Second Language Learning with Affective Factors and Deep Neural Networks Methods
Annotatsiya
The goal of the proposed study is to specify second language learning (SLL) and affective factors among variables of five language skills analyzed by using different Deep Neural Networks methods in freshman class of higher education students. Assessment levels of five language skills of the students have been showed by the percentages and whether there was significant difference between genders or five skills of learners analyzed by statistically by using DNN and FCNN. Questionnaire is evaluated to quantify students' affective factors as knowledge convenient for DNN input. Survey consists of four parts. First part, from 1 to 19 questions, deals with educational background of students with open-ended questions. From second to fourth part, the questionnaire is improved by using Likert's five-level response scale from 1, representing strong agreement to 5, representing strong disagreement. Major factors of these parts are affective factors; motivation, personality and attitude. Second part from 20 to 29 deals with personality, third part from 30 to 47 deals with motivation and last part from 48 to 54 deals with attitude. Data is transferred from survey answers to an excel sheet and converted to the numbers then analyzed with DNN methods. The importance of the study is to highlight right language skill by the help of affective factors for the development of developing second language learning.