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Showing 13 results for Interaction
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.
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
Faeze Delghandi, Ghasem Azadi Ahmadabadi,
Volume 1, Issue 2 (7-2014)
Volume 1, Issue 2 (7-2014)
Abstract
Background and Aim: Interaction in a digital library help users locating and accessing information and also assist them in creating knowledge, better perception, problem solving and recognition of dimension of resources. This paper tries to identify and introduce the components and elements that are used in interaction between user and system in search and retrieval of information in digital library.
Method: This paper investigate the different studies in the field of interactive search of digital library by the library approach and also identifies capabilities that can be used in digital library by the use of heuristic methods.
Results: The components of identified, interactive search including: 1. Interaction before search including using directories, applying ontology, and filtering. 2. Interaction in the time of search including annotation, suggesting the interactive terms by the use of subjective thesaurus. Syndicate list, colon searching, filtering. Interaction after search: motion, fragment, copying, collecting, cutting, segmentation, probing, rearrangement, re illustration, searching, visualization based on graph, personalization and recommendation systems.
Conclusion: With perception of interaction and interactive search, the efficient design and evaluation of digital library can be done. The designers of digital libraries can use different interaction according their resources. Librarian also can familiar with different interaction and would be able to give their users necessary education until they can search and retrieval efficiently.
Khadijeh Gholami, Shahnaz Mohammadi,
Volume 1, Issue 4 (3-2015)
Volume 1, Issue 4 (3-2015)
Abstract
Background and Aim: Pregnant women are the most active users of health information with regard to the growing use of the Internet as an important source of information. This research studies the information interactions of pregnant women in Ninisite forum, andis to create awareness of pregnant women. This site is the largest Persian-language site for women's issues, pregnancy and parenting.
Methods: This applied research uses qualitative content analysis and semiotic analysis techniques. Content analysis method was used to identify the topics of the exchanged messages. Semiotic method was used to explore the dimensions of trust between users. All messages in the two sections of before and during pregnancy in a 6-month period (from October to March 2013) were studied.
Results: Analyzing two forums related to before and during pregnancy showed that users talk about different aspects of life. Medical, sales, education, recreation, friendships and communing, religion and lifestyle and empathy were the main themes of discussions. Most common information needs of women are: infertility disorders, diagnosis and treatment, the symptoms and complications of pregnancy, the fetus disorders, the pregnancy and delivery and birth. Cognitive and affective dimensions were the main components of trust among users.
Conclusion: Results reveals the potential of forums for information exchange, therefore, applying forums for the dissemination of health information is recommended. It is also important that health officials control the quality of health information on the web.
Zeinab Sedighi, Alireza Isfandyari-Moghaddam, Atefeh Zarei,
Volume 3, Issue 2 (9-2016)
Volume 3, Issue 2 (9-2016)
Abstract
Purpose: Digital library user interfaces has a determining role in desirable performance of this kind of libraries. Digital Library service providers to the blind and deaf users will have their best performance when the users (deaf and blind users) could have a proper interaction with them. This study aims to evaluate and analyze the criteria related to user interface in digital libraries service provider to the blind and deaf users in the world.
Methodology: This is an applied and evaluative study. This study has been used library research method for extracting criteria and descriptive survey method for everything from evaluating library. A checklist consist of 13 criteria (Screen capabilities, Ability of organizing the screen, Search facilities, Links, Visual symbols and images, Options for customizing the texts, Help services and Tips, Error message alert, Interaction and feedback facilities, user interface Language, User controls, Library resources in various format, Study facilities of retrieved resources from the library) and 109 component is prepared. Then have been finalized by using the Delphi method and completed for each Library. 12 digital library service providers to blind and deaf user in the world perform the population.
Finding: Results of the study showed that the criteria of '' user interface language '' (with 97.92 %) and the criteria of '' user control'' (with 9.67 %) reached the highest and lowest scores respectively. Among the 12 digital libraries investigated worldwide, "Digital library for the blind" with respect 63.53 reached the maximum and "U.S. National Library Service for Blind" with respect 37.62 grasped the minimum levels.
Sara Behimehr, Yazdan Mansourian,
Volume 5, Issue 1 (6-2018)
Volume 5, Issue 1 (6-2018)
Abstract
Background and Aim: considering the significance of cognitive and psychological factors in human-information interaction, this study investigates the role of cognitive biases in academic information behavior of postgraduate students at Kharazmi University.
Methodology: this qualitative research used classic grounded theory as the method. The research population included all postgraduate of Kharazmi University who were at the writing up stage. Twenty-five students were chosen through a purposive convenient sampling process. The data were collected through semi-structured interviews and were analyzed based on the classic grounded theory coding and qualitative content analysis. Eisenberg & Berkowitz information behavior model was used as the framework.
Findings: The results showed that cognitive biases influence people's information behavior and 28 of cognitive biases were identified. Cognitive biases interfere on different six steps of Eisenberg & Berkowitz information behavior model. Biases can play a role in identifying and explaining information needs, selecting information resources, selecting information centers, using information, organizing and sharing information and evaluating the information seeking process.
Conclusions: cognitive biases can affect or be affected by other factors involved in information behavior. Failure in information access and information retrieval, information avoidance and also failure in resolving information needs are some of cognitive biases’ consequences. Cognitive biases should be studied in different contexts so that other aspects can be more thoroughly understood.
Dr Rouhollah Tavallaei, Dr Navid Nezafati, Mr Mohammad Milad Ahmadi,
Volume 6, Issue 1 (4-2019)
Volume 6, Issue 1 (4-2019)
Abstract
Background and Aim: Today, knowledge is essential to the survival and success of any organization. Given that they are the people who create, share and use knowledge, an organization cannot effectively use knowledge unless its employees are willing to share their knowledge and attract knowledge of others. On the other hand, Gamification involves combining the usual mechanisms in the games with the work space, in order to make things more appealing and to direct human behavior towards the goals of the system. Gamification is actually the use of game components on issues other than the game and emphasizes the use of entertainment and pleasure in the work. In this research, it has been tried to study and recognize the Gamification, its dimensions, mechanisms, dynamics, and models, using Grounded Theory method; and the following, we try to find a way to understand the role of the Gamification and apply it to the sharing and dissemination of knowledge.
Methods: Grounded Theory is an inductive method of theoretical discovery that allows the researcher to develop a report on the general characteristics of the subject; While simultaneously consolidating the basis of this report in empirical data observation. Using purposeful sampling, research data were collected using interviewing tools and analyzed through data analysis and coding principles.
Results: In this research, after formulating the stages of Grounded Theory and types of data encoding, in the end, a theory with visual modeling is presented and evaluated.
Conclusion: The dimensions of the proposed framework include the “organizational context”, “game-related dimensions”, and “other indicators that affect the Knowledge Sharing- Gamification”. Each of these dimensions includes lower-level components that are described in the model's description. For example, “organizational context” includes incomes, costs, platform, and so on, and “game-related dimensions” include the mechanisms, dynamics and aesthetics of the game
Heidar Mokhtari, Noroz Shafitabar Samakosh,
Volume 6, Issue 3 (10-2019)
Volume 6, Issue 3 (10-2019)
Abstract
Background and aim: Analyzing the newly-emerged field of human-information interaction (HII) needs to study the field from diferent aspects and perspectives. Religious literature, including among others the Koran is one of main resources for doing so. This study aimed to extract and explain the guidelines and main points related to HII by deeply reflecting on Koranic verses and chapters that regard this notion.
Method: By applying Koranic scientific and topical interpretion approach and using a qualitative content analysis, the related guidelines and points were extracted from the Koran by using a comprehensive and popular 10-volumed Persian interpretion book, Tafsīr Nūr authored by an Iranian scholar, Muhsin Qarā‘tī.
Findings: 25 Koranic guidelines on HII were found from the Koran. In total, the Koran has direct and indirect guidelines on HII, including information search, evaluation, dissemination, use and verification, and approaches of sending-receiving and using information.
Conclusion: Considering the Koranic guidelines on HII and conducting further research on them can resolve many challenges of encountring information of any types in the information age
, ,
Volume 7, Issue 1 (6-2020)
Volume 7, Issue 1 (6-2020)
Abstract
Aim: There is a framework of the organizational cybernetics so-called Viable System Model (VSM) that identifies a viable management plan in all general key channels of information that need to be maintained. The purpose was to evaluate the constructive facets of the organization and identify areas of organizations cybernetics.
Methodology: Theoretical analysis method evaluates the VSM among the scientific articles published from 2000 to 2020, which are indexed in Scopus and ISI databases. Thus, of 180 articles, the abstract and content of 51 were reviewed.
Findings: A viable system (VS) is composed of five interactive sub-systems which may mapped on aspects of organizational structure. The study of the organizations’ collaboration based on the type of environment among the selected articles stated that the highest rate of application of the VSM with 52.9% were in industrial units and then educational institutions with 21.6%.
Conclusion: Understanding the structure and operational functions, evaluating the complexities of systems and its subsets and guiding effective mechanisms in achieving the goals and maintaining the sustainability of the organization are the most important advantages and achievements of the VSM with the organizational cybernetic approach. Using the VSM of software allows users to structure, design and review the system levels of organizations well.
Hossein Narimani, Safiyeh Tahmasebi Limooni, Mitra Ghiasi,
Volume 8, Issue 1 (5-2021)
Volume 8, Issue 1 (5-2021)
Abstract
Purpose: The significance of collaboration of librarians and academicians in enhancing the students’ academic success and research capacity has been increasingly recognized by scholars. The purpose of this study is to provide a model of the impact of research interactions between faculty members and students with librarians on scientific products with a grounded theory approach.
Methodology: This is a qualitative approach using grounded theory research method. Data collection was a semi-structured interview using snowball sampling. Twenty interviews were conducted with faculty members of Mazandaran University and experts working at the University. Data analysis was performed in three stages of open coding, axial coding and selective coding based on which a qualitative research model was designed.
Findings: The results showed that out of a total of 264 open codes identified, 44 were central codes and in conclusion 10 selected codes were identified. A paradigm model including: Improving the research interactions of faculty members and students with librarians in scientific productions as a Axial category and causal conditions (training skills and information literacy, interaction and knowledge experiences), contextual factors (information and research consulting, resources Interventional conditions (research rules and regulations), strategies (evaluation of scientific output, management of research resources) and consequences (increase of scientific production, cost management and teamwork) was set.
Conclusion: Conferring inadequate studies inspecting factors affecting the interactions of faculty members and students with librarians on the scientific products of the University, results of this study will be beneficial in identifying the research priorities of the University and supporting researchers, enabling extensive and challenging research activities and improving research methods
Hamidreza Rostami, Shaban Elahi, Ali Moeini, Alireza Hassanzadeh,
Volume 8, Issue 4 (2-2022)
Volume 8, Issue 4 (2-2022)
Abstract
Background: Semantic technologies (STs) have made machine reasoning possible by providing intelligent data management methods. This capability has created new forms of interaction between humans and STs, which is called "semantic interaction." The increasing spread of this form of interaction in daily life reveals the need to identify the factors affecting it and introduce the requirements of a synergistic interaction, which in this study is interpreted as a model of symbiosis.
Purpose: The main purpose of this study is to investigate what, why, and how human-ST symbiosis occurs in the form of a symbiosis model. Providing such a model could be valuable in developing active strategies in the face of intelligent technologies.
Methodology: The study introduces actor-networks of human symbiosis based on the actor-network methodology. Data was collected through in-depth interviews with eight managers, experts, and users in the Computer Research Centers of Islamic Sciences (CRCIS) and examined using the actor network method. All phases of data collection, implementations, coding and analysis were done under NVivo software.
Findings: In the human-ST symbiosis, beside human eleven other actors: Semantic products, context, infrastructure, data, knowledge, social media, Web, scientific centers, organization, AI and ontology are identified. Their interaction establishes seven dynamic actors-networks of symbiosis: Product design and development, use, leadership and management, data, knowledge management, training and contextual conditions.
Conclusion: Semantic products alongside human beings are independent, autonomous, and self-aware actors who are able to go beyond mere mediation of change and govern social change in the Human-ST symbiosis. In such circumstances, man, as the creator and maintainer of the semantic product, in addition to strengthening the technical capabilities in the creation of the product, must entrust to the product the things that the product is able to do.
Purpose: The main purpose of this study is to investigate what, why, and how human-ST symbiosis occurs in the form of a symbiosis model. Providing such a model could be valuable in developing active strategies in the face of intelligent technologies.
Methodology: The study introduces actor-networks of human symbiosis based on the actor-network methodology. Data was collected through in-depth interviews with eight managers, experts, and users in the Computer Research Centers of Islamic Sciences (CRCIS) and examined using the actor network method. All phases of data collection, implementations, coding and analysis were done under NVivo software.
Findings: In the human-ST symbiosis, beside human eleven other actors: Semantic products, context, infrastructure, data, knowledge, social media, Web, scientific centers, organization, AI and ontology are identified. Their interaction establishes seven dynamic actors-networks of symbiosis: Product design and development, use, leadership and management, data, knowledge management, training and contextual conditions.
Conclusion: Semantic products alongside human beings are independent, autonomous, and self-aware actors who are able to go beyond mere mediation of change and govern social change in the Human-ST symbiosis. In such circumstances, man, as the creator and maintainer of the semantic product, in addition to strengthening the technical capabilities in the creation of the product, must entrust to the product the things that the product is able to do.
Jamileh Nateghifar, Soraya Ziaei, Esmat Momeni, Faezeh Delghandi,
Volume 8, Issue 4 (2-2022)
Volume 8, Issue 4 (2-2022)
Abstract
Background and Aim: The aim was to identify the targeted search, work duty, cultural and social-Organizational dimensions of information retrieval contexts using the Ingwersen and Jarvelin framework.
Method: Content analysis and systematic review of articles between 1991 and 2021(1371 – 1400) in domestic and foreign databases steered via: Scopus, Science Direct, Emerald, ProQuest, Eric, Comprehensive Humanities Portal, Noormags, Magiran, SED, and Civilica. Six steps including: Identifying keywords; retrieving Ingwersen and Jarvelin; Selection of the article by subject matter and validity of the sources; Search the background and extract a list of related articles; Prepare final articles and; Open coding were completed. Data collection was based on keywords extraction and texts information. Population, information sources, and validity of the results were guaranteed by the systematic classification process, based on past researches.
Findings: Keywords related to the dimensions of information system substances and retrieval of Ingwersen and Jarvelin was identified among 53 out of 3048 articles. Dimensions, criteria and open codes of identified matters, information retrieval context including: Comprehensiveness; Obstacle; Effectiveness; Information and process quality; Targeted search context includes; usability; Information and process quality. Task context includes: Information quality and work process / results. Cultural and social-organizational context including: socio-cognitive relations; The quality of the task result.
Conclusion: Real information is the result of human understanding of data sources during the process of interaction, communication and information. Interactive approach helps to understand human information behavior. Equally, interactive information retrieval provides theoretical framework for understanding its nature and analysis by considering the dimensions of the contexts.
Method: Content analysis and systematic review of articles between 1991 and 2021(1371 – 1400) in domestic and foreign databases steered via: Scopus, Science Direct, Emerald, ProQuest, Eric, Comprehensive Humanities Portal, Noormags, Magiran, SED, and Civilica. Six steps including: Identifying keywords; retrieving Ingwersen and Jarvelin; Selection of the article by subject matter and validity of the sources; Search the background and extract a list of related articles; Prepare final articles and; Open coding were completed. Data collection was based on keywords extraction and texts information. Population, information sources, and validity of the results were guaranteed by the systematic classification process, based on past researches.
Findings: Keywords related to the dimensions of information system substances and retrieval of Ingwersen and Jarvelin was identified among 53 out of 3048 articles. Dimensions, criteria and open codes of identified matters, information retrieval context including: Comprehensiveness; Obstacle; Effectiveness; Information and process quality; Targeted search context includes; usability; Information and process quality. Task context includes: Information quality and work process / results. Cultural and social-organizational context including: socio-cognitive relations; The quality of the task result.
Conclusion: Real information is the result of human understanding of data sources during the process of interaction, communication and information. Interactive approach helps to understand human information behavior. Equally, interactive information retrieval provides theoretical framework for understanding its nature and analysis by considering the dimensions of the contexts.
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