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• W3120342801 abstract "Language makes human communication possible. Apart from everyday applications, language can provide insights into individuals’ thinking and reasoning. Machine-based analyses of text are becoming widespread in business applications, but their utility in learning contexts are a neglected area of research. Therefore, the goal of the present work is to explore machine-assisted approaches to aid in the analysis of students’ written compositions. A method for extracting common topics from written text is applied to 78 student papers on technology and ethics. The primary tool for analysis is the Latent Dirichlet Allocation algorithm. The results suggest that this machine-based topic extraction method is effective and supports a promising prospect for enhancing classroom learning and instruction. The method may also prove beneficial in other applied applications, like those in clinical and counseling practice.&#x0D; References&#x0D; &#x0D; Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet Allocation. Journal of Machine Learning Research 3, 993-1022.&#x0D; Bruner, J. (1990). Acts of meaning. Cambridge, MA: Harvard University Press.&#x0D; Chen, K. Y. M., & Wang, Y. (2007). Latent dirichlet allocation. http://acsweb.ucsd.edu/~yuw176/ report/lda.pdf.&#x0D; Chung, C. K., & Pennebaker, J. W. (2008). Revealing dimensions of thinking in open-ended self-descriptions: An automated meaning extraction method for natural language. Journal of research in personality, 42(1), 96-132.&#x0D; Feldman, S. (1999). NLP meets the Jabberwocky: Natural language processing in information retrieval. Online Magazine, 23, 62-73. Retrieved from: http://www.onlinemag.net/OL1999/ feldmann5.html&#x0D; Mishlove, J. (2010). https://www.youtube.com/watch?v=0XTDLq34M18 (Accessed June 12, 2018).&#x0D; Ostrowski, D. A. (2015). Using latent dirichlet allocation for topic modelling in twitter. In Semantic Computing (ICSC), 2015 IEEE International Conference (pp. 493-497). IEEE.&#x0D; Pennebaker, J. W. (2004). Theories, therapies, and taxpayers: On the complexities of the expressive writing paradigm. Clinical Psychology: Science and Practice, 11(2), 138-142.&#x0D; Pennebaker, J.W., Boyd, R.L., Jordan, K., & Blackburn, K. (2015). The development and psychometric properties of LIWC 2015. Austin, TX: University of Texas at Austin.&#x0D; Pennebaker, J. W., Chung, C. K., Frazee, J., Lavergne, G. M., & Beaver, D. I. (2014). When small words foretell academic success: The case of college admissions essays. PLoS ONE, 9(12), e115844.&#x0D; Pennebaker, J. W., & King, L. A. (1999). Linguistic styles: Language use as an individual difference. Journal of Personality and Social Psychology, 77(6), 1296-1312.&#x0D; Recchia, G., Sahlgren, M., Kanerva, P., & Jones, M. N. (2015). Encoding sequential information in semantic space models: Comparing holographic reduced representation and random permutation. Computational intelligence and neuroscience, 2015, 1-18.&#x0D; Salzmann, Z. (2004). Language, Culture, and Society: An Introduction to Linguistic Anthropology (3rd ed). Westview Press.&#x0D; Schank, R. C., Goldman, N. M., Rieger III, C. J., & Riesbeck, C. (1973). MARGIE: Memory analysis response generation, and inference on English. In IJCAI, 3, 255-261.&#x0D; Taraban, R., Marcy, W. M., LaCour Jr., M. S., & Burgess II, R. A. (2017). Developing machine-assisted analysis of engineering students’ ethics course assignments. Proceedings of the American Society of Engineering Education (ASEE) Annual Conference, Columbus, OH. https://www.asee.org/public/conferences/78/papers/19234/view.&#x0D; Taraban, R., Marcy, W. M., LaCour, M. S., Pashley, D., & Keim, K. (2018). Do engineering students learn ethics from an ethics course? Proceedings of the American Society of Engineering Education – Gulf Southwest (ASEE-GSW) Annual Conference, Austin, TX. http://www.aseegsw18.com/papers.html.&#x0D; Taraban, R., & Marshall, P. H. (2017). Deep learning and competition in psycholinguistic research. East European Journal of Psycholinguistics, 4(2), 67-74.&#x0D; Weizenbaum, J. (1966). ELIZA—a computer program for the study of natural language communication between man and machine. Communications of the ACM, 9(1), 36-45.&#x0D; Winograd, T. (1972). Understanding natural language. New York: Academic Press.&#x0D;" @default.
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• W3120342801 date "2018-06-30" @default.
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• W3120342801 title "Finding a Common Ground in Human and Machine-Based Text Processing" @default.
• W3120342801 doi "https://doi.org/10.29038/eejpl.2018.5.1.tar" @default.
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