NATIONAL CONFERENCE ON MANAGEMENT, PSYCHOLOGY AND BEHAVIORAL SCIENCES | Year:2024 | دوره:6 |Papers Count:3

The purpose of this study was to predict academic procrastination based on personality traits, metacognitive strategies and intelligence beliefs in high school students in Kaboudar Ahang. The research method is descriptive-correlation. The statistical population of this study consisted of all high school students in Kaboudar Ahang city in the academic year 2020-2021, numbering 3611. The statistical sample was based on the rule of Tabachnik and Fidel (2007) 110 people who were randomly selected. Solomon and Roth Bloom (1984), Neo (1992) personality traits, Dawson and McInry (2004) cognitive and metacognitive strategies questionnaires, and Brown and Ryan (2003) IQ were used to collect data. According to the results of regression test, 45% of the changes in the criterion variable, academic procrastination, can be explained by the predictor variable (p <0. 01). According to the results of the correlation test, there was a significant positive relationship between neuroticism and academic procrastination with a correlation coefficient of-0. 584 (r =-0. 584) at the 99% level and between the trait of conscientiousness and academic procrastination with a correlation coefficient of 0. 308. 0-(r =-0. 308) There is a significant negative relationship at 99% level. There is a significant negative relationship between metacognitive strategies and academic procrastination with a correlation coefficient of-0. 399 (r =-0. 399) at the 99% level. There is a significant negative relationship between incremental IQ and academic procrastination with a correlation coefficient of-0. 236 (r =-0. 236) at the 99% level and between innate IQ and academic procrastination with a correlation coefficient of 0. 302 (= 0. 302). r) There is a significant positive relationship at 99% level.

Purpose: To study the research done in the field of artificial intelligence and its relationship with the diagnosis of autism neurobehavioral disorder using artificial intelligence tools. Method: The research method is a systematic review using the Prisma model. The statistical population was 147 researches retrieved from reliable databases (Jama Khabar Medical Journal, Science Direct, Google Scholar, PubMed, and Academic Jihad Scientific Information Center database), which were reviewed after screening 10 researches. Findings: 100% successful diagnosis of autism neurobehavioral disorder in autistic children over 4 years of age through artificial intelligence tools resulted in a revolution. In the conducted researches, researchers have not yet been able to propose an accurate method for the reliable diagnosis of this disorder in children under 4 years of age. Originality and value: This research or review of the studies conducted regarding the direct connection of artificial intelligence tools and algorithms in autism diagnosis is presented and in this research tools and algorithms are introduced and reviewed. Conclusion: Considering the expansion of the science of artificial intelligence in the field of psychology and the recognition of diseases with neurobehavioral disorders, including autism, it can be said that the path ahead is very satisfactory and in the very near future, children suspected of having autism can be diagnosed with tools Artificial intelligence started the identification and follow-up process as quickly as possible.

The present research considers the behavior of the seller as a dependent variable and identifies several factors that result in the formation of the salesperson’, s behavior. This study was carried out by the exploratory approach, the qualitative methodology and Grounded theory. To this end, we used open interviews with three questions with the content of the factors affecting the salesperson’, s behavior, the seller's response and behavior in engagement with the customers. The findings show that factors such as personal creativity, interest to the job, level of knowledge and awareness, purposefulness, experiences, level of earnings, skills, type of rules applied in the organization, attractiveness of organization and industry, customer performance, support of colleagues, sellers motivation and working environment can be considered as influencing factors on salesperson’, s behavior in the context of technical, personality, social and environmental variables and presented as a model. By carrying out interviews, coding and obtaining the contents, pattern of seller behavior is determined. Accordingly, the behavior of the salesperson is directly affected by technical, personality and social factors. Based on this model, factors affecting the salesperson’, s behavior are influenced by the "environmental variables", which indirectly affect the seller's behavior to some extent.