Cong, Qing

Dong, Xinfa

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ABSTRACTS Psychiatria Danubina, 2022; Vol. 34, Suppl. 1, pp 26-888  S366  Table 1. Questionnaire survey results of two groups of subjects before and after the experiment Group Before experiment After the experiment t P Experience group 24.6±4.2 29.5±4.4 0.127 0.006 Control group 24.7±3.8 24.8±3.6 0.845 0.712 t 0.674 0.284 - - P 0.773 0.005 - -  It can be seen from Table 1 that before the experiment, the P value of the questionnaire score t test of the experimental group and the control group was 0.773, which was greater than the significance level of 0.05. It is considered that the data difference was not statistically significant, that is, the severity of communication adaptation disorder symptoms of the two groups before the experiment was at the same level and comparable. After the experiment, the P value of the questionnaire score t test of the experimental group and the control group was 0.006. Far less than the significance level, it is considered that the data difference is statistically significant. Specifically, after the experiment, the average scores of the experimental group and the control group are 29.5 and 24.8 respectively. The former is significantly higher than the latter, indicating that the use of intelligent logistics system will aggravate the symptom severity of communication adaptation disorder. Conclusions: On the one hand, the promotion and application of intelligent logistics information system has brought more convenience to people’s life. People can know the location and status of logistics packages directly through mobile phone application without going to logistics outlets to inquire or call, and realize the unmanned management of package communities. But on the other hand, it also reduces the opportunities for people to communicate in life. For people with communication adaptation disorder, the reduction of communication opportunities may further aggravate their symptoms. In order to verify this conjecture, a comparative experiment is designed. The experimental results show that the average score of the questionnaire on communication adaptation disorder of the experimental group required to use the intelligent logistics information system after the experiment is 29.5, which is significantly higher than that of the control group, which proves that the use of the intelligent logistics system will aggravate the severity of the symptoms of communication adaptation disorder. The analysis found that this is because after using this tool, patients’ communication opportunities in daily life are further reduced, and their psychological views on the importance of communication are more negative. Some people even think that even if there is no communication in the future, it will not affect their life, so they relax their attention and Practice on communication, resulting in further deterioration of symptoms. Acknowledgement: The research is supported by: Chongqing Social Science Cultivation Project. No. 2021py46; Project Name: Research on Coordination and Linkage Mechanism of Emergency Material Support in Chengdu Chongqing Double City Economic Circle. *  *  *  *  * COMPUTER TEACHING STRATEGY BASED ON BIG DATA ENVIRONMENT FROM THE PERSPECTIVE OF EDUCATIONAL PSYCHOLOGY Xinfa Dong1 & Qing Cong2 1Student Affairs, Yellow River Conservancy Technical Institute, Kaifeng 475004, China 2Academic Affairs, Yellow River Conservancy Technical Institute, Kaifeng 475004, China Background: The research object of educational psychology is the learning process of learners and the teaching process of teachers. It is a collection of accumulated knowledge, wisdom and intuitive ideas of psychological methods and theories in the field of education. Teachers should master this discipline to effectively solve the problems in peacetime teaching. Its most intuitive application occurs in the typical two-way interaction between teachers and students in the teaching process. It requires teachers to carefully study all aspects including teaching content, teaching methods, teaching cases, teaching means and teaching environment. In addition, educational psychology also requires teachers to have a necessary understanding of students’ psychological state and activities, so as to further improve the effectiveness of teaching. Educational psychology holds that students, as independent individuals, have different thinking abilities and are in different psychological environments. With the approach of the era of the Internet of all things, the application of big data technology in the computer field is becoming more and more extensive, ABSTRACTS Psychiatria Danubina, 2022; Vol. 34, Suppl. 1, pp 26-888 S367 and the knowledge of big data is integrated into computer teaching. However, the knowledge and technology of big data are quite different from the traditional computer teaching content. For example, the former requires students to have more statistical and mathematical foundation, and needs more programming practice. These characteristics lead to an obvious increase in students’ learning difficulties. Therefore, we can use the method of educational psychology to analyze the psychological state and thinking mode of different students and find a more effective teaching method, so as to improve the teaching quality of computer specialty in our country. Objective: To explore the possibility and specific feasible measures of applying educational psychology methods to the teaching of big data and computer science-related majors, so as to improve the employment competitiveness and personal professional ability of graduates of related majors in China. Participants and methods: A nationally representative university in terms of teachers, teaching equipment and educational financial support was selected from domestic universities, and 204 college students majoring in big data and computer science who were willing to participate in the experiment were randomly selected as the research objects. The research objects were evenly divided into intervention group and control group. First, count the basic information of the two groups of students and confirm that there is no significant difference between their basic information. Then carry out the teaching experiment to let the two groups of students accept the teaching of big data and computer courses, and the teaching content is completely consistent. However, during the teaching of the experimental group, teachers are required to start from the perspective of educational psychology. Observe the psychological state of students and reasonably adjust the teaching progress and teaching content according to their psychological state. If necessary, carry out separate targeted teaching for some students, and give more encouragement when students encounter difficulties in learning. The control group was only taught in the traditional way. The teaching experiment lasts for one semester, and teachers are required to conduct a 100-point test on students’ curriculum professional ability before and after the experiment. After the experiment, the teachers will provide the quantitative scores of students’ abilities related to various courses to the research team in combination with the test data. Results: In the study, the measurement data were displayed in the form of mean ± standard deviation for t-test, and the counting data were displayed in the form of number or proportion of number for Chi-square test. The significance level was taken as 0.05. See Table 1 for the scores of the two groups of students after the experiment.  Table 1. Statistical results of professional ability scores of two groups of students after the experiment Comparison items Intervention group Control group P Change value (%) Big data knowledge 15.2±4.1 9.2±3.2 0.002 66.2 Big data processing 25.3±4.8 16.9±3.6 0.003 49.7 Fundamentals of computer 13.2±3.1 11.8±3.3 0.015 11.9 Computer programming 29.8±4.5 27.2±5.4 0.073 9.6  It can be seen from Table 1 that the scores of the two groups of students in various majors are different after the experiment. The P value of the quantitative score t-test of the two groups of students in computer programming is 0.073, which is greater than the significance level. It is considered that the data difference is not statistically significant, while the two groups of students in big data knowledge, big data processing. The P value of the quantitative score difference significance test of the three aspects of computer foundation is less than 0.05, which means that the data difference is significant. Specifically, the average scores of the experimental group on big data knowledge, big data processing and computer foundation are 15.2, 25.3 and 13.2 respectively, which are 66.2%, 49.7% and 11.9% higher than those of the control group. Conclusions: In view of the problems that students’ learning difficulty increases and learning efficiency decreases after integrating big data technology into the current university computer professional courses, this research is based on the analysis of the current teaching situation of computer major. Combined with the characteristics of big data teaching and educational psychology methods, a comparative teaching experiment is designed. The experimental results show that after the experiment, the quantitative score difference significance test P value of the experimental group in three aspects of big data knowledge, big data processing and computer foundation is less than 0.05, and it is considered that the data difference is significant. Specifically, the average scores of the experimental group based on big data knowledge, big data processing and computer were 15.2, 25.3 and 13.2 respectively, which were 66.2%, 49.7% and 11.9% higher than those of the control group. The data shows that using educational psychology to analyze students’ learning psychology and thinking state in the teaching process, and taking this as the basis to adjust the teaching content and teaching methods will help to improve the learning effect of students majoring in big data and computer technology. ABSTRACTS Psychiatria Danubina, 2022; Vol. 34, Suppl. 1, pp 26-888  S368 *  *  *  *  * ANALYSIS AND EXPLORATION OF FARMERS’ PSYCHOLOGY IN AGRICULTURAL SCIENCE AND TECHNOLOGY EXTENSION ACTIVITIES Jianhan He, Yuhua Wu & Zengyu Gao Institute of Cereal and Oil Crops, Hebei Academy of Agriculture and Forestry Sciences, Shijiazhuang 050000, China Background: With the development of science and technology in China, more and more scientific and technological products and applications are being applied in the field of agriculture. However, due to the overall low level of education, the deep influence of traditional ideas and the poor ability to accept and adapt to new things, the popularization of agricultural science and technology in some agricultural areas of China is greatly hindered. The root cause of these phenomena is a variety of bad psychology of farmers, among which the more typical psychological states are conformity psychology, conservative psychology, stability seeking psychology and contradictory psychology. The influence way of conformity psychology is that if most residents around farmers do not use scientific and technological products, they will probably choose not to apply. The influence mode of conservative psychology is that even if technicians have explained the application mode of scientific and technological products, farmers are reluctant to believe in the reliability of new technologies if they do not see successful use and economic benefits. It refers to the fear of using new technologies to reduce the yield of new products or reduce the quality of cultivation. Ambivalence means that although they want to adopt new technologies, they are skeptical about the effectiveness of new technologies and hesitate to take action. These psychological phenomena of farmers have certain objective rationality, but the extension personnel of agricultural science and technology products can also use psychology to improve the success rate of product promotion, which is the focus of this research. Objective: To explore the common psychology of farmers in the promotion of agricultural science and technology products and services and its impact on the promotion work, so as to put forward some countermeasures to improve the service promotion efficiency of agricultural science and technology products, so as to provide some useful decision-making basis for improving the level of agricultural science and technology and the output and quality of agricultural products in China. Objects and methods: Select a town with serious obstacles in the promotion of agricultural science and technology products, and then select two villages with roughly the same promotion level of agricultural science and technology products, farmers’ living habits, living environment, overall economic level, farmland area and the distribution of planted crop varieties. Select 100 villagers from each of the two villages to form an intervention group and a conventional group. Firstly, the relevant data statistics of the two groups are carried out, including the income level, gender, age, education level, family population, marriage, etc. After confirming that the differences of these data between the two groups are not statistically significant. The agricultural science and technology extension personnel shall be trained on the psychological status of farmers, so that the extension personnel can know the common psychological status of farmers in the face of these new technology products, and introduce some effective coping methods. After one year, the settlement rate of agricultural science and technology products and the annual output value of agricultural products of the two groups of farmers in recent two years (i.e., one year before and one year after training) were collected from the relevant departments of the local government. Results: The characteristics of all measurement types in the study were displayed by means of mean ± standard deviation, and t-test was conducted. The significance level of difference was set to 0.05. One year after the training, the settlement rate and annual output value of agricultural science and technology products of the two groups of farmers are listed in Table 1.  Table 1. Statistical results of experimental data of two groups of farmers Statistical indicators Within one year before training Within one year after training Settlement rate (%) Annual output value/￥ Settlement rate (%) Annual output value/￥ Intervention group 6 26541±1254 85 43116±1762 General group 7 26262±1466 53 27245±1537 t - 0.648 - 0.127 P - 0.952 - 0.001 

COMPUTER TEACHING STRATEGY BASED ON BIG DATA ENVIRONMENT FROM THE PERSPECTIVE OF EDUCATIONAL PSYCHOLOGY

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