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After reviewing the following instructions, read, summarize, and critique the attached article.

In Week 1 and Week 2 students began to learn the basics of APA Style. In Week 3 students transition from learning basic APA Style formatting to learning the basics of reading and writing about scholarly research. Students will critique many articles throughout their program; therefore, the skills addressed in this assignment are important to learn.  Your ability to critique a research article will improve considerable over time as you learn more about research methods and statistics. Focus on quality by limiting unnecessary "filler" information (the stuff many freshmen do in an undergraduate program).  Be very direct, factual, logical, and clear.

Assignment Instructions:
For this assignment, include the following: 1) cover page, 2) introduction, 3) two main sections (use section headings) including a summary (e.g., background, methods, results) and an article critique (e.g., strengths, limitations), 4) a conclusion (use a section heading), and 5) a references list (separate page).  Attached is an example of how your article critique might look.  Save the attached example to your Learner Toolbox.

Students new to research may be challenged with understanding how to critique an article and what to include in their critique.  To help you understand what information to include, review the attached example, as well as search the internet for other resources that might help you.  This is good practice as you will encounter many topics and activities unfamiliar to you during your graduate program.

This paper should be two pages long.  Review the Grading Rubric to better understand the exact criteria they will be graded on.

NOTE: This Week 3 assignment is your first opportunity to get help from the Graduate Online Writing Studio to improve your writing skills.  We highly encourage you to use this service.

References

Rakes, G. C., & Dunn, K. E. (2010). The impact of online graduate students' motivation and self-regulation on academic procrastination. Journal of Interactive Online Learning, 9(1), 78-93.

Running head: SUMMARY AND CRITIQUE OF VASCONCELOS’S 1

SUMMARY AND CRITIQUE OF VASCONCELOS’S 4

Summary and Critique of Vasconcelos’s Phenomenological Study of The Effect of Prayer on Organizational Life

Dan Kuchinka

Keiser University

Research, Ethics, and Scholarly Writing

Daniel G. J. Kuchinka, Ph.D.

Week 3 Article Critique

January, 2017

Summary and Critique of Vasconcelos’s Phenomenological Study of The Effect of Prayer on Organizational Life

The following is a summary and critique of Vasconcelos’s (2010) phenomenological study of workers and their use of prayer to cope with common challenges in the workplace. Following a summary of the study, the information will be evaluated as strengths, limitations, and ethical considerations are addressed.

Article Summary

According to Vasconcelos (2010), empirical research on the topic of prayer in the workplace is limited. Vasconcelos examined prayer in the workplace using phenomenology as a qualitative approach, designed to explore the topic in-depth. In his study, Vasconcelos examined a diverse sample (N = 28) of workers from Brazil. Data revealed 93% of participants prayed every day (61% many times during the day). A common theme that emerged was prayer varied by length, time of day, place, and circumstance. Vasconcelos (2010) found all participants agreed prayer brought a “pleasure or inner peace” (p. 374). It was discovered prayer was useful to help workers cope with the daily challenges in the workplace.

Article Critique

Like any other study, the research design and explanation of the study had several strengths, limitations, and ethical concerns.

Strengths and Limitations

Vasconcelos (2010) investigated a diverse sample in terms of age, education, and Christian affiliation (e.g., 74% of population is Catholic; p. 372). A limitation was the lack of participation from Jews and Muslims, which could have added significant breadth to the study. Prayer is part of the daily life of Muslims and could be viewed very differently.

Another strength of the study was the option to use email to respond to questions. This element of confidentiality may have helped promote honest and direct responses, without any fear of judgement. This strength of the study could also be viewed as a limitation. Prayer can be highly emotional and a lack of face-to-face interaction between the researcher and participant could have denied the investigator critical nonverbal and verbal, emotion-driven information.

Perhaps the most significant limitation of the study was the qualitative design. Although qualitative research can help understand a topic, the information cannot be generalized to the greater population. The same research design applied to a sample from a totally different population may have revealed dramatically different results.

Ethical Considerations

Vasconcelos (2010) appeared to have conducted the study and reported on the findings in an ethical manner. For example, the author clearly recognized the limitations of the sample. Although he did not recognize the lack of Jews and Muslims in the sample, which was not so much of an ethical issue but rather an oversight when discussing limitations. The author also valued privacy by allowing participants to respond using email. If data was collected in the workplace, workers may have endured negative outcomes from individuals or an organization that viewed negatively on prayer in the workplace.

Conclusion

The previous discussion addressed the strengths, limitations, and ethical considerations associated with Vasconcelos’s (2010) phenomenological study of prayer used in the workplace to help cope with common challenges. Consistent with a case study or phenomenological research design, the study opened doors for future research.

References

Vasconcelos, A. F. (2010). The effects of prayer on organizational life: A phenomenological study. Journal of Management and Organization, 16(3), 369-381. Retrieved from http://search.proquest.com/docview/748923600?accountid=35796

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78

The Impact of Online Graduate Students’ Motivation and Self-

Regulation on Academic Procrastination

Glenda C. Rakes

The University of Tennessee, Martin

Karee E. Dunn

The University of Arkansas

Abstract

With the rapid growth in online programs come concerns about how best to

support student learning in this segment of the university population. The purpose of this

study was to investigate the impact of effort regulation, a self-regulatory skill, and

intrinsic motivation on online graduate students’ levels of academic procrastination,

behavior that can adversely affect both the quality and quantity of student work. This

research was guided by one primary question: Are online graduate students’ intrinsic

motivation and use of effort regulation strategies predictive of procrastination? Results

indicated that as intrinsic motivation to learn and effort regulation decrease,

procrastination increases. Specific strategies for encouraging effort regulation and

intrinsic motivation in online graduate students are presented.

Introduction

Enrollments in online courses at universities in the United States have grown

substantially faster than enrollments in traditional courses over the past several years. For

example, in 2008, there was a 12.9% increase in students taking at least one online course

over the previous year. That growth greatly exceeds the increase of 1.2% in the overall

higher education population during the same time period (Allen & Seaman, 2008). With

this rapid growth come concerns about how best to support student learning in this

segment of the university population.

Interest in the role student self-regulation and motivation play in the online

learning environment has increased along with this dramatic growth in online learning

opportunities. Schunk and Zimmerman (1998) assert that self-regulated learning

strategies may be increasingly important as more students participate in distance learning

environments because instructors are not physically present. Thus, students need to be

more autonomous. Maintaining motivation may be more difficult for online students as

Rakes and Dunn

79

they face problems related to social isolation and technical issues that cause frustration

not as frequently experienced by students in face-to-face classes.

Research on the effects of academic self-regulation and motivation on learning

has demonstrated important links between the two constructs (Schunk, 2005). Students

with more developed self-regulatory cognitive skills tend to be more academically

motivated and learn more than others (Pintrich, 2003). The purpose of this study was to

investigate the impact of effort regulation, a self-regulatory skill, and intrinsic motivation

on online graduate students’ levels of academic procrastination. The results of this study

can provide online instructors with valuable insight into two malleable student

characteristics that may decrease student procrastination and increase student learning.

Motivation

Motivation is described as a process through which individuals instigate and

sustain goal-directed activity. Motivation is generally viewed as a process through which

an individual’s needs and desires are set in motion (Alexander & Murphy, 1998; Pintrich,

Marx, & Boyle, 1993). Academic motivation reflects students’ levels of persistence,

interest in the subject matter, and academic effort (DiPerna & Elliot, 1999); it is viewed

as a contributor to academic success (Alexander, 2006; Ames & Ames, 1985; Dweck &

Legget, 1988; Wylie, 1989).

While motivation is critically important to student learning (Pintrich & Schunk,

2002), lack of motivation is a frequent problem with students at all levels. All learning

environments present challenges, but the online environment presents unique challenges

because students bear more responsibility for their own learning than in many traditional

classes. Because of these challenges, students’ ability to influence their own motivation is

important (Wolters, Pintrich, & Karabenick, 2005).

One specific aspect of motivation is intrinsic motivation. It may be defined as the

performance of a task for the inherent satisfaction it brings an individual rather than for

some separate consequence (Ryan & Deci, 2000). Intrinsic motivation appears to

combine elements of Weiner’s (1974; 1980; 1986) attribution theory, Bandura’s (1977;

1993) work on self-efficacy, and other studies related to goal orientation (Pintrich, 2001).

Important to the present study is the fact that intrinsic motivation can be influenced

within the educational context (Deci & Ryan, 2004).

Intrinsic motivation increases when individuals attribute educational results to

internal factors they can control (attribution theory) (Weiner, 1980). Intrinsic motivation

is further increased when individuals believe they are capable of reaching desired goals

(self-efficacy) (Bandura, 1977; Lent, Brown & Larkin, 1986; Marsh, Walker, & Debus,

1991). Intrinsic motivation also increases when individuals are interested in mastering a

subject, rather than simply earning good grades (goal orientation) (Dweck, 1986;

Nicholls, 1984). When these factors converge and result in high levels of intrinsic

motivation, students are more likely to be successful learners (Alexander, 2006).

Self-Regulation

Self-regulated learning is described as an active process whereby learners

construct goals for learning. Learners monitor, regulate, and control their cognition,

motivation, and behavior. They are guided and constrained by their own goals and the

individual characteristics of a particular learning environment (Wolters, Pintrich, &

Rakes and Dunn

80

Karabenick (2005). Zimmerman (1989) described self-regulated learners as

“metacognitively, motivationally, and behaviorally active participants in their own

learning process” (p. 329). Self-regulatory activities impact individual students, their

level of achievement, and the learning context (Wolters, Pintrich, & Karabenick, 2005). It

is important for students to learn how to learn and take control of their efforts (effort

regulation).

One self-regulatory resource management strategy described by Pintrich, Smith,

Garcia, and McKeachie (1991) is effort regulation. Also referred to as volition (Corno,

1993), effort regulation refers to a learner’s ability to control his or her attention and

efforts even in situations that present distractions that may be perceived to be interesting.

Effort management is self-management, and reflects a commitment to completing

one’s study goals, even when there are difficulties or distractions. Effort

management is important to academic success because it not only signifies goal

commitment, but also regulates the continued use of learning strategies (Garcia &

McKeachie, 1991, p. 27).

Academic Procrastination

Shraw, Watkins, and Olafson (2007) define academic procrastination as

“intentionally delaying or deferring work that must be completed” (p. 12). Procrastination

is actually the opposite of motivation – the lack of intention or willingness to take action

(Ryan & Deci, 2000). 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 lower goal commitment, lower amount of

time allotted towards work (Morford, 2008), a decrease in course achievement (Akinsola,

Tella, & Tella, 2007), and a decrease in long-term learning (Schouwenburg, 1995).

Procrastination has also been correlated with lower levels of self-esteem (Harrington,

2005) and lower grades (Tuckman, 2002a; Tuckman, 2002b).

It is important to note that not all forms of procrastination lead to negative

consequences. Chu and Choi (2005) differentiate between passive procrastination and

active procrastination. While passive procrastinators allow the negative, indecisive

behavior to paralyze them, active procrastinators make deliberate decisions to

procrastinate because they prefer to work under pressure. In essence, active

procrastinators use procrastination as a positive academic strategy. They do not tend to

suffer the same negative academic consequences as passive procrastinators.

Steel (2007) also discusses the occasional use of the term procrastination to

describe positive behavior. He describes such use of the term by some researchers as

“functional delay” (p. 66). However, in his meta-analysis of the procrastination literature,

Steel asserts that such usage is secondary to the use of the term in the traditional, negative

sense. The use of the term procrastination in the present study refers to the primary,

passive, negative form of procrastination.

Factors Related to Procrastination

In a meta-analysis of procrastination research, Steel (2007) examined 691

previously examined correlates of procrastination. Most of the studies reviewed used

young undergraduate university students in traditional course settings. He found that

strong, consistent predictors of procrastination included task aversion, task delay, self-

Rakes and Dunn

81

efficacy, and impulsiveness. Additionally, he found that conscientiousness as

demonstrated by achievement motivation, organization, and self-control were also strong

predictors of procrastination behaviors. Steel’s results echo that of Solomon and

Rothblum (1994) who studied college students’ reasons for procrastination. They found

that procrastination involved a complex interaction of behavioral, affective, and cognitive

components, not simply a deficit in time management or poor study habits.

Both Onwuegbuzie and Jiao (2000) who studied graduate students in face-to-face

classes and Solomon and Rothblum (1984) who studied undergraduates in traditional

classes found that procrastination is strongly influenced by two factors: fear of failure and

task aversion, with fear of failure accounting for most of the procrastination behaviors. In

a related study, Flet et al. (1992) found that academic procrastination in undergraduate

students stems, in part, from anticipation of disapproval from those holding

perfectionistic standards for others. They also found that the fear of failure component of

procrastination was associated broadly with all the perfectionism dimensions.

Tuckman (2002b) studied procrastination in undergraduate students enrolled in a

Web-based course. He found that procrastinators used rationalization rather than self-

regulation, which resulted in lower course grades. This phenomenon occurred in spite of

the fact that the course was highly structured and enforced frequent deadlines throughout

the duration of the course. In another study, Tuckman compared high, moderate, and low

procrastinators in undergraduate students on their reported degree of self-regulation. He

found that the more self-regulation was used, the less procrastination resulted (Tuckman,

2002a).

Howell and Watson (2007) examined the relationships between procrastination,

goal orientation, and learning strategies among undergraduate students. They found that

disorganization and lower use of cognitive/metacognitive learning strategies were

positively related to procrastination. Morford (2008) found that low procrastinators

among undergraduates in traditional classes demonstrated higher commitments to goals

than high procrastinators. Tan, Ang, Klassen, Yeo, Wong, Huan, & Chong, (2008)

examined procrastination in undergraduate students and discovered that self-efficacy for

self-regulated learning was negatively related to procrastination.

Senecal, Koestner, and Vallerand (1995) found that junior college students who

were intrinsically motivated to perform well on academic tasks tended to procrastinate

less than students who are more extrinsically motivated to perform the same tasks. The

results led the researchers to the belief that procrastination is more of a motivational

problem rather than a problem of poor time management skills or simple laziness. Steel

(2007) also found that achievement motivation was a strong predictor of academic

procrastination.

Consequences of Academic Procrastination

Despite the obvious negative consequences of passive procrastination behaviors,

over 70% of undergraduate students in one study reported academic procrastination, with

about 20% reporting habitual procrastination (Schouwenburg, 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 in the online learning

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82

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, & 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. Elvers,

Polzella, and Graetz (2003) examined the differences between procrastination in

undergraduate students enrolled in online and face-to-face course sections of the same

course. Procrastination in the online sections was negatively correlated with exam scores,

but not in face-to-face sections.

If procrastination is prevalent in the online environment and detrimental to

student learning and performance, 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’ self-regulated learning strategies and motivation have on procrastination.

Procrastination, Self-regulated Learning Strategies, and Motivation

More specifically, it is important to understand this relationship because students’

self-regulated learning strategies and motivation are characteristics that can be addressed

and improved. Given the highly autonomous environment that is online education, the

need for highly developed levels of self-regulation is important (Artino & Stephens,

2007).

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, 1993, p. 7). Academic

motivation can be enhanced through the use of certain instructional strategies and through

course 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 graduate students, particularly in the online environment. If

cognitive self-regulated learning strategies and academic motivation influence online

students’ tendency to procrastinate, online faculty could avail themselves of means to

impact the tendency to procrastinate by specifically addressing self-regulated learning

strategies and motivation through the use of particular instructional strategies and through

course design.

The Present Study

Specific relationships should be identified between cognitive self-regulated

learning strategies, academic motivation, and procrastination, a particularly problematic

behavior among online students. This research was guided by one primary question: Are

online graduate students’ intrinsic motivation and use of effort regulation strategies

predictive of procrastination?

Intrinsic motivation and effort regulation, a specific cognitive self-regulated

learning strategy, were selected as predictors of procrastination because both were

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83

expected to be inversely related to procrastination (DiPerna & Elliott, 1999), and because

both are malleable student characteristics. Thus, if procrastination is identified in student

behavior, concerted faculty effort can be focused to address issues of intrinsic motivation

and effort regulation and yield positive impacts on student performance. Furthermore, if

intrinsic motivation and effort regulation are found to be predictive of procrastination,

online courses can be designed to take pre-emptive action against procrastination by

facilitating intrinsic motivation and increasing guidance for effort regulation.

Methodology

Sample

The convenience sample for this study consisted of 81 fully admitted graduate

students enrolled in an online masters program in education. The university from which

the sample was taken is an accredited mid-southern university that grants bachelors and

masters degrees. Respondent’s ages ranged from 21 to 57 with a mean age of 33. Eighty-

five percent (n=69) of the participants were female; 15% were male (n=12).

Measures

In order to measure self-regulated learning strategies, motivation, and

procrastination, participants completed the Motivated Strategies for Learning

Questionnaire (MSLQ) and the Procrastination Assessment Scale-Students (PASS).

Motivated Strategies for Learning Questionnaire. Intrinsic motivation and the

self-regulated learning strategy of effort regulation were assessed using appropriate

sections of the Motivated Strategies for Learning Questionnaire (MSLQ), a scale that was

developed from a social-cognitive perspective of motivation and self-regulated learning

(Pintrich et al., 1991). The MSLQ was designed to measure students’ motivation and self-

regulated learning strategies relative to a specific course.

Students rate themselves on a scale of 1-7 from “Not at all true of me now.” to

“Very true of me.” Scales are constructed by taking the mean of the items that comprise

that scale. Sample items from the intrinsic motivation scale include, “In a class like this, I

prefer course material that really challenges me so I can learn new things.” and “In a

class like this, I prefer course material that arouses my curiosity, even if it is difficult to

learn.” Sample items from the effort regulation scale include, “ I often feel so lazy or

bored when I study for this class that I quit before I finish what I planned to do.” and

“When course work is difficult, I give up or only study the easy parts.” Originally

validated on a sample (N=356) of undergraduate college students, Cronbach’s alpha

measured the internal consistency of items in the scales. Coefficient alphas are reported

for intrinsic goal orientation (.74) and effort regulation (.69) (Pintrich et al., 1991). The

reliability alpha for the intrinsic motivation scale for this sample was .73. The reliability

alpha for the effort regulation scale for this sample was .58. Although the reliability alpha

for effort regulation was low, it closely approached .60. Therefore, in light of the small

sample size, the scale was retained.

Procrastination Assessment Scale-Students. The Procrastination Assessment

Scale-Students (PASS) is the most widely used scale to measure academic

procrastination (Ferrari, Johnson, & McCown, 1995). It is a 44-item instrument that was

designed to measure the frequency of cognitive and behavioral aspects of procrastination.

Specifically, it measures the prevalence of academic procrastination and the reasons for

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procrastination. The authors (Solomon & Rothblum, 1984) define procrastination as a

passive act of procrastination, specifically as “the act of needlessly delaying tasks to the

point of experiencing subjective discomfort” (p. 503).

For purposes of the present study, the prevalence of academic procrastination

section was used. Respondents were asked to describe their behavior for specific

academic tasks such as writing a term paper, studying for exams, and weekly reading

assignments. Respondents answer the questions for each academic task using a 5-point

Likert scale for two questions: “To what degree do you procrastinate on this task?” (1 =

“Never Procrastinate” to 5 = “Always Procrastinate”) and “To what degree is

procrastination on this task a problem for you?” (1 = “Not at all a problem” to 5 = “Always

a problem.”) The sum of the two questions (prevalence and problem) of each

procrastination area was computed for a total score. A higher score is more indicative of

self-reported procrastination.

PASS was originally investigated on a sample of 323 undergraduate university

students. Cronbach’s alpha measured the internal consistency of items in the scales used

in this study. The individual coefficients for the different procrastination prevalence areas

were moderately high (e.g., for the essay questions the coefficient was .81). The

procrastination prevalence scale had a test/retest reliability of .74 for frequency (Ferrari,

Johnson, & McCown, 1995; Solomon & Rothblum, 1994). The reliability alpha for the

PASS for this sample was .61.

Procedures

All instruments were prepared for presentation on the Internet using Dragon,

survey software that is a companion to the FileMaker Pro database software. No personal

information was collected. All responses were voluntary and anonymous. Participants

were invited to participate via email and were asked to complete the questionnaire.

Results

In order to examine the relationship between the total score (frequency of

procrastination) on the PASS and the scores on the MSLQ motivation (intrinsic goal

orientation) and cognitive learning strategies (effort regulation) scale, the data were

analyzed using multiple regression to determine whether intrinsic motivation and effort

regulation were predictive of student procrastination. The PASS total prevalence of

procrastination score was entered as the dependent variable and MSLQ scores (intrinsic

goal orientation and effort regulation) were entered as the independent or predictor

variables. The sample size for the analyses was 81. The means, standard deviations, and

correlations among all the variables are shown in Table 1 below.

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Table 1

Means, Standard Deviations, and Correlations for Regression of Procrastination,

Intrinsic Motivation, and Effort Regulation (N = 81)

1 2 3

1. Procrastination 1.00

2. Intrinsic Motivation -.36 1.00

3. Effort Regulation -.38 .36 1.00

Mean 55.68 4.84 5.41

Standard

Deviation

16.83

1.12

1.05

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 the model (F(2, 78) = 2.751; p < .001) (see Table 2) with an

effect size of .25, which was particularly large for this sample.

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 correlations 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 2

below.

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Table 2

Regression Analysis of Procrastination on Intrinsic Motivation and Effort Regulation

__________________________________________