Using Collaborative Technology Policy and Social Media Strategies to Increase Retention in Online Courses

Audience Level: 
All
Institutional Level: 
Higher Ed
Streamed: 
Streamed
Special Session: 
Research
Abstract: 
The purpose of this quantitative study is to survey e-learning professionals regarding the design of e-learning courses and the higher education institutions in which they are employed. The goal is to determine the facilitating conditions that motivate them to include connective tools, as well as the perceptions of institutional innovativeness.
Extended Abstract: 
Student engagement and retention are at the forefront of higher education institutional effectiveness (Gazza & Hunker, 2014).  One of the issues faced by institutions with e-learning programs is low levels of student persistence in online courses (Beach, 2018; de Freitas, Morgan & Gibson, 2015; Rogers, 2017 & Shea, Bidjerano, 2014).  Research on how to retain students, particularly in e-learning courses is valuable to stakeholders and other decision-makers.  The use of connective tools to engage students in online courses is supported by existing literature (Fathema, Shannon, & Ross, 2015; Sharif & Cho, 2015).  This engagement is a factor associated with a higher retention rate among students (Gazza & Hunker, 2014).  Khalia and Ebner (2014), discussed factors associated with low retention in online courses, for example: students feeling isolated because of a lack of interactivity.  Doolan and Gilbert (2016) found a positive correlation between the use of connective tools to expand the boundaries of the face-to-face classroom and student retention.  In addition, several studies found a positive correlation between the use of connective tools in e-learning courses to facilitate connections and student retention (Hone & El Said, 2016; Lu, Chang, Yan, & Lin, 2016; Cela & Karina, 2015). 

In the proposed study, this researcher will identify the association between the facilitating conditions and organizational innovativeness in regards to use of connective tools in the development of online courses (Fathema, Shannon, & Ross, 2015; Sharif & Cho, 2015).  First, the researcher will gather information reported by e-learning professionals and their perceptions of the facilitating conditions that propel the use of connective tools. Next, the researcher will survey the e-learning professionals to understand their perceptions of institutional innovativeness.  Finally, the researcher will analyze the data collected from the survey to determine the relationship among the variables of facilitating conditions and organizational innovativeness.

Facilitating Conditions

Suboptimal tool implementation and higher education institutions’ inability to provide adequate support are factors of facilitating conditions in use of e-learning tools (Fathema, Shannon, & Ross, 2015; Sugar & Luterbach, 2016; Watson et al., 2016).  These two items are measured by the facilitating conditions portion of the unified theory of acceptance and use of technology (UTAUT) theory (Venkatesh, Morris, Davis, & Davis, 2003).  UTAUT has four foundational elements: performance expectancy, effort expectancy, social influence, and facilitating conditions (Oye, Iahad, & Rahim, 2014; Williams, Rana, & Dwivedi, 2015).  Facilitating conditions include training, institutional support, adequate resources, and funding, and can affect e-learning course development (Nur, Faslih, & Nur, 2017; Oye, Iahad, & Rahim, 2014; Zaharias & Pappas, 2016).  Facilitating conditions should be provided by the higher education institutions to encourage connective tool use in e-learning courses.

E-Learning Professionals, Facilitating Conditions, and Institutional Innovativeness

As the facilitating conditions are those not within the e-learning professionals’ purview, it is up to the learning organization to support the design model and tool choices made by e-learning professionals (Zaharias & Pappas, 2016).  This support is imperative to engage students in a collaborative and connective way (Sugar & Luterbach, 2016, Watson et al, 2016).  This researcher will illustrate how institutional innovativeness relates to facilitating conditions, which in turn affects  the design model and connective tools the e-learning professional implements in online courses (Camisón & Villar-Lópe, 2014; Lu, Chang, Yan & Lin, 2016; Oye, Iahad & Rahim, 2014).  The perceived level of institutional innovativeness is measured with McCrosky’s perceived organizational innovativeness scale (PORGI) (McCroskey, et al., 2014; DeMarzo, 2018).

Research Questions

            RQ1- Are there differences in e-learning professional’s perceptions of facilitating conditions (measured using the UTAUT questionnaire) by e-learning professionals’ job level, education level, years of experience, and type of institution?

            RQ2- Are there differences in e-learning professional’s perceptions of institutional innovativeness (measured using the Perceived Organizational Innovativeness Scale) by e-learning professionals’ job level, education level, years of experience, and type of institutions?

            RQ3- What is the relationship between perception of institutional innovativeness (measured using the Perceived Organizational Innovativeness Scale) and facilitating conditions (measured using the UTAUT questionnaire)?

 

References

Beach, M. (2018, June). When Great Teaching Is Not Enough: Utilizing Student Perception to Increase Retention in Online Learning. In EdMedia+ Innovate Learning (pp. 1940-1944). Association for the Advancement of Computing in Education (AACE).

Camisón, C., & Villar-López, A. (2014). Organizational innovation as an enabler of technological innovation capabilities and firm performance. Journal of business research67(1), 2891-2902.

Cela, R., & Karina, L. (2015). Social network analysis in eLearning environments. A study of learner's interactions from several perspectives.

de Freitas, S. I., Morgan, J., & Gibson, D. (2015). Will MOOCs transform learning and teaching in higher education? Engagement and course retention in online learning provision. British Journal of Educational Technology46(3), 455-471.

Doolan, M. A., & Gilbert, T. (2017). Student Choice: Blends of Technology beyond the University to support social interaction and social participation in learning. In E-Learning, E-Education, and Online Training (pp. 95-102). Springer, Cham.

Fathema, N., Shannon, D., & Ross, M. (2015). Expanding the technology acceptance model (TAM) to examine faculty use of learning management systems (LMSs) in higher education institutions. Journal of Online Learning & Teaching11(2).

Gazza, E. A., & Hunker, D. F. (2014). Facilitating student retention in online graduate nursing education programs: A review of the literature. Nurse education today34(7), 1125-1129.

Hone, K. S., & El Said, G. R. (2016). Exploring the factors affecting MOOC retention: A survey study. Computers & Education98, 157-168.

Khalil, H., & Ebner, M. (2014, June). MOOCs completion rates and possible methods to improve retention-A literature review. In EdMedia: World Conference on Educational Media and Technology (pp. 1305-1313). Association for the Advancement of Computing in Education (AACE).

Lu, H. K., Chang, K. C., Yan, M. J., & Lin, P. C. (2016). Organizational innovation for continuing education in higher education in Taiwan: The case of the chinese culture university. International Journal of Continuing Education and Lifelong Learning8(2), 128.

McCroskey, L. L., Teven, J. J., Minielli, M. C., & Richmond McCroskey, V. P. (2014). James C. McCroskey's instructional communication legacy: Collaborations, mentorships, teachers, and students. Communication Education63(4), 283-307.

Nur, M. N. A., Faslih, A., & Nur, M. N. A. (2017). Analysis of behaviour of e-learning users by unified theory of acceptance and use of technology (UTAUT) model A case study of vocational education in Halu Oleo University. Jurnal Vokasi Indonesia5(2).

Oye, N. D., Iahad, N. A., & Rahim, N. A. (2014). The history of UTAUT model and its impact on ICT acceptance and usage by academicians. Education and Information Technologies19(1), 251-270.

Rogers, M. S. (2017, October). The Pedagogical Variation Model for online Learning and Teaching: Increasing Retention Rates and Decreasing Drop-out Rates. In E-Learn: World Conference on E-Learning in Corporate, Government, Healthcare, and Higher Education (pp. 654-661). Association for the Advancement of Computing in Education (AACE).

Sharif, A., & Cho, S. (2015). 21st-Century instructional designers: Bridging the perceptual gaps between identity, practice, impact and professional development. International Journal of Educational Technology in Higher Education12(3), 72-85.

Shea, P., & Bidjerano, T. (2014). Does online learning impede degree completion? A national study of community college students. Computers & Education75, 103-111.

Sugar, W. A., & Luterbach, K. J. (2016). Using critical incidents of instructional design and multimedia production activities to investigate instructional designers’ current practices and roles. Educational Technology Research and Development64(2), 285-312.

Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly. 27 (3): 425–478.

Watson, S. L., Loizzo, J., Watson, W. R., Mueller, C., Lim, J., & Ertmer, P. A. (2016). Instructional design, facilitation, and perceived learning outcomes: an exploratory case study of a human trafficking MOOC for attitudinal change. Educational Technology Research and Development64(6), 1273-1300.

Williams, M. D., Rana, N. P., & Dwivedi, Y. K. (2015). The unified theory of acceptance and use of technology (UTAUT): A literature review. Journal of Enterprise Information Management28(3), 443-488.

Zaharias, P., & Pappas, C. (2016). Quality management of learning management systems: A user experience perspective. Current Issues in Emerging eLearning3(1), 5.

Conference Track:  Research Session Type:  Graduate Student Discovery Session
Conference Track: 
Leadership and Institutional Strategy
Session Type: 
Graduate Student Discovery Session Asynchronous
Intended Audience: 
Administrators
Design Thinkers
Faculty
Instructional Support
Training Professionals
Technologists
All Attendees
Researchers