goals of elementary school STEM teachers is that their students write their own explanations. However, analyzing answers to question that promotes writing is difficult and time consuming, so a system that supports teachers on this task is desirable. For elementary school students, the extension of the texts, is a basic component of several metrics of the complexity of their answers. In this paper we attempt to develop a set of predictors of the length of written responses to open questions. To do so, we use the history of hundreds elementary school students exposed to open questions posed by teachers on an online STEM platform. We analyze four different context-based personalized predictors. The predictors consider for each student the historical impact on the student answers of a limited number of keywords present on the question. We collected data along a whole year, taking the data of the first semester to train our predictors and evaluate them on the second semester. We found that with a history of as little as 20 questions, a context based personalized predictor beats a baseline predictor.
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References
Stevens, R.: The question as a measure of efficiency in instruction: a critical study classroom practice. Teach. Coll. Contrib. Educ. 48 (1912)
Emig, J.: Writing as a mode of learning. Coll. Compos. Commun. 28, 122–128 (1977)
Vygotssky, L.: Thought and Language. MIT Press (1986)
Urquhart, V.: Using Writing in Math to Deepen Student Learning. McREL (2009)
Rowe, M.: Wait time: slowing down may be a way of speeding up! J. Teach. Educ. 43–49 (1986)
Shahrill, M.: Review of effective teacher questioning in mathematics classrooms. Int. J. Human. Social Sci. 3(17) Sept (2013)
McNamara, D.; Graesser, A.: Coh-Metrix: an automated tool for theoretical and applied natural language processing. In: Applied Natural Language Processing: Identification, Investigation and Resolution, pp. 188–205. IGI Global (2011)
Blosser, P.: How to ask the right questions. The National Science Teachers Association (2000)
Tofade, T., Elsner, J., Haines, S.: Best practice strategies for effective use of questions as a teaching tool. Am. J. Pharma. Educ. 77(7) Article 155 (2013)
Chin, C.: Teacher questioning in science classrooms: what approaches stimulate productive thinking? J. Res. Sci. Teach. 44(6), 815–843, Aug (2007)
Scott, P.: Teacher talk and meaning making in science classrooms: A Vygotskian analysis and review. Studies in Science Education, 32, pp. 45--80 (1998)
Araya, R., Aljovin, E.: The effect of teacher questions on elementary school students’ written responses on an online STEM platform. In: Andre, T. (ed.) Advances in Human Factors in Training, Education, and Learning Sciences, vol. 596, pp. 372–382. Springer, Cham (2017)
Araya, R., Gormaz, R., Bahamondez, M., Aguirre, C., Calfucura, P., Jaure. P., Laborda, C.: ICT supported learning rises math achievement in low socio economic status schools. LNCS, vol. 9307, pp 383–388 (2015)
Winograd, K.: What fifth graders learn when they write their own math problems. Educ. Leader. 64(7), 64–66 (1992)
Pennington, J.; Socher, R.; Manning, C.: GloVe: global vectors for word representation. In: Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543 (2014). http://www.aclweb.org/anthology/D14–1162
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Funding from PIA-CONICYT Basal Funds for Centers of Excellence Project FB0003 is gratefully acknowledged and to the Fondef D15I10017 grant from CONICYT.
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Araya, R., Jiménez, A., Aguirre, C. (2018). Context-Based Personalized Predictors of the Length of Written Responses to Open-Ended Questions of Elementary School Students. In: Sieminski, A., Kozierkiewicz, A., Nunez, M., Ha, Q. (eds) Modern Approaches for Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 769. Springer, Cham. https://doi.org/10.1007/978-3-319-76081-0_12
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