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Dr. Mohammad Hassan Azimi, Dr. Shahnaz Khademizadeh, , ,
Volume 0, Issue 0 (5-2022)
Volume 0, Issue 0 (5-2022)
Abstract
Objective: Learning through mobile phone is a type of distance learning that takes place in many situations with social interaction and content through personal electronic devices. The purpose of the present study was to investigate the factors affecting the behavioral intention of learning graduate students of Shahid Chamran University of Ahvaz through mobile phones.
Method: The current research is applied in terms of its purpose and survey in terms of its execution
Findings: The results showed that all the structures of the theory of planned behavior and the technology acceptance model have an effect on the behavioral intention of the graduate students of Shahid Chamran University of Ahvaz to learn through mobile phones.
Conclusion: by considering the characteristics and needs of users and their applications in the virtual education system, buy or rent powerful servers for Providing virtual education services and taking into account the use of new educational technologies in evaluating the performance of faculty professors will improve the level of using educational systems while learning through mobile phones.
Abolghasem Karimi, Alireza Rousta, Majid Ahmadi, Saeid Daniali,
Volume 0, Issue 0 (5-2022)
Volume 0, Issue 0 (5-2022)
Abstract
Information search strategies change rapidly in continuous mode, and successive searches gradually teach the user what can be retrieved inside the system. Users must be able to interact with the system appropriately to achieve their information needs. The information revolution is not a new issue, but understanding information seeking behavior is important and vital in this era. In future projects, extraordinary efforts should be made to create intelligent systems. Therefore, success or failure in this matter is not the responsibility of the new information technology, but it requires awareness of human needs and his behavior to obtain information. Therefore, in the companies based in Pardis Technology Park, according to the technology-oriented approach and innovation, it is necessary to pay attention to the issue of market information learning and joint innovative capacities to improve the organization's performance. Generally, because the environment and technology are constantly and rapidly changing, and specifically, the demographic characteristics of customers and their expectations undergo changes and transformations, Certainly, in order to have a successful presence in the national and international arena and their dynamism, as well as greater adaptation to environmental changes, these companies should aim to activate their potentials through improving the performance of the organization. In fact, this research seeks to answer the question of what is the role of explaining the effect of market information learning ability and joint innovative capacities on the organization's performance with an information approach?
Methods and Materoal
This research is based on the applied purpose and according to the descriptive type of correlation, according to the model, we should use the structural equation modeling method. The statistical community of the research is top and middle managers of companies that are members of Pardis Technology Park in Tehran. Using Cochran's formula, 384 people were selected as a sample and 400 questionnaires were distributed by available sampling method and 387 questionnaires were collected in 50 companies. The data collection method of this research was done by library method and using books and articles, and questionnaire was used in the field method. To measure the variable of market information learning ability with 7 items, internal learning ability with 10 items, relational learning ability with 7 items and administrative innovation with 6 items from Teece et al., 1997; Weerawardena et al., 2015; and to measure the variable of service innovation with 4 items and organization performance with 5 items from Thakur & Hale, 2013 questionnaire; and to measure the variable of shared innovative capacities with 3 items, Oly Ndubisi et al., 2019 questionnaire was used.
Resultss and Discussion
Demographic characteristics of the sample include gender, age and education. In the method of structural equation modeling, the fit of the model is checked to ensure the accuracy and correctness of the findings before implementing the model to test the hypotheses. Also, Cronbach's alpha indices of combined reliability of average variance were extracted and the coefficient of determination was used to measure the model. The average values of the extracted variance of all 7 model structures are at the optimal level. The amount of Cronbach's alpha and composite reliability is for all model constructs, so the questionnaire has good and acceptable reliability. The amount of Cronbach's alpha and combined reliability for all model constructs is more than 0/7, so the questionnaire has acceptable and acceptable reliability. The value of R2 for the endogenous constructs of the research confirms the appropriateness of the fit of the structural model. The results of the Q2 criterion for the endogenous structure indicate the appropriate predictive power of the model regarding the endogenous constructs of the research and indicate that the fitting model has appropriate and favorable predictive power. Confirmatory factor analysis is used to measure the reliability and validity of the measurement scale. The final results of the confirmatory factor analysis are reported in Table No. 4. Factor loadings greater than 5% have good validity. Figure 1, shows the confirmatory factor analysis for the whole model. Figure 2, shows the analysis of significant coefficients for the whole model. Since all the numbers on the paths are above 1/96%, it indicates the significance of the paths, the appropriateness of the structural model and the confirmation of all research hypotheses. The GOF criterion was used for the overall fit of the model, Since three values of 0/01, 0/25 and 0/36 are determined as weak, medium and strong values for GOF and on the other hand, the coefficient is in a good condition (0/598), Therefore, the results show that the fit of the model in the statistical population of the research is appropriate. Also, the t-test for all hypotheses is greater than 1/96. Therefore, with 95% confidence, the relationship between the variables is significant.
Conclusion
The present research was done by explaining the effect of market information learning ability and joint innovative capacities on the performance of the organization with an information approach. Organizational performance is a valuable activity at the community level. It also has a fundamental role in the economy and is considered as a useful tool to achieve economic growth and benefits of the organization. In fact, it is like an umbrella that includes all concepts related to the success and activities of the entire organization. In this research, 7 hypotheses were tested and the results of the hypothesis analysis indicate that the ability to learn market information plays a role in the internal learning ability (0/945) and the ability to learn interfaces (0/785) and internal learning ability (0/220) and interface learning ability (0/662) have been effective on administrative innovation. Also, administrative innovation has had an effect on the organization's performance (0/488), and shared innovative capacities have a significant effect on service innovation (0/904). Finally, service innovation has played a role in the organization's performance (0/590).
Methods and Materoal
This research is based on the applied purpose and according to the descriptive type of correlation, according to the model, we should use the structural equation modeling method. The statistical community of the research is top and middle managers of companies that are members of Pardis Technology Park in Tehran. Using Cochran's formula, 384 people were selected as a sample and 400 questionnaires were distributed by available sampling method and 387 questionnaires were collected in 50 companies. The data collection method of this research was done by library method and using books and articles, and questionnaire was used in the field method. To measure the variable of market information learning ability with 7 items, internal learning ability with 10 items, relational learning ability with 7 items and administrative innovation with 6 items from Teece et al., 1997; Weerawardena et al., 2015; and to measure the variable of service innovation with 4 items and organization performance with 5 items from Thakur & Hale, 2013 questionnaire; and to measure the variable of shared innovative capacities with 3 items, Oly Ndubisi et al., 2019 questionnaire was used.
Resultss and Discussion
Demographic characteristics of the sample include gender, age and education. In the method of structural equation modeling, the fit of the model is checked to ensure the accuracy and correctness of the findings before implementing the model to test the hypotheses. Also, Cronbach's alpha indices of combined reliability of average variance were extracted and the coefficient of determination was used to measure the model. The average values of the extracted variance of all 7 model structures are at the optimal level. The amount of Cronbach's alpha and composite reliability is for all model constructs, so the questionnaire has good and acceptable reliability. The amount of Cronbach's alpha and combined reliability for all model constructs is more than 0/7, so the questionnaire has acceptable and acceptable reliability. The value of R2 for the endogenous constructs of the research confirms the appropriateness of the fit of the structural model. The results of the Q2 criterion for the endogenous structure indicate the appropriate predictive power of the model regarding the endogenous constructs of the research and indicate that the fitting model has appropriate and favorable predictive power. Confirmatory factor analysis is used to measure the reliability and validity of the measurement scale. The final results of the confirmatory factor analysis are reported in Table No. 4. Factor loadings greater than 5% have good validity. Figure 1, shows the confirmatory factor analysis for the whole model. Figure 2, shows the analysis of significant coefficients for the whole model. Since all the numbers on the paths are above 1/96%, it indicates the significance of the paths, the appropriateness of the structural model and the confirmation of all research hypotheses. The GOF criterion was used for the overall fit of the model, Since three values of 0/01, 0/25 and 0/36 are determined as weak, medium and strong values for GOF and on the other hand, the coefficient is in a good condition (0/598), Therefore, the results show that the fit of the model in the statistical population of the research is appropriate. Also, the t-test for all hypotheses is greater than 1/96. Therefore, with 95% confidence, the relationship between the variables is significant.
Conclusion
The present research was done by explaining the effect of market information learning ability and joint innovative capacities on the performance of the organization with an information approach. Organizational performance is a valuable activity at the community level. It also has a fundamental role in the economy and is considered as a useful tool to achieve economic growth and benefits of the organization. In fact, it is like an umbrella that includes all concepts related to the success and activities of the entire organization. In this research, 7 hypotheses were tested and the results of the hypothesis analysis indicate that the ability to learn market information plays a role in the internal learning ability (0/945) and the ability to learn interfaces (0/785) and internal learning ability (0/220) and interface learning ability (0/662) have been effective on administrative innovation. Also, administrative innovation has had an effect on the organization's performance (0/488), and shared innovative capacities have a significant effect on service innovation (0/904). Finally, service innovation has played a role in the organization's performance (0/590).
The efficiency in digital library web pages(Presenting a model for Payame Noor University libraries)
Dr Saeed Ghaffari, Dr Masoud Bakhtiari,
Volume 0, Issue 0 (5-2022)
Volume 0, Issue 0 (5-2022)
Abstract
Purpose: The purpose of this research is to discover the state of the user interface in the web pages of digital libraries and to provide a model for the libraries of Payam Noor University based on the criteria found in the texts and sources.
Research method: The research method is survey and descriptive. The research tool used to evaluate libraries is the Nowruz check list (2018) consisting of 10 main criteria and 114 sub-components. The statistical population of the research is 10 digital libraries in America, Australia, France, Switzerland, New Zealand, Holland, Poland, Japan, India and Italy. Descriptive statistics and SPSS and Excel software were used to analyze the data of this research. Also, the questions and hypothesis of the research were tested by using t-test and Friedman's test.
Research findings: The digital library of America has won the first rank in compliance with the standards with 70.42%. Then the digital libraries of France with a score of 62.07 and Switzerland with a score of 61.59% are in the second and third places. Italian digital libraries are at the bottom of these libraries with 40.14% compliance. Also, among the criteria, the interface language criterion with 35.98% of compliance has been paid more attention by libraries than other criteria. The criterion of user control with 6.70% compliance has the lowest compliance rate among libraries.
Conclusion: In the libraries of Payam Noor University, in addition to following the general search components, special components such as proximity search, related keyword suggestions, and marking of search results are suggested in order to save the user's time. Also, the existence of the site map, information display criteria, reducing the time spent by the user and attracting and maintaining the user need attention.
Research method: The research method is survey and descriptive. The research tool used to evaluate libraries is the Nowruz check list (2018) consisting of 10 main criteria and 114 sub-components. The statistical population of the research is 10 digital libraries in America, Australia, France, Switzerland, New Zealand, Holland, Poland, Japan, India and Italy. Descriptive statistics and SPSS and Excel software were used to analyze the data of this research. Also, the questions and hypothesis of the research were tested by using t-test and Friedman's test.
Research findings: The digital library of America has won the first rank in compliance with the standards with 70.42%. Then the digital libraries of France with a score of 62.07 and Switzerland with a score of 61.59% are in the second and third places. Italian digital libraries are at the bottom of these libraries with 40.14% compliance. Also, among the criteria, the interface language criterion with 35.98% of compliance has been paid more attention by libraries than other criteria. The criterion of user control with 6.70% compliance has the lowest compliance rate among libraries.
Conclusion: In the libraries of Payam Noor University, in addition to following the general search components, special components such as proximity search, related keyword suggestions, and marking of search results are suggested in order to save the user's time. Also, the existence of the site map, information display criteria, reducing the time spent by the user and attracting and maintaining the user need attention.
Maryam Tavosi, Nader Naghshineh, Mohammad Zerehsaz, Siamak Mahboub,
Volume 0, Issue 0 (5-2022)
Volume 0, Issue 0 (5-2022)
Abstract
Purpose: Beauty is widely used in the field of art, but when it enters the field of human-computer interaction, it takes the name of "computational aesthetics". Knowing the dimensions of aesthetics can help web designers to design a better user interface for users. The current research aims to identify, rank, and propose a conceptual framework for the aesthetic components of digital images on the web.
Methodology: The present research was carried out with the meta-synthesis method. The documents retrieved from 6 treasure databases, IRANDOC, ISC, SID, Google-Scholar, Emerald, and Web of Science, were retrieved and analyzed with a targeted keyword search and a systematic approach, including 1278 documents. The number of 54 documents were selected and included in the study with the PRISMA approach. The importance coefficient of the identified codes was calculated by Shannon's qualitative content analysis method. EndNote software was used to store and read documents carefully.
Findings: First, the basic conceptual framework was drawn based on the aesthetic theories of Kant, Berlyne, Leibniz, Adorno, Birkhoff, and Husserl, along with 15 documents in English, containing 2 categories, 4 concepts, and 22 aesthetic codes. Then, by performing meta-synthesis, the mentioned framework was upgraded to 2 categories, 4 concepts, and 32 codes. According to Shannon's formula, the two codes "symmetry" and "non-complexity" in the category of objective aesthetics and the two codes "attractive color combination" and "moderate complexity" in the category of subjective aesthetics were identified as having the highest "coefficient of importance".
Conclusion: It is equally important to pay attention to the codes of subjective aesthetics along with objective aesthetics. The current research emphasizes the scientific cooperation of two groups of experts in computer science and humanities to accurately perceive aesthetics and better interaction between humans and computers. The proposed conceptual framework is the first at the national (Iran) and international levels.
Methodology: The present research was carried out with the meta-synthesis method. The documents retrieved from 6 treasure databases, IRANDOC, ISC, SID, Google-Scholar, Emerald, and Web of Science, were retrieved and analyzed with a targeted keyword search and a systematic approach, including 1278 documents. The number of 54 documents were selected and included in the study with the PRISMA approach. The importance coefficient of the identified codes was calculated by Shannon's qualitative content analysis method. EndNote software was used to store and read documents carefully.
Findings: First, the basic conceptual framework was drawn based on the aesthetic theories of Kant, Berlyne, Leibniz, Adorno, Birkhoff, and Husserl, along with 15 documents in English, containing 2 categories, 4 concepts, and 22 aesthetic codes. Then, by performing meta-synthesis, the mentioned framework was upgraded to 2 categories, 4 concepts, and 32 codes. According to Shannon's formula, the two codes "symmetry" and "non-complexity" in the category of objective aesthetics and the two codes "attractive color combination" and "moderate complexity" in the category of subjective aesthetics were identified as having the highest "coefficient of importance".
Conclusion: It is equally important to pay attention to the codes of subjective aesthetics along with objective aesthetics. The current research emphasizes the scientific cooperation of two groups of experts in computer science and humanities to accurately perceive aesthetics and better interaction between humans and computers. The proposed conceptual framework is the first at the national (Iran) and international levels.
Saeed Rouhi Shalemaie, Mohammad Khandan, Ali Shabani,
Volume 0, Issue 0 (5-2022)
Volume 0, Issue 0 (5-2022)
Abstract
Introduction
The present research aims to design a model for intergenerational knowledge sharing in order to identify the dimensions and rank the Factors and Components influencing intergenerational knowledge sharing in the car leasing industry.
Methods and Materoal
Considering the conceptual framework of the present study and the nature and type of available data and information for presenting a conceptual model of intergenerational knowledge sharing in the leasing industry, the research method utilized is an exploratory mixed-methods approach. This study is fundamental in its outcomes, has a practical nature, and is also critical in terms of its paradigm. The statistical population of this research comprises two sections: the qualitative part consists of 17 experts and specialists from the leasing industry, while the quantitative part includes a total of 970 employees currently working in this industry. Based on Cochran's formula and with a 95% margin of error, a sample of 275 individuals was selected. To ensure greater confidence, an additional 25% was added to the minimum sample size, leading to 343 questionnaires being sent to employees. Ultimately, 336 complete and valid questionnaires were returned, which were used for analysis in this research. Non-probability purposive sampling was employed for sample selection. Purposive sampling involves selecting a portion of the population based on the researcher's (or experts and specialists') judgment. In this method, sample acceptance criteria are defined, and individuals are selected for the survey regarding the research subject based on these criteria. In this research, the criteria for purposive sampling to select experts in the qualitative section were: 1) Leasing industry experts with more than 5 years of experience. 2) Leasing industry experts holding master's and doctoral degrees. After conducting interviews with selected individuals and upon reaching saturation in responses, with the agreement of the supervisors and advisors, the theoretical saturation was achieved, and the number of samples is detailed in the table below. Additionally, in the quantitative section, Cochran's formula was utilized, resulting in a selection of 336 employees from the leasing industry through simple random sampling. The data collection for this research was based on library studies including books, articles, websites, and relevant Persian and English internet information portals. Given the scarcity of library resources on the research topic, the most significant source used has been the internet and various databases, which has added to the importance of the research and the currency of information. For data collection in both qualitative and quantitative sections, field methods and tools such as semi-structured interviews and questionnaires were employed, which will be elaborated upon further. Semi-structured interviews are among the most common types of interviews used in social qualitative research. These interviews can be both structured and unstructured, and are sometimes referred to as in-depth interviews, where all respondents are asked similar questions and can freely answer the questions. In this research, for the semi-structured interviews, common questions were utilized based on the opinions of experts and professionals in the leasing industry, and the responses derived from these questions were transformed into specific components through descriptive analysis with the help of open, axial, and selective coding. For conducting field studies, a questionnaire has been utilized. Accordingly, based on the research objectives and questions, the research tool, namely the questionnaire, was designed. To gather information, both the questionnaire and semi-structured interviews were employed. In this research, categories were used to analyze the semi-structured interviews. The categories are often labeled as codes or keywords; however, anything that is labeled has the capability to organize and systematize the data, often functioning even as analytical codes. Analytical codes are the result of an analytical process that goes beyond merely identifying a topic. The coding of information was also analyzed using MaxQDA software. After collecting the conducted interviews and extracting their indicators, we entered them into MaxQDA and categorized them into groups and sets, each related to one of the main indicators. In the code system section of MAXQDA software, we established a hierarchical arrangement of codes and subcodes. In this research, descriptive statistics including frequency, percentage, mean, and standard deviation were used to analyze the obtained data from the samples. Additionally, in the inferential statistics section, the structural equation modeling method was employed. These analyses were conducted using SPSS and Smart PLS 2.0 statistical software.
Resultss and Discussion
The findings in the quantitative section indicated that 55 percent of the respondents were male and 45 percent were female. The majority of the sample had over 15 years of work experience (80 percent). The education level of 80 percent of the individuals was at the master's level, and the most common age range in the group was 30 to 50 years, accounting for 90 percent. The qualitative findings showed that 43.8 percent of the respondents were male and 56.3 percent were female. The majority of the sample had over 15 years of work experience (51.2 percent). The education level of 45.5 percent of individuals was at the master's or doctoral level, and the most common age range in this group was 40 to 50 years, comprising 39.6 percent. The results indicated that the standard deviation values were mostly below 1, with only a few below 2. This finding suggests that the data has low dispersion, and responses were primarily in alignment with each other. Additionally, to assess the normality or non-normality of the distribution of variables among the respondents, skewness and kurtosis values were utilized. Given that the skewness and kurtosis values were below 2, we can conclude that the data has a normal distribution. The findings indicated that the mean of the knowledge sharing variable is above the expected level, with a mean of 3.85 for knowledge sharing. Thus, the evaluation of the sample's opinions showed that the mean of the items related to the knowledge sharing variable is above average. Descriptive statistics revealed that the mean of the external environment variable is also above the expected level, with an average of 3.77. Consequently, the evaluation of the sample's opinions indicated that the mean of the items related to the external environment variable is above average as well. A review of the descriptive statistics showed that the mean of the innovation variable is higher than the expected level. Innovation had an average score of 3.65. Therefore, the evaluation of the sample's opinions indicated that the mean scores related to the variable of innovation are above the average level. The results obtained from the descriptive statistics review showed that the mean of the foresight variable is higher than the expected level, with an average of 3.43. Consequently, the evaluation of the sample's opinions indicated that the mean scores related to the variable of foresight are above the average level. The results from the descriptive statistics review indicated that the mean of the reactive variable is higher than the expected level, with an average of 3.88. Therefore, the evaluation of the sample's opinions showed that the mean scores related to the reactive variable are above the average level. The results obtained from the descriptive statistics review indicated that the mean of the analytical variable is higher than the expected level, with an average of 3.79. Hence, the evaluation of the sample's opinions indicated that the mean scores related to the analytical variable are above the average level. The results from the descriptive statistics review showed that the mean of the information technology governance variable is higher than the expected level, with an average of 3.71. Therefore, the evaluation of the sample's opinions indicated that the mean scores related to the information technology governance variable are above the average level. The results from the descriptive statistics review showed that the mean of the organizational structural variable is higher than the expected level, with an average of 3.57. Thus, the evaluation of the sample's opinions indicated that the mean scores related to the organizational structural variable are above the average level. The results obtained from the descriptive statistics review indicated that the mean of the learning organization variable is higher than the expected level, with an average of 3.71. Thus, the evaluation of the sample's opinions indicated that the mean scores related to the learning organization variable are above the average level. The results from the descriptive statistics review showed that the mean of the organizational learning variable is higher than the expected level, with an average of 3.54. The evaluation of the sample opinions indicated that the mean of the items related to the variable of organizational learning is above the average level. The results from the descriptive statistics showed that the mean for the variable of knowledge management is above the expected level, with knowledge management having a mean of 3.50. Therefore, the assessment of the sample opinions revealed that the mean of the items related to the variable of knowledge management is above the average level. The components of knowledge sharing, external environment, innovation, foresight, responsiveness, analysis, information technology governance, organizational structure, learning organization, organizational learning, and knowledge management have a direct and significant impact on inter-generational knowledge sharing in the leasing industry. Based on the results from the structural equation modeling, it is observed that knowledge sharing has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.362. Hence, it can be said that for a 36% increase in knowledge sharing, the transfer of inter-generational knowledge sharing also increases by 36%. The external environment has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.331. Therefore, it can be stated that for a 33% increase in the external environment, the transfer of inter-generational knowledge sharing also increases by 33%. Innovation has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.322. Consequently, it can be said that for a 32% increase in the innovation environment, the transfer of inter-generational knowledge sharing also increases by 32%. Foresight has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.376. Thus, it can be stated that for a 38% increase in foresight, the transfer of inter-generational knowledge sharing also increases by 38%. Responsiveness has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.301. Therefore, it can be concluded that for a 30% increase in responsiveness, the transfer of inter-generational knowledge sharing also increases by 30%. An analysis of intergenerational knowledge sharing shows a significant and positive relationship, with a standardized effect size of 0.338. Therefore, it can be said that for every 34% increase in the analytic aspect, intergenerational knowledge sharing also increases by 34%. The governance of information technology has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.329. Thus, it can be stated that for every 33% increase in information technology governance, intergenerational knowledge sharing also increases by 33%. Organizational structure has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.377. Accordingly, it can be inferred that for every 38% increase in organizational structure, intergenerational knowledge sharing increases by 38%. A learning organization has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.347. Thus, it can be said that for every 35% increase in learning organizations, intergenerational knowledge sharing also increases by 35%. Organizational learning has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.353. Therefore, it can be stated that for every 35% increase in organizational learning, intergenerational knowledge sharing increases by 35%. Knowledge management shows a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.967. Thus, it can be concluded that for every 97% increase in knowledge management, intergenerational knowledge sharing also increases by 97%.
Conclusion
Based on the results obtained, the components (knowledge sharing, external environment, innovation, foresight, reaction, analytical, information technology governance, organizational structure, learning organization, organizational learning, knowledge management) were identified as the main components, while the components (planning and organizing information technology, acquiring and implementing information technology, delivery and support for information technology, monitoring and evaluating information technology, complexity, formalization, centralization and decentralization, personal capabilities and skills, patterns and mental models, shared vision and goals, team learning, systems thinking) were considered as sub-components affecting intergenerational knowledge sharing in the leasing industry. According to the assessments conducted, the components (knowledge management (97%), organizational structure (38%), foresight (38%), knowledge sharing (36%), organizational learning (35%), learning organization (35%), analytical (34%), external environment (33%), information technology governance (33%), innovation (32%), and reaction (30%)) ranked in this order as having the highest impact on intergenerational knowledge sharing in the leasing industry. It was found that, from the specialists' perspective, the intergenerational knowledge sharing model in the leasing industry aligns well with the needs of this industry. This knowledge sharing model can enhance operational processes, improve service quality, and increase productivity. Furthermore, this model can facilitate the transfer of experiences and knowledge to future generations, thereby contributing to the advancement of the leasing industry. Overall, specialists believed that the intergenerational knowledge sharing model in the leasing industry is well-suited to its needs and can support its performance and progress. Based on the analysis obtained and the identification of components (knowledge sharing, external environment, innovation, foresight, reaction, analytical, information technology governance, organizational structure, learning organization, organizational learning, knowledge management), it can be concluded that all these components present a suitable model for improving the performance of the automotive leasing industry, and it is recommended that this model be considered for advancing the goals and success of this industry.
The present research aims to design a model for intergenerational knowledge sharing in order to identify the dimensions and rank the Factors and Components influencing intergenerational knowledge sharing in the car leasing industry.
Methods and Materoal
Considering the conceptual framework of the present study and the nature and type of available data and information for presenting a conceptual model of intergenerational knowledge sharing in the leasing industry, the research method utilized is an exploratory mixed-methods approach. This study is fundamental in its outcomes, has a practical nature, and is also critical in terms of its paradigm. The statistical population of this research comprises two sections: the qualitative part consists of 17 experts and specialists from the leasing industry, while the quantitative part includes a total of 970 employees currently working in this industry. Based on Cochran's formula and with a 95% margin of error, a sample of 275 individuals was selected. To ensure greater confidence, an additional 25% was added to the minimum sample size, leading to 343 questionnaires being sent to employees. Ultimately, 336 complete and valid questionnaires were returned, which were used for analysis in this research. Non-probability purposive sampling was employed for sample selection. Purposive sampling involves selecting a portion of the population based on the researcher's (or experts and specialists') judgment. In this method, sample acceptance criteria are defined, and individuals are selected for the survey regarding the research subject based on these criteria. In this research, the criteria for purposive sampling to select experts in the qualitative section were: 1) Leasing industry experts with more than 5 years of experience. 2) Leasing industry experts holding master's and doctoral degrees. After conducting interviews with selected individuals and upon reaching saturation in responses, with the agreement of the supervisors and advisors, the theoretical saturation was achieved, and the number of samples is detailed in the table below. Additionally, in the quantitative section, Cochran's formula was utilized, resulting in a selection of 336 employees from the leasing industry through simple random sampling. The data collection for this research was based on library studies including books, articles, websites, and relevant Persian and English internet information portals. Given the scarcity of library resources on the research topic, the most significant source used has been the internet and various databases, which has added to the importance of the research and the currency of information. For data collection in both qualitative and quantitative sections, field methods and tools such as semi-structured interviews and questionnaires were employed, which will be elaborated upon further. Semi-structured interviews are among the most common types of interviews used in social qualitative research. These interviews can be both structured and unstructured, and are sometimes referred to as in-depth interviews, where all respondents are asked similar questions and can freely answer the questions. In this research, for the semi-structured interviews, common questions were utilized based on the opinions of experts and professionals in the leasing industry, and the responses derived from these questions were transformed into specific components through descriptive analysis with the help of open, axial, and selective coding. For conducting field studies, a questionnaire has been utilized. Accordingly, based on the research objectives and questions, the research tool, namely the questionnaire, was designed. To gather information, both the questionnaire and semi-structured interviews were employed. In this research, categories were used to analyze the semi-structured interviews. The categories are often labeled as codes or keywords; however, anything that is labeled has the capability to organize and systematize the data, often functioning even as analytical codes. Analytical codes are the result of an analytical process that goes beyond merely identifying a topic. The coding of information was also analyzed using MaxQDA software. After collecting the conducted interviews and extracting their indicators, we entered them into MaxQDA and categorized them into groups and sets, each related to one of the main indicators. In the code system section of MAXQDA software, we established a hierarchical arrangement of codes and subcodes. In this research, descriptive statistics including frequency, percentage, mean, and standard deviation were used to analyze the obtained data from the samples. Additionally, in the inferential statistics section, the structural equation modeling method was employed. These analyses were conducted using SPSS and Smart PLS 2.0 statistical software.
Resultss and Discussion
The findings in the quantitative section indicated that 55 percent of the respondents were male and 45 percent were female. The majority of the sample had over 15 years of work experience (80 percent). The education level of 80 percent of the individuals was at the master's level, and the most common age range in the group was 30 to 50 years, accounting for 90 percent. The qualitative findings showed that 43.8 percent of the respondents were male and 56.3 percent were female. The majority of the sample had over 15 years of work experience (51.2 percent). The education level of 45.5 percent of individuals was at the master's or doctoral level, and the most common age range in this group was 40 to 50 years, comprising 39.6 percent. The results indicated that the standard deviation values were mostly below 1, with only a few below 2. This finding suggests that the data has low dispersion, and responses were primarily in alignment with each other. Additionally, to assess the normality or non-normality of the distribution of variables among the respondents, skewness and kurtosis values were utilized. Given that the skewness and kurtosis values were below 2, we can conclude that the data has a normal distribution. The findings indicated that the mean of the knowledge sharing variable is above the expected level, with a mean of 3.85 for knowledge sharing. Thus, the evaluation of the sample's opinions showed that the mean of the items related to the knowledge sharing variable is above average. Descriptive statistics revealed that the mean of the external environment variable is also above the expected level, with an average of 3.77. Consequently, the evaluation of the sample's opinions indicated that the mean of the items related to the external environment variable is above average as well. A review of the descriptive statistics showed that the mean of the innovation variable is higher than the expected level. Innovation had an average score of 3.65. Therefore, the evaluation of the sample's opinions indicated that the mean scores related to the variable of innovation are above the average level. The results obtained from the descriptive statistics review showed that the mean of the foresight variable is higher than the expected level, with an average of 3.43. Consequently, the evaluation of the sample's opinions indicated that the mean scores related to the variable of foresight are above the average level. The results from the descriptive statistics review indicated that the mean of the reactive variable is higher than the expected level, with an average of 3.88. Therefore, the evaluation of the sample's opinions showed that the mean scores related to the reactive variable are above the average level. The results obtained from the descriptive statistics review indicated that the mean of the analytical variable is higher than the expected level, with an average of 3.79. Hence, the evaluation of the sample's opinions indicated that the mean scores related to the analytical variable are above the average level. The results from the descriptive statistics review showed that the mean of the information technology governance variable is higher than the expected level, with an average of 3.71. Therefore, the evaluation of the sample's opinions indicated that the mean scores related to the information technology governance variable are above the average level. The results from the descriptive statistics review showed that the mean of the organizational structural variable is higher than the expected level, with an average of 3.57. Thus, the evaluation of the sample's opinions indicated that the mean scores related to the organizational structural variable are above the average level. The results obtained from the descriptive statistics review indicated that the mean of the learning organization variable is higher than the expected level, with an average of 3.71. Thus, the evaluation of the sample's opinions indicated that the mean scores related to the learning organization variable are above the average level. The results from the descriptive statistics review showed that the mean of the organizational learning variable is higher than the expected level, with an average of 3.54. The evaluation of the sample opinions indicated that the mean of the items related to the variable of organizational learning is above the average level. The results from the descriptive statistics showed that the mean for the variable of knowledge management is above the expected level, with knowledge management having a mean of 3.50. Therefore, the assessment of the sample opinions revealed that the mean of the items related to the variable of knowledge management is above the average level. The components of knowledge sharing, external environment, innovation, foresight, responsiveness, analysis, information technology governance, organizational structure, learning organization, organizational learning, and knowledge management have a direct and significant impact on inter-generational knowledge sharing in the leasing industry. Based on the results from the structural equation modeling, it is observed that knowledge sharing has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.362. Hence, it can be said that for a 36% increase in knowledge sharing, the transfer of inter-generational knowledge sharing also increases by 36%. The external environment has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.331. Therefore, it can be stated that for a 33% increase in the external environment, the transfer of inter-generational knowledge sharing also increases by 33%. Innovation has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.322. Consequently, it can be said that for a 32% increase in the innovation environment, the transfer of inter-generational knowledge sharing also increases by 32%. Foresight has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.376. Thus, it can be stated that for a 38% increase in foresight, the transfer of inter-generational knowledge sharing also increases by 38%. Responsiveness has a significant positive relationship with the transfer of inter-generational knowledge sharing, with a standardized effect size of 0.301. Therefore, it can be concluded that for a 30% increase in responsiveness, the transfer of inter-generational knowledge sharing also increases by 30%. An analysis of intergenerational knowledge sharing shows a significant and positive relationship, with a standardized effect size of 0.338. Therefore, it can be said that for every 34% increase in the analytic aspect, intergenerational knowledge sharing also increases by 34%. The governance of information technology has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.329. Thus, it can be stated that for every 33% increase in information technology governance, intergenerational knowledge sharing also increases by 33%. Organizational structure has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.377. Accordingly, it can be inferred that for every 38% increase in organizational structure, intergenerational knowledge sharing increases by 38%. A learning organization has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.347. Thus, it can be said that for every 35% increase in learning organizations, intergenerational knowledge sharing also increases by 35%. Organizational learning has a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.353. Therefore, it can be stated that for every 35% increase in organizational learning, intergenerational knowledge sharing increases by 35%. Knowledge management shows a significant and positive relationship with intergenerational knowledge sharing, with a standardized effect size of 0.967. Thus, it can be concluded that for every 97% increase in knowledge management, intergenerational knowledge sharing also increases by 97%.
Conclusion
Based on the results obtained, the components (knowledge sharing, external environment, innovation, foresight, reaction, analytical, information technology governance, organizational structure, learning organization, organizational learning, knowledge management) were identified as the main components, while the components (planning and organizing information technology, acquiring and implementing information technology, delivery and support for information technology, monitoring and evaluating information technology, complexity, formalization, centralization and decentralization, personal capabilities and skills, patterns and mental models, shared vision and goals, team learning, systems thinking) were considered as sub-components affecting intergenerational knowledge sharing in the leasing industry. According to the assessments conducted, the components (knowledge management (97%), organizational structure (38%), foresight (38%), knowledge sharing (36%), organizational learning (35%), learning organization (35%), analytical (34%), external environment (33%), information technology governance (33%), innovation (32%), and reaction (30%)) ranked in this order as having the highest impact on intergenerational knowledge sharing in the leasing industry. It was found that, from the specialists' perspective, the intergenerational knowledge sharing model in the leasing industry aligns well with the needs of this industry. This knowledge sharing model can enhance operational processes, improve service quality, and increase productivity. Furthermore, this model can facilitate the transfer of experiences and knowledge to future generations, thereby contributing to the advancement of the leasing industry. Overall, specialists believed that the intergenerational knowledge sharing model in the leasing industry is well-suited to its needs and can support its performance and progress. Based on the analysis obtained and the identification of components (knowledge sharing, external environment, innovation, foresight, reaction, analytical, information technology governance, organizational structure, learning organization, organizational learning, knowledge management), it can be concluded that all these components present a suitable model for improving the performance of the automotive leasing industry, and it is recommended that this model be considered for advancing the goals and success of this industry.
Afshin Motaghi Destenaei, Ali Karami, Milad Piri Fath Abad,
Volume 0, Issue 0 (5-2022)
Volume 0, Issue 0 (5-2022)
Abstract
Introduction
The idea of creating smart machines and artificial intelligence has been around for centuries and dates back to at least the 14th century. Although the application of artificial intelligence in education is a very new field, but during the last 25 years, artificial intelligence has made achievements in some fields. Which has also affected education of course, criticisms have also been raised against excessive optimism towards contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.
Methods and Materoal
This study was done using SWOT analysis method and its data collection method is also a library
Resultss and Discussion
Text In general, artificial intelligence as a catalyst for teaching and learning with the help of computers is a field with many applications. The teaching of science, technology, engineering and mathematics subjects can be enhanced with artificial intelligence-based software systems. Another potential strength is the potential of AI systems to serve learners across schools, borders, and platforms in creating ecosystems of interactive learning tools. Additionally, AI systems in education may be used to evaluate different learning models throughout the school. Without strong artificial intelligence, tutoring systems cannot provide rapid feedback to learners and enable stimulating interaction. With a realistic view, weak to moderate and strong artificial intelligence have a good ability to support teaching and learning and facilitate the daily work of teachers.
Intelligent learning systems often have less artificial intelligence than expected, especially when it comes to interacting with students. Baker (2016) in a critical position classified many of the existing education systems under stupid education systems. His concept for online learning is to enhance data-driven human intelligence rather than data-driven artificial intelligence. In order to more dynamically use AI in education, there is a need for training data, one of the problems that arise is how to ensure that the data is real and free from bias. As stated by Popenici and Kerr (2017), complex AI algorithms are designed by human programmers who are likely to include their own agendas or biases in the development of the system. An important aspect of high-level machine intelligence is that it customizes learning for each student, but in doing so it intervenes by standardizing content and what is expected of the student.
As reviewed by Lakin et al. (2016), it is hard to see a future where teachers are replaced by artificial intelligence systems or robots. A more positive and realistic scenario is that the role of the teacher evolves and transforms, freeing teachers from tedious daily tasks. In addition, AI in education has the potential to relieve the teacher of the burden of having all the knowledge and information that can be relevant to students. A possible use of artificial intelligence in education in the future is in the form of robots (collaborative robots) that help teachers in their daily work and tailor the learning experience to each student, for example in recording and analyzing the work of these students. And report to the teacher. The use of intelligent learning systems can provide customized instruction or instant feedback to students at any time of the day. But the depth of customization is one of the truly critical features, not superficial and personalized learning. Studies show that developers of intelligent instructional systems have been successful in their goal of adapting and surpassing computer-assisted instruction (CAI) and human teacher training in raising student test scores.
The negative change in the role of the teacher may be caused by the design of stereotypical courses with low-level multiple-choice questions and the use of teachers as content developers. Most school curricula and teacher training programs are not well prepared to take advantage of the benefits of artificial intelligence in education due to not providing artificial intelligence courses to their teachers. If teachers are not trained in the use of artificial intelligence, this can lead to misuse of the technology, for example in protecting privacy and using personal data for influence. According to Nicholas and Holmes (2018), an ethical framework should be established for the use of artificial intelligence in education, and even if adopted, it should be continuously discussed and updated to allow for the capabilities and scope of artificial intelligence and the potential use of reflect it. A growing concern among many education workers is the fear of unemployment as high-level machine intelligence systems completely take over the teaching profession. According to Popenici and Kerr (2017), artificial intelligence currently has the potential to replace a large number of teaching assistants and administrative staff in education, and therefore it is more important to investigate its impact on education. Studies show that widespread use of high-level AI systems may disrupt students' ability to learn independently and develop 21st century skills such as problem solving and critical thinking. Finally, the most severe threat to students may be AI. Surveillance cameras with built-in facial recognition. Along with machine learning, facial recognition is one area where AI is advancing much faster than AI ethics. By using this technology, schools may collect students' biometric information, for example, under the pretext of reducing the many working hours that employees spend on registration and attendance. Support using artificial intelligence systems in education and robotics is certainly an opportunity, but social robots are still in their infancy and have limited social skills. In the near future, a realistic opportunity lies in the development of robots that can provide personalized content and rapid feedback. As in the manufacturing industry, teachers will soon be able to reprogram the cobots using block programming code that doesn't require advanced programming skills. Of course, there are also threats, and for purely economic reasons, we will probably experience cases where teachers are replaced by artificial intelligence solutions in education. Universities with financial problems may be tempted to try solutions, such as Deakin University in Australia, which offers a service where any student who asks can expect tailored information and advice. However, since the common concern is how to submit assignments and how to pay for parking, such systems pose a threat to administrative staff rather than teachers. Finally, as with AI in general, ethics is a major and immediate challenge in the use of AI in education, even though the threats posed by AI in education may not be as dramatic as in other AI areas. Automatic will not be useful. Quality teaching is a complex and creative profession involving improvisation and spontaneity where humans are not easily replaced. In general evaluation, it can be said that there are many ways that artificial intelligence can help students. From identifying signs of effort to creating a more interactive and personalized learning program.
Here are four ways that artificial intelligence can have a positive impact on student learning; Personalized learning: The ability to respond to personalized learning needs is one of the most positive benefits of artificial intelligence in education. Artificial intelligence technology can easily adapt to different learning styles. AI technology can analyze students' past performance and create tailored curricula and settings based on past performance. When it comes to personalized learning, AI can also point students in the right direction for resources and other useful data and information. Artificial intelligence has the ability to provide personalized study plans for students without having to wait for interventions from learning professionals. All while meeting the overall goal of making learning easier and helping students engage with content more effectively. Ultimately, where AI really helps personalized learning is in its ability to reach students on a massive scale. With overcrowded classrooms at the elementary school level and classrooms of hundreds at the secondary level, AI can help personalize education for all students at once, making it easier for everyone to succeed. Tutoring: Sometimes students need extra help, and AI allows you to access on-demand tutoring without an in-person or live tutoring session. Because the AI uses algorithms to adapt, it can quickly change to cover the areas where students need the most support. Just like a human tutor who adapts to a student's learning style and ability to absorb information, AI tutoring systems are very useful in their ability to focus on improving and deepening student learning as a whole. The main advantage of AI-based tutoring technology is the ability to help students understand complex concepts and terms on a mass level. Finally, with artificial intelligence, access to tutoring is no longer limited to those who can afford it. In addition, instructors can spend less time helping those who do not understand the concepts. Assessment and grading: A large part of teachers' time is spent grading assignments. Artificial intelligence technology can help speed up this process. Additionally, when it comes to grading assignments, AI technology can help analyze and get feedback from students on things like grammar, content, and vocabulary. By removing this part of teachers' duties, they can focus on other aspects of teaching that are more important, such as lesson planning and student engagement. Finally, one of the biggest benefits of automated assessment is that it eliminates human error, biases, and mistakes. It can also give each student an outline of where they went wrong and how they can improve, without taking up extra time from teachers. Improving student interaction: Artificial intelligence can engage students in educational content and make learning more interesting. One of the ways that educators and teachers can incorporate artificial intelligence into the classroom is through the use of catboats. The ability of catboats to personalize and adapt to students' learning styles creates more opportunities to keep students engaged, and the fact that catboats can be accessed anytime or anywhere means that students they can work at their own pace and continue their learning outside of traditional classroom time. The fact that AI improves engagement is exciting for course planners and administrators. This means they can deliver highly personalized and interactive learning in their courses, regardless of the subject, helping to amplify the impact on people's lives. Discussed how artificial intelligence can be useful for students. In addition there is great potential impact on coaches and teachers – particularly in ways it can save time.
The three advantages of artificial intelligence in education for teachers are: 1- Predictive analysis an interesting and emerging area of artificial intelligence in education is prediction. AI can analyze data and predict which students might fall behind due to the educational gap. Predictive analytics is exciting for educators because it means students struggling with learning challenges can be identified earlier and given the tools they need to succeed. Additionally, early intervention means that students who otherwise fail or struggle might have the opportunity to become successful students by giving them the right tools to help them succeed. 2-Advanced educational methods one of the methods of using artificial intelligence in education is to improve teaching methods. Today, due to the vast amount of content and information, teachers often have little time to organize alternative learning methods without spending more than hours of classroom time. Using artificial intelligence technology, teachers have the ability to quickly put together games and simulations that help students practice and learn the lessons being taught without spending more time on lesson planning. It saves a lot of time for teachers. 3- Facilitating evaluations and grading if you ask any teacher, they will tell you that assessment is one of the most time-consuming parts of the job. One of the exciting areas of artificial intelligence in education is the use of artificial intelligence technology to improve and speed up the assessment and grading process. For example, assessments can be done in real time instead of lengthy home marking. This not only saves time for teachers, but also improves students' understanding of the material in the moment.
Conclusion
The research findings show that there are both opportunities and threats regarding the role of artificial intelligence in contemporary education. In many ways, AI appears to have a promotional mode. But like other areas of advertising, it has the potential to grow with specific applications in educational and learning activities. The results of the research show that the awareness of artificial intelligence and the study of the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence instead of using artificial intelligence in education
The idea of creating smart machines and artificial intelligence has been around for centuries and dates back to at least the 14th century. Although the application of artificial intelligence in education is a very new field, but during the last 25 years, artificial intelligence has made achievements in some fields. Which has also affected education of course, criticisms have also been raised against excessive optimism towards contemporary artificial intelligence research. Little research has been done on the expectations of the role of artificial intelligence in education and its potential impact on education. The purpose of this study is to analyze and investigate the role of artificial intelligence in education.
Methods and Materoal
This study was done using SWOT analysis method and its data collection method is also a library
Resultss and Discussion
Text In general, artificial intelligence as a catalyst for teaching and learning with the help of computers is a field with many applications. The teaching of science, technology, engineering and mathematics subjects can be enhanced with artificial intelligence-based software systems. Another potential strength is the potential of AI systems to serve learners across schools, borders, and platforms in creating ecosystems of interactive learning tools. Additionally, AI systems in education may be used to evaluate different learning models throughout the school. Without strong artificial intelligence, tutoring systems cannot provide rapid feedback to learners and enable stimulating interaction. With a realistic view, weak to moderate and strong artificial intelligence have a good ability to support teaching and learning and facilitate the daily work of teachers.
Intelligent learning systems often have less artificial intelligence than expected, especially when it comes to interacting with students. Baker (2016) in a critical position classified many of the existing education systems under stupid education systems. His concept for online learning is to enhance data-driven human intelligence rather than data-driven artificial intelligence. In order to more dynamically use AI in education, there is a need for training data, one of the problems that arise is how to ensure that the data is real and free from bias. As stated by Popenici and Kerr (2017), complex AI algorithms are designed by human programmers who are likely to include their own agendas or biases in the development of the system. An important aspect of high-level machine intelligence is that it customizes learning for each student, but in doing so it intervenes by standardizing content and what is expected of the student.
As reviewed by Lakin et al. (2016), it is hard to see a future where teachers are replaced by artificial intelligence systems or robots. A more positive and realistic scenario is that the role of the teacher evolves and transforms, freeing teachers from tedious daily tasks. In addition, AI in education has the potential to relieve the teacher of the burden of having all the knowledge and information that can be relevant to students. A possible use of artificial intelligence in education in the future is in the form of robots (collaborative robots) that help teachers in their daily work and tailor the learning experience to each student, for example in recording and analyzing the work of these students. And report to the teacher. The use of intelligent learning systems can provide customized instruction or instant feedback to students at any time of the day. But the depth of customization is one of the truly critical features, not superficial and personalized learning. Studies show that developers of intelligent instructional systems have been successful in their goal of adapting and surpassing computer-assisted instruction (CAI) and human teacher training in raising student test scores.
The negative change in the role of the teacher may be caused by the design of stereotypical courses with low-level multiple-choice questions and the use of teachers as content developers. Most school curricula and teacher training programs are not well prepared to take advantage of the benefits of artificial intelligence in education due to not providing artificial intelligence courses to their teachers. If teachers are not trained in the use of artificial intelligence, this can lead to misuse of the technology, for example in protecting privacy and using personal data for influence. According to Nicholas and Holmes (2018), an ethical framework should be established for the use of artificial intelligence in education, and even if adopted, it should be continuously discussed and updated to allow for the capabilities and scope of artificial intelligence and the potential use of reflect it. A growing concern among many education workers is the fear of unemployment as high-level machine intelligence systems completely take over the teaching profession. According to Popenici and Kerr (2017), artificial intelligence currently has the potential to replace a large number of teaching assistants and administrative staff in education, and therefore it is more important to investigate its impact on education. Studies show that widespread use of high-level AI systems may disrupt students' ability to learn independently and develop 21st century skills such as problem solving and critical thinking. Finally, the most severe threat to students may be AI. Surveillance cameras with built-in facial recognition. Along with machine learning, facial recognition is one area where AI is advancing much faster than AI ethics. By using this technology, schools may collect students' biometric information, for example, under the pretext of reducing the many working hours that employees spend on registration and attendance. Support using artificial intelligence systems in education and robotics is certainly an opportunity, but social robots are still in their infancy and have limited social skills. In the near future, a realistic opportunity lies in the development of robots that can provide personalized content and rapid feedback. As in the manufacturing industry, teachers will soon be able to reprogram the cobots using block programming code that doesn't require advanced programming skills. Of course, there are also threats, and for purely economic reasons, we will probably experience cases where teachers are replaced by artificial intelligence solutions in education. Universities with financial problems may be tempted to try solutions, such as Deakin University in Australia, which offers a service where any student who asks can expect tailored information and advice. However, since the common concern is how to submit assignments and how to pay for parking, such systems pose a threat to administrative staff rather than teachers. Finally, as with AI in general, ethics is a major and immediate challenge in the use of AI in education, even though the threats posed by AI in education may not be as dramatic as in other AI areas. Automatic will not be useful. Quality teaching is a complex and creative profession involving improvisation and spontaneity where humans are not easily replaced. In general evaluation, it can be said that there are many ways that artificial intelligence can help students. From identifying signs of effort to creating a more interactive and personalized learning program.
Here are four ways that artificial intelligence can have a positive impact on student learning; Personalized learning: The ability to respond to personalized learning needs is one of the most positive benefits of artificial intelligence in education. Artificial intelligence technology can easily adapt to different learning styles. AI technology can analyze students' past performance and create tailored curricula and settings based on past performance. When it comes to personalized learning, AI can also point students in the right direction for resources and other useful data and information. Artificial intelligence has the ability to provide personalized study plans for students without having to wait for interventions from learning professionals. All while meeting the overall goal of making learning easier and helping students engage with content more effectively. Ultimately, where AI really helps personalized learning is in its ability to reach students on a massive scale. With overcrowded classrooms at the elementary school level and classrooms of hundreds at the secondary level, AI can help personalize education for all students at once, making it easier for everyone to succeed. Tutoring: Sometimes students need extra help, and AI allows you to access on-demand tutoring without an in-person or live tutoring session. Because the AI uses algorithms to adapt, it can quickly change to cover the areas where students need the most support. Just like a human tutor who adapts to a student's learning style and ability to absorb information, AI tutoring systems are very useful in their ability to focus on improving and deepening student learning as a whole. The main advantage of AI-based tutoring technology is the ability to help students understand complex concepts and terms on a mass level. Finally, with artificial intelligence, access to tutoring is no longer limited to those who can afford it. In addition, instructors can spend less time helping those who do not understand the concepts. Assessment and grading: A large part of teachers' time is spent grading assignments. Artificial intelligence technology can help speed up this process. Additionally, when it comes to grading assignments, AI technology can help analyze and get feedback from students on things like grammar, content, and vocabulary. By removing this part of teachers' duties, they can focus on other aspects of teaching that are more important, such as lesson planning and student engagement. Finally, one of the biggest benefits of automated assessment is that it eliminates human error, biases, and mistakes. It can also give each student an outline of where they went wrong and how they can improve, without taking up extra time from teachers. Improving student interaction: Artificial intelligence can engage students in educational content and make learning more interesting. One of the ways that educators and teachers can incorporate artificial intelligence into the classroom is through the use of catboats. The ability of catboats to personalize and adapt to students' learning styles creates more opportunities to keep students engaged, and the fact that catboats can be accessed anytime or anywhere means that students they can work at their own pace and continue their learning outside of traditional classroom time. The fact that AI improves engagement is exciting for course planners and administrators. This means they can deliver highly personalized and interactive learning in their courses, regardless of the subject, helping to amplify the impact on people's lives. Discussed how artificial intelligence can be useful for students. In addition there is great potential impact on coaches and teachers – particularly in ways it can save time.
The three advantages of artificial intelligence in education for teachers are: 1- Predictive analysis an interesting and emerging area of artificial intelligence in education is prediction. AI can analyze data and predict which students might fall behind due to the educational gap. Predictive analytics is exciting for educators because it means students struggling with learning challenges can be identified earlier and given the tools they need to succeed. Additionally, early intervention means that students who otherwise fail or struggle might have the opportunity to become successful students by giving them the right tools to help them succeed. 2-Advanced educational methods one of the methods of using artificial intelligence in education is to improve teaching methods. Today, due to the vast amount of content and information, teachers often have little time to organize alternative learning methods without spending more than hours of classroom time. Using artificial intelligence technology, teachers have the ability to quickly put together games and simulations that help students practice and learn the lessons being taught without spending more time on lesson planning. It saves a lot of time for teachers. 3- Facilitating evaluations and grading if you ask any teacher, they will tell you that assessment is one of the most time-consuming parts of the job. One of the exciting areas of artificial intelligence in education is the use of artificial intelligence technology to improve and speed up the assessment and grading process. For example, assessments can be done in real time instead of lengthy home marking. This not only saves time for teachers, but also improves students' understanding of the material in the moment.
Conclusion
The research findings show that there are both opportunities and threats regarding the role of artificial intelligence in contemporary education. In many ways, AI appears to have a promotional mode. But like other areas of advertising, it has the potential to grow with specific applications in educational and learning activities. The results of the research show that the awareness of artificial intelligence and the study of the role of artificial intelligence in education will reduce the risk of substituting artificial intelligence instead of using artificial intelligence in education
Mahdi Akbari Golzar, Dr Ahmad Naderi,
Volume 0, Issue 0 (5-2022)
Volume 0, Issue 0 (5-2022)
Abstract
Purpose: Several articles have systematically reviewed the studies conducted in the field of blockchain in the country using the meta-combination method, and all of them focus on the review of foreign articles. Due to the growth and spread of blockchain technology in the country and the increase in the scope of domestic research in relation to it, a systematic review of the research done inside the country also seemed necessary. In this regard, the aim of this article is to systematically review internal articles in the blockchain field, focusing on the human-computer interaction (HCI) field of study.
Method: In this study, 985 articles were identified with the search criteria determined by the qualitative meta-combination method, which finally led to 111 final articles after applying targeted filters. The focus of this review is to summarize the HCI literature on blockchain technology. The literature review was organized in four comprehensive stages, following the PRISMA systematic review protocol, and then the collected data were analyzed thematically.
Findings: Articles related to human interaction with blockchain technology were divided into four subject categories, each of which is divided into three separate sub-topics, which are:
1. Decentralized economy and smart contracts
2. User understanding and participation of blockchain technology and cryptocurrencies
3. Application of blockchain technology in a specific field
4. Jurisprudence and legal issues around blockchain and cryptocurrencies.
Conclusion: despite the very good growth of studies related to blockchain technology and human relationship with it in Iran, the atmosphere governing these studies is still significantly different from the international environment, and domestic researchers in this field should pay more attention to emerging socio-technical fields and Its interdisciplinary research as well as the rapid developments of the technological field to better understand human social life.
Sorush Fathi, , , ,
Volume 0, Issue 0 (5-2022)
Volume 0, Issue 0 (5-2022)
Abstract
The current research aims to qualitatively examine the lived experience of capable female heads of households in the economic field in order to extract a model or a native model from their lived process that other female heads of households can also benefit from. In terms of method, this research is an applied research and it was conducted in a qualitative way using the foundational data theory method. The statistical population of the research was made up of economically capable household heads living in Tehran. In order to conduct the interview, the snowball sampling method was used. Interviews were conducted by the researcher in person and until the saturation point was reached. Therefore, the researcher has conducted interviews with 25 female heads of households who are capable in the economic field until the stage of theoretical saturation. Findings: In general, 931 conceptual codes were extracted from the interviews. The results showed that in the structural conditions, the prevailing gender culture, the weakness of the legal and political system in supporting women heads of households, the lack of social justice and the disproportion of employment conditions with the needs of women heads of households had a significant effect on their progress or lack of progress. Background conditions, lack of a spouse, responsibility for children and providing for the family's livelihood needs, and in most cases the lack of basic facilities for empowerment have created complex conditions, and intervening factors include the lack of facilities, such as not having a babysitter or a person who is in the absence of a mother. take care of children In the meantime, the lack of sufficient literacy for proper employment had led to many problems, which economically capable women heads of households have overcome these crises and managed them well. The presence of support organizations can be considered as an effective element. Optimal use of services has played an effective role in improving their conditions. It has been very helpful to create and prepare the society for vulnerable women to enter the labor market, obtain financial and social credit, provide facilities, training and rehabilitation, and even create job skills that are provided in support organizations. This has caused effects and consequences such as livelihood provision and family management to be on the path of empowerment. Women heads of households who are capable in the field of economy did not have enough experience and economic skills from the beginning, and even did not have the necessary preparation to become heads. Economic empowerment will reduce deprivation and social exclusion and join them in society and social groups. In addition to this, economic capability will increase their capacity to improve their personal and social status. Therefore, the agency of women in the empowerment process as effective social forces should be considered by policymakers.
Fatemeh Zarmehr, Asefeh Asemi, Mitra Pashootanizade,
Volume 1, Issue 1 (4-2014)
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The purpose of the present study was to investigate the opinion of blind and visually impaired of the possibility of employing gadgets in delivering resources and information services to them.
Method: The research method was descriptive - survey and questionnaires were used to collect data. The data was analyzed by descriptive and inferential statistics.
Results: The result showed that respondent approved the use of information resources and services through variety of gadgets and the fact that gadgets are generally more desirable. Based on the agreement in regard to the gadgets used as resources and information services to blind and visually impaired it was found that mobile phone and pocket PC were in the first place (56.7%), Google glasses in second place(55%) e-book readers in third place (54.4%), tablets in fourth place (54.2%) and RFID systems in fifth place (44.1%).
Conclusion: Based on the findings of the study it is concluded that blinds ability to use gadgets in optimal utilization of resources and information services is possible and more than desirable. As a results we can make proper use of the new technology in order to present resources and information services to blinds and visually impaired
Fatemeh Zandian, Anahita Dashti, Mohammad Hasanzade,
Volume 1, Issue 1 (4-2014)
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The main objective of this research was to investigate the use of full-text databases in the LIS theses of Tehran State Universities within the years 2005 and 2009.
Method: For this purpose, the total of 9952 citations related to 172 existing theses in the academic central libraries were studied. The data collected were analyzed by the bibliometrics and citation analysis methods.
Results: The results showed that only 499 (5.01%) out of the total of 9952 citations were of full-text databases. By the use of Bradford law, the Emerald, Iran Doc. Pro Quest and Science Direct were chosen as the core databases in LIS. There was no statistically significance difference in the use of full-text databases with regard to database, university and subject. Citing the linguistic distribution of full-text data showed that most citations were in English language. Finally, the amount of the use of full-text databases by the theses has not been increased between the years 2005 and 2009.
Conclusion: The results obtained in the present research showed that the use of databases was very low and no growing trend for citing databases did show. Also English language was Dominant language in citing databases
Fatima Fahimnia, Parvin Goodarzian,
Volume 1, Issue 1 (4-2014)
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The present investigation intends to compare and review various user interfaces from user standpoint and to ascertain its linkage with user satisfaction.
Method: The research incorporated a descriptive survey of University of Tehran graduate student body. Using a targeted sampling, graduate students from the faculties of chemistry and Biology were selected. The instruments used were: A researcher-constructed questionnaire based on Nielsen’s 10 component model; and QUIS user satisfaction questionnaire. Each database UI were evaluated based on five indicators of general features, search features, retrieval features, display features and user-friendliness.
Results: Findings indicated that with an average of 3.88 the Elsevier database had the highest score. Users surveyed believed that the quality of user interface was in better shape than other UI. One should note that this advantage is not indicative of overall database quality and the relatively better interaction on the part of users could be ascribed to their familiarity with the said database and its resource location process.
Conclusion: The main purpose of designing and developing a database is to accruing user satisfaction in search and retrieval process. Hence User satisfaction directly connected to user interface
Maryam Sarafzadeh, Soheila Alavi,
Volume 1, Issue 1 (4-2014)
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The present paper discusses results of a study which aimed to explore the knowledge and use of Online social networking by MLIS students in Iran and to explore their perceptions of using that technology for academic and professional purposes, and challenges they face for using them.
Method: The research method was explorative and empirical. Data was collected through a web-based survey questionnaire containing both open and close ended question. The link of questionnaire was emailed to MLIS students in Tehran. 113 students completed the questionnaire.
Results: The results show that 35 percent of respondents were not users of SNS. Internet filtering in Iran was identified as the major barrier on using SNS by MLIS students. Negative perceptions toward SNSs were identified as another big barrier for using SNSs by MLIS students. 22% of respondents felt that social networking would waste their time and some 14% saw on benefit on online social networking. 36% of respondents trusted very little to information released in SNSs. MLIS students were not sophisticated users of social networking sites. They have good level of skills on basic features of social networking sites. However, in more advanced features of SNSs such as privacy settings their skills seemed limited. Respondents to the questionnaire showed positive feedback on using SNS for academic and professional purposes.
Conclusion: There are several advantages for integrating SNS in LIS education. Preparing students for employing those technologies on their work places upon their graduation; increasing the employability of LIS students; reaping the educational benefits that SNSs offer and preparing students to teach social networking literacy competencies to others are some of the advantages of incorporating SNSs into LIS education. Authors suggest that skills to master online social networking must be taught in LIS education
Mohammad Hassanzade, Sakineh Alipour-Irangh, ,
Volume 1, Issue 1 (4-2014)
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The purpose of this study was to investigate the relationship between social capital and knowledge sharing at national information centers in Iran.
Method: This applied research was carried out using two questionnaires and a checklist. Data were collected from all the managers, but stratified random sample of staff members of three: main national information centers (National library, Regional Information Center of Science and Technology [RICEST], and Iranian Institute for Information Science and Technology).
Results: research findings indicated that: A) from managers point of view, lowest relationship between social capital and knowledge sharing belonged to the national library. But results gained from staff members credited the lowest situation to the RICEST; B) all of three information centers coined low range of knowledge sharing, therein, National Library with mean 2.17 out of 5 was the tallest among others; C) National Library was the highest among other centers regarding three dimensions (Relations, Trust and Shared norms) of social capital; D) Relationship between knowledge sharing and trust in all three institutes was significant But in Iranian Institute for Information science and Technology the relationship was more outstanding than others; and E) there was significant relationship between shared norms and knowledge sharing in all institutes.
Conclusion: In order for the National Information centers to improve the knowledge sharing culture among their staff members they should internalize mutual trust, provide them with shared norms and improve organizational communication
Yazdan Mansourian, Omid Alipour,
Volume 1, Issue 1 (4-2014)
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The present study aims to explore the coverage and reflection of Knowledge and Information Science news in the Iranian press. Iran Newspaper which is one of the main public newspapers in the country has been selected as the case for this study.
Method: This study used content analysis as its research methodology and adopted an inductive approach in data analysis. All the pages of this newspaper during a year (from March 2011 to March 2012), were downloaded from the newspaper website and their contents were inductively analyzed to identify Knowledge and Information Science related news.
Results: Iran Newspaper, as one of the most well-known and public press in the country, has covered the Knowledge and Information Science news with a reasonable coverage and comprehensiveness. However, the reports are mainly descriptive with less analytic perspective and there is a gap for more analytical news. The collected data were categorized into seven categories and all the concepts and subcategories were presented in the tables. The main identified categories in this study include: Tehran International Book events, other book exhibitions and cultural events, meetings and book awards, reflection of the news related to book and publication industry, scientometrics, reading and its promotion and hinders.
Conclusion: There is a gap of the news from ILISA (Iranian Library and Information Science Association) and also popular notes written by Knowledge and Information Science scholars, librarians and Knowledge and Information Science students. As a result it seems ILISA could be more active in publishing its news and use popular press to introduce this area to the public and promotes its activities in the public domains.
Zeynab Ebrahimi-Milajerd, Nosrat Riahinia,
Volume 1, Issue 1 (4-2014)
Volume 1, Issue 1 (4-2014)
Abstract
Background and Aim: The purpose of this study was to investigate the extent of observing the standards of indexing (ISO 999-1996, BS 3700) of Library and Information Sciences books.
Method: The study used descriptive-analytical methodology and the population consisted of all the Persian books, written and translated, in the field of Library and Information Sciences published from 2006 to 2012 with the back-of-book index. To collect the data, a checklist based on the indexing standards of ISO 1996-999 and B.S. 3700 was used.
Results: The findings indicated that among the 424 books studied, 26% had index, while 71% lacks it. Overall, the extent of observing the evaluation criteria in the book indices in the field of Library and Information Sciences was 56%.
Conclusion: The results suggest that the extent of providing the index for the books in the field of Library and Information Sciences has a relatively optimal condition compared with those in other fields. However, considering the purposes and applications of the field of Library and Information Sciences, the standards in the indices have been poorly observed. Time-consuming nature of indexing, lack of time and accelerating pace of publishing and offering books, economic issues, lack of expert human resources, and lack of enough knowledge about the significance of index are some of the obstacles for preparing the index for the books.
Marziye Saeidizadeh, Azam Sanatjoo, Mohsen Nokarizi,
Volume 2, Issue 1 (4-2015)
Volume 2, Issue 1 (4-2015)
Abstract
Background and Aim: this study aimed to explore the impact of objective complexity and Product of work task on user's interactive information searching behavior.
Method: The research population consisted of MSc students of Ferdowsi university of Mashhad enrolled in 2012-13 academic year. In 3 stages of sampling (random stratified, quota, and voluntary sampling), 30 cases were selected. Each of these cases searched 6 different types of simulated work tasks. Students’ Interactive Information searching behavior was measured by “loud thinking reports” and “log files”. Also, reliable questionnaire (Cronbach's alpha= 0.88) was applied to measure subjective complexity. Validity of tools was verified through Faculties of KIS at Ferdowsi university of Mashhad.
Results: By increasing level of objective complexity, users view more databases, search engines and web OPACs. Also they use more queries and select resources, visit and analyze more results and spend more time during search process. Totally, they feel less satisfaction and success. It was also found that most participants spend more time to search IR systems related to tasks including intellectual product. Also, they iterate queries, use more search facilities, view, analyze and select more search results and resources. At the end, in spite of more interaction, satisfaction and perception of search success were reported not favorable.
Conclusion: It is necessary to pay attention to work task characteristics in designing interactive and personalized IR systems. So, recommendations raised from this study are appropriate to improve and develop Interactive IR systems.
Kobra Babaei, Zahed Bigdeli,
Volume 2, Issue 1 (4-2015)
Volume 2, Issue 1 (4-2015)
Abstract
Background and Aim: The need for cognition to the extent of involvement of the individual in intellectual activities, to assess individual differences in motivation for information processing, individual differences in the tendency of people to engage in and enjoy effortful thinking and, finally, refers to the processing of sound messages. The purpose of this study was to investigate the effect of need for cognition on students' collaborative information behavior.
Method: This study is applied research .Among 340 students graduate from the Faculty of Psychology and Educational Sciences in Allameh Tabatabai University, 181 students were selected randomly and measures need to recognize the information behavior participatory out the questionnaires.
Results: The research findings indicate the need to recognize significant differences by gender and field of study participants were women. Students seeking a partnership relationship between need for cognition and behavior were observed. Information seeking behavior in terms of the need to know their students significant difference was found and thus the need to know the information seeking behavior of effective student participation.
Conclusion: the need for cognition is effective on Students’ collaborative information behavior (CIB).
Nosrat Riahinia, Forough Rahimi, , Leili Allahbakhshian,
Volume 2, Issue 1 (4-2015)
Volume 2, Issue 1 (4-2015)
Abstract
Background and Aim: The main aim of Information storage and retrieval systems is keeping and retrieving the related information means providing the related documents with users’ needs or requests. This study aimed to answer this question that how much are the system relevance and User- Oriented relevance are matched in SID, SCI and Google Scholar databases.
Method: In this study 15 keywords of the most repeated ones that were related to “Human Information Interaction” and its subheadings were selected and searched both in Persian and in English in the mentioned databases for two one week periods. The results were arranged according to the system relevance based on the retrieval and displaying order. From each search the first 10 results were selected and sent to the subject experts and asked them to rank from 1 to 10. Data were descriptively and analytically (using Spearman correlation test) analyzed by SPSS software.
Results: Subject experts’ relevance score in Persian was lower in ISC than SID and higher than Google Scholar. The most subject relevant records were in the third score of system relevance. The records with the lowest system relevance score also had the lowest subject experts’ relevance score. SID in Persian had a strong and positive relation between the both scores but there was no relation in ISC. The highest matching level of the both scores was seen in SID in both languages on the both periods which means more likely to retrieve relevant records.
Conclusion: There is a similar retrieval pattern in both languages with subject expert’s view in SID showing the highest precision which was the lowest in Google scholar in Persian
Mohammadreza Shekari, Fatima Fahimnia, Gholam Reza Heydari,
Volume 2, Issue 1 (4-2015)
Volume 2, Issue 1 (4-2015)
Abstract
Background and Aim: The aim of this study is to assess the Iranian Personal electronic information management of knowledge and information science and medical Library and Information Sciences faculty members based on the Jones model.
Method: This study is kind of application research and in terms of data collection is descriptive and analytical study. The statistical population included faculty of knowledge and information science and medical library and information science in Iranian state universities. Data were collected by a questionnaire that its validity is confirmed by the ISprofessors, and its reliability was measured by Cronbach's alpha coefficient (0.81). To analyse the data, descriptive and inferential statistic using SPSS statistical software was used.
Results: Analysis of the data showed that the average of activities in finding and re-finding were 4.0101, in storage was 4.0783, in organization was 3.2424, in maintaining were 2.8106, in security were 3.1162, in measuring and evaluation was 3.7803, in making sense of things was 3.9192 and total average personal electronic information management was 3.5659. Findings showed that there was nosignificant difference in the personal electronic information management in terms of gender and organizational affiliation, but in terms of the scientific and age there, there were significant differences among faculty members. The differences were found between the scientific degrees of associate professor and professor, and between the age group 36-45 years old and up to 56 years old.
Conclusion: The results indicated that faculty members in the age group 46-55 years old as well as associate degree had the best performance. Also, faculty members of medical library and information science had been used greater extent personal information management activities and personal information management quality women had better than men. Faculty members had desirable operation to Retrieve and store information, but they do not well maintained this information and do not provide appropriate security measures. In general, management of personal electronic information was in the relatively good level.
Fatemeh Navidi,
Volume 2, Issue 1 (4-2015)
Volume 2, Issue 1 (4-2015)
Abstract
Background and Aim: Considering the increasing number of users who interact with online social networks, it can be inferred that these networks have become an essential part of users' lives and play different roles in their everyday life. Therefore, the present study aims to explore the role of these networks in users' everyday-life information seeking.
Method: This research is an applied research with qualitative approach and it was conducted using thematic analysis method. This method includes a semi - structured interview with active users of online social networks.
Results: Results indicate that online social networks play different roles in the users' lives, such as entertainment, education, communication and interactions; accompanied by, news, favorite contents, and up-to-date information; but, these networks face some challenges that affect information seeking behavior of users which compels users to utilize active information seeking.
Conclusion: Richer social capital and diversity of users in an individual's social network leads to the access to more qualitative information which in turn increases the probability of finding the required information and achieving the expected results with the least effort.
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