Software Construction for the Estimation of the Linguistic Level and Test Difficulty
Keywords:Linguistic level software, test, linguistic level, text, readability rates
For this survey a new linguistic level evaluation and test measurement software has been created. This particular software has assisted in detection matters regarding readability and it has also allowed text readability measurement with the use of common grading systems, including readability measurement formulas. This system accepts various examination topics, which are classified according to the level of difficulty and where all kinds of tests are represented and it controls all the linguistic level and difficulty goals. The choice of topics and its inclusion is conducted with the sampling method. During this experimental application of our software, a field survey was conducted during which not only university students but also a lot of internet users were called to evaluate this programme.
. Ajina, A., Laouiti, M., & Msolli, B. (2016). Guiding through the Fog: Does annual report readability reveal earnings management? Research in International Business and Finance.
. Bengio, Y., Ducharme, R., Vincent, P., Janvin, C., De Mulder, W., Bethard, S., … Mikolov, T. (2015). Statistical Language Models Based on Neural Networks. Computer Speech & Language.
. Bormuth, J. R. (2006). Readability: A New Approach. Reading Research Quarterly.
. Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery.
. Catanzaro, B., Sundaram, N., & Keutzer, K. (2008). Fast support vector machine training and classification on graphics processors.
. Caylor, J. S., Sticht, T. G., Fox, L. C., & Ford, J. P. (1973). Methodologies for Determining Chapelle Reading Requirements of Military Occupational Specialties. In Human Resources Research Organization, Alexandria, VA.
. Chapelle, O., Haffner, P., & Vapnik, V. N. (1999). Support vector machines for histogram-based image classification. IEEE Transactions on Neural Networks.
. Chih-Wei Hsu, Chih-Chung Chang, C.-J. L. (2008). A Practical Guide to Support Vector Classification. In BJU international.
. Contreras, A., García-Alonso, R., Echenique, M., & Daye-Contreras, F. (1999). The SOL formulas for converting SMOG readability scores between health education materials written in Spanish, English, and French. Journal of Health Communication.
. Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning.
. Dale, E., & Chall, J. S. (1948). A formula for predicting readability: Instructions. Educational Research Bulletin.
. Dale, E., & Chall, J. S. (1949). The concept of readability. Elementary English.
. Daniela D, M. V., & Maria Celeste Pirozzoli, A. (2013). Application of a Readability Score in Informed Consent forms for Clinical Studies. Journal of Clinical Research & Bioethics.
. Mikros, G. K. (2013). Authorship Attribution and Gender Identification in Greek Blogs. Methods and Applications of Quantitative Linguistics.
. Mikros, G. K., & Argiri, E. K. (2007). Investigating topic influence in authorship attribution. CEUR Workshop Proceedings.
. Mikros, George, & Perifanos, K. (2015). Authorship Attribution in Greek Tweets Using Author’s Multilevel N-Gram Profiles. American Printer.
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