Explaining student retention based on psychological characteristics, previous experiences, academic background, management and computer skills in the e-learning environment.

Ojaghi, N.; Esmaeili, Z.; Sarmadi, M.R.; Saeidipour, B.

doi:10.22061/jte.2018.3294.1840

Document Type : Original Research Paper

Authors

Department of Education and Psychology, Payame Noor University, Tehran, Iran

https://doi.org/10.22061/jte.2018.3294.1840

Abstract

Background and Objective:e-learning is a new way of teaching that provides and manages learning opportunities to transfer knowledge and skills through the Internet and computer networks. At present, traditional teaching methods are no longer able to meet the needs of continuous growth and development of educational skills. New technologies offer more, newer and more exciting learning opportunities, such as the opportunity to gain a learning experience tailored to each student's ability and way of learning. e-learning through the use of information and communication technology breaks down the boundaries of access and time and provides new tools for learners to learn. On the other hand, because in Iran it is not possible to benefit from higher education in the form of face-to-face classes for all applicants, the expansion of e-learning provides a golden opportunity for the young, dynamic and student generation. Designing optimal e-learning services is a complex task and requires interdisciplinary skills. This research was conducted to investigate the relationship between psychological characteristics, previous experiences, academic background, management and computer skills and student retention in an e-learning environment.
Methods: The present research is applied -quantitative in terms of its purpose and descriptive-survey in terms of collecting data. The statistical population in this study encompasses the electronics active students of Payamenoor University. Using 41 researcher-made questions, students were asked to specify their views in relation to any questions in a 5-point Likert scale (from totally agree to totally disagree with). The content validity of the research instrument was approved by five thesis supervisors, consultants, and experts. A confirmatory factor analysis was used to determine the validity of the measurement instrument's structure. All of the questions variables were fitted with factor load. The reliability of the instrument was confirmed by the Cronbach's alpha coefficient which was equal to 0.83.
Findings: The results of data collection after adjustment and tabling were analyzed by statistical tests (exploratory). The results of Pearson correlation test showed a significant relationship between academic background and student retention (r=.17), previous experiences and student retention (r=.14), computer management skills and student retention (r=.47), and psychological characteristics and student retention (r=.52).
Conclusion: The results of simultaneous regression also showed that predictor variables account for about 76% of student retention changes.

Keywords

20.1001.1.20080441.1398.13.3.11.6

Main Subjects

e-learning

COPYRIGHTS
©2019 The author(s). This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, as long as the original authors and source are cited. No permission is required from the authors or the publishers.

References

[1] Halkett R. E-learning: Research and how to survive it. Industrial and Commercial Training. 2011; 34(2): 80-82.

[2] Lee BC., Yoon J, Lee I. Learners acceptance of e-learning in south korea: Theories and results. Computers & Education. 2009; 53: 1320-1329

[3] Carr S. As distance education comes of age, the challenge is keeping the students. Chronicle ofHigher Education. 2000; 46)23): 39-41.

[4] Levy Y. Comparing dropouts and persistence in e-learning courses. Computers & Education. 2007; 48(2): 185-204.

[5] Tello SF. An analysis of student persistence in online education. International Journal of Information and Communication Technology Education. 2007; 3(3): 47-62.

[6] Berg ZL, Huang YP. A model for sustainable student retention: A holistic perspective on the student dropout problem with special attention to e-Learning. DEOSNEWS. 2004; 13(5): 97-108.

[7] Frankola K. Why online learners drop out. Workforce. 2001; 8(3): 53-58.

[8] Diaz DP. Online drop rate revisited. Technology Source; 2002.

[9] Patterson B, McFadden C. Attrition in online and campus degree programs.OJDLA, 12(2); 2009

[10] Lee MC. Explaining and predicting users’ continuance intention toward e-learning: An extension of the expectation–confirmation model. Computers & Education. 2010; 54(2): 506-516.

[11] Willging PA, Johnson SD. Factors that influence students’ decision to dropout of online courses. Journal of Asynchronous Learning Networks. 2004; 8(4): 105–118.

[12] Ally M. Foundations of educational theory for online learning. In Anderson, T. and Elloumi, F. (Eds.) Theory and practice of online learning (pp. 15-44). Canada: Athabasca University; 2004

[13] 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.

[14] Carr S. As distance education comes of age, the challenge is keeping the students. Chronicle of Higher Education. 2000; 46(23): 39-41.

[15] Nesler M. Factors associated with retention in a distance-based liberal arts program.Paper presented at the northeast association for institutional research. Newport, Wales; 1999.

[16] Kember D. A longitudinal-process model of drop-out from distance education. The Journal of Higher Education. 1989; 60(3): 278-301.

[17] Parker A. Identifying predictors of academic persistence in distance education. United States Distance Learning Association Journal. 2003; 17(1): 55–61.

[18] Fortoft N. Predicting persistence in distance learning programs. Paper presented at the mid-western education research meeting, Chicago, IL; 1995.

[19] Svedberg MK.Self-directed learning and persistence in online asynchronous undergraduate programs (doctoral dissertation). Virginia polytechnic institute and state university, US; 2010.

[20] Joo Y, Joung S, Sim W. Structural relationships among internal locus of control, institutional support, ﬂow, and learner persistence in cyber universities. Computers in Human Behavior. 2011; 27(2): 714–722.

[21] 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.

[22] Krause N, Stryker S. Stress and well-being: The buffering role of locus of control beliefs. Social Science & Medicine. 1984; 18(9): 783–790.

[23]Babaei M. Introduction to electronic learning. Tehran: Iran's Science and Information Technology Research Institute and Chapar Publishing; 2010. Persian

[24] Hart C. Factors associate sociated with student persistence in an online program off study: A review of the literature. Journal of Interactive Online Learning. 2012; 11(1):19-42.

[25] Osborn V. Identifying at-risk students in videoconferencing and web-based distance education. American Journal of Distance Education. 2011; 15(1): 41–54.

[26] 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.

[27] Cheung LLW, Kan ACN. Evaluation of factors related to student performance in a distance-learning business communication course. Journal of Education for Business. 2002; 77(5): 257.

[28] Castles J. Persistence and the adult learner: Factors affecting persistence in open university students. Active Learning in Higher Education. 2004; 5(2): 166-179.

[29] Holder B. 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.

[30] 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): 107-118.

[31] Mu¨ller T. Persistence of women in online degree-completion programs. International Review of Research in Open and Distance Learning. 2008; 9(2): 1-18.

[32] Dupin-Bryant P. Pre-entry variables related to retention in online distance education. American Journal of Distance Education. 2004; 18(4): 199–206.

[33] 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.

[34] 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.

[35] Pituch KA, Lee YK. The influence of system characteristics on e-learning use. Computers & Education. 2004; 47: 222-244.