Please help. I have an assignment in my research methods class to examine the methods section of a scholarly article. My assignment is to pick an article (attached), read it, and be able to understand, synthesize, and critically assess the methods section of the paper. I will include the information that I already have so it will be easier for my tutor to answer the other questions. I have attached the article. I am only looking at hypothesis 4 to answer these questions in regards to the entire research study.
Research Question: What are college students doing while they are studying? This is an investigation of multitasking behaviors.
Hypothesis 4: subjective fatigue and multitasking behaviors will be positively correlated.
Independent variable: subjective fatigue
Dependent variable: multitasking behavior (frequency and duration)
Answer as many questions as you can. My questions are as follows:
-Are the variables categorical or continuous?
-Statistic used to test?
-Value of test statistic?
-Significance level (p value)?
-Is the hypothesis confirmed?
-What is the raw data (mean, etc.)
-What is the magnitude (if any)?
It is really helpful if you can summarize all the quantitative stuff in a single sentence or two in plain English, like this: ?The study found that females had lower average Communication Anxiety scores (mean = 17.2) than males did (mean = 21.3), but there were no differences on other dependent measures.?
Assess the study, beginning with a simple statement of the author?s conclusions. Are their internal or external validity problems? Was reliability reported? Was it sufficient? Was the sample sufficient? Is there a way the study could have been done differently to make it more sound? Are there plausible rival hypotheses that should have been considered? How much confidence do you have that the findings are indicative of an actual, real relationship? Were there important variables that were left out?
Connect the conclusions to the article?s conclusions: Do you agree about what they decided? Make sure your assessment deals with Type 1 and Type 2 error (don?t criticize their study for Type 2 errors if they found significance), and focus on the overall strength of the study, not nitpicky errors.
State your conclusions clearly. What did the study prove? Was it a good or bad study? How much confidence do you have in the findings?
Computers & Education 75 (2014) 19?29 Contents lists available at ScienceDirect Computers & Education
journal homepage: www.elsevier.com/locate/compedu What else do college students ?do? while studying? An
investigation of multitasking
Charles Calderwood a, *, Phillip L. Ackerman b, Erin Marie Conklin b
b Virginia Commonwealth University, Department of Psychology, 806 West Franklin Street, P.O. Box 842018, Richmond, VA, 23284-2018, USA
Georgia Institute of Technology, School of Psychology, 654 Cherry Street, Atlanta, GA, 30332-0170, USA a r t i c l e i n f o a b s t r a c t Article history:
Received 26 September 2013
Received in revised form
30 January 2014
Accepted 1 February 2014
Available online 18 February 2014 We investigated the frequency and duration of distractions and media multitasking among college
students engaged in a 3-h solitary study/homework session. Participant distractions were assessed with
three different kinds of apparatus with increasing levels of potential intrusiveness: remote surveillance
cameras, a head-mounted point-of-view video camera, and a mobile eyetracker. No evidence was obtained to indicate that method of assessment impacted multitasking behaviors. On average, students
spent 73 min of the session listening to music while studying. In addition, students engaged with an
average of 35 distractions of 6 s or longer over the course of 3 h, with an aggregated mean duration of
25 min. Higher homework task motivation and self-ef?cacy to concentrate on homework were associated with less frequent and shorter duration multitasking behaviors, while greater negative affect was
linked to longer duration multitasking behaviors during the session. We discuss the implications of these
data for assessment and for understanding the nature of distractions and media multitasking during
Ó 2014 Elsevier Ltd. All rights reserved. Keywords:
Media in education
Learning strategies 1. Introduction
For school children, the amount of schoolwork completed at home typically increases with increasing grade levels. For example, the
typical 9th grade student is in the classroom for about 30 h per week, and has 7½ h per week of assigned homework. A 12th grade student
typically will spend the same amount of weekly time in the classroom, but will have about 10 h per week of homework. In contrast, when
students reach college, they will spend only approximately 15 h per week in the classroom, but are expected to spend 30 or more h per week
engaged in studying and homework outside of the classroom. While cultural differences may exist in the amount of homework assigned in
different countries (e.g., Chen & Stevenson, 1989), the trend of more homework assigned with increasing grade levels has been empirically
supported (see Cooper, Robinson, & Patall, 2006; Cooper & Valentine, 2001).
The increased prevalence of cell phones, other communication technologies, and portable audio devices in contemporary college student
populations (see Jacobsen & Forste, 2011) has created the potential for signi?cant attentional con?icts when students complete schoolwork
outside of the classroom. One major source of con?ict stems from a desire to engage in non-schoolwork activities. A second major source of
con?ict results from a lack of intrinsic interest in homework activities, and a desire to do anything other than study (Leone & Richards, 1989).
In combination, these con?icts likely exacerbate the appeal of using technological devices in the study environment, as these sources of
distraction present an easy outlet for the alleviation of boredom during homework completion. Distractions and media multitasking are
important issues to study in college student populations, as these students experience little parental or instructor oversight of their study
habits. These issues are also particularly salient for members of the current generation of college students, who have been dubbed the
?Multitasking Generation? (Wallis, 2006) due to the ubiquity with which they incorporate technology into their daily lives.
1.1. Quantifying college student media multitasking
Despite the widespread recognition of the pervasiveness of technology in contemporary college student life, investigators have yet to
objectively explore the frequency and duration with which students multitask with media in their homework environment. Instead,
* Corresponding author. Tel.: þ1 804 828 8352.
E-mail addresses: firstname.lastname@example.org, email@example.com (C. Calderwood).
0360-1315/Ó 2014 Elsevier Ltd. All rights reserved. 20 C. Calderwood et al. / Computers & Education 75 (2014) 19?29 researchers have placed primary emphasis on observing or experimentally manipulating multitasking behaviors in classroom environments
(e.g., Hembrooke & Gay, 2003; Kraushaar & Novak, 2010; Sana, Weston, & Cepeda, 2013). Those studies which have featured explorations of
media multitasking outside of the classroom have relied almost exclusively on self-report data (e.g., Jacobsen & Forste, 2011); despite a lack
of evidence that students can or are willing to accurately report multitasking behaviors. In a seven day experience sampling study of student
internet use, Moreno et al. (2012) found that the correlation between students? estimated hours per day using the internet and the summation of several within-day concurrent internet-use reports was only r ¼ .31, suggesting that students have a limited ability or willingness
to accurately estimate their media use. Due to these limitations, there is a strong need for research that objectively quanti?es media
multitasking in college students completing schoolwork outside of the classroom.
An overreliance on self-report measures in media multitasking studies has also created a dearth of knowledge regarding alternative
methodological approaches to examine student multitasking. A variety of observational technologies, such as surveillance systems, headmounted video cameras, and eyetracking devices, have the potential to be useful tools to explore media multitasking. However, research on
these observational technologies has not yet been extended to the homework environment. While such technologies may allow for a more
accurate assessment of rates of student multitasking, it is also possible that these more intrusive observational technologies alter student
behavior by engendering participant reactivity effects (see Whitley, 2002). Accordingly, it is necessary to analyze both the degree to which
alternative observational technologies allow for the quanti?cation of multitasking behaviors and whether these technologies are differentially associated with participant reactivity effects.
1.2. Exploring why college students media multitask
In addition to placing primary emphasis on subjective reports of media multitasking, investigators have generally focused on how
students multitask with media and who is likely to engage in media multitasking, rather than why students do or do not engage in these
behaviors (see, for example, Foehr, 2006). As investigators have linked media multitasking to impaired academic task performance (e.g., Fox,
Rosen, & Crawford, 2009), there is a paradox as to why students would choose to multitask with media during homework completion (see
Wang & Tchernev, 2012). Wang and Tchernev (2012) have recently provided evidence to suggest that, although perceived cognitive needs
usually drive the initiation of multitasking behaviors, multitasking with media primarily satis?es emotional needs. Despite this recognition,
there have been no studies to link objective observations of multitasking behaviors during homework completion to mood or task motivation in college student samples.
The ?rst step in exploring the relationship of mood and task motivation to multitasking during homework completion is to establish
whether these processes change over the course of the homework period. Mental work refers to activities accomplished against resistance
(Dodge, 1913). All else being equal, sustained periods of mental work are theorized to drain cognitive and attentional resources (Hockey,
1997; Kahneman, 1973). In turn, the depletion of cognitive and attentional resources has been shown to have implications for mood and
motivational processes (see Hockey, 2011). Although empirical researchers have observed subjective mood decrements during periods of
sustained academic task performance (e.g., Ackerman & Kanfer, 2009), these ?ndings have not yet been generalized to the homework
environment. Based on predictions derived from attentional and cognitive resource theories, we predict that decrements in mood and
motivation will accompany sustained periods of homework completion.
Hypothesis 1. Mood and motivation will be impaired over sustained periods of homework completion.
While resource-based perspectives imply that engagement in homework tasks will impair mood and task motivation, they do not directly
address which mood and motivational processes are related to multitasking behaviors. In the following sections, we review three sets of
mood and motivational variables which are likely to be associated with multitasking behaviors during homework completion. Throughout
these sections, we provide speci?c hypotheses regarding the relationships of these mood and motivational variables to multitasking.
1.2.1. Negative and positive affect
Negative and positive affect (NA and PA) respectively refer to the experience of negative and positive mood states (Watson, Clark, &
Tellegen, 1988). Theorists have argued that affective experiences have important implications for the allocation of cognitive resources
between on-task thoughts and off-task distractions (Beal, Weiss, Barros, & MacDermid, 2005). However, there is evidence to suggest that
negative and positive affective experiences are differentially related to this resource allocation process. NA has been consistently linked to
engagement in ruminative thought (see Thomsen, 2006 for a review), which has been proposed as a factor in the allocation of cognitive
resources to off-task distractions (Beal et al., 2005). In contrast, PA has been theorized to broaden attentional and cognitive resources
(Carver, 2003; Fredrickson, 2001), and positive mood states have been associated with more careful processing of goal-relevant (i.e., ontask) information (see Aspinwall, 1998). Based on theories linking affective experiences to cognitive resource allocation and past empirical ?ndings regarding the effects of NA and PA on cognitive resources and attention, we anticipate NA to be linked to more frequent and
longer duration multitasking behaviors, while we expect PA to be associated with less frequent and shorter duration multitasking behaviors.
Hypothesis 2. NA and multitasking behaviors will be positively correlated.
Hypothesis 3. PA and multitasking behaviors will be negatively correlated.
1.2.2. Subjective fatigue
Subjective fatigue refers to feelings of tiredness or lack of energy that are not related exclusively to exertion (see Brown & Schutte, 2006).
As homework tasks are identi?ed by several characteristics commonly associated with fatigue (see Ackerman, Calderwood, & Conklin, 2012
for a review), sustained periods of homework activity are likely to correspond to increasing levels of fatigue over time. As it relates to off-task
distractions, Davis (1946) was one of the ?rst researchers to identify that some individuals divert attention away from primary tasks under
conditions of fatigue. While not studied in relation to media multitasking speci?cally, researchers have generally supported this observation,
?nding that the ability to regulate goal-directed perceptual and motor processes is compromised under fatiguing conditions (e.g., van der C. Calderwood et al. / Computers & Education 75 (2014) 19?29 21 Linden, Frese, & Meijman, 2003). Therefore, students experiencing subjective fatigue while completing homework are more likely to engage
with off-task distractions in their study environment, as fatigue compromises their ability to regulate their goal-directed homework
behavior. Based on these lines of empirical evidence, we predict that higher subjective fatigue will be associated with more frequent and
longer duration multitasking.
Hypothesis 4. Subjective fatigue and multitasking behaviors will be positively correlated.
1.2.3. Task motivation and self-ef?cacy
Homework task motivation refers to a drive to perform well on homework tasks, while homework self-ef?cacy re?ects a judgment of
one?s abilities to accomplish homework tasks (for a discussion of self-ef?cacy and task motivation constructs, see Bandura, 1982; Deci, 1975,
respectively). These motivational processes are seen as integral in the achievement of goal-directed behavior, and have important implications for academic task performance and related outcomes (see Pajares, 1996 for a review). The ability to resist distraction is seen as a
component of the regulation of behavior in the service of accomplishing goal-directed behavior (see Zimmerman, Bandura, & MartinezPons, 1992). Applied to the homework environment, individuals who exhibit a higher motivation and self-ef?cacy to perform well and
focus on their homework tasks should be less likely to engage with off-task distractions in their homework environment. Based on this
reasoning, we expect that higher homework task motivation and self-ef?cacy to focus on homework tasks will be associated with less
frequent and shorter duration multitasking.
Hypothesis 5. Homework task motivation and multitasking behaviors will be negatively correlated.
Hypothesis 6. Homework self-ef?cacy and multitasking behaviors will be negatively correlated.
2. Goals of the current investigation
There were three primary goals of the current investigation. First, we used a variety of observational methods to determine how many
interruptions college students engage with, the duration of these interruptions, and the proportion of time spent media multitasking during
homework completion. Second, we sought to determine whether potentially intrusive objective means of assessing multitasking behaviors
have reactive effects on student behaviors. To accomplish this goal, we tested for potential behavioral differences when students were
monitored with: (1) A system of four surveillance cameras; (2) A system of four surveillance cameras and a head-mounted point-of-view
(POV) camera; and (3) A system of four surveillance cameras and a mobile eyetracker. Third, we sought to explore the relationships of
distraction frequency and duration to self-report measures of affect, fatigue, motivation, and self-ef?cacy during homework completion.
Deriving our hypotheses from theoretical models of cognitive resources, resource allocation, and task motivation, we expected higher NA
and fatigue to be associated with greater multitasking behaviors, while we anticipated higher PA, homework task motivation, and selfef?cacy to be linked to fewer multitasking behaviors.
Although other studies have examined student media multitasking in a laboratory setting (e.g., Ie, Haller, Langer, & Courvoisier, 2012),
they have typically done so using somewhat arti?cial laboratory tasks. To increase the external validity of our study results, we explored
media multitasking in a less constrained environment, by having students complete their own homework during the study session. As such,
we consider the current investigation to be complementary to studies utilizing more constrained tasks.
Sixty undergraduate students at the Georgia Institute of Technology participated in the study. Participants were recruited through
recruitment ?yers posted on campus and in-class announcements in undergraduate psychology classes. Participants were offered course
extra credit for their participation. Inclusion criteria were that students were currently enrolled in at least one course each of math, science,
and one other subject. Participants were randomly assigned to one of three conditions. Because of hardware/software failures, eyetracking
data were lost for two participants. Therefore, complete data were available for 58 participants (N ¼ 58).
Participants were instructed to bring 3 h of homework comprising three different subjects to the laboratory. They were told that they
could bring their laptop, mp3 player/CDs or other audio materials, and cell phone to the study, and that they would be allowed to use all of
these items. We also informed participants that we would provide an Internet-connected desktop computer and a printer for them to use
during the session. On arrival, participants in the POV and mobile eyetracker conditions were ?tted with the respective apparatus. Participants were instructed to ?do your homework as you would anywhere else,? and asked to complete a short questionnaire at the beginning
of each hour of the session. They were allowed to leave the room if necessary (e.g., for a bathroom break) by ?rst removing any apparatus, or
to eat or drink in an area away from the computer. Self-report questionnaires, which took approximately 3 min to complete, were
administered at 0 min, 63 min, and 126 min after the equipment calibration protocols were completed.
Participants were provided with a desk, a computer connected to the Internet, a printer, a JVC ?boombox? with an mp3 input cable, and a
?at work surface. Partitions were setup in the work area to limit the participant?s view of other areas of the laboratory (see Fig. 1). A research
assistant entered the room once each hour to administer brief questionnaires or to swap out a memory card. Other than these brief interruptions, research assistants did not intrude during the 3 h homework session.
Four small high-de?nition cameras and a microphone were placed to observe the participant?s activities while studying/completing
homework. One dome camera was placed overhead, one camera above and in front of the participant, and two cameras above and on either 22 C. Calderwood et al. / Computers & Education 75 (2014) 19?29 Fig. 1. Student workstation layout as seen from four surveillance cameras. Clockwise from upper left: Overhead dome camera display, distant camera from the right of the
workstation, distant camera from the left and behind the workstation, and computer table display from behind and above the workstation. Shown in the displays are the computer,
workstation chair, printer, and work surfaces. The boombox (with mp3 input access) can be seen in the upper part of the ?rst camera display, on the ?oor behind the workstation. side behind the participant (a still sample from the cameras is shown in Fig. 1). Video was recorded from all four cameras simultaneously.
The cameras were active in all three experimental conditions.
3.3. Experimental conditions
3.3.1. POV Camera condition
In this condition, participants had a small high-de?nition V.I.O. POV camera attached via a headband. The physical apparatus is shown in
Fig. 2a, and a screen-shot from the POV is shown in Fig. 2b. Twenty participants were randomly assigned to the POV condition (n ¼ 20).
3.3.2. Mobile eyetracker condition
In this condition, participants wore a Tobii mobile eyetracker device, which is similar to a large pair of glasses (see Fig. 3a). The mobile
eyetracker contains a small video camera that records where the participant is looking, and the playback system overlays a red dot (see the
web version of this article) indicating eye ?xations and gaze movements over the video stream (see Fig. 3b). Twenty participants were
randomly assigned to this condition, but due to equipment failures, complete data were only available for 18 participants (n ¼ 18).
3.3.3. Surveillance-only condition
In this condition, participants wore no recording devices during the session. Twenty participants were randomly assigned to this
condition (n ¼ 20).
3.4. Data coding
Recorded videos from the three sets of devices were played-back while research assistants coded the frequency and duration of any nonhomework-related events drawn from ten different distraction categories (see Table 1). Distraction categories were developed from
commonly reported behaviors in a 5-day pilot study in which students self-reported their multitasking behaviors while completing
homework. All video footage included a running time-stamp used to calculate distraction duration. Coders were instructed to record the
following pieces of information any time that a participant engaged in a behavior corresponding to one or more of the distraction categories:
1) The distraction category or categories to which the behavior(s) belonged; 2) The start time of the behavior(s) on the time-stamp; 3) The
end time of the behavior(s) on the time-stamp; and 4) Any additional comments necessary to identify the behavior(s) in question. The
recorded videos for a subsample of 20 participants were randomly selected to be re-coded by a second research assistant to estimate interrater agreement. Inter-rater agreement was quanti?ed via computation of an intraclass correlation coef?cient (see Shrout & Fleiss, 1979),
with values closer to 1 representing greater agreement. The estimated inter-rater agreement for multitasking frequency was ICC ¼ .72, while
the estimated inter-rater agreement for multitasking duration was ICC ¼ .90. C. Calderwood et al. / Computers & Education 75 (2014) 19?29 23 Fig. 2. (a) Upper panel. V.I.O. point-of-view (POV) camera, mounted on headband; (b) Lower panel. Still screen-shot from POV camera. 3.5. Self-report measures
A brief 34-item measure of state affect, fatigue, self-ef?cacy, and positive motivation (comprised of items from the Positive and Negative
Affect Schedule; Watson et al., 1988; Pro?le of Mood States, McNair, Lorr, & Droppleman, 2003; and locally developed items) was
administered at the beginning of each hour of the laboratory session. This measure was drawn from a subset of items developed in a
previous study of mood, fatigue, and motivation during sustained academic task performance (see Ackerman & Kanfer, 2009).
3.5.1. Negative and positive affect
Twelve items referring to the experience of negative mood states (a ¼ .83?.86 across the three hourly administrations in the session) and
seven items referring to the experience of positive mood states (a ¼ .90?.91). An example negative affect item is ?distressed?, while an
example positive affect item is ?enthusiastic.? Students were asked to indicate the degree to which each statement described how they
currently felt on a 5-point Likert-type scale (1 ¼ Very slightly or not at all, 2 ¼ A little, 3 ¼ Moderately, 4 ¼ Quite a bit, 5 ¼ Extremely).
3.5.2. Subjective fatigue
Twelve items referring to feelings of fatigue, sluggishness, stiffness or strain in neck or eyes (a ¼ .84?.87). An example item is ?worn out.?
Students rated the degree to which each statement described how they currently felt on a 5-point Likert-type scale (1 ¼ Very slightly or not at
all, 2 ¼ A little, 3 ¼ Moderately, 4 ¼ Quite a bit, 5 ¼ Extremely).
3.5.3. Homework task motivation
Five items referring to motivation to perform well on homework tasks and assignments (a ¼ .77?.93). An example item is ?I am pushing
myself to work hard.? Students provided a rating of the degree to which each statement described their current attitude on a 6-point Likerttype scale (1 ¼ Strongly disagree, 2 ¼ Moderately disagree, 3 ¼ Slightly disagree, 4 ¼ Slightly agree, 5 ¼ Moderately agree, 6 ¼ Strongly agree).
Five items referring to the con?dence that the student can concentrate on his/her homework/study activities in the next hour (a ¼ .83?.87).
An example item is ?In the next hour, how con?dent are you that you can concentrate on your homework/study activities. 50% of the time??
Students rated their con?dence to concentrate on their homework/study activities on a 9-point Likert-type scale (0 ¼ No con?dence, 24 C. Calderwood et al. / Computers & Education 75 (2014) 19?29 Fig. 3. (a) Upper panel. Tobii mobile eyetracker; (b) Lower panel. Still screen-shot from mobile eyetracker. Red circles indicate eye ?xations, and lines indicate shifts in gaze. (For
interpretation of the references to color in this ?gure legend, the reader is referred to the web version of this article.) 1 ¼ Extremely little con?dence, 2 ¼ Very little con?dence, 3 ¼ Somewhat con?dent, 4 ¼ Moderately con?dent, 5 ¼ Quite con?dent, 6 ¼ Very
con?dent, 7 ¼ Extremely con?dent, 8 ¼ Certain).
4. Results The results are presented in ?ve sections. First, we compare codings derived from the surveillance cameras against codings derived from
the POV camera and eyetracker. Second, we present a descriptive analysis of the frequency of student engagement with various sources of
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