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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.
Mohammad Ramin Naderi, Yazdan Mansourian,
Volume 1, Issue 3 (12-2014)
Volume 1, Issue 3 (12-2014)
Abstract
Background and Aim: This study attempts to propose a suggestive model for theorising in the field of Inquiry-Based Information Behaviour (IBiB).
Method: To achieve the research aim, Piaget’s Cognitive Development Theory, Dewey’s Constructivist Theory, as well as IBL Pedagogy were analysed. Taking into account the current information behaviour models and theories which are developed based on the so called theories, we employed deductive reasoning to propose our suggestive model. Methodologically, this interpretivist study is done based on library method and implemented qualitative content analysis technique.
Results: Admitting different cognitive competencies in different age-groups as well as the role of active experience in cognitive development, each child needs her own customized cognitive-appropriate experience to be able to engage with the learning process. The specific style and rate of cognitive development makes children unique learners.
Conclusion: The proposed preliminary conceptual model showed that active learning, open inquiry-based practices, and children’s personalized methods for responding to learning and cognitive needs, all have information ethos. And, children’s IBiB determines the extent to which they could succeed in the above processes. Testing this hypothesis, the IBiB theory which explains this phenomenon needs to be developed.
Mahsa Fardehosseiny, Mohammad Harandipour,
Volume 2, Issue 1 (4-2015)
Volume 2, Issue 1 (4-2015)
Abstract
Background and Aim: In order to comprehension of interactive content and content production standards, and also users interaction with LMSs, and their behavior in dealing with information, the aim of this paper is to examine the users interaction information provided in the eFront application, an open source Learning Management System, by emphasizing SCORM standard.
Method: The method that used in this study, is Netnograghy which is a qualitative method and it is ethnography in online society. All the data is collected online by using the software reports.
Results: by comparing users’ behavior between two courses in eFront application we understand that in courses which the content is provided by text and images need to have an online teacher and that content is not enough. The users who was attended in course “Installing virtual reference desk" not only need information in text format and images but also need to interact with teacher directly. In the first three chapters of this course which have made in film format, users had fewer problems. The users who have attended in course with web conference passed the course more successful than before. In the course "EndNote” which content have made in film format, users could make good communication with information and had less problem.
Conclusion: Provide educational content in the context of "interactive learning management system" should be the terms and conditions. You can not just put the text and image content to reach the desired result. Although content in the form of educational films can provide more help to the user, to achieve better results, we need to fully interactive content which is available to users, in order to control their activity in learning environment.
Hassan Rastegarpour, Faezeh Movahedi, Zeynab Barkhordari,
Volume 2, Issue 4 (3-2016)
Volume 2, Issue 4 (3-2016)
Abstract
Background and Aim: Generally, where learning and teaching concerns, instructional technology’s presence had been certain. Instructional technology by having the standards and components of a profession needs ethics. Therefore, as a professional, an instructional technologist has to yield to ethical criteria, knowledge, standards, and appreciate ethical responsibilities in order to succeed. To recognize the ethical responsibilities in any profession, its stakeholders should be identified first.
Method: This research thoroughly analyzes and goes over the main points of definitions of instructional technology, and explains the educational technology as a profession. The existence of the term “ethics” in the definition of the field, and studying instructional technology as a profession is an entrance into a professional discipline and articulating its code of ethics.
Result: Findings reveals that in Instructional Design Technology discipline, the change in the definition, modifys the functions and responsibilities of professionals in the field. Hence, the field encompasses five separate activities, yet these interaction closely with one another. These activities are: application, development, management, design, and evaluation. Technologists may be involved in one or more of the aforementioned activities, but what is of most value is the ethics in all of them.
Conclusion: It may be concluded that Instructional Technology enjoys professional ethics. So, it should be categorized as a profession withholding rights and ethical responsibility. Stakeholders in this field have dual responsibilities, due to its inherent nature of face-to-face and virtual learning activities.
Leila Khalili, Aziz Hedayati Khoshemehr, Samad Rasoulzadeh Aghdam, Behnaz Sheibany,
Volume 4, Issue 2 (9-2017)
Volume 4, Issue 2 (9-2017)
Abstract
Background and Aim: Information literacy is a key element in the development of an independent and effective learning in higher education in the 21st century. The aim of this study was to investigate the relationship between information literacy and learning motivation in undergraduate students. In addition, the mean score of information literacy was measured based on educational and demographic variables.
Methods: This applied research carried out with a quantitative approach and survey method. The data collection instrument was a questionnaire. The study population was undergraduate students at Azarbaijan Shahid Madani University and 379 samples were selected using stratified random sampling. Descriptive and inferential statistics analyses (Pearson correlation, independent T and ANOVA) were performed using SPSS software.
Results: Students' information literacy based on five standards was 2.48. Based on the findings of the Pearson correlation coefficient with 99 percent confident there was positive and significant relationship between information literacy and learning motivation. In addition there was positive and significant relationship between information literacy and Grade Point Average (GPA) of students with 99 percent confident. Based on independent T-test was not observed a significant difference in male and female students' information literacy. ANOVA test showed a significant difference with 99 percent confident between information literacy and entrance year of students. Based on ANOVA test there was no significant differences on students' information literacy based on field of study.
Conclusion: Students' information literacy unsatisfactory. Information literacy had a positive relationship with learning motivation. Also there was a positive relationship between information literacy and academic performance (GPA). Information literacy of senior students was more than freshmen. Information literacy of students in four areas of study (Humanities, Engineering, Agriculture and Science) was almost similar.
Miss Soheila Khoeini, Dr Nader Naghshineh,
Volume 6, Issue 3 (10-2019)
Volume 6, Issue 3 (10-2019)
Abstract
Background and Aim: E-learning is an important topic in the educational settings and students are significant prerequisites of it, who have an essential role for the acceptance and effective use of e-learning management systems so that knowing their attitudes and mental models is essential for the successful implementation of such a method. Therefore, the aim of this study was to investigate the Adoption Rate of students' mental model with the structure of the learning management system of the University of Tehran using the card sorting method.
Methodology: Research had qualitative approach with card sorting and interview tools. Usabilitest software, descriptive statistics, distance matrix, and hierarchical clustering were used to analyze the data. Sample consisted of 15 postgraduate students at Tehran University (second semester of the academic year 2019-2020) that were interacting with the learning management system (Moodle).
Findings: Findings indicate that out of 42 cards examined, the status and classification of 36 cards (85%) in the learning management system were fully consistent with the participants' mental model and only in some cases such as "Help" and" Recent lessons referred" according to their mental model, users expected these sections to be placed in other categories. As well as labeling; 66% of users found the "settings" tag more appropriate than their "preferences" and the function of some, such as "medal management", "medal preferences" was unclear to them. Also, the categories presented in the three sections: "User Profile", "Quick Access" and "My Lessons" were approved by users.
Conclusion: The results show that the degree of adaptation of students' mental model to the structure of the learning management system of the University of Tehran is at a desirable level.
Mr Sajjad Mohammadian, Dr Nader Naghshineh, Dr Maryam Nakhoda,
Volume 8, Issue 2 (9-2021)
Volume 8, Issue 2 (9-2021)
Abstract
Background and Aim: The meaning of cross-domain recommendation is that instead of dealing with each domain independently, transfer knowledge gained in one domain (source) to another domain (target) and use it. The present article systematically reviews the research in this field in terms of foundations, applications and challenges.
Method: The Prisma guidelines had been used. Search in Persian and English scientific information sources with related keywords were conducted and 98 English language sources were found in the period 2007 to 2021. Applying the initial refinement, inclusion and exclusion criteria by experts, 28 English documents were selected to enter in the systematic review.
Findings: There are four levels of cross-domain recommendations: Attributes, types, items and systems. Machine learning algorithms are used to predict user rating in cross-domain recommendations, and three categories of: Prediction, ranking, and classification criteria are used to evaluate predictions based on confusion matrix. Cross-domain recommendations can be used to increase the accuracy of recommendations, resolve cold start problems, cross-sell, and improve personalization by transferring knowledge between domains. The most challengeable recommendations of cross-domain is the differences between domains. These differences include the mismatch between the properties of the domains and/or unclear relationships between the domains. In addition, differences in domain size and poor performance of basic algorithms in predicting user rating are other challenges in cross-domain recommendations.
Conclusion: While this subject has been shaped in the last decade, but the keen attention of computer science and information researchers shows its importance. Items level are the main category of cross-domain recommendations. Due to the formation of e-business groups, in the future, cross-domain recommendations at the system level will be given more consideration. Cross-domain recommendations could be used to improve the performance of recommender systems, user modeling in human-computer interaction, and e-commerce.
Ms. Maryam Abolghasemi, Dr. Fatemeh Fahimnia,
Volume 8, Issue 4 (2-2022)
Volume 8, Issue 4 (2-2022)
Abstract
Background and Aim: In processing large data, scientists have to perform the tedious task of analyzing hefty bulk of data. Machine learning techniques are a potential solution to this problem. In citizen science, human and artificial intelligence may be unified to facilitate this effort. Considering the ambiguities in machine performance and management of user-generated data, this paper aims to explain how machine learning can be combined with the active citizenship concept. In addition, it discusses the necessary conditions for advancing the citizen science and beyond.
Method: The review method and comprehensive systematic study was applied to assess the concept of machine learning, citizen science and human-computer interaction.
Results: Many research problems seem to be computationally insolvable and may demand human cognitive skills. Therefore, due to classification activities which are performed in the majority of large-scale citizenship science projects, in addition to participants who may learn lessons about the science, machines also learn lessons about human and imitate him and slowly its learning capacity enhances over time. Artificial intelligence, particularly machine learning is a debatable topic with related ambiguities and biases which should strongly take into consideration.
Conclusion: The application of machine learning techniques carries many advantages including classification time cut and masterful evaluations in the process of making decisions on big data sets. However, algorithms usually act as a black box where data biases are not observable at first glance. Taking this problem into consideration may mitigate serious risks arising from the application of such techniques.
, , , ,
Volume 9, Issue 2 (9-2022)
Volume 9, Issue 2 (9-2022)
Abstract
Aim: The purpose is to provide a comprehensive overview of the current state of research in the field of flipped learning and classroom. It is a science metrics attempt to extract and analyze bibliographic networks based on the international scientific indexing (ISI)
Methodology: Systematic search technique was applied: A set of scientific productions indexed in the field of flipped learning and classroom between 1990 and 2018 extracted from the Web of Science Database (3259 records). VOS viewer software was used to analyze bibliographic networks using data mining technique. Text mining techniques were implemented on keywords, titles, and abstracts in order to network scientific graphs.
Findings: Analyzes of published scientific product collections by year, Universities and institutions of higher education and countries are revealed in tables. Bibliographic networking techniques have been applied to countries and Universities to identify the extent of their contribution to science production. The findings of this study were also used to evaluate the quality of scientific output through the number of citations.
Conclusion: Flipped learning is a new educational topic which its significance to other subjects has not been explored nor its various dimensions have been sufficiently and effectively researched.
Professor Saleh Rahimi, Ms Fatemeh Rahimi, Samira Daniali,
Volume 9, Issue 3 (10-2022)
Volume 9, Issue 3 (10-2022)
Abstract
Purpose: Due to increasing the amount of information and the importance of applying images in various fields, the significance of visual literacy is obvious and research showed that visual literacy has a positive effect on learning. So, the purpose of this study was to determine the importance of visual literacy and to identify the necessity of its use in education and learning, as well as to dictate the components of visual literacy to improve learning.
Methodology: Using the library method and utilizing descriptive research method, the literature in the field of visual literacy has been reviewed
Findings: Visual literacy is typically considered as an ability to evaluate, analyze and interpret the meanings of images and their use, also as a main concept is contemplated.
Conclusion: Visual literacy standards help us study, analyze, understand, and recognize visual elements based on a reasonable and pre-defined framework. It makes people benefit more appropriately from information resources in the form of symbols and images. The concept of visual literacy is evolving and requires more advanced tools. Therefore, students must be acquainted with the tools of this technique.
Methodology: Using the library method and utilizing descriptive research method, the literature in the field of visual literacy has been reviewed
Findings: Visual literacy is typically considered as an ability to evaluate, analyze and interpret the meanings of images and their use, also as a main concept is contemplated.
Conclusion: Visual literacy standards help us study, analyze, understand, and recognize visual elements based on a reasonable and pre-defined framework. It makes people benefit more appropriately from information resources in the form of symbols and images. The concept of visual literacy is evolving and requires more advanced tools. Therefore, students must be acquainted with the tools of this technique.
Mansour Torkiantabar,
Volume 10, Issue 1 (6-2023)
Volume 10, Issue 1 (6-2023)
Abstract
Purpose: This study was conducted to evaluate the organizational learning process of university library librarians based on the Marquardt model.
Methodology: This research is applied in terms of purpose and descriptive-survey method. The statistical population includes 552 librarians at the libraries of the Islamic Azad University. The sample size was estimated to be 227 people using the Krejcie Morgan table. To collect the required data for the research, a researcher-made questionnaire was used to assess the five components of learning organization based on the Marquardt model (learning dynamics, organizational transformation, empowerment of individuals, knowledge management, and application of technology). The collected data were analyzed using SPSS26 statistical software and statistical formulas of Kolmogorov-Smirnov, Cronbach's alpha, and t-test.
Findings: The results showed that organizational learning in the libraries of Islamic Azad University has a favorable situation and this situation for all five components (learning dynamics, organizational change, empowerment, knowledge management, and technology application) Was also approved.
Conclusion: Although the results of this study indicate the favorable situation of organizational learning in the libraries of Islamic Azad University, the continuation of this type of learning due to the many changes and developments that we see in the world today for all organizations, especially types Libraries are a basic necessity.
Methodology: This research is applied in terms of purpose and descriptive-survey method. The statistical population includes 552 librarians at the libraries of the Islamic Azad University. The sample size was estimated to be 227 people using the Krejcie Morgan table. To collect the required data for the research, a researcher-made questionnaire was used to assess the five components of learning organization based on the Marquardt model (learning dynamics, organizational transformation, empowerment of individuals, knowledge management, and application of technology). The collected data were analyzed using SPSS26 statistical software and statistical formulas of Kolmogorov-Smirnov, Cronbach's alpha, and t-test.
Findings: The results showed that organizational learning in the libraries of Islamic Azad University has a favorable situation and this situation for all five components (learning dynamics, organizational change, empowerment, knowledge management, and technology application) Was also approved.
Conclusion: Although the results of this study indicate the favorable situation of organizational learning in the libraries of Islamic Azad University, the continuation of this type of learning due to the many changes and developments that we see in the world today for all organizations, especially types Libraries are a basic necessity.
Ebrahim Khalil Abbasi, Alireza Badeleh,
Volume 10, Issue 2 (9-2023)
Volume 10, Issue 2 (9-2023)
Abstract
Objectives: Nowadays, the learning management system is one of the key tools of university education systems. This system increases the efficiency and learning power of students because it activates the students' mind and facilitates the understanding of knowledge. In addition, the use of learning management systems increases students' motivation for active learning and improves their self-confidence in academic exams. Also, these systems reduce the duration of teaching and the workload of teachers and are effective on students' visual literacy.
The design of learning management systems is the result of the collaboration of educational experts and software engineers, who identify the required capabilities and the expected service quality by considering various psychological, social, cultural, educational, and technical aspects. These capabilities and qualities are the basis of the software design of the system that is ultimately used by students and teachers, and the success and failure of the system largely depends on their satisfaction. Therefore, knowing the expectations of students, teachers, and other users using learning management systems and then implementing these expectations in the design and development of these systems is of key importance.
In Farhangian University, education is based on physical and online education, and some university courses have been always held online. Therefore, designing and implementing effective online education software is a high priority for Farhangian University. In addition, the use of information and communication technologies in education is one of the values highly highlighted in the document of Fundamental Evolution of Education.
We identified only a few research that has been conducted to study the expectations of Farhangian University teachers from learning management systems. To fill this gap, in this article we aim at understanding the expectations of teachers of Farhangian University from the learning management systems. Our main objectives are to find out to what extent the teachers feel satisfied with using these systems, what software features of these systems are highly requested by the teachers, what are the main defects the teachers have observed in these systems, and what are the main values of using these systems for education.
Methodology: The research method of this research is based on the qualitative research method of content analysis with comparative categorisation approach to identify and represent the experiences and expectations of Farhangian University teachers from the university's learning management system. For more interaction with the teachers, the interview method was used to gather their expectations, in which they answered the questions of a pre-designed interview. The interview questions were finalized after applying the opinions of some experts in software, educational sciences, and educational technology. The statistical population of the research was all teachers and lecturers of Farhangian University in the academic year 1402-1401. In this research, teachers who had practical and useful experiences in organizing online classes as well as who were experts in this field were invited for an interview, and 12 colleagues declared their presence for the interview. The authors conducted the interview process in parallel and then jointly completed the data forms. Qualitative interviews were conducted in accordance with its standards in a time between 30 to 60 minutes, physically on site or online on the platform of social networks. First, the aim of the interview and its topic were explained to each interviewee and then the interview was conducted. Considering the semi-structured nature of the interview, in addition to the pre-designed questions, other related questions were also asked to clarify the meaning of the answers during the interview. Also, the authors used some examples when there were ambiguities between the interviewer and the participated teachers.In this research, to analyze the data obtained from the interviews, three types of coding including open coding, axial coding, and selective coding were used. In the open coding, the main sentences related to the topic of the research were extracted from the data forms and recorded as primary codes. Then, the created codes were placed in a category based on the similarities. In the stage of axial coding, the primary codes and categories that were created in the open coding were compared with each other and similar items were merged and finally the axial categories were extracted. Then, for similar codes that were placed in the same category, by doing this process, several main topics were obtained that show the main basis of the interviews. If there is an equivalent standard or term for a subject in the field of education or software engineering, that equivalent and definition has been used in the article. Selective coding, based on the results of open coding and axial coding, defines the final framework of categories. The synthesised results then are reported in this article.
Findings: The results show that half of the teachers are satisfied with the learning management systems, three teachers are with relative satisfaction, and the remaining three are unsatisfied. Regarding the expected software features from these systems, we categorised the identified features into two main categories namely generic and user-interface-specific features. Because the participants in the interview do not have specialized knowledge of software engineering, in responding to their expectations from the systems, they expressed a combination of capabilities, quality attributes, and other requirements. Therefore, capability in this research means the general requirements of the participants, which can be a functional requirement, non-functional requirement (quality attributes), software capability restrictions, or a combination of them.Assisting teachers during the online class, learnability, usability, adaptability, error detection and prevention, facilitating the interaction between teachers and students are examples of the generic software features expected by the teachers. The participants also expect that the system provides them information of any events and tasks being progressed. In addition, they expect the system should be flexible and extendable enough so that they can add new tasks and generate new reports.In terms of the user-interface-specific features, the teachers expect the systems provide hedonic, simple, and customisable user interface. They also expect easy access to the system features. In addition, better content representation is another requirement the teacher expressed. The defects observed by the teachers are also categorised in generic and user-interface-specific defects. As for the generic defects, the teachers reported that it is not easy to learn the learning management systems. They also complained about the audio and video latency during the online classes. Another defect reported is that performing some tasks is time consuming which requires going through several steps. Poor and busy user interfaces are main defects reported by most of the teachers regarding the interface of the learning management systems. Inconsistent representation of the application in the mobile devices is another user interface defect. We have also understood that better time management, improved social interaction, and decreased stress of using information and communication technologies for education are the main values indicated by the teachers as the consequences of using the learning management systems. Being respectful and self-disciplined are other values reported by the teachers.
Conclusion: It can be concluded that despite the good features of learning management systems and the teachers’ relative satisfaction with them, there is a need for these systems to be flexible according to the needs of users and give the teachers more control on the education process. Also, interaction with students and features that improve people's participation in the education process are among the most important expectations of teachers from these systems. Moreover, the user interface of learning management systems is very important for teachers because it provides their interaction with students. Teachers expect to be able to adapt the system and especially their user interface according to their needs and add new features at the lowest cost.
We also synthesized several design guidelines for the system developers. The system should provide support for both expert and novice teachers. Expert teachers expect more customization capabilities and novice teachers expect more advice and automatic error handling. Moreover, the system should help the novice teacher to be experienced in using the system continuously. As each teacher has his/her own teaching strategy, another guideline is to design the system so that each teacher takes control on the education and teaching process individually.
Since an online learning system is an interactive application, its user interface design is very important. User-friendly and customisable user interface which nicely represented in different devices with different resolutions is a high priority guideline. This design guideline becomes even more important when we know that most of the students use their mobile phones for participating in online classes. We finally highlighted the importance of considering values in the design and development of learning management systems. In these systems, capabilities such as speaking in turn, the need to obtain permission to speak, remaining silent when others are speaking, and being neat during online presentation are aspects of respect. Their accomplishment, therefore, requires relevant software features. Moreover, the interaction between teachers and students necessitates establishment of ethical rules and standards to be respected by the users and the software. The privacy and security are two important subjects that requires more attention in learning management systems.
The design of learning management systems is the result of the collaboration of educational experts and software engineers, who identify the required capabilities and the expected service quality by considering various psychological, social, cultural, educational, and technical aspects. These capabilities and qualities are the basis of the software design of the system that is ultimately used by students and teachers, and the success and failure of the system largely depends on their satisfaction. Therefore, knowing the expectations of students, teachers, and other users using learning management systems and then implementing these expectations in the design and development of these systems is of key importance.
In Farhangian University, education is based on physical and online education, and some university courses have been always held online. Therefore, designing and implementing effective online education software is a high priority for Farhangian University. In addition, the use of information and communication technologies in education is one of the values highly highlighted in the document of Fundamental Evolution of Education.
We identified only a few research that has been conducted to study the expectations of Farhangian University teachers from learning management systems. To fill this gap, in this article we aim at understanding the expectations of teachers of Farhangian University from the learning management systems. Our main objectives are to find out to what extent the teachers feel satisfied with using these systems, what software features of these systems are highly requested by the teachers, what are the main defects the teachers have observed in these systems, and what are the main values of using these systems for education.
Methodology: The research method of this research is based on the qualitative research method of content analysis with comparative categorisation approach to identify and represent the experiences and expectations of Farhangian University teachers from the university's learning management system. For more interaction with the teachers, the interview method was used to gather their expectations, in which they answered the questions of a pre-designed interview. The interview questions were finalized after applying the opinions of some experts in software, educational sciences, and educational technology. The statistical population of the research was all teachers and lecturers of Farhangian University in the academic year 1402-1401. In this research, teachers who had practical and useful experiences in organizing online classes as well as who were experts in this field were invited for an interview, and 12 colleagues declared their presence for the interview. The authors conducted the interview process in parallel and then jointly completed the data forms. Qualitative interviews were conducted in accordance with its standards in a time between 30 to 60 minutes, physically on site or online on the platform of social networks. First, the aim of the interview and its topic were explained to each interviewee and then the interview was conducted. Considering the semi-structured nature of the interview, in addition to the pre-designed questions, other related questions were also asked to clarify the meaning of the answers during the interview. Also, the authors used some examples when there were ambiguities between the interviewer and the participated teachers.In this research, to analyze the data obtained from the interviews, three types of coding including open coding, axial coding, and selective coding were used. In the open coding, the main sentences related to the topic of the research were extracted from the data forms and recorded as primary codes. Then, the created codes were placed in a category based on the similarities. In the stage of axial coding, the primary codes and categories that were created in the open coding were compared with each other and similar items were merged and finally the axial categories were extracted. Then, for similar codes that were placed in the same category, by doing this process, several main topics were obtained that show the main basis of the interviews. If there is an equivalent standard or term for a subject in the field of education or software engineering, that equivalent and definition has been used in the article. Selective coding, based on the results of open coding and axial coding, defines the final framework of categories. The synthesised results then are reported in this article.
Findings: The results show that half of the teachers are satisfied with the learning management systems, three teachers are with relative satisfaction, and the remaining three are unsatisfied. Regarding the expected software features from these systems, we categorised the identified features into two main categories namely generic and user-interface-specific features. Because the participants in the interview do not have specialized knowledge of software engineering, in responding to their expectations from the systems, they expressed a combination of capabilities, quality attributes, and other requirements. Therefore, capability in this research means the general requirements of the participants, which can be a functional requirement, non-functional requirement (quality attributes), software capability restrictions, or a combination of them.Assisting teachers during the online class, learnability, usability, adaptability, error detection and prevention, facilitating the interaction between teachers and students are examples of the generic software features expected by the teachers. The participants also expect that the system provides them information of any events and tasks being progressed. In addition, they expect the system should be flexible and extendable enough so that they can add new tasks and generate new reports.In terms of the user-interface-specific features, the teachers expect the systems provide hedonic, simple, and customisable user interface. They also expect easy access to the system features. In addition, better content representation is another requirement the teacher expressed. The defects observed by the teachers are also categorised in generic and user-interface-specific defects. As for the generic defects, the teachers reported that it is not easy to learn the learning management systems. They also complained about the audio and video latency during the online classes. Another defect reported is that performing some tasks is time consuming which requires going through several steps. Poor and busy user interfaces are main defects reported by most of the teachers regarding the interface of the learning management systems. Inconsistent representation of the application in the mobile devices is another user interface defect. We have also understood that better time management, improved social interaction, and decreased stress of using information and communication technologies for education are the main values indicated by the teachers as the consequences of using the learning management systems. Being respectful and self-disciplined are other values reported by the teachers.
Conclusion: It can be concluded that despite the good features of learning management systems and the teachers’ relative satisfaction with them, there is a need for these systems to be flexible according to the needs of users and give the teachers more control on the education process. Also, interaction with students and features that improve people's participation in the education process are among the most important expectations of teachers from these systems. Moreover, the user interface of learning management systems is very important for teachers because it provides their interaction with students. Teachers expect to be able to adapt the system and especially their user interface according to their needs and add new features at the lowest cost.
We also synthesized several design guidelines for the system developers. The system should provide support for both expert and novice teachers. Expert teachers expect more customization capabilities and novice teachers expect more advice and automatic error handling. Moreover, the system should help the novice teacher to be experienced in using the system continuously. As each teacher has his/her own teaching strategy, another guideline is to design the system so that each teacher takes control on the education and teaching process individually.
Since an online learning system is an interactive application, its user interface design is very important. User-friendly and customisable user interface which nicely represented in different devices with different resolutions is a high priority guideline. This design guideline becomes even more important when we know that most of the students use their mobile phones for participating in online classes. We finally highlighted the importance of considering values in the design and development of learning management systems. In these systems, capabilities such as speaking in turn, the need to obtain permission to speak, remaining silent when others are speaking, and being neat during online presentation are aspects of respect. Their accomplishment, therefore, requires relevant software features. Moreover, the interaction between teachers and students necessitates establishment of ethical rules and standards to be respected by the users and the software. The privacy and security are two important subjects that requires more attention in learning management systems.
Miss Maryam Emamverdi*, Dr Fatemeh Fahimnia, Dr Sepideh Fahimifar,
Volume 10, Issue 4 (3-2024)
Volume 10, Issue 4 (3-2024)
Abstract
Background and Purpose: In digital humanities, citizens encounter in the creation and consumption of cultural resources, with activities such as transcription and transliteration of text, text correction, punctuation, Classification, description of metadata, labeling, division and annotation of features leading to the enrichment of digitization or production Digital corpuses with different types of annotations. Digital Annotation Tools (DAT) facilitate the development of new digital-based reading strategies and allow learners to highlight digital texts and add textual, video, and image annotations. Social annotation can become a "novice art" for teaching literature. Also, citizen science projects emphasize that education should be considered as a high priority goal. The successful development of a practical method to use the collaborative annotation system to increase the academic output of digital humanities researchers and improve reading in the digital space is still a valuable research topic. Computer-aided transcription tools can speed up the process of reading and transcribing texts. An automatic method for converting Persian texts into phonetic chains has been presented, but in this context, the problem is not limited to automatic phonetic transcription, where the absence of short vowel letters (short vowels) in replacing Arabic letters with Latin characters (letters) leads to poor readability of transliterated text. Reading skill requires many prerequisites, one of them is phonological awareness. The purpose of this research was to Investigate the changes in users' learning and reading experience using annotation types and Investigate the impact of international transliteration training on users' learning and reading experience.
Materials and methods: The approach of this research was quantitative and the research method was semi-experimental and its design was pre-test-post-test. The statistical population includes 30 non-Persian speakers who had no familiarity with the Persian language. In order to reach the target audience and distribute the questionnaires, some Persian language teaching institutions such as Dehkhoda Institute, Persian language teaching center of universities. Also, the sites of some scientific social networks such as Research Gate, Linkedin and Academia were referred and the profiles of people were observed. In order to carry out the research, 30 non-Persian speaking people were selected from the statistical population using a non-probability and targeted sampling method and were randomly assigned into two experimental and control groups (15 people in the experimental group and 15 people in the control group). In terms of gender, 12 were men and 18 were women (40% men and 60% women). In terms of age group, most of them were between 19 and 50 years old, and the average age was 33 years old. In terms of nationality, 2 Iraqis, 2 Americans, 5 Chinese, 2 French, 3 German, 5 Ukrainian, 1 English, 1 Swiss, 3 Japanese, 1 Canadian, 2 Andalusian, 2 Italian and 1 person was Indian. In terms of education level, 12 people were in the doctoral level, 9 people were in the master's level, and 6 people were in the bachelor's level. An interface was designed to collect data. a few verses of Saadi's poems in which words that have the same written appearance but have completely different pronunciations, meanings and grammatical characteristics, were searched and selected. Depending on whether there are different options for pronouncing words or not, we deliberately included possible but incorrect pronunciations for some random words in the question so that they would listen to the voice of the local announcer and then choose one from among the transliterated words of the poem. In each pre-test and post-test stage, users were faced with 23 disambiguation items (in the form of disambiguation by clicking on the correct option among multiple choice options) and 2 correction items (by typing the correct form in the corresponding field). For all 23 questions, there were real words in the Persian language.To design the intervention activity of international transliteration education, the Persian book of the first year of elementary school and the second and third sections, which include the signs of the Persian language, were used. The structure of each sign (letter) in the book to read consists of five parts: big picture - small pictures with the word - written text - new sign - learning activities. Under each sign and thumbnails of each word, international transliteration and audio file were added. The training files were provided to the experimental group and trainings were provided in 4 sessions of 30 minutes and Skype and Google Meet communication software were used. Users of both groups were evaluated twice (before and after the intervention of international phonetic training). The tools used in this research were two questionnaires to perform practical work, one of which was used in the pre-test phase and the other in the post-test phase. After the pre-test, the experimental group received an interactive training support introducing the work program and IPA (International Phonetic Association, 1999) in order to improve the learning experience and develop the educational structure, while the control group did not receive this intervention during the research process. To calculate the distance between the answer chosen by the user and the correct answer in the poem, the method of calculating phonetic distances by Faradi et al (2019) was used.
Findings: For analysis, three independent variables were identified for each item (question); Two of them were interval variables, i.e., word length and Lonstein's phonological weighted distance between the selected answer and the correct answer, the other parameter was a nominal parameter that indicated whether the correct answer was among the selected options or not. The average scores in the learning and user reading experience of the control group in the pre-test was equal to 18.6 and in the post-test it was equal to 19.8, while the average scores in the learning and user reading experience of the experimental group before the international transliteration training was equal to 19.1 and after the international transliteration training, it increased to 23.2. For this purpose, the t-test of independent groups was used after the international transliteration training. The result of independent group t-test indicates the change of scores in users' learning and reading experience between the two experimental and control group after the international transliteration training. The resulting t (3.08) is greater than the critical table's t (1.96), so it can be said that there is a significant difference between the scores in the learning and reading experience of the users between the two experimental and control groups after the international transliteration training (p 0.05 < ). Therefore, according to the evidence, it can be said that the types of annotations (audio, transliteration) have resulted in a significant change in the learning and reading experience of users. In order to answer the second question, "What effect does international transliteration training have on users' experience in reading Persian language poems?" In the descriptive analysis of the information, first the statistical indicators related to the basic variables of the research were calculated. The results showed that in the pre-test stage, the average distance from the correct answer (error rate) of the experimental group was 4.09 and in the post-test stage it reached 1.1, which shows a significant decrease. In the control group, the average distance from the correct answer (error rate) was equal to 3.4 and in the post-test stage it reached 3.6, which did not change much. According to the quasi-experimental design of this research and the use of pre-test and post-test in it and in order to control the effect of the pre-test in it, to analyze the relevant data, the statistical model of covariance analysis has been implemented. Before running any covariance analysis, it was necessary to have homogeneous regression slopes and a linear relationship between the auxiliary random variable and the dependent variable. For this purpose, the interaction between the random variable and the independent variable has been investigated. On this basis, the assumptions of this statistical method, i.e. the assumption of normality of distribution, equality of error variances and homogeneity of coefficients and regression, have been investigated. In the inferential methods section, in order to answer the second question, "What effect does international transliteration training have on the experience of users in reading Persian language poems?" Between-group covariance analysis was used, and the results are presented in separate tables. Due to the fact that the studied groups have been selected completely randomly in compliance with all conditions, therefore, the distribution of the data was completely normal. Another important assumption of covariance analysis is the homogeneity of regression coefficients. Based on the results, it was observed that the interaction of the pre-test distance from the correct answer (error rate) with the independent variable is significant at the confidence level of 0.95 (p < 0.05). One of the assumptions of covariance analysis is the assumption of equality of error variances (homogeneity of variances), which Levin's F was used to check this assumption (to check the post-test of the dependent variable). Based on the reported results, it was observed that the homogeneity of variances is significant at the confidence level of 0.95 (p < 0.05). In order to analyze the results and changes of the dependent scale of the research, univariate covariance analysis between groups was used. The results showed that considering the pre-test scores "distance from the correct answer (error rate)", the difference between the trained group and the control group is significant in the post-test stage. (p<0.05). Distance from the correct answer (error rate) (F = 12.3). The total effect size is equal to 0.314. Considering the eta square, it can be said that 31.4% of the changes and the reduction of the distance from the correct answer (error rate) are caused by the influence of the independent variable (i.e. intervention and international phonetic training), which is in accordance with Cohen's criteria, close to It is moderate and statistically significant. (p<0.05). Therefore, it can be said that international phonetic training has been effective on the distance from the correct answer (error rate).
Conclusion: Level-based achievement was confirmed in the results of the users' learning and reading tests. Annotations act as an attention strategy. Vocabulary marking and adding various annotations and international transliteration were effective for non-Persian speakers to learn and read Persian poetry.The results of this research also highlight the important role of international phonetic training in phonetic transcription and improving the second language reading experience and show the effectiveness of international phonetic training intervention and phonological memory training as a tool to strengthen second language vocabulary development. The limitations of this research include ignoring the influence of the users' native languages, the difference in the phonological systems of different languages, relatively small sample size, different age groups and different education levels, the influence and relationships of these factors. The output of the text-to-phonetic chain conversion program is used in automatic text-to-speech conversion, correct phonetic writing of texts, teaching Persian to non-Persian speakers, dictionary writing, etc. By performing phonetic transcription work in the form of manual disambiguation or correction, users create a database with possible corrections that are suggested to be used for various purposes, including increasing the accuracy of machine transliteration. Using a digital annotation tool that allows more accurate tracking of how each user interacts with the text (through log files, tracking eye movements in the number of times focusing on a word, attention creation mechanisms) Important points for future empirical work in this field. Implementation of computer-based assessment using machine learning method leads to efficient implementation and improvement of PA skills. Although solutions for computer-based phonological awareness assessment for children have been provided in Persian language, computer-based phonological awareness assessment can also be used in teaching reading literature. Implementation of computer-based assessment using machine learning method leads to efficient implementation and improvement of PA skills. Although solutions for computer-based phonological awareness assessment for children have been provided in Persian language, computer-based phonological awareness assessment can also be considered in teaching reading literature.
Materials and methods: The approach of this research was quantitative and the research method was semi-experimental and its design was pre-test-post-test. The statistical population includes 30 non-Persian speakers who had no familiarity with the Persian language. In order to reach the target audience and distribute the questionnaires, some Persian language teaching institutions such as Dehkhoda Institute, Persian language teaching center of universities. Also, the sites of some scientific social networks such as Research Gate, Linkedin and Academia were referred and the profiles of people were observed. In order to carry out the research, 30 non-Persian speaking people were selected from the statistical population using a non-probability and targeted sampling method and were randomly assigned into two experimental and control groups (15 people in the experimental group and 15 people in the control group). In terms of gender, 12 were men and 18 were women (40% men and 60% women). In terms of age group, most of them were between 19 and 50 years old, and the average age was 33 years old. In terms of nationality, 2 Iraqis, 2 Americans, 5 Chinese, 2 French, 3 German, 5 Ukrainian, 1 English, 1 Swiss, 3 Japanese, 1 Canadian, 2 Andalusian, 2 Italian and 1 person was Indian. In terms of education level, 12 people were in the doctoral level, 9 people were in the master's level, and 6 people were in the bachelor's level. An interface was designed to collect data. a few verses of Saadi's poems in which words that have the same written appearance but have completely different pronunciations, meanings and grammatical characteristics, were searched and selected. Depending on whether there are different options for pronouncing words or not, we deliberately included possible but incorrect pronunciations for some random words in the question so that they would listen to the voice of the local announcer and then choose one from among the transliterated words of the poem. In each pre-test and post-test stage, users were faced with 23 disambiguation items (in the form of disambiguation by clicking on the correct option among multiple choice options) and 2 correction items (by typing the correct form in the corresponding field). For all 23 questions, there were real words in the Persian language.To design the intervention activity of international transliteration education, the Persian book of the first year of elementary school and the second and third sections, which include the signs of the Persian language, were used. The structure of each sign (letter) in the book to read consists of five parts: big picture - small pictures with the word - written text - new sign - learning activities. Under each sign and thumbnails of each word, international transliteration and audio file were added. The training files were provided to the experimental group and trainings were provided in 4 sessions of 30 minutes and Skype and Google Meet communication software were used. Users of both groups were evaluated twice (before and after the intervention of international phonetic training). The tools used in this research were two questionnaires to perform practical work, one of which was used in the pre-test phase and the other in the post-test phase. After the pre-test, the experimental group received an interactive training support introducing the work program and IPA (International Phonetic Association, 1999) in order to improve the learning experience and develop the educational structure, while the control group did not receive this intervention during the research process. To calculate the distance between the answer chosen by the user and the correct answer in the poem, the method of calculating phonetic distances by Faradi et al (2019) was used.
Findings: For analysis, three independent variables were identified for each item (question); Two of them were interval variables, i.e., word length and Lonstein's phonological weighted distance between the selected answer and the correct answer, the other parameter was a nominal parameter that indicated whether the correct answer was among the selected options or not. The average scores in the learning and user reading experience of the control group in the pre-test was equal to 18.6 and in the post-test it was equal to 19.8, while the average scores in the learning and user reading experience of the experimental group before the international transliteration training was equal to 19.1 and after the international transliteration training, it increased to 23.2. For this purpose, the t-test of independent groups was used after the international transliteration training. The result of independent group t-test indicates the change of scores in users' learning and reading experience between the two experimental and control group after the international transliteration training. The resulting t (3.08) is greater than the critical table's t (1.96), so it can be said that there is a significant difference between the scores in the learning and reading experience of the users between the two experimental and control groups after the international transliteration training (p 0.05 < ). Therefore, according to the evidence, it can be said that the types of annotations (audio, transliteration) have resulted in a significant change in the learning and reading experience of users. In order to answer the second question, "What effect does international transliteration training have on users' experience in reading Persian language poems?" In the descriptive analysis of the information, first the statistical indicators related to the basic variables of the research were calculated. The results showed that in the pre-test stage, the average distance from the correct answer (error rate) of the experimental group was 4.09 and in the post-test stage it reached 1.1, which shows a significant decrease. In the control group, the average distance from the correct answer (error rate) was equal to 3.4 and in the post-test stage it reached 3.6, which did not change much. According to the quasi-experimental design of this research and the use of pre-test and post-test in it and in order to control the effect of the pre-test in it, to analyze the relevant data, the statistical model of covariance analysis has been implemented. Before running any covariance analysis, it was necessary to have homogeneous regression slopes and a linear relationship between the auxiliary random variable and the dependent variable. For this purpose, the interaction between the random variable and the independent variable has been investigated. On this basis, the assumptions of this statistical method, i.e. the assumption of normality of distribution, equality of error variances and homogeneity of coefficients and regression, have been investigated. In the inferential methods section, in order to answer the second question, "What effect does international transliteration training have on the experience of users in reading Persian language poems?" Between-group covariance analysis was used, and the results are presented in separate tables. Due to the fact that the studied groups have been selected completely randomly in compliance with all conditions, therefore, the distribution of the data was completely normal. Another important assumption of covariance analysis is the homogeneity of regression coefficients. Based on the results, it was observed that the interaction of the pre-test distance from the correct answer (error rate) with the independent variable is significant at the confidence level of 0.95 (p < 0.05). One of the assumptions of covariance analysis is the assumption of equality of error variances (homogeneity of variances), which Levin's F was used to check this assumption (to check the post-test of the dependent variable). Based on the reported results, it was observed that the homogeneity of variances is significant at the confidence level of 0.95 (p < 0.05). In order to analyze the results and changes of the dependent scale of the research, univariate covariance analysis between groups was used. The results showed that considering the pre-test scores "distance from the correct answer (error rate)", the difference between the trained group and the control group is significant in the post-test stage. (p<0.05). Distance from the correct answer (error rate) (F = 12.3). The total effect size is equal to 0.314. Considering the eta square, it can be said that 31.4% of the changes and the reduction of the distance from the correct answer (error rate) are caused by the influence of the independent variable (i.e. intervention and international phonetic training), which is in accordance with Cohen's criteria, close to It is moderate and statistically significant. (p<0.05). Therefore, it can be said that international phonetic training has been effective on the distance from the correct answer (error rate).
Conclusion: Level-based achievement was confirmed in the results of the users' learning and reading tests. Annotations act as an attention strategy. Vocabulary marking and adding various annotations and international transliteration were effective for non-Persian speakers to learn and read Persian poetry.The results of this research also highlight the important role of international phonetic training in phonetic transcription and improving the second language reading experience and show the effectiveness of international phonetic training intervention and phonological memory training as a tool to strengthen second language vocabulary development. The limitations of this research include ignoring the influence of the users' native languages, the difference in the phonological systems of different languages, relatively small sample size, different age groups and different education levels, the influence and relationships of these factors. The output of the text-to-phonetic chain conversion program is used in automatic text-to-speech conversion, correct phonetic writing of texts, teaching Persian to non-Persian speakers, dictionary writing, etc. By performing phonetic transcription work in the form of manual disambiguation or correction, users create a database with possible corrections that are suggested to be used for various purposes, including increasing the accuracy of machine transliteration. Using a digital annotation tool that allows more accurate tracking of how each user interacts with the text (through log files, tracking eye movements in the number of times focusing on a word, attention creation mechanisms) Important points for future empirical work in this field. Implementation of computer-based assessment using machine learning method leads to efficient implementation and improvement of PA skills. Although solutions for computer-based phonological awareness assessment for children have been provided in Persian language, computer-based phonological awareness assessment can also be used in teaching reading literature. Implementation of computer-based assessment using machine learning method leads to efficient implementation and improvement of PA skills. Although solutions for computer-based phonological awareness assessment for children have been provided in Persian language, computer-based phonological awareness assessment can also be considered in teaching reading literature.
Abolghasem Karimi, Alireza Rousta, Majid Ahmadi, Saeid Daniali,
Volume 11, Issue 1 (6-2024)
Volume 11, Issue 1 (6-2024)
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).
Afshin Motaghi Destenaei, Ali Karami, Milad Piri Fath Abad,
Volume 11, Issue 2 (9-2024)
Volume 11, Issue 2 (9-2024)
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
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