Human Information Interaction

Designing a research data management literacy model for faculty members in humanities and social sciences at Iranian public universities (affiliated with the Ministry of Science)

nadjla hariri

Introduction
Introduction There are many data collections in decision-making and every day a large number of these data are collected in research projects by humans or by devices and in this data, to better understand the issues related to data, we need to first understand the data and the literacy related to them. Data literacy is defined as information by reading, creating and communicating with data: that we can find data, make information about it, learn the tools to work with data, have less management of it. We can have, analyze and refine data, learn to share data and make simple decisions.
Research data management includes; production, access, tools, storage and reuse of research data with sufficient and easy-to-use help in virtual research infrastructures that form the main part of the monitoring cycle, which itself includes ideation. It is to create or receive, evaluate, select, ingest, preserve, store, access, reuse (Cox and Verban, 2014).
Studies on research data management are now common, while there is a global ease of research data, but it continues to be difficult to keep data easily accessible. Session, we know more than yesterday about the role of research data in the design and implementation of new research, but the trends and infrastructure to support researchers in research data management, need. (Varana, 2024).
Considering the research that has been conducted on research data management literacy, the aim of this study is to determine the components and indicators of management literacy. ) and to provide a suitable model for research data management literacy.
Methods and Materoal
The present study was conducted with a quantitative and survey method and aimed at evaluating and validating the tool built using the proposed research model. The statistical population of the National Institute of Higher Education Research and Planning was 112 academic centers affiliated with the Ministry of Science and the total number of faculty members of the humanities and social sciences of the country's public universities was 8,441. Due to the large volume of data, 360 people were selected using cluster sampling. Then, the questionnaire was completed and descriptive statistical methods (mean, deviation indices, frequency table) and inferential statistics (structural equation modeling and exploratory factor analysis) and SPSS and Smart Pls software were used to analyze the data.
Resultss and Discussion
The findings indicate that the six factors of stakeholders, services, policy, types of literacy, data cycle, and financial issues are critical together, explaining 60 percent of the total variance of changes. Also, the highest level of the level is related to the stakeholders factor with a mean of 4.09 and a standard deviation of 0.57, followed by the factors of services, policy, data life cycle, types of literacy, and financial issues, respectively. Using the Pearson correlation coefficient test, it was shown that all components of research data management literacy have a positive and significant correlation with the set at the 0.01 error level. The coefficients of the factor loadings of the subscales of research data management literacy also have a good understanding of the concept of their analysis and have a strong and significant correlation with their belief.
Conclusion
Research data management contributes to scientific integrity at different levels. When research data management literacy is sufficient, research data are accurate, complete, valid, and reliable. The risk of losing or damaging data, as well as the risk of unauthorized access, is minimized. In addition, research data can be shared with others with minimal effort and individuals can easily confirm the results.
The relationships between the components and indicators of research data management literacy from the perspective of faculty members in the humanities and social sciences of Iranian public universities show that higher than any of these components in improving the quality and efficiency of research, research data management literacy has a positive effect. The search for understanding the methods and infrastructures related to data management is a research for individuals to achieve better research results and valuable results. The results of this study show that different levels of research data management literacy among university professors know, and also need to have literacy skills in research data management that they do and create. Collecting, processing, validating, publishing, sharing, and archiving data are involved, and this is a characteristic of good research data management.

Analysis of Users' Interaction with E-Marketers' Posts on Instagram Social Network

Mohammad Moradi

Social networks and their increasing influence among different users in all parts of the world have made these networks become suitable tools for advertising and e-commerce. Today, businesses have come to understand that social networks are and will continue to be a means of doing business. Instagram is a popular social network based on video and images. This social network is known as one of the powerful marketing tools. The number of views, likes and comments on social networks, including Instagram, plays a significant role in customer decision-making; Because they pay attention to the opinions and reception of other audiences towards that product or post and are influenced. This research analyzes what factors create posts with different levels of popularity. For this purpose, the factors affecting the number of views, likes and comments in an Instagram social network post are extracted and their weight and importance are calculated based on the regression model. Finally, the decision tree model is presented for forecasting and management in order to increase the number of visits, likes and comments.
Methods and Materoal
In this research, the type of research is based on the purpose of applied research. At first, library studies have been used in order to extract factors affecting the amount of visits, likes and comments in Instagram social network marketing posts. The statistical population includes all articles related to the factors affecting visits, likes and comments. The probability sampling method of simple random samples has been used and 30 articles in this field have been reviewed. Then, the data related to the factors identified from the previous stage have been extracted from the pages of big marketers on the Instagram social network. Then, using the extracted data and using the regression model, the weight and importance of each factor affecting the number of visits, likes, and comments of Instagram social network marketers' posts has been calculated. Finally, a decision tree model has been created to predict the status (rate of visits, likes and comments) of a marketing post on the Instagram social network based on the characteristics of that post.
Resultss and Discussion
Directly, factors such as the number of posts, the number of followers, the type of post, the content of the post and the time of the post are potential factors that affect the number of views, likes and comments. According to the obtained results, the "post content with survey" factor with a positive sign and a coefficient of 420,290.616 had the most positive effect on the label, which is the number of visits to a post. The factor "discount post content" with a positive sign and a coefficient of 5417.751 has had the most positive effect on the label, which is the liking of a post. The factor "discount post content" with a positive sign and a coefficient of 2164.016 has had the most positive effect on the label, which is the amount of comments on a post. Also, the type of image post with a regression coefficient of 565.153 and a negative sign in the investigation of factors affecting the number of comments shows that the use of video posts will increase the comments and interaction of customers.

Conclusion
Most of the researches conducted, such as Gkikas et al (2022), Torbarina, Jelenc & Brkljačić (2020), Wahid & Gunarto (2022), etc., only investigated the influence of a few specific factors on the likes and comments of social media posts, and a comprehensive set of factors has not been investigated. Also, these factors were only for checking likes or opinions and not checking both cases. Most importantly, in the studies conducted, only the positive or negative impact of a factor on the number of likes and opinions has been discussed, and their importance has not been determined. In this research, various factors affecting the number of visits, likes and comments of social network posts were investigated. Also, the importance of each factor was determined. In addition, a decision tree model was presented to manage related pages and posts in order to achieve increased likes and comments. Based on the extracted effective factors, calculating the weight and importance of each factor and the created decision tree model, posts can be managed to increase the number of visits, likes and comments

Proposing a conceptual framework for the aesthetics of images on the web: Meta-synthesis and ranking components identified

Nader Naghshineh

Philosophical inquiry into art and beauty within the Western tradition can be traced back to ancient Greece. However, the concept of aesthetic experience gained prominence in the eighteenth century (Stanford Encyclopedia of Philosophy, entry on aesthetic experience, January 20, 2023). According to the Macmillan Dictionary, the term "aesthetics" was coined in Germany during this period and did not achieve acceptance in the English language until the nineteenth century (Macmillan Dictionary). Furthermore, as noted by Boo et al. (2018), the term is derived from the Latin phrase "aisthitiki," which translates to "perception through sensation." The Merriam-Webster Dictionary defines aesthetics as "pleasing appearance." The fundamental meaning of beauty is encapsulated in the notion of "maintaining unity amidst diversity" (Moshagen & Tilsch, 2010, as cited in Venture, 1876).
While beauty is a widely discussed concept in the field of art, it assumes a different significance within human-computer interaction, where it is referred to as "computational aesthetics." In 1994, Jakob Nielsen proposed a set of ten influential factors designed to enhance user interaction systems. Among these factors is the principle of "aesthetic and minimalist design," which highlights the importance of reducing clutter in user interfaces. Understanding the dimensions of aesthetics can assist web designers in creating improved user interfaces. The current research aims to identify, rank, and propose a conceptual framework for the aesthetic components of digital images on the web. The rapid expansion of web-based technologies has led to an increasing volume of data and information production. Concurrently, the understanding of aesthetics—previously discussed in non-web or offline contexts—has now emerged in online environments utilizing digital tools. Moreover, cognitive sciences have gained particular significance in contemporary research priorities. According to Wong and Borman (2014), websites must not only be usable but also visually appealing. Despite extensive research conducted in usability, psychological aspects related to aesthetics within web environments have received considerably less attention (Wong & Borman, 2014). This study aims to address this gap by focusing on identifying the characteristics of images in web environments from an aesthetic perspective.
Methods and Materials
The present research was conducted using a meta-synthesis method. Documents were retrieved from six databases: IRANDOC, ISC, SID, Google Scholar, Emerald, and Web of Science, utilizing a targeted keyword search and systematic approach that included 1,278 documents. Out of these, 54 documents were selected for inclusion in the study following the PRISMA approach. The importance coefficient of the identified codes was calculated using Shannon's qualitative content analysis method. EndNote software was employed for careful document storage and review. Initially, a foundational conceptual framework comprising 22 aesthetic characteristics for web images was developed based on insights from scholars and established sources. Subsequently, through meta-analysis, this framework was expanded to include 32 aesthetic codes applicable to images in web environments.
Results and Discussion
The basic conceptual framework was developed based on aesthetic theories from Kant, Berlyne, Leibniz, Adorno, Birkhoff, and Husserl, incorporating insights from 15 English-language documents that contained two categories, four concepts, and 22 aesthetic codes. Through meta-synthesis, this framework was enhanced to include two categories, four concepts, and 32 codes. In order of priority, the codes "symmetry or proportion" and "lack of complexity" exhibited the highest Shannon importance coefficient within the category of objective aesthetics and classical aesthetic concepts. Additionally, the codes "appealing color combination" and "moderate complexity—not too low and not too high (similar to Berlyne's theory of stimulus complexity)" were identified as having significant relevance within subjective aesthetics and classical notions of beauty. The category of subjective aesthetics pertains to users' perceptions as subjects interpreting images within web environments; conversely, objective aesthetics relates to the design of uploaded images themselves as objects within this interaction. Classical aesthetic concepts address elements that are independent of meaning and appearance; in contrast, semantic aesthetics focuses on aspects related to meaning and associations rather than mere appearances.
Conclusion
It is essential to consider both subjective and objective aesthetic codes equally. This research underscores the importance of scientific collaboration between experts in computer science and humanities to enhance understanding of aesthetics and improve human-computer interactions. The proposed conceptual framework represents a pioneering effort at both national (Iran) and international levels. It is recommended that developers of the Python library "Athec" utilize this conceptual framework to more accurately define the aesthetic characteristics of digital images within web environments by incorporating a broader range of aesthetic codes into their library programming.

Designing a Workplace Digital Curriculum Based on Artificial Intelligence Components

Kourosh Fathi Vagargah

Businesses affected by digital transformations are facing new employee management and development needs. Employees in these companies not only need to acquire the right technical skills, but also have the mindset to help them cope with the new challenges of the digital workforce in the modern world. These changes and needs that are subsequently created in the development path lead to a digital transformation in the training of managers, as trainers and training professionals need to transition to new work forms to find, create and use digital tools to help future managers, companies and employees. The evolving literature of electronic human resource management expresses its challenges and potential. Stone et al. (2015) found that data-driven decision-making environments in the field of human interactions have a high ability to evaluate recruitment volunteers, improve staff levels, as well as provide digital tools for employee training and development. However, most studies in electronic Human Resource Management have concluded that more innovation is needed to improve the efficiency and performance of these digital tools.
In 2010, ifenthaler stated that in the not-too-distant future, when learners become active builders of their learning environments, setting individual goals and creating content structures for the knowledge and content they want to master, we may see the emergence of the true meaning of Constructivism (Ifenthaler, 2010) and that is now when eifenthaler mentioned it 12 years ago, and on this basis, the fundamental issue of research can be seen as the mismatch of the current situation.education and human resource development with new technologies. The digital age requires digital transformation in the most important context of humanity, the platform of teaching and learning. On the other hand, although the severity of the covid-19 pandemic has decreased and training has been resumed from the virtual platform, in the digital world and the volume of available data and the moment-to-moment updating of information, it is never possible to transfer them through face-to-face training. On the other hand, a person does not have the capacity to learn all the information and data available, so it is desirable that what he learns is based on his personal development, interests and expertise to make learning deeper and more effective. So this research seeks to address or adjust these issues to take a step towards improving the education and Human Resource Development situation in the country, and this will be achieved by designing a model of AI-based digital curriculum. To this end, the current research questions include:
1. What are the components of AI from the point of view of commentators?
2. What is the concept of digital curriculum from the point of view of commentators?
3. What are the coordinates of the AI-based digital curriculum model?
Methods and Materoal
Based on the purpose, the present research is applied, and in terms of data collection, it is a qualitative design. Among the various qualitative methods, the grounded theory method of the foundation was used with the constructivist approach of Charmaz. The current research community is all specialists in the field of curriculum, educational technology, educational technology and artificial intelligence, and the samples included 23 specialists. In order to collect information, semi-structured interview, observation and study of documents were used. In order to analyze the data in this research, the three-step method of Susanne Friese including noticing, collecting and thinking was done with the help of Atlas t.i software.
Resultss and Discussion

What are the components of AI from the point of view of commentators?

The components of artificial intelligence consisted of 5 Main and 19 sub-categories. These include charting systems (algorithm, phase logic, classification), learning systems (supervised learning ,unsupervised learning, hybrid knowledge - based systems, reinforcement learning, learning from incomplete data), semantic systems (self-learning, semantic similarity, natural language understanding, prediction), control of complex systems (dealing with nonlinear problems, expert system), neural network model (problem solving, optimization, flexibility, reasoning).
2. What is the concept of digital curriculum from the point of view of commentators?
The concept of digital curriculum has 6 Main and 33 sub-categories. These categories include digital curriculum objectives (increasing the capacity of program design by teachers, developing cognitive skills, meaningful learning experiences, participatory learning opportunities, educational dynamics, research-oriented, educational justice, self-learning), digital curriculum features (stable yet flexible, transforming learning into a lifelong process, balancing the learner and learning environment, using technology in the classroom, digital teaching culture, high compliance capacity), digital curriculum tools (educational games, digital laboratories, electronic libraries, simulators, environmental features of the digital curriculum (interactive, flexible, classroom Networking lessons, personalization of the learning environment), digital curriculum resources (Smart Textbooks,personalization of learning resources, web-based resources, open educational resources, textbooks), evaluation methods in the digital curriculum (online tests, video dialogue, video recorded by the learner, online critical texts, digital evaluation tools, quizzes).
3. What are the coordinates of the AI-based digital curriculum model?
phase curriculum model includes phase1 curriculum (learning based on specific pattern, classification and organization of content, linear learning, learning under external supervision, reinforcement learning and mutual understanding of language), phase2 curriculum (combined knowledge in learning, optimal building learning, learning from incomplete data, reasoning-based learning, predicting the learning process and facing learning problems) and phase3 curriculum (facing non-linear problems, deep learning, unsupervised learning, expertise in learning, semantic parallelism, self-directed learning and flexibility in learning).
Conclusion
Digital transformations have significantly changed teaching and learning practices. The present study examines the new needs of employee development and empowerment in the digital age, identifying the components of artificial intelligence and digital curriculum. The main objective of the present study is to define the components of artificial intelligence and then apply them in the form of digital curriculum elements. In other words, the digital curriculum in the workplace is defined by the components and functions of artificial intelligence.This model is designed based on the phase logic of artificial intelligence and can help to improve the design of the digital workplace curriculum. Based on the background studies, no research was found that could organize the digital workplace curriculum in this way, and therefore, the findings of the current research and the final output were completely unique.

Analysis of personality traits affecting the information-seeking behavior of graduate students of Tabriz University

afshin hamdipour

Information Seeking Behavior is a broad term encompassing a series of actions undertaken to articulate individuals’ information needs, search for information, evaluate it and select relevant data, ultimately leading to its use (Ozowa and Aba, 2017). According to Case and Given (2016), information-seeking is an integral part of human life. They note that humans frequently feel the need for information and actively seek it throughout their daily lives. In their research, which examined the information-seeking behaviors of professionals from various fields, including physicians, nurses, managers, engineers, journalists, customers, and other groups, the authors found significant differences in the information-seeking behaviors of various professions. These differences can be attributed to professional roles, work environments, and specific information needs. As a dimension of human behavior, information-seeking is influenced by numerous factors. Given the critical role of psychological aspects in shaping human information-seeking behavior and their impact on the interaction between humans and information, addressing these factors is vital. The increasing focus on user-centered (human-centered) studies in recent decades highlights the importance of such studies. This research explores personality traits that influence the information-seeking behavior of graduate students at the University of Tabriz.
Methods and Materials
This study used a descriptive-survey method. The statistical population comprised 2,826 graduate students (2,258 master’s and 568 doctoral students from 17 faculties at the University of Tabriz, excluding dependent units and the international campus, during the first semester of the 2022-2023 academic year. The students were enrolled in four fields: humanities, basic sciences, engineering, and agriculture. Using Cochran’s formula, the sample size was calculated to be 338 students selected through stratified random sampling. The study employed a localized version of John and Srivastava’s (1999) questionnaire for data collection. The questionnaire included two sections: six demographic items and 42 items rated on a five-point Likert scale to assess information-seeking behavior and five personality traits (Extraversion, Conscientiousness, Agreeableness, Openness to Experience, Neuroticism). Validity was ensured through expert review by five faculty members, and reliability was confirmed using Cronbach’s alpha, with coefficients ranging from 0.588 to 0.903. Data were analyzed using descriptive statistics (frequency, mean, standard deviation) and inferential statistics (multiple linear regression). Skewness and kurtosis coefficients that fell within ±2 confirmed the normal distribution of the data.
Results and Discussion
The findings of the present study showed that all five dimensions of personality traits (extroversion, conscientiousness, adaptability, acceptance of experience, and neuroticism with averages of 4.13, 3.94, 3.99, 4.11, and 2.69 respectively) have a significant effect on the information-seeking behavior of graduate students at Tabriz University; Specifically, Extraversion, Conscientiousness, Agreeableness, and Openness to Experience demonstrated positive effects, while Neuroticism exhibited a negative effect. Other results showed that among information-seeking behaviors, "referring to the Internet to obtain information" has the highest priority among students, with an average of 4.72. In the extraversion dimension, "being friendly in the process of acquiring information" is the most important, with an average of 4.34. In the dimension of conscientiousness, "observance of order in the process of obtaining information" has the highest average score, with an average score of 4.22. In the adaptability dimension, "tendency to cooperate with others during information searching" has the highest score with an average of 4.29. In the experience acceptance dimension, "having an active imagination in the information seeking process" has the highest rank with an average of 4.42. In the dimension of neuroticism, "being nervous in the process of finding information" is the highest average score (3.03). The results of multiple linear regression also showed that the independent variables, extroversion, conscientiousness, adaptability, acceptance of experience, and neuroticism are significant predictors of information-seeking behavior, which explain 25.6% of the changes related to the dependent variable. Among the 5 independent variables, the conscientiousness variable, with a beta coefficient of 0.220, made a greater contribution than the other variables.
Conclusion
The findings of this study confirmed the effect of five important personality traits on information-seeking behavior. It is expected that librarians and information specialists will consider the different aspects of personality traits in information-seeking behavior and pay attention to the fact that knowledge of these issues will help them to provide effective information services to students. According to the findings of the present study, it is recommended that the libraries of University of Tabriz establish information systems based on individual student differences to facilitate an optimal environment for information searching. In addition, organizing workshops on communication skills can help students perform more effectively in information-seeking activities. These skills can be beneficial for both extroverted and even neurotic students. It is also recommended that information system designers tailor their systems and services based on the needs and personality traits of students. Furthermore, it is recommended that librarians receive the necessary training to identify students’ individual characteristics and differences and provide information services tailored to their personality traits during interactions with users. Finally, offering psychological counseling and stress management support for students can help them reduce their anxiety and improve their performance in information-seeking activities. This is particularly beneficial for students with high neuroticism levels.

Evaluation of the E-Learning System at Kharazmi University Based on the HELAM Conceptual Model

Margan Kian

Objective: Virtual classes, virtual schools, smart schools, and virtual universities, and generally, electronic learning, are considered reliable capacities and capabilities for developing academic skills. The aim of this study is to evaluate the e-learning program at Kharazmi University using the HELAM conceptual model.
Method: This research is practical in terms of its objective and descriptive-survey in terms of method. A quantitative approach was used to collect data. The statistical population consisted of postgraduate students at Kharazmi University. The sample size was 536 postgraduate students, and stratified random sampling was used. A standardized researcher-made questionnaire was used for data collection. The main structure of the questionnaire is based on the HELAM model, along with an additional “overall satisfaction” factor, which was adapted and translated using specialized literature and relevant research. For data analysis, various statistical tests including one-sample t-test and one-way ANOVA in SPSS, and confirmatory factor analysis in R software were used.
Findings: The results indicated that the status of Kharazmi University’s e-learning program, evaluated using the HELAM conceptual model and its seven aspects (student attitude, instructor attitude, system quality, content quality, service quality, support issues, and overall satisfaction), is significantly above the community average with over 99% confidence. Moreover, the support issues aspect showed a significant difference compared to other dimensions, followed by content quality and service quality, which are close to each other and separated from other sub-scales, while system quality, instructor attitude, overall satisfaction, and student attitude have the lowest mean rankings.
Conclusion: Managers and experts at the Information and Communication Technology Center of Kharazmi University should take measures to improve system quality, instructor attitude, overall satisfaction, and student attitude aspects to enhance their performance in parallel with support issues.