Academic journal article Interdisciplinary Journal of e-Skills and Lifelong Learning

The U-Curve of E-Learning: Course Website and Online Video Use in Blended and Distance Learning

Academic journal article Interdisciplinary Journal of e-Skills and Lifelong Learning

The U-Curve of E-Learning: Course Website and Online Video Use in Blended and Distance Learning

Article excerpt

Introduction

Online technologies have greatly enhanced distance and blended learning over the last 20 years. However, perseverance is considered harder in e-learning environments. The flexibility and availability of e-learning, particularly online video lectures, make it easier for students to procrastinate. While in informal e-learning courses, such as Massive Open Online Courses (MOOCS), procrastination may cause withdrawal, in formal e-learning contexts we would expect most students to have a u-curve e-learning pattern similar to that of the familiar traditional face-to-face learning cycle. At the beginning of the semester students may be enthusiastic or feel committed and allocate adequate attention resources to learning, during the middle their learning effort decreases, toward the end it increases, and just prior to the exam they exert the greatest efforts to study the course content.

Online video lectures are sometimes perceived as substitutes for face-to-face class meetings (Copley, 2007). There is a growing trend of online video lecture use in distance learning, blended learning, traditional face-to-face learning that migrates to blended models, MOOCS, and diverse sorts of life-long learning. Thus, it is important to study the temporal use patterns (Grinberg, Naaman, Shaw, & Lotan, 2013) of online video lectures and analyze their implications for procrastination and e-learning processes.

The purpose of this study is to examine temporal use patterns of course Websites and focus on the use of online video lectures on their own, as well as relative to general use patterns of course Websites, as expressed in pageviews of their homepage.

The majority of prior studies of procrastination in learning (e.g., Ackerman & Gross, 2005; Cao, 2012; Kachgal, Hansen, & Nutter, 2001; Ozer, Demir, & Ferrari, 2009; Wang, He, & Li, 2013), and online learning (e.g., Michinov, Brunot, Le Bohec, Juhel, & Delaval, 2011; Rakes & Dunn, 2010) were based on surveys, in which students self-reported their perceptions. Some studies, such as Ariely and Wertenbroch (2002), used experiments. However, just a few studies that investigated procrastination in learning (e.g., Levy & Ramim, 2012) were based on objective data of student behavior (Hershkovitz & Nachmias, 2009) or utilized data analytics (LaValle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011).

This study used Google Analytics (Clifton, 2012) to examine e-learning temporal patterns (Grinberg et al., 2013) of a sample that included 8,977 students who were enrolled in selected five courses during the years 2012 and 2013. Diverse types of courses were included in the sample in order to explore other factors that may affect e-learning and online video lecture temporal use patterns, as well as increase the generalizability of the findings. The sample included compulsory and elective courses, fully online and blended courses, as well as undergraduate and master of business administration (MBA) ones, and various topics: finance, information systems analysis, political science, project management, and strategy. All courses have been offering online video lectures for at least two years prior to the investigated period. Hence, this study examined continued use (Bhattacherjee, 2001; Geri, & Naor-Elaiza, 2008) patterns rather than adoption of online video lectures.

We used an interdisciplinary theoretical background, which included procrastination literature (Steel, 2007), cognitive fit theory (Adipat, Zhang, & Zhou, 2011; Vessey, 1991), attention economy (Davenport & Beck, 2001; Geri & Geri, 2011; Shapiro & Varian, 1999), distance learning (Moore & Kearsley, 2011), informing science (Cohen, 1999, 2009), and more, to develop hypotheses regarding the following research questions:

* What are the general temporal use patterns of course Websites?

* What are the temporal use patterns of online video lectures? …

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