Project RITE

Both effort regulation and intrinsic motivation among online graduate students in this study had a significant unique influence on procrastination. Results indicated that as intrinsic motivation to learn and effort regulation decrease, procrastination increases. Since procrastination has a negative influence on student performance, the findings provide important information for online teachers trying to develop strategies that will improve student achievement in online courses.
Individually, both effort regulation and intrinsic motivation influence procrastination behavior and can be viewed as characteristics that can be influenced by specific strategies that can be used by online instructors in an effort to reduce procrastination. The results of this study indicate that together, these two factors powerfully influence procrastination.

Regression Results

Multiple linear regression was used to determine whether intrinsic motivation and effort regulation are predictive of student procrastination. Intrinsic motivation and effort regulation were measured by online graduate students’ scores on the respective scales on the MSLQ, and entered as the independent or predictor variables. Student procrastination was measured by the online graduate students’ total score on the PASS, and entered as the dependent variable. The sample size for the analyses was 81 representing all online graduate students who completed the surveys.

Preliminary examination of the results indicated there was no extreme multicollinearity in the data (all variance inflation factors were less than 2). Exploratory analysis also indicated that the assumptions underlying the application of multiple linear regression (independence, normality, heteroschedasticity, and linearity) were met. The regression results indicated that the set of independent variables significantly influenced 19.8% of the variance in learner-centered beliefs (F(2, 78) = 2.751; p < .001) (see Table 1) with an effect size was particularly large for this small sample at .25. Both of the independent variables had a significant unique influence on procrastination. In order of importance, they were effort regulation (t = -2.63, p < .01) and intrinsic motivation (t = -2.34, p < .05).

The negative correlation between intrinsic motivation and effort regulation as they relate to procrastination (-.36 and -.38, respectively) indicate that as intrinsic motivation to learn and effort regulation decrease, procrastination increases. Beta weights and partial correlations are presented in Table 1.

Table 1
Regression Analysis of Procrastination on Intrinsic Motivation and Effort Regulation
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Variable                        b                      Beta        Partial           t
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Intrinsic Mot.            -3.82                -.25            -.24           -2.34**
Effort Reg.                 -4.56                -.29             -.27          -2.63*
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Note. *p < .01. **p < .05. R2 = .198. R2 change = .178.

A total of 81 responses to the questionnaire have been received. The data has been downloaded. Analysis of the data is underway.

The measures have been posted to the Web. Data collection is ongoing.

The Research Problem

Procrastination. Schraw (2007) defines academic procrastination as “intentionally delaying or deferring work that must be completed” (p. 12). Research indicates that procrastination adversely affects academic progress because it limits both the quality and quantity of student work. Procrastination leads to a number of negative results, including a decrease in long-term learning. Despite the obvious consequences of this behavior, over 70% of undergraduate students in one study reported academic procrastination, with about 20% reporting habitual procrastination (Schouwenberg, 1995). Graduate students in another study demonstrated an even greater tendency to procrastinate on academic tasks at a rate of up to 3.5 times that of a comparison group of undergraduate students (Onwuegbuzie, 2004)

For many students, the tendency to procrastinate increases further in the online learning environment. In traditional classes, the requirement to attend lectures forces students to focus on class materials on a regular basis. At least part of their study time is distributed equally across the semester (Elvers, Polzella, and Graetz, 2003). Online students do not participate in regular class meetings, so there is an increased tendency to procrastinate and “cram” more study into less time, often resulting in poorer learning outcomes.

Motivation and Self-Regulation. Research on the effects of academic self-regulation and motivation on learning have demonstrated important links between the two (Schunk, 2005). Students with more developed self-regulatory cognitive skills tend to be more academically motivated and learn more than others (Pintrich, 2003).

Specific relationships should be identified between cognitive self-regulated learning strategies, academic motivation, and a particularly problematic behavior among online students: procrastination. This research will be guided by one primary question: Are intrinsic motivation and effort regulation as measured by the MSLQ predictive of student procrastination as measured by the PASS in graduate students enrolled in an online graduate course?

The Project Significance

If procrastination is prevalent in the online environment and detrimental to student learning, it is important for online faculty to identify factors that may reduce students’ tendency to procrastinate. Because procrastination can lead to decreased academic performance, it is important to better understand the influence students’ learning strategies and motivation have on procrastination.

More specifically, it is important to understand this relationship because students’ learning strategies and motivation are characteristics that can be addressed and improved. Self-regulated learning strategies can be addressed through instructional design, direct instruction, and modeling (Paris & Winograd, 2001; Perels, Gurtler, & Schmitz, 2005)). “Motivation to learn is alterable; it can be positively or negatively affected by the task, the environment, the teacher and the learner” (Angelo, 1003, p. 7). Academic motivation can be enhanced in a variety of ways including instructional strategies and design (Komarraju, 2008), social interaction with other students and faculty (Yang, Tsai, Kim, Cho, & Laffey, 2006), and by positively influencing student belief in the value of academic tasks and in their ability to successfully complete them (Angelo, 1993).

Researchers have just begun to fully explore the issue of procrastination in online courses with undergraduate students. Little research appears in the literature regarding procrastination behavior in online graduate students. If cognitive self-regulated learning strategies and academic motivation influence online students’ tendency to procrastinate, online faculty, and UT Online faculty in particular, could avail themselves of means to impact the tendency to procrastinate by specifically addressing learning strategies and motivation in course design.