درخت حوزه‌های تخصصی

The present study aims to identify the effectiveness of cognitive rehabilitation on different types of attention in the third-grade students with specific learning disabilities (reading) considering moderating role of gender. As a quasi-experimental research, the study uses pretest-posttest design with control group. Façade testing, a helpful tool to identify the reading difficulties and dyslexia in elementary school, is performed to determine the research sample among the third-grade students in Tabriz. The students who score above average survey response rate are identified as specific learning-disability learners (reading). Among these students, 60 (four groups of 15) are selected by simple random sampling method and randomly divided into two experimental groups (two groups of 15, one group female students and the other male students who received cognitive rehabilitation training program) and two control groups. Cognitive rehabilitation training is delivered through Smart Sound training software (8-level computer-based cognitive rehabilitation in reading disorder). Attention is also measured by focused/sustained attention and scattered attention test designed in Sina Institute. To analyze the data, multivariate analysis of covariance is used. The results indicate cognitive rehabilitation is effective in focused/sustained attention, scattered attention, and reaction time in specific learning-disability students and gender does not have a moderating effect. Accordingly, cognitive rehabilitation does not have meaningful effect in the increase of focused attention, scattered attention and reaction time in male and female students.

The present study aimed to investigate the association between academic self-actualization, self-regulation, and classroom socio-psychological climate with students' academic motivation with the mediation of achievement emotions in high school students. The statistical population of this cross-sectional correlational study consisted of all high school students in Tehran in 2019, with 485 students selected using multistage cluster sampling. The research instruments included the Academic Motivation Scale (AMS), Self-Actualization Scale (SAS), Motivated Strategies for Learning Questionnaire (MSLQ), Classroom Socio-psychological climate, and Achievement Emotions Questionnaire (AEQ). Data were analyzed by structural equation modeling (SEM) in SPSS and Lisrel software. The results showed that academic self-actualization, achievement emotions, and classroom socio-psychological climate had a positive association with academic motivation in the high school students (p<0.01). Moreover, there was a significant association between self-regulation and academic motivation among the students (p<0.01). The findings indicated that academic self-actualization, self-regulation, and classroom socio-psychological climate had an effect on students' academic motivation, with achievement emotions acting as a mediator (p<0.01).

Online learning platforms have become commonplace in modern society today, but high dropout rates and decrement students’ performance still require more attention in such online learning environments. The purpose of this research is to accelerate the identification of students at risk of academic failure in order to take appropriate corrective action. Therefore, we have proposed model to achieve this goal and ultimately improve the performance of students and faculty. Then, for early prediction of students at risk of academic failure, the short-term memory neural network (LSTM) and the widely used support vector algorithm have been used to analyze students’ time based behaviors using data from the University of Tehran e-learning system. To demonstrate the optimal performance of the predictive algorithm, we compared the LSTM network with the support vector algorithm with different evaluation criteria. The results show that the use of LSTM network for early prediction of students at risk provides higher predictive accuracy compared to the support vector machine algorithm. In this research, our method in predicting students’ performance with LSTM network has achieved 94% accuracy and with support vector machine algorithm has achieved 88% accuracy. In addition, the Area Under the Curve (AUC) was 0.936 and 0.882, respectively, using the LSTM algorithm and the support vector machine. Therefore, according to the obtained results, it can be seen that our proposed algorithm has an important and effective contribution to improving the final performance of teachers and students during the course.