In collaboration with Payame Noor University and Iranian Electronic Learning Association

Document Type : scientific-research

Authors

1 Assistant Professor, Department of Educational Sciences, Payame Noor University, Tehran, Iran

2 research expert of Azarbaijan shahid Madani University

10.30473/idej.2023.66989.1140

Abstract

The present study was done with the aim of presenting student retention pattern in e-learning environment of Payame Noor University. This research was carried out using an exploratory (qualitative-quantitative) method. In the qualitative section, the Meta-Synthesis Research method was used to infer the pattern. Using qualitative method, the literature investigation and survey of experts through Delphi method were developed to a set of the dimensions and components affecting the electronics student retention. And the primary pattern was designed with 5 dimensions and 14 components. Then, in a quantitative section, a survey research method was used to validate the pattern and confirm it, and a questionnaire was used for collecting required data. The results of data collection after adjustment and tabling were analyzed by statistical tests (exploratory and confirmatory factor analysis(. To confirm the content validity of the research tool the researcher used the experts and also to determine the validity of the tool measurement construct, she/he used confirmatory factor analysis. All of the questions’ variables were fitted with factor load. The reliability of the tool was confirmed by the Cronbach's alpha coefficient. According to research findings, the components and final dimensions of student retention pattern in the e-learning environment were identified and classified in 5 dimensions including learner, quality of educational services, teacher, environment, technology, and 14 components including psychological characteristics, previous experiences, academic background, computer management skills, interaction, course design, organizational support, teacher's knowledge, teacher's skills, teacher's attitude, supportive environment, job commitment, quality of technology and Internet quality.

Keywords

Main Subjects

Article Title [Persian]

ارائه الگوی ماندگاری دانشجو در محیط یادگیری الکترونیکی (مطالعه موردی دانشگاه پیام نور)

Authors [Persian]

  • محبوبه اسلمی 1
  • ناهید اوجاقی 1
  • سکینه جعفریان 2

1 استادیار رشته علوم تربیتی، دانشگاه پیام نور، تهران

2 کارشناس پژوهشی دانشگاه شهید مدنی آذربایجان

Abstract [Persian]

 
روش‌: این پژوهش با استفاده از روش آمیخته اکتشافی (کیفی-کمی) انجام شد. در بخش کیفی برای استنباط الگو از روش فراترکیب استفاده گردید. بدین صورت که بر مبنای روش کیفی به بررسی ادبیات و نظرسنجی از خبرگان به صورت دلفی به مجموعه ای از ابعاد و مؤلفه های تاثیر گذار بر ماندگاری دانشجوی الکترونیک دست یافته و الگوی اولیه با 5 بعد، 14 مؤلفه و 57 شاخص طراحی شد و سپس در بخش کمی به منظور اعتبار سنجی الگو و تأیید آن از روش پیمایشی و برای گرد آوری داده های مورد نیاز از پرسشنامه استفاده شد. نتایج حاصل از گردآوری داده ها پس از تنظیم و تلخیص با استفاده از آزمونهای آماری (تحلیل عاملی اکتشافی و تأییدی) مورد تجزیه و تحلیل قرار گرفت. برای تأیید روایی محتوایی ابزار پژوهش از خبرگان امر و همچنین به منظور تعیین روایی سازه ابزار اندازه گیری، از تحلیل عاملی تأییدی استفاده شد. که تمامی متغیر گویه ها دارای بار عاملی مناسب بودند. پایایی ابزار نیز با استفاده از آلفای کرونباخ مورد تأیید قرار گرفت.
یافته‌ها: طبق یافته های پژوهش مؤلفه ها و ابعاد نهایی الگوی ماندگاری دانشجو در محیط یادگیری الکترونیک در 5 بعد، فراگیر، کیفیت خدمات آموزشی، مربی، محیط، فناوری، و 14 مؤلفه(ویژگی های روانشناختی، تجارب قبلی، پیشینه تحصیلی،مهارت های مدیریت کامپیوتری، تعامل، طراحی دوره، حمایت سازمانی، دانش مربی، مهارت مربی ، نگرش مربی ، محیط حمایتی، تعهد کاری، کیفیت تکنولوژی و کیفیت اینترنت) شناسایی و طبقه‌بندی شدند.

Keywords [Persian]

  • الگو
  • ماندگاری
  • محیط الکترونیکی
  • References

    • Mahmodi M, Ebrahimzade I. The Analysis of Iranian Students’ Persistence in Online Education. irpodl.2015; 16( 1). [Persian].
    • Carr S. As distance education comes of age, the challenge is keeping the students. chronicle of higher education. 2000; 46(23): 39-41.
    • Levy Y. Comparing dropouts and persistence in e-learning courses. computers & education.2007; 48(2):185-204.
    • Tello SF. An analysis of student persistence in online education. international journal of information and communication technology education.2007; 3(3):47-62.
    • Tinto V. Dropout from higher education: A theoretical synthesis of recent research. review of educational research.1975; 45(1): 89-125. http://dx.doi.org/10.3102/00346543045001089.
    • Morris LV, Finnegan C, Wu S. Tracking student behavior, persistence, and achievement in online courses. the internet and higher education.2005; 8(3): 221–231.
    • Nesler Factors associated with retention in a distance-based liberal arts program.Paper presented at the Northeast Association for Institutional Research. Newport; 1999.
    • Kember D. Open learning courses for adults: A model of student progress. Englewood Cliffs. NJ: educational technology publications; 1995.
    • Parker A. A study of variables that predict dropout from distance education. international journal of educational technology.1999;1(2):1–12.
    • Fjortoft N. Predicting persistence in distance learning programs. Paper presented at the Mid-Western Education Research Meeting. Chicago;1995: IL.
    • Svedberg MK. Self-Directed Learning and Persistence in Online Asynchronous Undergraduate Programs [dissertation]. St Lucia, Qld: Virginia Polytechnic Institute and State University;2010.
    • Sun PC,  Tsai R J,  Finger G, Chen YY, Yeh  What drives a successful e-Lear earning? An empirical investigation of the critica itical factors influencing learner satisfaction. compututers & education. 2008;50(4):1183-202.
    • Lee y, Choi j,Taehyun K. Discriminating factors between completers of and dropouts from online learning courses. british journal of educational technology. 2013; 44 ( 2).
    • Parker A. Identifying predictors of academic persistence in distance education. united states distance learning association journal .2003; 17(1): 55–61.
    • Gianakos I. Predictors of coping with work stress: The influences of sex, gender role, social desirability, and locus of control. Sex Roles. 2002; 46(5): 149–158.
    • Krause N, Stryker S. Stress and well-being: The buffering role of locus of control beliefs. social science & medicine;1984; 18(9): 783–790.
    • Koufaris M. Applying the technology acceptance model and flow theory to online consumer behavior. Information systems research. 2002; 3(2), 205-223.
    • Hsu CL, Chang KC, Chen MC. Flow experience and internet shopping behavior: investigating the moderating effect of consumer characteristics. systems research and behavioral science.2012; 29(3),317-323.
    • Chen H, Wigand RT, Nilan MS. Exploring web users’ optimal flow experiences. information technology & people;2000: 3(4), 263-281.
    • Kiili K. Digital game-based learning: towards an experiential gaming model. Iinternet and higher education. 2005: 8(1), 13-24.
    • Pearce Engaging the learner: how can the flow experience support e-learning?. In Proceedings of world conference on elearning in corporate, government, healthcare, and higher education. Chesapeake (Virginia). 2005.
    • Lee MKO, Cheung CMK, Chen Z. Acceptance of internet-based learning medium: The role of extrinsic and intrinsic motivation. information and management. 2005; 42(8):1095–1104.
    • Venkatesh V, Brown SA. A longitudinal investigation of personal computers in homes: Adoption determinants and emerging challenges. MIS Quarterly. 2001; 25(1): 71–102.
    • Lin C S, Wu Sh, Tsai R J. Integrating perceived playfulness into expectation-confirmation model for web portal context. Information and Management. 2005; 42(5): 683–693.
    • Sheng Z, Jue Z, Weiwei T. Extending TAM for online learning systems: An intrinsic motivation perspective. tsinghua science and technology. 2008; 13(3): 312-317.
    • Osborn V. Identifying at-risk students in videoconferencing and web-based distance education. american journal of distance education. 2001;15(1):41–54.
    • Ivankova NV, Stick SL. Students’ persistence in a distributed doctoral program in educational leadership in higher education: A mixed methods study. research in higher education. 2007; 48(1): 93–135.
    • Chyung SY. Systematic and systemic approaches to reducing attrition rates in online higher education. emerican journal of distance education. 2001;15(3): 36–49.
    • Castles J. Persistence and the adult learner: Factors affecting persistence in Open University students. active learning in higher education .2004; 5(2):166–179.
    • Holder An investigation of hope, academics, environment, and motivation as predictors of persistence in higher education online programs. internet and higher education. 2007; 10(4): 245–260.
    • Moore K, Bartkovich J, Fetzner M, Ison S. Success in cyberspace: Student retention in       online courses. journal of applied research in the community college. 2003; 10(2): 12.
    • Dupin-Bryant P. Pre-entry variables related to retention in online distance education. american journal of distance education. 2004; 18(4): 199–206.
    • Xenos M, Pierrakeas C, Pintelas P. A survey on student dropout rates and dropout causes concerning the students in the course of informatics of the Hellenic Open University. computers & education. 2002; 39(4): 361–377.
    • Hong KS. Relationships between student’ and instructional variables with satisfaction and learning from a web-based course. the internet and higher education. 2002; 5: 267–281.
    • Roca JC, Gagne M. Understanding e-learning continuance intention in the workplace: A self-determination theory perspective. computers in human behavior. 2008; 24:1585–1604.
    • Pituch KA, Lee Y K. The influence of system characteristics on e-learning use. computers & education.2006; ۴٧; ٢٢٢-٢۴۴.
    • Perry B, Boman J, Care WD, Edwards M, Park C. Why do students withdraw from online graduate nursing and health studies education? journal of educators online. 2008; 5(1): 1–17.
    • Bocchi J, Eastman JK, Swift C O. Retaining the online learner: Profile of students in an online MBA program and implications for teaching them. joeb. 2004;79(4):245–253.
    • Barbera E, Clara M, Jennifer A. Linder-Vanberschot. Factors Influencing Student Satisfaction and Perceived Learning in Online Courses. elea. 2013; 10( 3).
    • Young A, Norgard C. Assessing the quality of online sources from the students' Perspective, Internet and higher education. 2006; 9: 107-115.
    • Harper KC, Chen K, Yen DC. Distance learning, virtual classrooms, and teaching pedagogy in the Internet environment. technology in society. 2004; ٢۶: ۵٨۵-۵٩٨.
    • Muilenburg LY, Berge ZL. Barriers to distance education: A factor analytic study. the american journal of distance education. 2001; 11(2): 39–54.
    • Clay MN, Rowland  S, Packard A. Improving undergraduate online retention through gated advisement and redundant communication. journal of college student retention: research, theory and practice. 2009; 10(1): 93–102.
    • Pigliapoco E, Bogliolo A. The effects of psychological sense of community in online and faceto- face academic courses. international journal of emerging technologies in learning. 2008;3 (4): 60–69.
    • Gush T. University teacher competencies in a virtual teaching/learning environment: Analysis of a teacher training experience. teaching and teacher education. 2010; 26( 2): 199-206.
    • Alvarez IT, Guasch A. University teacher roles and competencies in online learning Environments: a theoretical analysis of teaching and learning Practices. european journal of teacher education. 2009; 32( 3); 321–336.
    • Smith T. Fifty-one competencies for online instruction. the journal of educators online.2005; 2(2): 1-18. Retrieved from http://www.thejeo.com/Ted%20Smith%20Final.
    • Soong BMH, Chan HC, Chua BC, Loh KF. Critical success factors for on-line course resources. compedu. 2001;36(2), 101–120. 10.1016/S0360-1315(00)00044-0.
    • Bhuasiri w, Xaymoungkhoun B, Hangjung ZH, Rho J, Ciganek A. Critical success factors for e-learning in developing countries: A comparative analysis between ICT experts and faculty. compedu.2012;58: 843–855.
    • Arbaugh JB. Managing the on-line classroom: a study of technological and behavioral characteristics of web-based MBA. jhtmr. 2002;(2): 203–223.
    • Thurmond VA, Wambach K, Connors, HR. Evaluation of student satisfaction: determining the impact of a web-based environment by controlling for student characteristics. ajde. 2002;16(3):169–189. http://dx.doi.org/10.1207/S15389286AJDE1603_4.
    • Webster Hackley P. Teaching effectiveness in technology-mediated distance learning. academy of management journal.1997; 40(6): 1282–1309.
    • Frydenberg Persistence in University Continuing Education Online Classes, the international journal of research in open and distance learning, University of California Irvine USA.2007; 8( 3).
    • Kemp WC. Persistence of adult learners in distance education. american journal of distance education.2002; 16(2): 65.
    • Packham G, Jones P, Miller C, Thomas B. E-learning and retention: Key factors influencing student withdrawal. education training. 2004; 46(6/7):335–342.
    • Pierrakeas C, Xenos M, Panagiotakopoulos C, Vergidis D. A comparative study of dropout rates and causes for two different distance education courses. international review of research in open and distance learning. 2004; 5(2): 1–13.
    • Arbaugh JB. Virtual classroom characteristics and student satisfaction with internet-based MBA courses. journal of management education. 2000; 24(1):32–54. http://journals.sagepub.com/doi/abs/10.1177/105256290002400104.
    • Arasti Z, Sefidgar A, Zaefarian R. Explanation the Role of the Personal, Environmental and System Factors on the Success of Entrepreneurship Electronic Learning in University of Tehran. jed. 2016; 8(1): 61–79. https://jed.ut.ac.ir/article_55469_7469.html. [Persian[.