فهرست مطالب

مجله تعامل انسان و اطلاعات
سال یازدهم شماره 3 (پاییز 1403)

  • تاریخ انتشار: 1403/09/11
  • تعداد عناوین: 6
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  • مژگان عروجی، نجلا حریری*، فهیمه باب الحوائجی صفحات 1-20
    هدف

    نظر به پژوهش هایی که درباره سواد مدیریت داده های پژوهشی انجام گرفته است، هدف این پژوهش، تعیین میزان مولفه ها و شاخص های سواد مدیریت داده های پژوهشی اعضای هیئت علمی رشته های علوم انسانی و اجتماعی دانشگاه های دولتی ایران (وابسته به وزارت علوم) و ارائه الگوی مناسب با آن است.

    روش پژوهش: 

    پژوهش حاضر با رویکرد کمی و روش پیمایشی و با هدف تایید و اعتباریابی ابزار ساخته شده با استفاده از مدل پیشنهادی پژوهش انجام گرفته است. جامعه آماری شامل 360 نفر از اعضای هیئت علمی گروه علوم انسانی و اجتماعی دانشگاه های دولتی کشور که از طریق روش نمونه گیری خوشه ای انتخاب شدند، پرسشنامه تکمیل و برای تحلیل داده ها از روش آماری توصیفی (میانگین، انحراف معیار، جدول توزیع فراوانی) و آمار استنباطی (مدل سازی معادلات ساختاری و تحلیل عاملی اکتشافی) و نرم افزارهای SPSS و Smart Pls استفاده شده است.

    یافته ها

    حاکی از آن است که شش عامل ذینفعان، خدمات، سیاست، انواع سواد، چرخه حیات داده و مسائل مالی روی هم رفته 60 درصد از واریانس کل متغیرها را تبیین می کنند. همچنین بالاترین میزان میانگین مربوط به عامل ذینفعان با میانگین 4/09 و انحراف معیار 0/57 است و پس از آن به ترتیب عامل خدمات، سیاست، چرخه حیات داده، انواع سواد و مسائل مالی در رتبه های بعدی قرار گرفتند.

    نتیجه گیری

    رابطه مولفه ها و شاخص های سواد مدیریت داده های پژوهشی از دیدگاه اعضای هیئت علمی رشته های علوم انسانی و اجتماعی دانشگاه های دولتی ایران نشان دهنده اهمیت بالای هر یک از این مولفه ها در ارتقاء کیفیت و کارآمدی پژوهش ها است و تاثیر مثبتی در سواد مدیریت داده های پژوهشی دارد. تلاش برای شناخت فرآیندها و زیرساخت های مرتبط با مدیریت داده های پژوهشی به افراد جهت دستیابی به نتایج تحقیقات بهتر و گسترده تر ارزشمند است.

    کلیدواژگان: مدیریت داده های پژوهشی، سواد اطلاعاتی، مدیریت داده، سواد مدیریت داده های پژوهشی
  • ارزشیابی نظام آموزش الکترونیکی دانشگاه خوارزمی مبتنی بر مدل مفهومی HELAM
    نیوشا باقری، مرجان کیان*، مسعود گرامی پور، علی عظیمی، یوسف مهدوی نسب صفحه 2
    هدف

    کلاس های مجازی، مدارس مجازی، مدارس هوشمند و دانشگاه مجازی، و به طورکلی آموزش الکترونیکی، از ظرفیت ها و قابلیت های قابل اتکا برای توسعه مهارت های تحصیلی محسوب می شود. هدف این مطالعه ارزشیابی برنامه آموزش الکترونیکی دانشگاه خوارزمی با استفاده از مدل مفهومی هلام است.

    روش

    پژوهش حاضر ازنظر هدف کاربردی و ازلحاظ روش توصیفی، پیمایشی است. همچنین برای جمع آوری داده ها از روش کمی استفاده شد. جامعه آماری این پژوهش، دانشجویان تحصیلات تکمیلی دانشگاه خوارزمی بود. حجم نمونه تعداد 536 نفر از دانشجویان تحصیلات تکمیلی دانشگاه خوارزمی و روش نمونه گیری، تصادفی طبقه بندی شده بود. برای گردآوری داده ها از پرسش نامه محقق ساخته استانداردشده استفاده شد. در واقع، ساختار اصلی پرسشنامه همان پرسشنامه مبتنی بر مدل HELAM به علاوه عامل «رضایت کلی» است که از متون تخصصی و ادبیات پژوهش مربوطه برای اصلاح و ترجمه آن کمک گرفته شد. به منظور تجزیه وتحلیل داده ها از آزمون های آماری مختلف ازجمله t تک متغیره و تحلیل واریانس یک طرفه در نرم افزار SPSS و از تحلیل عاملی تاییدی در نرم افزار R استفاده شد.

    یافته ها

    نتایج نشان داد که وضعیت برنامه آموزش الکترونیکی دانشگاه خوارزمی با استفاده از مدل مفهومی هلام و هر 7 بعد آن (نگرش دانشجویی، نگرش اساتید، کیفیت سیستم، کیفیت محتوا، کیفیت خدمات، مسائل حمایتی و رضایت کلی)، با بیش از 99% اطمینان مقداری فراتر از میانگین جامعه دارد. علاوه براین، بعد مسائل حمایتی تفاوت معناداری با دیگر ابعاد دارد، سپس کیفیت محتوا و کیفیت خدمات نزدیک به یکدیگر و دورتر از سایر زیرمقیاس ها هستند، و نهایتا ابعاد کیفیت سیستم، نگرش اساتید، رضایت کلی و نگرش دانشجویی نیز دارای پایین ترین میانگین رتبه هستند.

    نتیجه گیری

    مدیران و کارشناسان مرکز فناوری اطلاعات و ارتباطات دانشگاه خوارزمی باید اقداماتی را درجهت بهبود ابعاد کیفیت سیستم، نگرش اساتید، رضایت کلی و نگرش دانشجویی انجام دهند تا عملکرد آن ها نیز به موازات مسائل حمایتی ارتقا یابد.

    کلیدواژگان: ارزشیابی، آموزش الکترونیکی، دانشگاه خوارزمی، پژوهش کمی، مدل مفهومی HELAM
  • محمد مرادی* صفحات 21-39
    زمینه و هدف

    شبکه های اجتماعی و نفوذ هر چه بیشتر آن ها در میان کاربران مختلف در تمام نقاط دنیا باعث شده است که این شبکه ها به ابزارهایی مناسب برای تبلیغات و تجارت الکترونیک تبدیل شوند. شبکه اجتماعی اینستاگرام به عنوان یکی از ابزارهای قدرتمند بازاریابی شناخته می شود و افزایش بازدید، پسند و نظر در اینستاگرام، نقش مهمی در دیده شدن کسب وکارها دارد. هدف این پژوهش تحلیل رفتار کاربران در مواجهه با پست های بازاریابان الکترونیکی در شبکه اجتماعی اینستاگرام به منظور افزایش تعاملات کاربران با پست های بازاریابان الکترونیکی می باشد.

    روش پژوهش:

     طبق مطالعات کتابخانه ای، عوامل تاثیرگذار بر میزان بازدید، پسند و نظر پست های بازاریابان الکترونیکی در شبکه اجتماعی اینستاگرام استخراج شده است. پس از استخراج داده های متناسب با هر عامل شناسایی شده، به محاسبه وزن و اهمیت هر یک بر اساس مدل رگرسیون پرداخته شده است. همچنین با استفاده از تکنیک های داده کاوی به ایجاد مدل طبقه بند درخت تصمیم برای پیش بینی و مدیریت پست ها به منظور افزایش میزان بازدید، پسند و نظر، ایجاد شده است.

    یافته ها

    به طور مستقیم، عواملی مانند «تعداد پست ها»، «تعداد دنبال کنندگان»، «نوع پست»، «محتوای پست» و «زمان پست» عوامل بالقوه ای هستند که بر تعداد بازدید، پسند ها و نظر ها تاثیرگذار هستند. طبق نتایج بدست آمده عامل «محتوای پست با موضوع نظرسنجی» با دارا بودن علامت مثبت و ضریب 420290.616 بیشترین تاثیر مثبت را بر میزان بازدید یک پست داشته است. عامل «محتوای پست با موضوع تخفیف» با دارا بودن علامت مثبت و ضریب 5417.751 بیشترین تاثیر مثبت را بر میزان پسند یک پست داشته است. عامل «محتوای پست با موضوع تخفیف» با دارا بودن علامت مثبت و ضریب 2164.016 بیشترین تاثیر مثبت را بر میزان نظر یک پست داشته است.

    نتیجه گیری

    بر اساس عوامل موثر استخراج شده، محاسبه وزن و اهمیت هر عامل و مدل درخت تصمیم ایجادشده می توان به مدیریت پست ها در جهت افزایش میزان بازدید، پسند و نظر پرداخت.

    کلیدواژگان: شبکه های اجتماعی، بازاریابی الکترونیکی، تحلیل رفتار کاربران، داده کاوی
  • مریم طاوسی، نادر نقشینه*، محمد زره ساز، سیامک محبوب صفحات 40-70

    زمینه و هدف:

     زیبایی در حوزه هنر بسیار کاربرد دارد، اما وقتی وارد حوزه تعامل انسان و کامپیوتر می شود نام «زیبایی شناسی محاسباتی» به خود می گیرد. شناخت ابعاد زیبایی شناسی می تواند به طراحان وب کمک کند رابط کاربری بهتری برای کاربران طراحی کنند. پژوهش حاضر قصد دارد مولفه های زیبایی شناسی تصاویر دیجیتالی در محیط وب را شناسایی، رتبه بندی و یک چارچوب مفهومی پیشنهاد نماید.

    روش تحقیق: 

    پژوهش حاضر با روش فراترکیب انجام شده است. اسناد بازیابی شده از 6 پایگاه گنج ایرانداک، جهاد دانشگاهی، پایگاه استنادی جهان اسلام، گوگل اسکولار، امرالد و وب آو ساینس، با جستجوی هدفمند کلیدواژه ای و رویکرد نظام مند مشتمل بر 1278 سند، بازیابی و تحلیل شدند. تعداد 54 سند با رویکرد PRISMA انتخاب و وارد مطالعه شدند. ضریب اهمیت کدهای شناسایی شده با روش تحلیل محتوای کیفی شانون محاسبه شد. از نرم افزار EndNote برای مطالعه دقیق اسناد استفاده شد.

    یافته ها

    ابتدا چارچوب مفهومی پایه بر اساس نظریات زیباشناختی کانت و برلین و لایب نیتس و آدورنو و بیرکهوف و هوسرل به انضمام 15 سند به زبان انگلیسی، حاوی 2 مقوله و 4 مفهوم و 22 کد زیباشناسی، ترسیم شد. سپس با انجام فراترکیب، چارچوب مذکور به 2 مقوله و 4 مفهوم و 32 کد ارتقا یافت. بر اساس فرمول شانون، دو کد «تقارن» و «عدم پیچیدگی» در مقوله زیباشناسی عینی و دو کد «ترکیب رنگ جذاب» و «پیچیدگی متوسط» در مقوله زیباشناسی ذهنی دارای بیشترین ضریب اهمیت شناسایی شدند.

    نتیجه گیری

    توجه به کدهای زیبایی شناسی ذهنی در کنار زیباشناسی عینی به صورت توامان اهمیت دارد. تحقیق حاضر بر همکاری علمی دو گروه متخصصان علوم کامپیوتر و علوم انسانی در راستای ادراک دقیق زیباشناسی و تعامل بهتر میان انسان و رایانه تاکید دارد. چارچوب مفهومی پیشنهادی، نخستین در سطح ملی(ایران) و بین المللی است.

    کلیدواژگان: زیبایی شناسی محاسباتی، تعامل انسان و رایانه، پیچیدگی بصری، ادراک زیبایی شناسی، بینایی ماشین
  • فرهاد فتحی، کورش فتحی واجارگاه*، اسماعیل جعفری، مجتبی وحیدی اصل صفحات 71-92
    زمینه و هدف

    تحولات دیجیتال وظهور هوش مصنوعی درعرصه آموزش ویادگیری به ویژه در زمینه آموزش مدیران و منابع انسانی نیازمند تغییرات اساسی و نوآوری در رویکردهای آموزشی است. در همین راستا هدف پژوهش حاضر طراحی برنامه درسی دیجیتال در محیط کار مبتنی بر مولفه های هوش مصنوعی بود.

    روش

    پژوهش حاضر بر اساس هدف، کاربردی و از لحاظ نحوه گردآوری داده ها، طرح کیفی است.از میان روش های مختلف کیفی، از روش نظریه داده بنیاد با رویکرد سازنده گرایی شارماز استفاده شد. جامعه پژوهش حاضر، کلیه متخصصان حوزه برنامه درسی، فناوری آموزشی، تکنولوژی آموزشی و هوش مصنوعی هستند و اطلاع رسان ها شامل 23 نفر از متخصصان بودند. به منظور گردآوری اطلاعات، از مصاحبه نیمه ساختاریافته، مشاهده و مطالعه اسناد و مدارک استفاده گردید. به منظور تجزیه و تحلیل اطلاعات در این پژوهش، از روش سه مرحله ای سوزان فریز شامل توجه، جمع آوری و تفکر به کمک نرم افزار اطلس تی آی7 انجام شد.

    یافته ها

    الگوی برنامه درسی فازی شامل برنامه درسی فاز1 (یادگیری مبتنی بر الگوی مشخص، طبقه بندی و سازماندهی محتوا، یادگیری خطی، یادگیری تحت نظارت بیرونی، یادگیری تقویتی و ادراک متقابل زبان)، برنامه درسی فاز2 (دانش ترکیبی در یادگیری، بهینه سازی یادگیری، یادگیری از داده های ناقص، یادگیری مبتنی بر استدلال، پیش بینی روند یادگیری و مواجه با مسائل یادگیری) و برنامه درسی فاز 3 (مواجه با مسائل غیرخطی، یادگیری عمیق، یادگیری بدون نظارت، خبرگی در یادگیری، تشابه یابی معنایی، یادگیری خودراهبر و انعطاف پذیری در یادگیری) بود.

    نتیجه گیری

    این الگو بر پایه منطق فازی هوش مصنوعی طراحی شده است و می تواند به بهبود طراحی برنامه درسی دیجیتال در محیط کار کمک کند. بر اساس مطالعات پیشینه، هیچ پژوهشی به این صورت یافت نشد که بتواند برنامه درسی دیجیتال در محیط کار رابه این نحو سازماندهی کرده  و بنابراین، یافته های پژوهش حاضر و خروجی نهایی، کاملا منحصر به فرد بوده است.

    کلیدواژگان: الگوی برنامه درسی، برنامه درسی دیجیتال، برنامه درسی محیط کار، برنامه درسی فازی، هوش مصنوعی
  • افشین حمدی پور*، هاشم عطاپور، نگین کژایی صفحات 97-112
    زمینه و هدف

    رفتار اطلاع یابی از جنبه های مختلفی رفتار انسان تاثیر می پذیرد. هدف از پژوهش حاضر، شناخت ویژگی های شخصیتی موثر بر رفتار اطلاع یابی دانشجویان تحصیلات تکمیلی دانشگاه تبریز است.

    روش پژوهش: 

    پژوهش حاضر کاربردی است که به روش توصیفی- پیمایشی انجام شده است. جامعه آماری پژوهش، کلیه دانشجویان تحصیلات تکمیلی دانشگاه تبریز (2826N=) بود، که 338 نفر با روش طبقه ای تصادفی به عنوان نمونه آماری انتخاب و پرسشنامه بین آن ها توزیع شد. به منظور تحلیل داده ها از آمار توصیفی و استنباطی (فراوانی، میانگین و انحراف معیار و رگرسیون خطی چندگانه) استفاده شد.

    یافته ها

    یافته های پژوهش حاضر نشان داد که تمام پنج بعد ویژگی های شخصیتی (برون گرایی، وظیفه شناسی، سازگاری، استقبال از تجربه و روان رنجوری) بر رفتار اطلاع یابی دانشجویان تحصیلات تکمیلی دانشگاه تبریز تاثیر معنی داری دارند؛ به طوریکه برون گرایی، وظیفه شناسی، سازگاری و استقبال از تجربه تاثیر مثبت و روان رنجوری تاثیر منفی بر رفتار اطلاع یابی آنها دارد. یافته های رگرسیون خطی چندگانه نیز نشان داد از بین متغیرهای مستقل، متغیرهای برون گرایی، وظیفه شناسی، سازگاری، استقبال از تجربه و روان رنجوری پیش بین های معنی داری در رفتار اطلاع یابی هستند که در مجموع 6/25 درصد از تغییرات مربوط به متغیر وابسته را تبیین می کنند. در بین 5 متغیر مستقل، سهم متغیر وظیفه شناسی با ضریب بتای 0/220بیش از سایر متغیرها بوده است.

    نتیجه گیری

    یافته های پژوهش حاضر، تاثیر پنج ویژگی مهم شخصیت را در رفتار اطلاع یابی تایید کرد. انتظار می رود کتابداران و متخصصان اطلاعات در بررسی رفتار اطلاع یابی ابعاد مختلف ویژگی های شخصیتی را مدنظر قرار دهند و به این نکته توجه نمایند که آگاهی از ویژگی های شخصیتی، آن ها را در ارائه خدمات اطلاعاتی موثر به دانشجویان یاری خواهد کرد.

    کلیدواژگان: رفتار اطلاع یابی، نیاز اطلاعاتی، ویژگی های شخصیتی، تفاوت های فردی، دانشجویان تحصیلات تکمیلی
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  • Mozhgan Oroji, Nadjla Hariri*, Fahimeh Babalhavaeji Pages 1-20
    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.

    Keywords: Research Data Management, Information Literacy, Data Management, Research Data Management Literacy
  • Evaluating the E-Learning system of Kharazmi University using the HELAM Conceptual Model
    Niusha Bagheri, Margan Kian*, Masoud Gramipour, Ali Azimi, Youssef Mahdavi Nesab Page 2
    Purpose

    Institutions such as virtual classes, schools, and universities are essential tools for enhancing academic skills. This study aims to investigate the effectiveness of Kharazmi University's e-learning program by applying the HELAM conceptual model as a framework for evaluation.

    Research method

    This study employed a survey research design. The target population consisted of graduate students at Kharazmi University, from which a random sample of 536 postgraduate students was selected using stratified random sampling method. A researcher-made questionnaire, based on the HELAM model and supplemented with an "overall satisfaction" factor, was used to collect data. The questionnaire was refined and translated using specialized texts and relevant research literature. Data analysis was conducted using various statistical tests, including one-sample t test and one-way analysis of variance (ANOVA) in SPSS software, as well as confirmatory factor analysis in R software.

    Findings

    The findings revealed that Kharazmi University's e-learning program, as evaluated using the HELAM conceptual model, exceeded the societal average in all seven dimensions with a high degree of confidence (>99%). Notably, the support issues dimension stood out as significantly different from the others. The dimensions of content quality and service quality were found to be closely related, yet distinct from the other subscales. Finally, the dimensions of system quality, professors' attitude, overall satisfaction, and student attitude had the lowest average rankings

    Conclusion

    To further enhance the e-learning program, the managers and experts at Kharazmi University's Information and Communication Technology Center should focus on improving the system quality, professors' attitude, overall satisfaction, and student attitude dimensions. By doing so, they can elevate the performance of these areas to match the already strong support issues dimension, ultimately leading to a more comprehensive and effective e-learning experience.

    Keywords: Evaluation, E-Learning, Kharazmi University, Quantitative Research, HELAM Conceptual Model
  • Mohammad Moradi* Pages 21-39

    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

    Keywords: Social Networks, E-Commerce, User Behavior Analysis, Data Mining
  • Maryam Tavosi, Nader Naghshineh*, Mohammad Zerehsaz, Siamak Mahboub Pages 40-70

    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 teninfluential 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.

    Keywords: Computational Aesthetics, Human-Computer Interaction, Visual Complexity, Aesthetic Perception, Computer Vision
  • Farhad Fathi, Kourosh Fathi Vagargah*, Esmaeil Jafari, Mojtaba Vahidi Asl Pages 71-92

    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, wemay 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 educationalresources, 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.

    Keywords: Curriculum Model, Digital Curriculum, Workplace Curriculum, Phase Curriculum, Artificial Intelligence
  • Afshin Hamdipour*, Hashem Atapour, Negin Kajaiee Pages 97-112

    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.

    Keywords: Information-Seeking Behavior, Information Need, Personality Traits, Individual Differences, Postgraduate Students