algorithm

This paper introduces a set of English grammar symbols that the author has developed to enhance students’ understanding and consequently, application of the English grammar rules. A pretest-posttest control-group design was carried out in which the samples were students in two girls’ senior high schools (N=135, P ≤ 0.05) divided into two groups: the Treatment which received grammar lessons with grammar symbols; and the Control which received grammar lessons without the symbols. The experiment lasted for 30 hours spanned in three months. The statistical test revealed a significant higher gain scores for the Treatment group. Thus, the author strongly recommends using these symbols (or similar ones with the same characteristics) at least for two reasons. Firstly, students do not have to memorize all of them (72 tense symbols and 50 other symbols). That is, with just a few rules to learn, and then applying the existing algorithm, other symbols are easily shaped. Secondly, using these symbols enables teachers and students to have a general idea as to what to expect next because several grammatical rules and formulae can be predicted in advance.

The purpose of the present study was to validate the Adaboost machine learning and probit regression in the prediction of Management's overconfidence at present and in the future. It also compares the predicted models obtained during the years 2012 to 2017. The samples of the research were the companies admitted to the Tehran Stock Exchange, (financial data of 1292 companies/year in total). Data collection in the theoretical part of the study benefitted from the content analysis international research paper in library method and for calculating the data's Excel software was used, and in order to test the research hypotheses, Matlab 2017 and Eviews10.0 were used. The empirical findings demonstrate that The Adaboost's algorithm nonlinear prediction model represents the highest power in learning and prediction (performance of this model) the managerial over-confidence for this year and the following year, proved to be better than the probit regression prediction model.