Abstract
Text mining is a research field that has developed different techniques to find relevant information in unstructured data, such as texts. This article tries to verify whether the automatic extraction of information from texts can help students in reading comprehension activities. Two studies involving control and experimental groups were carried out with students of 5th and 8th grade in order to evaluate whether a particular text mining tool could effectively help students improve their scores in a reading task. We also wanted to verify if the use of the tool by students in different grades could yield different outcomes. Results showed that the mining tool helped 5th graders to improve their scores, but it was not so effective for 8th graders. These results indicate the potential of the proposed tool especially for learners who are still developing their reading skills.
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References
Allahyari, M., et al.: A brief survey of text mining: classification, clustering and extraction techniques. In: Proceedings of Conference on Knowledge Discovery and Data Mining - KDD, Halifax, Canada (2017)
Beers, K.: When Kids Can’t Read. What Teachers Can Do. Heinemann, Portsmouth (2003)
Block, C.C., Pressley, M.: Comprehension Instruction: Research-Based Best Practices. Solving Problems in the Teaching of Literacy. Guilford Press, New York (2002)
Chang, K.E., Sung, Y.T., Chen, S.F.: Learning through computer-based concept mapping with scaffolding aid. J. Comput. Assist. Learn. 17(1), 21–33 (2001)
Chein, M., Mugnier, M.-L.: Graph-Based Knowledge Representation: Computational Foundations of Conceptual Graphs, 1st edn. Springer, London (2008)
Cordón, L.A., Day, J.D.: Strategy use on standardized reading comprehension tests. J. Educ. Psychol. 88(2), 288–295 (1996)
Di Giacomo, D., Cofini, V., Di Mascio, T., Rosita, C.M., Fiorenzi, D., Gennari, R., Vittorini, P.: The silent reading supported by adaptive learning technology: influence in the children outcomes. Comput. Hum. Behav. 55(1), 1125–1130 (2016)
Guastello, E.F., Beasley, T.M., Sinatra, R.C.: Concept mapping effects on science content comprehension of low-achieving inner-city seventh graders. Remedial Spec. Educ. 21(6), 356–364 (2000)
Hall, T., Strangman, N.: Graphic organizers. National Center on Accessing the General Curriculum, Wakefield (2002)
He, W.: Examining students’ online interaction in a live video streaming environment using data mining and text mining. Comput. Hum. Behav. 29(1), 90–102 (2013). https://doi.org/10.1016/j.chb.2012.07.020
Hsu, J.-L., Chou, H.-W., Chang, H.-H.: EduMiner: using text mining for automatic formative assessment. Expert Syst. Appl. 38(4), 3431–3439 (2011)
Hyerle, D.: Visual Tools for Transforming Information Into Knowledge. SAGE, Thousand Oaks (2008)
Jamshidifarsani, H., Garbaya, S., Lim, T., Blazevic, P., Ritchie, J.M.: Technology-based reading intervention programs for elementary grades: an analytical review. Comput. Educ. 128(1), 427–451 (2019)
Jenner, J.: A bridge to reading and writing literacy: developing oral language skills in young children. Pacific Educator (2003)
Manoli, P., Papadopoulou, M.: Graphic organizers as a reading strategy: research findings and issues. Creative Educ. 3, 348–356 (2012). https://doi.org/10.4236/ce.2012.33055
Marzano, R.J., Pickering, D.J., Pollock, J.E.: Classroom Instruction that Works: Research-Based Strategies for Increasing Student Achievement. Association for Supervision and Curriculum Development, Alexandria (2001)
Nandhini, K., Balasundaram, S.R.: Improving readability through extractive summaries for learners with reading difficulties. Egyptian Inform. J. 14(1), 195–204 (2013)
National Reading Panel: Teaching children to read: an evidence-based assessment of the scientific research literature on reading and its implications for reading instruction. NIH Publication. No. 00-4769 (2000) https://doi.org/10.1002/ppul.1950070418
Nesbit, J.C., Adesope, O.O.: Learning with concept and knowledge maps: a meta-analysis. J. Rev. Educ. Res. 76(3), 413–448 (2006)
Reader, W., Hammond, N.: Computer-based tools to support learning from hypertext: concept mapping tools and beyond. Comput. Educ. 12, 99–106 (1994)
Reategui, E., Epstein, D., Lorenzatti, A., Klemann, M., Sobek: a text mining tool for educational applications. In: International Conference on Data Mining, Las Vegas, USA, pp. 59–64 (2011)
Roman-Sanchez, J.M.: Self-regulated learning procedure for university students: the meaningful text-reading strategy. Electron. J. Res. Educ. Psychol. 2(1), 113–132 (2004)
Romero, C., Cazorla, M., Buzón, O.: Meaninful learning using concept maps as a learning strategy. J. Technol. Sci. 7(3), 313–332 (2017)
Schenker, A.: Graph-theoretic techniques for web content mining. Ph.D. thesis, University of South Florida, Tampa, FL (2003)
Shatnawi, S., Gaber, M.M., Cocea, M.: Text stream mining for massive open online courses: review and perspectives. Syst. Sci. Control Eng. 2(1), 664–676 (2014)
Sweeny, S.M.: Writing for the instant messaging and text messaging generation: using new literacies to support writing instruction. J. Adolesc. Adult Literacy 54(2), 121–130 (2010)
Tankersley, K.: The Threads of Reading: Strategies for Literacy Development. ASDC, Alexandria (2003)
Tovani, C.: I Read But I Don’t Get It. Comprehension Strategies for Young Adolescent Readers. Stenhouse Publishers, Portsmouth (2000)
Trend, D.: The End of Reading: From Gutenberg to Grand Theft Auto. Peter Lang Publishing, New York (2010)
Warschauer, Mark: Laptops and Literacy: Learning in the Wireless Classroom. Teachers College Press, New York (2006)
Wiecha, J.L., Sobol, A.M., Peterson, K.E., Gortmaker, S.L.: Household television access: associations with screen time, reading and homework among youth. Ambul. Pediatr. 1(5), 244–251 (2001)
Xu, Y., Reynolds, N.: Using text mining techniques to analyze students’ written responses to a teacher leadership dilemma. Int. J. Comput. Theory Eng. 4(4), 575 (2012)
Yu, C.H., DiGangi, S.A., Jannasch-Pennell, A.: Using text mining for improving student experience management in higher education. In: Tripathi, P., Mukerji, S. (eds.) Cases on Innovations in Educational Marketing: Transnational and Technological Strategies, pp. 196–213. Information Science Reference, Hershey (2011). https://doi.org/10.4018/978-1-60960-599-5.ch012
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Reategui, E., Epstein, D., Bastiani, E., Carniato, M. (2020). Can Text Mining Support Reading Comprehension?. In: Gennari, R., et al. Methodologies and Intelligent Systems for Technology Enhanced Learning, 9th International Conference. MIS4TEL 2019. Advances in Intelligent Systems and Computing, vol 1007 . Springer, Cham. https://doi.org/10.1007/978-3-030-23990-9_5
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