6Websites, Books, Independent Studies 6 Journal Articles Summ.docx

6
Websites, Books, Independent Studies: 6 Journal Articles
Summarized Here
APA Citation REQUIRED (Refer to APA Writer’s Manual, 6th
ed.)
Fill out both areas for 1 Article on each page (6 pages)
Sample Citation in APA 6th edition:
Arbelo, F. (2016). Pre-entry doctoral admission variables and
retention at a Hispanic Serving
Institution. International Journal of Doctoral Education, 11, 269
– 284.
http://www.informingscience.org/Publications/3545
Academic Journal Articles:
APA Citation (Refer to APA Writer’s Manual, 6th ed.)
Citation here
Selection
Explanation
Source: Primary or Secondary
Information Classification:
(Self-contained study/ Research findings / Professional
Association/ Unanalyzed Data / Compiled Statistics, etc.)
How and why is this information pertinent to your selected
topic?

Citation here
Selection
Explanation
Issues / Topics Covered
Author(s):
Research Question(s) addressed:
Research Subjects: (pre-K, 9th graders, elementary school
students, etc.)
Research setting:
(Public school, 3rd grade class, Charter school, adult learning
center, etc.)
Methodology:
Findings:
Conclusions:
Special Circumstances/Limitations:
Future Implications:
topic?
2nd Article

Citation here:
Selection
Explanation
topic?
Citation here:
Selection
Explanation
Author(s):
students, etc.)
Research setting:
center, etc.)

Methodology:
Findings:
Conclusions:
topic?
3rd Article
Citation here:
Selection
Explanation
topic?

Selection
Explanation
Author(s):
students, etc.)
Research setting:
center, etc.)
Methodology:
Findings:
Conclusions:
topic?
4th Article

Citation here:
Selection
Explanation
topic?
Selection
Explanation
Author(s):
students, etc.)
Research setting:
center, etc.)
Methodology:

Findings:
Conclusions:
topic?
5th Article
Citation here:
Selection
Explanation
topic?

Selection
Explanation
Author(s):
students, etc.)
Research setting:
center, etc.)
Methodology:
Findings:
Conclusions:
topic?
6th Article
Citation here:

Selection
Explanation
topic?
Selection
Explanation
Author(s):
students, etc.)
Research setting:
center, etc.)
Methodology:
Findings:

Conclusions:
topic?
International Review of Research in Open and Distributed
Learning
Volume 18, Number 2
April – 2017
Analysis of Time-on-Task, Behavior Experiences, and
Performance in Two Online Courses with Different
Authentic Learning Tasks
Sanghoon Park
University of South Florida
Abstract
This paper reports the findings of a comparative analysis of
online learner behavioral interactions, time-

on-task, attendance, and performance at different points
throughout a semester (beginning, during, and
end) based on two online courses: one course offering authentic
discussion-based learning activities and
the other course offering authentic design/development-based
learning activities. Web log data were
collected to determine the number of learner behavioral
interactions with the Moodle learning management
system (LMS), the number of behavioral interactions with peers,
the time-on-task for weekly tasks, and the
recorded attendance. Student performance on weekly tasks was
also collected from the course data.
Behavioral interactions with the Moodle LMS included resource
viewing activities and
uploading/downloading file activities. Behavioral interactions
with peers included discussion postings,
discussion responses, and discussion viewing activities. A series
of Mann-Whitney tests were conducted to
compare the two types of behavioral interactions between the
two courses. Additionally, each student's
behavioral interactions were visually presented to show the
pattern of their interactions. The results
indicated that, at the beginning of the semester, students who
were involved in authentic

design/development-based learning activities showed a
significantly higher number of behavioral
interactions with the Moodle LMS than did students involved in
authentic discussion-based learning
activities. However, in the middle of the semester, students
engaged in authentic discussion-based learning
activities showed a significantly higher number of behavioral
interactions with peers than did students
involved in authentic design/development-based learning
activities. Additionally, students who were given
authentic design/development-based learning activities received
higher performance scores both during
the semester and at the end of the semester and they showed
overall higher performance scores than
Performance in Two Online Courses with Different Authentic
Learning Tasks
Park
214
students who were given authentic discussion-based learning
activities. No differences were found between
the two groups with respect to time-on-task or attendance.

Keywords: authentic learning task, behavioral experience,
online learning, Web log data, time-on-task
Introduction
The number of online courses has been growing rapidly across
the nation in both K-12 and higher education.
According to the U.S. Department of Education’s National
Center for Education Statistics (NCES),
approximately half of all K-12 school districts nationwide
(55%) had students enrolled in at least one online
course (National Center for Education Statistics [NCES], 2011).
In higher education, more than 7.1 million
students are taking at least one online course (Allen & Seaman,
2014). These numbers are projected to grow
exponentially as more universities are striving to meet the
increasing demand for online courses. Online
courses are expected to provide formal learning opportunities at
the higher education level using various
learning management platforms (Moller, Foshay, & Huett, 2008;
Shea & Bidjerano, 2014; Wallace, 2010).
Consequently, E-learning systems, or learning management
systems (LMSs), are being advanced to provide
students with high quality learning experiences and high quality
educational services in their online courses

(Mahajan, Sodhi, & Mahajan, 2016).
Although the quality of an online learning experience can be
defined and interpreted differently by the
various stakeholders involved, previous studies identified both
time flexibility and authentic learning tasks
as two key factors affecting successful online learning. Time
flexibility has been regarded as the most
appealing option for online learning (Romero & Barberà, 2011)
as it allows online learners to determine the
duration, pace, and synchronicity of the learning activities
(Arneberg et al., 2007; Collis, Vingerhoets, &
Moonen, 1997; Van den Brande, 1994). Recently, Romero and
Barberà (2011) divided time flexibility into
two constructs, instructional time and learner time, and asserted
the need for studies that consider the time
attributes of learners, such as time-on-task quality. Authentic
tasks form the other aspect of successful
online learning. Based on the constructivist learning model,
online students learn more effectively when
they are engaged in learning tasks that are relevant and/or
authentic to them (Herrington, Oliver, & Reeves,
2006). Such tasks help learners develop authentic learning
experiences through activities that emulate real-

life problems and take place in an authentic learning
environment (Roblyer & Edwards, 2000). Authentic
learning activities can take many different forms and have been
shown to provide many benefits for online
learners (Lebow & Wager, 1994). For example, authentic tasks
offer the opportunity to examine the task
from different perspectives using a variety of available online
resources. Additionally, authentic tasks can
be integrated across different subject areas to encourage diverse
roles and engage expertise from various
interdisciplinary perspectives (Herrington et al., 2006). To
maximize the benefits of authentic tasks,
Herrington, Oliver, and Reeves (2006) suggested a design model
that involves three elements of authentic
learning: tasks, learners, and technologies. After exploring the
respective roles of the learner, the task and
the technology, they concluded that synergy among these
elements is a strong contributor to the success of
online learning. Therefore, online learning must be designed to
incorporate authentic learning tasks that
are facilitated by, and can be completed using, multiple types of
technologies (Parker, Maor, & Herrington,
2013).

Learning Tasks
Park
215
In summary, both time flexibility and authentic learning tasks
are important aspects of a successful online
learning experience. However, few studies have investigated
how online students show different behavioral
interactions during time-on-tasks with different authentic
learning tasks, although higher levels of online
activity were found to be always associated with better final
grades greater student satisfaction (Cho & Kim,
2013). Therefore, the purpose of this study was to compare
behavioral interactions, time-on-task,
attendance, and performance between two online courses
employing different types of authentic tasks.
Web Log Data Analysis
Web log data analysis or Web usage analysis is one of the most
commonly used methods to analyze online
behaviors using electronic records of a system-user interactions
(Taksa, Spink, & Jansen, 2009). Web logs
are the collection of digital traces that provide valuable

information about each individual learner’s actions
in an online course (Mahajan et al., 2016). Many recent LMSs,
such as CANVAS, or the newly upgraded
LMSs, such as Blackboard or Moodle, offer various sets of Web
log data in the form of learning analytics.
The data usually contain course log history, number of views
for each page, number of comments,
punctuality of assignment submission, and other technology
usage. Web log files also contain a list of user
actions that occurred during a certain period of time (Grace,
Maheswari, & Nagamalai, 2011). This vast
amount of data allows instructors and researchers to find
valuable information about learners’ online
course behaviors, such as how many times per day and how
often students log in, how many times and how
often they post to discussion boards, how many students submit
assignments on time, how much time they
spend on each learning task, etc. Web log data also provides
personal information about online learners,
such as each student’s profile and achievement scores, and each
student’s behavioral interaction data, such
as content viewing, discussion posting, assignment submission,
writing, test taking, task performances, and
communications with peers/instructor (Mostow et al., 2005).

The data can be presented in the form of
visualization to support students and instructors in the
understanding of their learning/teaching
experiences. Therefore, the Web log analysis method offers a
promising approach to better understand the
behavioral interactions of online learners at different points
during the semester. Researchers can use Web
log data to describe or make inferences about learning events
without intruding the learning event or
involving direct elicitation of data from online learners (Jansen,
Taksa, & Spink, 2009). Although Web log
data is a source of valuable information to understand online
behaviors, it also has to be noted that
researchers must be careful when interpreting the data with a
fair amount of caution because Web log data
could be misleading. For example, an online student might
appear to be online for a longer time than she/he
actually participated in a learning activity. Therefore, prior to
conducting the Web log analysis, a researcher
needs to understand the type of behavioral data to be analyzed
based on the research questions and
articulates the situational and contextual factors of the log data.
Using the timestamps showing when the
Web log was recorded, the researcher can make the observation

of behaviors at certain point of time and
decide the validity of the online behavior (Jansen et al., 2009).
Behavioral Interactions
Previous studies have shown the benefits of analyzing Web log
data to understand the online learning
behaviors of students. Hellwege, Gleadow, and McNaught
(1996) conducted a study of the behavioral
patterns of online learners while studying a geology Web site
and reported that learners show a pattern of
accessing the most recent lecture notes prior to accessing the
Web site materials. Sheard, Albrecht, and
Butbul (2005) analyzed Web log files and found that knowing
when students access various resources helps
Learning Tasks
Park
216
instructors understand the students’ preferred learning patterns.
While analyzing log data to investigate
learning effectiveness, Peled and Rashty (1999) found that the
most popular online activities were, in

general, passive activities, such as retrieving information, rather
than contributing activities. Dringus and
Ellis (2005) reported on how to analyze asynchronous
discussion form usage data to evaluate the progress
of a threaded discussion. Several recent studies showed a
positive link between students' online activities
and their final course grades. For example, Valsamidis and
Democritus (2011) examined the relationship
between student activity level and student grades in an e-
learning environment and found that the quality
of learning content is closely related to student grades. Also,
Dawson, McWilliam, and Tan (2008) found
that when students spend more time in online activities and
course discussions, they earned higher final
grades. Similarly, Macfadyen and Dawson (2010) reported that
the numbers of messages postings, email
correspondences, and completed assessments were positively
correlated with students' final course grades.
Most recently, Wei, Peng, and Chou's study (2015) showed the
positive correlations between the number of
discussion postings, reading material viewings, and logins with
students' final exam scores. Although the
previous studies utilized Web log data to investigate the
relationships between students' behavioral

interactions and learning achievement, few studies examined
how online students' behavioral interactions
are different at different phases of online learning when
involved in two types of authentic learning tasks.
Online Learning Experience
The overall online learning experience consists of continuous
behavioral interactions that are generated
while completing a series of learning tasks (Park, 2015).
Therefore, an examination of the nature of the
learning tasks and the influences of the learning tasks on
student behaviors is needed. The examined short-
term learning experiences can then be combined to create a big
picture of the online learning experience
within a course. According to Veletsianos and Doering (2010),
the experience of online learners must be
studied throughout the semester due to the long-term nature of
online learning programs. To analyze the
pattern of behavioral interactions, this study employed time and
tasks as two analysis frames because both
time and tasks form essential dimensions of a behavioral
experience, as shown in Figure 1. An online
learning experience begins at the starting point (first day of the
course) and ends at the ending point (last

day of the course). In between those two points, a series of
learning tasks are presented to provide learners
with diverse learning experiences. As the course continues, the
learner continues to interact with learning
tasks and eventually accumulates learning experiences by
completing the learning tasks (Park, 2016).
Students learning experiences are built up from the previous
learning tasks because learning tasks are not
separated from each other as shown in the spiral area in Figure
1. Hence, to analyze behavioral interactions
in online learning, both the type of learning tasks and the time-
on-task must be analyzed simultaneously.
In this paper, the researcher gathered and utilized Web log data
to visualize the behavioral interaction
patterns of online learners during the course of a semester and
to compare the behavioral interactions
between two online courses requiring different types of learning
tasks.
Learning Tasks
Park

217
Figure 1. Online learning experience - time and tasks.
Research Questions
1. Do online learners' behavioral interactions with Moodle LMS
differ between a course employing
authentic discussion-based learning tasks and a course
employing authentic design/development-
based learning tasks?
2. Do online learners' behavioral interactions with peers differ
between a course employing authentic
discussion-based learning tasks and a course employing
authentic design/development-based
learning tasks?
3. Does online learners' time-on-task differ between a course
employing authentic discussion-based
learning tasks and a course employing authentic
design/development-based learning tasks?
4. Does online learners' attendance differ between a course
employing authentic discussion-based
learning tasks and a course employing authentic
design/development-based learning tasks?

5. Does online learners' academic performance differ between a
course employing authentic
discussion-based learning tasks and a course employing
authentic design/development-based
learning tasks?
Method
Setting
In this study, the researcher purposefully selected two online
courses as units of analysis. The two courses
were purposefully selected because of the different learning
approach that each course employed to design
authentic learning activities and the extent to which technology
was used. Course A activities were designed
based on the constructivist learning approach while Course B
activities were designed based on the
constructionist learning approach. Both the constructionist
approach and constructivist approach hold the
basic assumption that students build knowledge of their own
and continuously reconstruct it through
personal experiences with their surrounding external realities.
However, the constructionist approach is

Learning Tasks
Park
218
different from the constructivist approach in that constructionist
learning begins with a view of learning as
a construction of knowledge through constructing tangible
projects or creating digital artifacts (Kafai, 2006;
Papert, 1991). The title of Course A was Program Evaluation, in
which the major course activities consisted
of textbook reading, weekly online discussions, and a final
evaluation plan proposal. Students enrolled in
this course were expected to read the textbook, participate in
weekly discussion activities, and complete a
program evaluation plan. Course B was titled Instructional
Multimedia Design/Development, which
consisted of a series of hands-on tasks to design and develop
multimedia materials using different
multimedia authoring programs. Students were required to
review related literature and tutorials on
multimedia design during the semester and to create audio-
based, visual-based, and Web-based
multimedia materials through a series of hands-on-activities.

The comparison of course requirements, key
learning activities, authentic tasks, and technology use between
the two online courses is presented in Table
1.
Table 1
Comparison of Course Requirements, Key Learning Activities,
Authentic Tasks, and Technology use
Between Courses
Course A Course B
Course* requirements Textbook reading & online discussion
Multimedia design/development
Key learning activities
discussion
online discussion
port

design/development
design/development
module design/development
rt
world scenario and presented with
contextualized data for weekly
discussions.
-defined
and open to multiple
interpretations.
each discussion topic.
variety of related documents and
Web resources.
course outcome (program
evaluation plan proposal) that
could be used in their own
organization.

dents were guided to design and
create instructional multimedia
materials to solve a performance
problem that they identified in their
own fields.
scope of each multimedia project to
solve the unique performance
problems they had identified.
different multimedia programs and
apply various design principles that
were related to their own projects.
Web based learning module that
can be used as an intervention to
solve the identified performance
problem in their own organizations.
Technology use Students utilized the following
technology to share their ideas and
insights via weekly discussions
Students utilized the following
technology to design and create
instructional multimedia materials
Learning Tasks
Park

219
based on the constructivist learning
approach:
-Word
-PowerPoint
based on the constructionist
learning approach:
design tools

Note.
* A course in this study refers to a general online class that
delivers a series of lessons and learning tasks
(online lectures, readings, assignments, quizzes, design and
development activities, etc.).
** Authentic tasks were designed based on 10 characteristics of
authentic activities/tasks defined by
Herrington et al. (2006).
Both courses were delivered via Moodle LMS and met the
Quality Matters (QM) standards. Moodle is an
open-source LMS that helps educators create online learning
courses. It has been used as a popular
alternative to proprietary commercial online learning solutions
and is distributed free of charge under open
source licensing (Romero, Ventura, & Garcia, 2008). QM
specifies a standard set employed to certify the
quality of an online course (www.qualitymatters.org). Both
courses A and B in this study met the required
standards for high quality online course design after a rigorous
review process by two certified QM
reviewers.
Participants
As two courses with different online learning tasks were

purposefully selected, 22 graduate students who
were enrolled in two 8 week long online courses participated in
this study. Twelve students were enrolled
in Course A, and 10 students were enrolled in Course B.
Excluding four students, two who dropped from
each course due to personal reasons, the data reported in this
paper concern 18 participants, 10 students (4
male and 6 female) in Course A and 8 students (all female) in
Course B, with a mean age of 32.60 years (SD
= 5.76) and 35.25 years (SD = 9.66), respectively. The average
number of online courses the study
participants had taken previously was 11.40 (SD = 4.88) for
Course A and 11.38 (SD = 12.28) for Course B,
indicating no significant difference between the two courses.
However, it should be noted that the number
of students who had not previously taken more than 10 online
courses was higher in Course B (five
participants) than in Course A (three participants). Fifteen of
the 18 participants were teachers: five taught
elementary school, five taught middle school, and five taught
high school. Of the remaining three
participants, one was an administrative assistant, one was a
curriculum director, and one was an
instructional designer.

Learning Tasks
Park
220
Figure 2. Example of Web log data screen.
Data
In this study, the researcher examined behavioral interactions
by utilizing students’ Web log data acquired
from Moodle LMS used in this study (Figure 2). The obtained
sets of data were significant for this study
because they contained timestamp-sequenced interaction
activities that are automatically recorded for each
student with pre-determined activity categories such as view
discussion, post discussion, view resources,
etc. Hence, it clearly showed the type of activities each student
followed in order to complete a given online
learning task. The researcher also ensured the accuracy of data
by following the process to decide the

validity of the online behavior (Jansen et al., 2009). First, the
researcher clearly defined the type of
behavioral data to be analyzed based on the research questions
(Table 2), and second, the researcher
articulated the situational and contextual factors of the log data
by cross-examining the given online tasks
and recorded students activities. Lastly, the researcher checked
the timestamps for each activity to confirm
Learning Tasks
Park
221
the time and the length of data recorded. The data were then
converted to Excel file format and computed
based on three semester phases for further analysis. These
phases were phase 1 ─ beginning of the semester,
phase 2 ─ during the semester, and phase 3 ─ end of the
semester. An example of a Web log data screen is
presented in Figure 2. The data show online learner behaviors in
chronological order. Based on the type of
behavioral activities, the researcher identified two categories of
behavioral interactions that affect task

completion: interactions with the Moodle LMS and interactions
with peers. Table 2 presents the two
categories of behavior interactions and example behaviors for
each category.
Table 2
Categories of Behavioral Interactions and Description
Categories of behavioral
interaction
Behavior description
(Operational definition)
Interactions with Moodle
LMS
(# of times quiz participation - quiz completion and submission)
(# of visits to the Resource page)
(# of visits to files page - file uploading and file downloadng)
Interactions with peers
(# of times discussion viewed)

(# of times discussion posted - making initial posts)
(# of times discussion responded - making comments or replies)
Among the identified behaviors, quiz taking was excluded from
the analysis because it was a behavioral
interaction that only applied to Course A. Student attendance
and time-on-task were collected from
recorded attendance data and each student’s weekly reported
time-on-task. Weekly performance scores
were also collected from the course instructors and from the
students with student permission. Due to the
different grading systems, task scores from the two courses
were converted to a 1 (minimum) to 100
(maximum) scale and combined based on the corresponding
weeks for each phase.
Results
Collected data were analyzed to answer each of the five
research questions. Table 3 displays the descriptive
statistics of behavioral interactions with the Moodle LMS,
behavioral interactions with peers, time-on-task,
attendance, and performance for each of the two courses.

A series of Mann-Whitney tests (Field, 2013), the non-
parametric equivalent of the independent samples t-
test, were used to compare the two types of behavioral
interactions, time-on-task, attendance, and
performance between the two courses. The Mann-Whitney test
was selected for use in this study because
the data did not meet the requirements for a parametric test, and
the Mann-Whitney test has the advantage
of being used for small samples of subjects, (i.e., between five
and 20 participants) (Nachar, 2008).
Learning Tasks
Park
222
RQ1: Do online learners' behavioral interactions with the
Moodle LMS differ between
a course employing authentic discussion-based learning tasks
and a course employing
authentic design/development-based learning tasks?
The average number of behavioral interactions with the Moodle
LMS between the two courses was
compared using the Mann-Whitney test. Among the three phases
compared, the average number of Moodle

LMS interactions in phase 1 (weeks 2/3) was significantly
different, as revealed in Figure 3. In phase 1, the
average number of Moodle LMS interactions in Course B (M =
32.00, Mdn = 31.50) was significantly higher
than the average number of Moodle LMS interactions in Course
A (M = 19.60, Mdn = 20.00), U = 12.00, z
= - 2.50, p < .05, r = -.59, thus revealing a large effect size
(Field, 2013). In phases 2 and 3, however, the
average number of Moodle LMS interactions were not
significantly different between the two courses.
Nonetheless, the total number of Moodle LMS interactions
between the two courses was significantly
different as the total number of Moodle LMS interactions in
Course B (M = 74.13, Mdn = 73.50) was
significantly higher than the average number of Moodle LMS
interactions in Course A (M = 59.70, Mdn =
65.50), U = 17.50, z = - 2.01, p < .05, r = -.47, indicating a
medium to large effect size.
Figure 3. Average number of behavioral interactions with the
Moodle LMS for each phase of the semester
for two courses.

0
5
10
15
20
25
30
35
Phase1 Phase2 Phase3
Course A
Course B
Learning Tasks
Park
223
Table 3

Descriptive Statistics of Behavioral Interactions, Time,
Attendance, and Performance
Phase 1 (Weeks 2/3) Phase 2 (Weeks 4/5/6) Phase 3 (Weeks
7/8) All three phases
Course A
(n = 10)
Course B
(n = 8)
Course A
(n = 10)
Course B
(n = 8)
Course A
(n = 10)
Course B
(n = 8)
Course A
(n = 10)
Course B
(n = 8)
M SD M SD M SD M SD M SD M SD M SD M
SD
Behavioral
interactions a

LMS
interactions
19.60(5.36) 32.00(9.37) 23.10(6.72) 19.63(7.15) 17.00(5.77)
22.50(12.09) 59.70(10.46) 74.13(19.28)
Peer
interactions
64.50(24.04) 86.75(42.60) 126.30(57.88) 58.25(25.39)
65.70(41.17) 48.25(22.38) 256.50(99.69) …
lable at ScienceDirect
Computers in Human Behavior 86 (2018) 174e180
Contents lists avai
Computers in Human Behavior
journal homepage: www.elsevier.com/locate/comphumbeh
Full length article
Effect of uninterrupted time-on-task on students’ success in
Massive
Open Online Courses (MOOCs)
Youngjin Lee
University of Kansas, 1122 W. Campus Rd. (Room 413)
Lawrence, KS 66045, USA
a r t i c l e i n f o
Article history:
Available online 23 April 2018

Keywords:
Massive open online course (MOOC)
Log file analysis
Academic success
Self-regulated learning (SRL)
Time-on-task
E-mail address: [email protected]
https://doi.org/10.1016/j.chb.2018.04.043
0747-5632/© 2018 Elsevier Ltd. All rights reserved.
a b s t r a c t
This study investigated the relationship between uninterrupted
time-on-task and academic success of
students enrolled in a Massive Open Online Course (MOOC).
The variables representing uninterrupted
time-on-task, such as number and duration of uninterrupted
consecutive learning activities, were mined
from the log files capturing how 4286 students tried to learn
Newtonian mechanics concepts in a MOOC.
These variables were used as predictors in the logistic
regression model estimating the likelihood of
students getting a course certificate at the end of the semester.
The analysis results indicate that the
predictive power of the logistic regression model, which was
assessed by Area Under the Precision-Recall
Curve (AUPRC), depends on the value of off-task activity
threshold time, and the likelihood of students
getting a course certificate increases as students were doing
more uninterrupted learning activities over
a longer period of time. The findings from this study suggest
that a simple count of learning activities,
which has been used as a proxy for time-on-task in previous
studies, may not accurately describe student
learning in the computer-based learning environment because it
does not reflect the quality, such as
uninterrupted durations, of those learning activities.

© 2018 Elsevier Ltd. All rights reserved.
Since Carroll (1963) proposed a model of school learning, time-
on-task, the amount of time students are actively engaged in
learning, has been considered an important variable that can
explain academic success of students. Earlier studies conducted
with undergraduate students in traditional face-to-face courses
found that self-reported days of study per week (Allen, Lerner,
&
Hinrichsen, 1972) and self-reported hours of study per week
(Wagstaff & Mahmoudi, 1976) were positively correlated with
the
GPA of students. Similarly, Wagner, Schober, and Spiel (2008)
re-
ported that secondary school students who spent more time
working on homework obtained a better grade.
Although self-reported survey is the most common form of data
collection method employed in the research investigating time-
on-
task, it inherently contains errors because students report their
time-on-task after they completed the learning tasks, instead of
while performing them. Creating a journal of time-on-task while
performing the learning task can improve the accuracy of self-
reported time-on-task to some extent. This approach, however,
may generate a different type of error because creating a journal
entry disrupts the natural flow of learning processes of students.
To
address these issues, researchers started analyzing log files of
computer-based learning environments when they need to esti-
mate time-on-task of students. Since computer-based learning
environments, such as Massive Open Online Courses (MOOCs),
can
capture learning behaviors of students in detail without inter-
rupting their learning processes, it is relatively easy to estimate
the

time-on-task of students. Wellman and Marcinkiewicz (2004)
used
a frequency of accessing Web pages of an online course as a
proxy
for the time-on-task of college students, and found that it was
positively correlated with the achievement of students measured
by pre- and post-tests. Similarly, Cho and Shen (2013) reported
that
the amount of time spent in the Learning Management System
(LMS) is positively correlated with the total points students
earned
in the course although they did not explain how the LMS
computed
the time-on-task values they analyzed in their study.
Even though log file analysis allows researchers to better esti-
mate the time-on-task of students in computer-based learning
environments, it also presents a new challenge. If students stop
using the learning environment, engage in an alternative task
for a
while, and return to what they were doing, the log file would
not be
able to recognize this off-task activity because the learning
session
in the system is preserved as long as the Web browser window
remains open. In order to address this issue, time-on-task values
mailto:[email protected]
http://crossmark.crossref.org/dialog/?doi=10.1016/j.chb.2018.04
.043&domain=pdf
www.sciencedirect.com/science/journal/07475632
www.elsevier.com/locate/comphumbeh

Y. Lee / Computers in Human Behavior 86 (2018) 174e180 175
longer than a pre-determined threshold, which typically ranges
from 10 to 60 min, are often discarded from the analysis (Ba-
Omar
& Petrounias, 2007; del Valle & Duffy, 2007; Munk & Drlík,
2011;
Wise, Speer, Marbouti, & Hsiao, 2012). Although Kovanovi�c
et al.
(2015)'s experiments show that the threshold value for deter-
mining off-task activities has an impact on the subsequent data
analysis, many studies analyzing log files of computer-based
learning environments did not take into account off-task activ-
ities when they examined the relationship between time-on-task
and students' academic success. In addition, most studies
focusing on time-on-task were descriptive in nature, and did not
investigate whether time-on-task can predict the academic
success
of students.
The study reported in this paper analyzed the log files of a
MOOC to infer uninterrupted time-on-task of students by
excluding
off-task activities from the analysis, and examined the effect of
uninterrupted time-on-task on the academic success of students
in
the MOOC. More specifically, this study has two research
questions:
(1) Does the threshold value determining off-task activities
have an
impact on the predictive power of the model estimating the like-
lihood of students getting a course certificate in the MOOC?;
and
(2) What is the relationship between uninterrupted time-on-task
inferred from the log files of a MOOC and students’ success in
getting a course certificate? The rest of the paper is organized

as
follows. After reviewing related work, the method section
describes
the MOOC and its log files analyzed in this study, and explains
in
detail how the predictive models estimating the likelihood of
stu-
dents getting a course certificate were developed. The results
sec-
tion compares the predictive power of the developed models,
and
examines the relationship between uninterrupted time-on-task
and the likelihood of students getting a course certificate, fol-
lowed by discussion.
1. Related work
1.1. Completion rate and attrition in MOOCs
Completion rate and attrition in MOOCs have been extensively
studied in recent years. Breslow et al. (2013) examined how
154,763 students who signed up for a physics MOOC in spring
2012.
They found that 15% of registrants tried to complete the first
homework assignment, 6% of them passed the midterm exam,
and
only 5% of them earned the course certificate at the end of the
semester. Similarly, Ho et al. (2014) reported that about 5% of
reg-
istrants were able to earn the course certificate when they exam-
ined the completion rate of students enrolled in seventeen
HarvardX and MITx MOOCs between fall 2012 and summer
2013.
Jordan (2015) investigated the completion rates of more than
hundred MOOCs having a different grading scheme and varying
length of study. She found that the completion rate of MOOCs

varied widely (from 0.7% to 52.1%), depending on the length of
study (higher completion rates for shorter MOOCs), start date
(higher completion rates for recent MOOCs), and assessment
type
(higher completion rates when MOOCs adopt an automatic
grading
scheme). When Goldberg et al. (2015) investigated the
relationship
between participants' level of education and engagement in their
completion of a MOOC on dementia, they found that 38% of
regis-
trants were able to complete the course, and the discussion
board
activity was a significant predictor for their course completion.
Crossley, Paquette, Dascalu, McNamara, and Baker (2016)
examined
whether students' online activity and the language they produce
in
the discussion forum can predict the course completion. Of 320
students who completed at least one graded assignment and pro-
duced at least 50 words in discussion forum,187 students were
able
to complete the course successfully (completion rate ¼ 58%). In
Hone and SaidEl (2016)'s survey study of 379 participants, 32%
of
survey respondents were able to complete an entire course, and
there was no significant difference in the course completion rate
by
gender, level of study (undergraduate or postgraduate) or
MOOC
platform used by students. In Pursel, Zhang, Jablokow, Choi,
and
Velegol (2017)'s study, 5.6% of students who registered for a
MOOC on creativity and innovation were able to complete the
course with a statement of accomplishment. They also found
that

registering after the course launch date is negatively correlated
with the course completion while the frequency of watching a
video, posting a message and providing a comment are
positively
correlated with the course completion.
Several studies indicate that study time or time management is
the most common reason for disengaging from MOOCs. Nawrot
and
Doucet (2014) reported that about 70% of the survey
respondents
(N ¼ 508) indicated that bad time management (e.g., bad time
or-
ganization, conflicting real life responsibilities, too much time
consuming course, left behind due to illness or work) was the
main
reason for their MOOC withdrawal decision. Kizilcec and
Halawa
(2015) analyzed the survey responses of 1968 students who
were
sampled from twenty MOOCs to investigate reasons for
attrition.
Their analysis revealed four reasons for disengaging: time
issues,
course difficulty, format and content, and goals and
expectations
(in the order of significance). 84% of survey respondents
mentioned
that they did not have enough time for the course, and half of
those
respondents also indicated that they are easily distracted from
the
course. Zheng, Rosson, Shih, and Carroll (2015)'s interview
with 18
students taking a MOOC revealed that high workload,
challenging

course content, lack of time, lack of pressure, no sense of
commu-
nity, social influence and lengthy course start-up were the
factors
relevant to course drop-out. Similarly, Eriksson, Adawi, and
St€ohr
(2016) found that the learner's ability to find and manage time
was the most frequently mentioned reason for course drop-out
when they interviewed 34 students selected from two MOOCs
with a different completion rate. Other reasons for
disengagement
include the learner's perception of course content, the learner's
perception of course design, and the learner's social situation
and
characteristics.
1.2. Effect of time-on-task on academic performance in
computer-
based learning environments
In the last decade, researchers studied the effect of time-on-task
on academic performance of students learning in the computer-
based learning environment. Lustria (2007) found that college
students who spent more time in using interactive Web sites
containing health related information performed better in the
comprehension test. Louw, Muller, and Tredoux (2008)
examined
the importance of various predictors such as pre-existing level
of
mathematics ability, degree of access to computers outside of
school, time spent on computers both inside and outside of
school
and on the computer-based tutoring system used in the study,
computer literacy, confidence in using information technology,
motivation for mathematics, degree of enjoyment of learning
mathematics, intention to study after school, language used at

home and parental encouragement. Of these predictors, they
found
that time spent on the computer-based tutoring environment was
the strongest variable predicting the academic success of high
school students participating in their study. When Krause,
Stark,
and Mandl (2009) investigated how 137 college students learned
statistics in the computer-based learning environment, they
found
that the time-on-task is significantly positively correlated with
the
learning outcome of students. Macfadyen and Dawson (2010)'s
log
file analysis of an LMS showed that number of log-ins and time
spent in the LMS can explain more than 30% of variance in the
final
Fig. 1. An example of 8.MReVx log files analyzed in the study.
Y. Lee / Computers in Human Behavior 86 (2018) 174e180176
grade of college students. Cho and Shen (2013) reported that
time-
on-task logged in the LMS, along with effort regulation, can
predict
students' academic achievement in the course. Goldhammer et
al.
(2014) investigated how the effect of time-on-task on the
learning performance in the computer-based learning
environment
is moderated by task difficulty and student skill using linear
mixed
models. When students were solving problems in the computer-
based learning environment, time-on-task increased with task
difficulty, and was positively related to the performance of stu-

dents. However, the positive effect of time-on-task decreased as
skill levels of students is increasing. Landers and Landers
(2015)
studied the effect of time-on-task in the gamified learning envi-
ronment. They reported that college students who learned in the
gamified learning environment spent much more time using the
learning environment than students who used non-gamified
learning environment, which in turn improved their academic
performance in the course. Although these studies reported
time-
on-task as an important predictor for academic success of stu-
dents learning in the computer-based learning environment,
none
of them took into account off-task activities of students, which
may
have an impact on the predictive power of time-on-task.
2. Method
2.1. Context
This study analyzed the log files that captured how 12,981
students who signed up for a MOOC called “edX 8.MReVx Me-
chanics Review (hereafter, 8.MReVx)” interacted with various
learning resources during the Summer 2014 semester. 83% of
12,981 registrants were male and 17% of them were female. Age
of
registrants varied from 15 to 75: 45% of them were under 26,
39% of
them were in the range of 26 and 40, and 16% of them were age
41
and above. 25% of registrants had an advanced degree, 35% of
them
had a college degree, and 38% of them had a secondary diploma
or
less. Geographic distribution included the US (27% of

registrants),
India (18%), UK (3.6%), Brazil (2.8%) and others.
8.MReVx, which was offered by MIT, is designed to provide a
comprehensive overview of Newtonian mechanics and greater
expertise with problem-solving. 8.MReVx provides various
learning
resources, such as e-texts, videos, discussion boards, wiki,
check-
points, weekly homework problems, quizzes, midterm exam and
final exam, to help students learn Newtonian mechanics
concepts.
Students did not need to use an external resource, such as
textbook,
because the course was designed to be self-contained; it
provided
all required information through its e-texts and wiki.
Checkpoints
are easier problems embedded within e-texts as formative as-
sessments whereas homework problems, quizzes, midterm exam
and final exam are more difficult problems provided throughout
the 12-week long semester as summative assessments. Students
were given a week to work on formative and summative assess-
ment problems which were due on Sunday at midnight. The
achievement of students was determined by checkpoints (8%),
homework problems (34%), quizzes (36%), midterm exam (7%)
and
final exam (16%), and students who scored more than 60% of
the
maximum possible points received a course certificate. For
further
explorations of course structure and available learning
resources,
see the archived course at
https://courses.edx.org/courses/MITx/8.
MReVx/2T2014/info.

2.2. Procedures
Of 12,981 students who registered in 8.MReVx, this study
focused on students who solved at least one assessment problem
(N ¼ 4286) because problem solving is the most important
learning
activity for earning a certificate in this course; in 8.MReVx, all
available points were allocated to solving formative and
summative
assessment problems. Of these 4286 students who solved at
least
one assessment problem, only 434 students earned a course cer-
tificate at the end of the semester.
Fig. 1 shows a snippet of log files analyzed in this study. Each
row is a timestamped database transaction representing a
specific
learning activity experienced by one particular student enrolled
in
the MOOC. One can easily see that the student solved a problem
([check_problem_correct] in row 14 in Fig. 1), watched the
same
video twice ([play_video] in row 15 and 17 in Fig. 1), and then
solved another problem ([check_problem correct] in row 19 in
Fig. 1). In total, there were 12,981 log files like the one shown
in
Fig. 1 because each log file captured what one student did while
taking the course. These log files were imported into an SQLite
(https://www.sqlite.org) database, and Python (https://www.
python.org) and R (https://www.r-project.org) scripts were
devel-
oped to pre-process the merged log files in the imported
database.
Pre-processing of the merged log files consists of the following

steps. First, examine “Event” and “Resource Name” columns in
the
database to create a list of unique problems available in the
entire
course. Second, for each student in the database, examine how
many problems he or she tried to solve. Third, select a subset of
students who attempted to solve at least one problem. Fourth,
compute the time-on-task for all learning activities of selected
students (e.g. [play_video] in row 17 in Fig. 1), by subtracting
its
timestamp value (e.g., 6/24/14 16:05:21.327 in row 17 in Fig.1)
from
the timestamp value of the subsequent learning activity (e.g.,
6/24/
14 16:13:28.982 in row 18 in Fig. 1). Finally, using the
computed
time-on-task values, create variables representing the frequency
and the duration of uninterrupted time-on-task such as (1)
number
of learning chunks per week (NLC); (2) number of learning
activities
per week (NLA); (3) duration of all learning chunks per week
(TLC);
and (4) median of duration of learning chunks per week
(MedTLC).
Figure 2 explains how these variables are defined and computed
in this study. Each row in Fig. 2 represents a specific learning
ac-
tivity, such as solving a problem or watching a video, one
particular
MOOC student experienced during one week period. A learning
chunk is defined as a group of consecutive learning activities
not
interrupted by an off-task activity, an activity whose time-on-
task

value is larger than a predetermined threshold value (e.g., 10,
30
or 60 min). Fig. 2 indicates that the student had three learning
chunks this week (NLC ¼ 3) that are separated by two off-task
ac-
tivities. Number of learning activities per week is the count of
in-
dividual learning activity whose time-on-task value is smaller
than
the predetermined off-task activity threshold time (NLA ¼ 10 in
Fig. 2). Duration of all learning chunks can be obtained by
summing
up the duration of individual learning chunk observed in the
week
(i.e., TLC ¼ TLC,1 þ TLC,2 þ TLC,3; TLC,1 ¼ TOT1 þ TOT2 þ
TOT3;
https://courses.edx.org/courses/MITx/8.MReVx/2T2014/info
https://courses.edx.org/courses/MITx/8.MReVx/2T2014/info
https://www.sqlite.org
https://www.python.org
https://www.python.org
https://www.r-project.org
Fig. 2. Learning chunk (LCi), off-task activity, time-on-task
(TOTi), and duration of
learning chunk (TLC,i) identified in the log file.
Y. Lee / Computers in Human Behavior 86 (2018) 174e180 177
TLC,2 ¼ TOT4 þ TOT5 þ TOT6; TLC,3 ¼ TOT7 þ TOT8 þ
TOT9 þ TOT10).
Finally, median of duration of learning chunks
(MedTLC ¼ Median({TLC,1, TLC,2, TLC,3}) captures, on
average, how long
students were engaged in meaningful learning activities before

getting distracted by an off-task action.
Log transform was applied to these variables in order to
mitigate
heavy skewness and incompatible ranges. Table 1 summarizes
descriptive statistics of unscaled and log-transformed variables
pre-processed with different off-task activity threshold values.
2.3. Predictive models estimating likelihood of students getting
course certificate
In order to answer the research questions, three logistic
regression models (base, main effect and interaction) were
devel-
oped. The response variable of logistic regression model was
whether or not students earned a course certificate at the end of
the
Table 1
Mean, median, standard deviation and interquartile range of
unscaled and log-
transformed variables representing the frequency and the
duration of uninter-
rupted time-on-task.
Variable Mean Median SD IQR
Off-task threshold ¼ 10 min
NLC 8.75 5.00 10.13 9.00
NLA 112.48 66.00 121.73 135.25
TLC 6826.12 3626.35 7947.68 8041.12
MedTLC 658.48 491.57 618.97 543.15
log(NLC) 0.82 0.78 0.37 0.60
log(NLA) 1.83 1.83 0.46 0.75
log(TLC) 3.55 3.56 0.54 0.79
log(MedTLC) 6.12 6.20 0.93 1.08
NLC 5.40 3.50 5.51 5.00

NLA 116.35 67.94 126.34 137.62
TLC 10638.27 5265.48 13310.41 12161.47
MedTLC 1501.39 1159.74 1371.65 1461.46
log(NLC) 0.69 0.65 0.31 0.43
log(NLA) 1.84 1.84 0.46 0.75
log(TLC) 3.70 3.72 0.59 0.83
log(MedTLC) 6.88 7.06 1.08 1.28
NLC 4.53 3.00 4.24 4.00
NLA 117.30 68.00 127.53 138.75
TLC 13134.35 6316.93 17047.45 14899.79
MedTLC 2029.12 1583.05 1914.13 2152.61
log(NLC) 0.65 0.60 0.28 0.37
log(NLA) 1.85 1.84 0.46 0.75
log(TLC) 3.77 3.80 0.61 0.86
log(MedTLC) 7.18 7.37 1.14 1.37
semester. The base model predicts a probability for getting a
course
certificate based on two explanatory variables, number of
learning
chunks per week (NLC) and number of learning activities per
week
(NLA). The base model uses frequencies of uninterrupted
learning
activities as a proxy for the time-on-task of MOOC students as
was
done in the previous study. The main effect model includes two
additional duration-based explanatory variables, duration of all
learning chunks per week (TLC) and median of duration of
learning
chunks per week (MedTLC), in order to investigate whether the
duration-based time-on-task variables proposed in this study can
improve the predictive power of logistic regression model.
Finally,
the interaction model adds interaction terms to the main effect
model in order to examine the importance of interactions

between
main effect explanatory variables. To investigate the effect of
off-
task threshold value on the predictive power of logistic regres-
sion model, each model was fit to data sets pre-processed with
three different off-task activity threshold values frequently used
in
educational data mining research (10, 30 and 60 min). Table 2
summarizes the resulting nine predictive models compared in
this study in terms of the off-task threshold value and the
explanatory variables.
3. Results
3.1. Effect of off-task threshold value on predictive power of
logistic
regression model
In order to investigate whether an off-task activity threshold
value has an impact on the predictive power of logistic
regression
model, data sets pre-processed with three different off-task
activity
threshold values (10, 30 and 60 min) were divided into training
and
test sets, each logistic regression model (base, main effect and
interaction) was fit to the three training sets, and the predictive
power of each model was evaluated on the three test sets. When
creating training and test sets, which consist of 80% and 20% of
the
original data sets, stratified random sampling was used to
ensure
that the ratio of positive (students who earned a course
certificate)
to negative instances (students who did not earn a course
certifi-

cate) in both sets is similar. Since the test sets were not used in
building logistic regression models, which were fit only to the
training sets, they can play a role of future unseen data
providing an
unbiased measure of predictive power of the model.
Accuracy and Area Under the receiver operating characteristic
Curve (AUC) are two most frequently used performance metrics
when comparing predictive power of binary classification
models.
However, accuracy and AUC are not appropriate performance
measurements for the data set analyzed in this study because it
is
heavily imbalanced to negative instances. Because only 434 stu-
dents, out of 4286 students who solved at least one problem, got
a
course certificate, the proportion of negative instance, students
who did not earn a course certificate, is approximately 0.90 in
both
training and test sets. Therefore, a simple model that always
pre-
dicts everyone will not get a course certificate will achieve a
90%
accuracy. Despite the high accuracy, however, such a model is
useless because it does not provide any meaningful information
about who will get a course certificate. Likewise, AUC is not an
appropriate performance measure because it would give an
overly
optimistic result based on the high percentage of correct
classifi-
cations of the majority class. In order to address these issues,
this
study used Area Under the Precision-Recall Curve (AUPRC)
(Davis &
Goadrich, 2006) when comparing the predictive power of
logistic

regression model.
Table 3 compares AUC and AUPRC of nine logistic regression
models tested in this study. As explained above, AUC values do
not
change much because of the large number of negative instances
in
Table 2
Nine logistic regression models compared in the study.
Model Type Off-task Threshold Explanatory Variables
M1 Base 10 min log(NLC), log(NLA)
M4 Main effect 10 min log(NLC), log(NLA), log(TLC),
log(MedTLC)
log(MedTLC)
log(MedTLC)
M7 Interaction 10 min log(NLC), log(NLA), log(TLC),
log(MedTLC), log(NLC):log(NLA), log(NLC):log(TLC),
log(NLC):log(MedTLC)
Note. “:” in the explanatory variable name indicates an
interaction between the corresponding main effect variables.

Table 4
Summary of logistic regression analysis for variables predicting
students’ success in
getting a course certificate.
Explanatory Variable Estimate Standard Error p-value
log(NLC) 1.37 0.33 <0.0001
*
log(NLA) 2.27 0.32 <0.0001
*
log(TLC) 0.78 0.44 0.078
log(MedTLC) 0.62 0.24 0.011
*
log(NLC):log(NLA) �1.45 0.25 <0.0001*
log(NLC):log(TLC) 0.30 0.24 0.21
log(NLC):log(MedTLC) 0.24 0.16 0.12
Note. *p < .05; “:” in the explanatory variable name indicates
an interaction between
the corresponding main effect variables.
Y. Lee / Computers in Human Behavior 86 (2018) 174e180178
the data. All nine logistic regression models show a very similar
AUC value ranging from 0.922 to 0.938 regardless of the off-
task
activity threshold value or the explanatory variables included in
the model. On the other hand, AUPRC values are comparable
only in
the same model type (base, main effect and interaction). In
addi-

tion, prediction models including more explanatory variables
ach-
ieve a larger AUPRC value. On average, AUPRC of the main
effect
models is about 32% larger than that of the base models,
indicating
that the two duration-related time-on-task variables proposed in
this study (TLC and MedTLC) are important in discriminating
stu-
dents who earned a course certificate from students who did not.
On the other hand, the difference between main effect and inter-
action models is smaller. On average, AUPRC of the interaction
models is approximately 4% larger than that of the main effect
models.
3.2. Relationship between uninterrupted time-on-task and
students’ success in MOOC
This study elects to examine the importance of explanatory
variables in the interaction model with 60-min threshold (M9)
in
relation to the student's success in getting a course certificate
because this model achieved the largest AUPRC value. The
results
from logistic regression analysis suggest that the more learning
activities (NLA) and learning chunks (NLC) students have and
the
longer their average learning chunk (MedTLC) is, the more
likely
they will earn a course certificate; when log(NLA), log(NLC)
and
log(MedTLC) increase by one standard deviation, the log odds
of
students getting a course certificate increase by 2.27, 1.37 and
0.62,
respectively (see Table 4).

Of three interactions, only the interaction between number of
learning chunks per week and number of learning activities per
week, log(NLC):log(NLA), is statistically significant. The
large, nega-
tive value of the interaction coefficient indicates that students
are
less likely to get a course certificate if they are engaged in
many
learning activities in a …
See discussions, stats, and author profiles for this publication
at: https://www.researchgate.net/publication/303691151
Off-task behavior in elementary school children
Article in Learning and Instruction · August 2016
DOI: 10.1016/j.learninstruc.2016.04.003
CITATIONS
26
READS
1,909
7 authors, including:
Some of the authors of this publication are also working on
these related projects:
Learning analytics assessment of cognitive presence View
project

Physics Playground View project
Karrie E Godwin
Kent State University
23 PUBLICATIONS 297 CITATIONS
SEE PROFILE
Ma. Victoria Almeda
TERC
SEE PROFILE
Shimin Kai
Teachers College
SEE PROFILE
Ryan Baker
University of Pennsylvania
389 PUBLICATIONS 9,419 CITATIONS
SEE PROFILE
All content following this page was uploaded by Ma. Victoria

Almeda on 09 May 2019.
The user has requested enhancement of the downloaded file.
https://www.researchgate.net/publication/303691151_Off-
task_behavior_in_elementary_school_children?enrichId=rgreq-
018efa77e15a1cc9f970d361d912e0ef-
XXX&enrichSource=Y292ZXJQYWdlOzMwMzY5MTE1MTtBU
zo3NTY2NjQ0Mjg5MzcyMzlAMTU1NzQxNDI2NjYzNw%3D%
3D&el=1_x_2&_esc=publicationCoverPdf
https://www.researchgate.net/publication/303691151_Off-
task_behavior_in_elementary_school_children?enrichId=rgreq-
018efa77e15a1cc9f970d361d912e0ef-
https://www.researchgate.net/project/Learning-analytics-
assessment-of-cognitive-presence?enrichId=rgreq-
018efa77e15a1cc9f970d361d912e0ef-
https://www.researchgate.net/project/Physics-
Playground?enrichId=rgreq-
018efa77e15a1cc9f970d361d912e0ef-
https://www.researchgate.net/?enrichId=rgreq-
018efa77e15a1cc9f970d361d912e0ef-
https://www.researchgate.net/profile/Karrie_Godwin?enrichId=r
greq-018efa77e15a1cc9f970d361d912e0ef-

https://www.researchgate.net/institution/Kent_State_University?
enrichId=rgreq-018efa77e15a1cc9f970d361d912e0ef-
https://www.researchgate.net/profile/Ma_Victoria_Almeda?enri
chId=rgreq-018efa77e15a1cc9f970d361d912e0ef-
https://www.researchgate.net/profile/Shimin_Kai?enrichId=rgre
q-018efa77e15a1cc9f970d361d912e0ef-

https://www.researchgate.net/institution/Teachers_College?enri
https://www.researchgate.net/profile/Ryan_Baker14?enrichId=r
https://www.researchgate.net/institution/University_of_Pennsyl
vania?enrichId=rgreq-018efa77e15a1cc9f970d361d912e0ef-

lable at ScienceDirect
Learning and Instruction 44 (2016) 128e143
Contents lists avai
Learning and Instruction
journal homepage: www.elsevier.com/locate/learninstruc
Off-task behavior in elementary school children
Karrie E. Godwin a, *, Ma. V. Almeda b, Howard Seltman c,
Shimin Kai b,
Mandi D. Skerbetz d, Ryan S. Baker b, Anna V. Fisher a
a Carnegie Mellon University, Department of Psychology, 5000
Forbes Avenue, Pittsburgh, PA 15213, USA
b Teachers College Columbia, Department of Human
Development, 525W 120th Street, New York, NY 10027, USA
c Carnegie Mellon University, Department of Statistics, 5000
Forbes Avenue, Pittsburgh, PA 15213, USA
d South Fayette Township School District, 3680 Old Oakdale
Road, McDonald, PA 15057, USA
a r t i c l e i n f o
Article history:
Received 14 August 2014
Received in revised form
17 April 2016
Accepted 23 April 2016
Available online 31 May 2016

Keywords:
Off-task behavior
Attention
Time on-task
Instructional design
* Corresponding author. Carnegie Mellon University
PIER, 336E Baker Hall, 5000 Forbes Avenue, Pittsburg
E-mail address: [email protected] (K.E.
http://dx.doi.org/10.1016/j.learninstruc.2016.04.003
0959-4752/© 2016 Elsevier Ltd. All rights reserved.
a b s t r a c t
This paper reports results from a large-scale observational study
investigating attention allocation during
instructional activities in elementary school students
(kindergarten through fourth-grade). In Study 1, 22
classrooms participated while a more diverse sample of 30
classrooms participated in Study 2. This work
investigated temporal patterns in children's attention allocation
by collecting observational data on
children's on- and off-task behaviors at three different time
points (i.e., beginning, middle, and end of the
school year) [Study 1]. We also investigated whether patterns of
attention allocation changed as a
function of student characteristics (gender, grade-level, SES),
teachers' instructional design choices
(instructional format and duration of an instructional activity),
and school type (private, parochial, public
charter schools) [Studies 1 & 2]. Children's patterns of attention
allocation fluctuated over the course of
the school year. Female students were found to be more on-task
compared to male students. On-task
behavior tended to decline as the instructional duration

increased. The lowest rates of on-task
behavior were observed while children were engaged in whole-
group instructional formats. An effect
of school type was found with higher proportions of on-task
behavior observed in parochial schools.
However, the effect of grade-level was equivocal across studies.
These findings can begin to form a
foundation for the development of research-based guidelines for
instructional design aimed to support
engagement among students in elementary classrooms.
© 2016 Elsevier Ltd. All rights reserved.
Loss of instructional time due to off-task behavior is a well-
established problem in educational settings, recognized by re-
searchers (e.g., Carroll, 1963; Karweit & Slavin, 1981; Lee,
Kelly, &
Nyre, 1999) and practitioners (e.g., Lemov, 2010) for over a
hun-
dred years (cf. Currie, 1884 as cited in Berliner, 1990). Off-task
behavior has been documented to negatively impact academic
achievement, although the magnitude of this impact is unstable
across studies (for reviews see Frederick & Walberg, 1980;
Goodman, 1990). Off-task behavior is an indicator that students'
attention is not focused on the instructional activity. The link
be-
tween the quality of attention and task performance has also
been
documented in the cognitive psychology literature (e.g.,
Choudhury
& Gorman, 2000; Dixon & Salley, 2007; DeMarie-Dreblow &
Miller,
1988). Despite considerable prior research on off-task behavior,
, Department of Psychology &
h, PA 15213, USA.
Godwin).
designing effective, easy to implement, and scalable

interventions
to reduce off-task behavior has been challenging. Roberts
(2002)
suggests that many existing interventions may be unsuccessful
because they do not take into sufficient account the conditions
that
lead to off-task behavior. The goal of the present study is to
eluci-
date some of the factors involved in off-task behavior in
elementary
school settings.
1. Off-task behavior in elementary school students
There is a variety of reasons why loss of instructional time oc-
curs in schools; these reasons include but are not limited to:
weather (e.g., snow days), sudden onset interruptions (e.g., an-
nouncements over the loudspeakers), and special events.
However,
student inattentiveness (i.e., off-task behavior during
instructional
time) has been found to be the biggest factor that accounts for
loss
of instructional time (Karweit & Slavin, 1981). Prior research
esti-
mates that elementary school students spend between 10% and
50%
Delta:1_given name
Delta:1_surname
Delta:1_given name
Delta:1_surname
Delta:1_given name
Delta:1_surname
Delta:1_given name
mailto:[email protected]
http://crossmark.crossref.org/dialog/?doi=10.1016/j.learninstruc

.2016.04.003&domain=pdf
www.sciencedirect.com/science/journal/09594752
http://www.elsevier.com/locate/learninstruc
K.E. Godwin et al. / Learning and Instruction 44 (2016)
128e143 129
of their time off-task in regular education classrooms (e.g.,
Fisher
et al., 1980; Karweit & Slavin, 1981; Lee et al., 1999; Lloyd &
Loper, 1986). Inattention or off-task behavior is a serious chal-
lenge educators face. In fact, off-task behavior has been
identified
as one of the most common reasons for student referrals
(Roberts,
2001). While eliminating all off-task behavior is not a realistic
expectation, reducing rates of off-task behavior is an important
goal
given the challenges that off-task behavior causes for classroom
management as well as the potential implications of off-task
behavior on academic achievement.
A large number of prior studies have examined off-task
behavior
in elementary school students; however, the generalizability of
prior work is limited due to its relatively narrow scope. For
example, some studies observed a few classrooms within a
single
grade level (e.g., Lahaderne, 1968; Samuels & Turnure, 1974).
Other
studies involved a large number of classrooms (e.g., 18 to 25
classrooms), but only observed a small subset of students within
each classroom (e.g., Fisher et al., 1980; Karweit & Slavin,

1981).
Indeed, the wide range in estimates of off-task behavior
reported in
the literature may be partially attributed to the relatively small
sample sizes utilized in prior research, as small samples are
more
susceptible to the influence of extreme data points. In order to
establish a more comprehensive understanding of children's on
and off-task behaviors during early and middle childhood,
research
examining children's patterns of attention allocation on a larger
scale and across multiple grade-levels is clearly needed.
The present work makes a contribution to the field due to its
size
and scope: this work includes a large sample size both in terms
of
the number of classrooms which were recruited (e.g., Study 1:
22
classrooms, Study 2: 30 classrooms) as well as the number of
children within each classroom who were observed (i.e., all stu-
dents in attendance were included in the study). In contrast to
some prior work which tended to focus on one particular grade
or a
small range of grade-levels, this work conducts observations
across
a broad range of grade-levels in elementary school (i.e., kinder-
garten through fourth-grade).
A second contribution of this work pertains to the detailed
coding scheme that was employed to provide a more nuanced
examination of the sources of off-task behavior common in
elementary school settings. Previous work examining off-task
behavior in classrooms tended to treat off-task behavior as a
uni-
tary construct (e.g., Carnine, 1976; Frederick, Walberg, &

Rasher,
1979; Karweit & Slavin, 1981). Consequently, the sources of
chil-
dren's off-task behavior remain underspecified. In the present
study we delineate common types of off-task behavior including
off-task peer interactions, self-distraction, and off-task
behaviors
directed towards aspects of the classroom environment.
Identifying
common types of off-task behavior in elementary school
settings is
critical as interventions targeting inattention will be successful
only
to the extent that they adequately address the source of
children's
off-task behavior. The types of off-task behavior measured in
the
present work were based on the results of a teacher survey.
Thirty
elementary school teachers were asked to rate the frequency of
students' off-task behaviors on a scale from 1 to 4, where 1 in-
dicates that the behavior occurs rarely and 4 indicates that the
behavior is very frequent. Peers (M ¼ 3.21, SD ¼ 0.62) and
self-
distractions (M ¼ 2.62, SD ¼ 0.90) were identified by teachers
as
frequent sources of off-task behavior. Additionally, walking
around
the classroom (or being out of one's seat) was identified as a
frequent off-task behavior by 14 of the teachers (M ¼ 2.50,
SD ¼ 0.85). Off-task behavior relating to the environment was
identified as another common source of distraction (M ¼ 1.83,
SD ¼ 0.85). Studying off-task behaviors associated with the
class-
room environment is of particular interest because of the
hypoth-

esized link between off-task behavior and visual design features
of
elementary school classrooms (e.g., Fisher, Godwin, & Seltmen,
2014; Godwin & Fisher, 2011). Primary classrooms often
contain
large amounts of stimulating sensory displays intended to
increase
children's motivation and engagement (Barrett, Zhang, Moffat,
&
Kobbacy, 2012; Tarr, 2004; Thompson & Raisor, 2013).
However,
there is no empirical evidence demonstrating that this design
choice increases motivation and engagement. By contrast, large
amounts of stimulating displays in classrooms have been
described
as “visual bombardment” (Bullard, 2010, p. 110) and a
“cacophony
of imagery” (Tarr, 2004, p. 1). Barrett and colleagues (Barrett
et al.,
2012) recently reported that, contrary to their initial hypothesis,
high amounts of color (i.e., the degree and manner in which
color
was utilized in the classroom walls, furniture, and displays) was
negatively associated with elementary school children's
achieve-
ment scores (although note that a follow-up study by Barrett,
Davies, Zhang, and Barrett (2015) reported that very low
amounts
of color are also negatively associated with achievement,
suggest-
ing that there may be a level of visual stimulation that is
optimal for
classroom settings). Furthermore, there is recent experimental
evidence supporting the notion that highly decorated learning
environments may actually promote off-task behavior in young
children and thereby decrease learning (Fisher et al., 2014;

Godwin
& Fisher, 2011).
Despite a large number of studies documenting rates of off-task
behavior in elementary school students, there has been limited
research examining the factors associated with off-task
behavior.
The present work aims to address this gap in the literature by
conducting an exploratory study which examines four main
research questions: (1) Do patterns of attention allocation
change
over the course of the school year? (2) Are student
characteristics
(e.g., gender, grade-level, and SES) related to children's
attention
allocation patterns? (3) Are instructional design strategies (e.g.,
instructional format and duration of instructional activity)
related
to children's tendency to engage in on and off-task behavior?
(4) Is
school-type related to children's attention allocation patterns?
Below we briefly discuss how each of these factors may be
related
to patterns of attention allocation in elementary school students.
There has been limited investigation of the variability in chil-
dren's patterns of attention allocation as a function of time
(Martin
et al., 2015). The idea that students' attentional capacity
fluctuates
over the course of the school day is a common belief in
education
circles. Indeed, teachers' report that they modify their
instruction
in response to fluctuations in students' levels of attention by
avoiding challenging instructional activities following lunch or

at
the end of the school day (for discussion see Muyskens &
Ysseldyke,
1998; Ammons, Booker, & Killmon, 1995). Observational
research
examining the effect of time of day on school children's
classroom
behaviors has found that inappropriate behaviors are more
frequent in the afternoon compared to the morning (Muyskens &
Ysseldyke, 1998). Similar findings have been obtained with
chil-
dren who have attention deficit disorders (e.g., Antrop, Roeyers,
&
De Baecke, 2005; Zagar & Bowers, 1983). Furthermore, studies
us-
ing performance-based measures of attention (e.g., paper and
pencil visual search tasks in which participants are asked to
locate
and cross out a target object from a group of distractors) have
found
that performance on tests of attention is highest in the mid-
morning and declines mid-day, although there is some
variability
in the observed attention patterns for preschool children and
stu-
dents in primary grades (e.g., Janvier & Testu, 2007). Although
levels of attention have been found to oscillate over the course
of
the school day, it is currently an open question if and how
patterns
of attention allocation change across the school year.
Specifically,
the proportion of on-task behavior as well as the prevalence of
different types of off-task behavior may fluctuate as children
become more familiar with their teacher, school rules, peers,
and

128e143130
their classroom environment. For example, self-distractions may
be
more common in the beginning of the school year, but as time
progresses children may become better acquainted with their
classmates, leading to more off-task behavior directed towards
peers in the middle and end of the school year. Another possible
outcome is that children may habituate to their classroom visual
environment. Therefore, off-task behavior directed at the visual
features of the classroom may decrease from the beginning of
the
school year compared to the end of the school year.
Student characteristics also likely influence children's patterns
of attention allocation. For example, prior work suggests that
males
exhibit more off-task behavior compared to females (Marks,
2000;
Matthews, Ponitz, & Morrison, 2009). Consequently, it is of
interest
to investigate whether gender differences emerge in a large
sample
of elementary school children and to investigate whether the
specific types of off-task behavior children engage in vary as a
function of gender. Grade-level is another factor that may
contribute to children's tendency to engage in off-task behavior
as
prior research has documented that the ability to engage in
selec-
tive sustained attention improves with age (e.g., Bartgis,
Thomas,
Lefler, & Hartung, 2008; for review see; Fisher & Kloos, 2016;

White, 1970). Additionally, it is possible that specific types of
off-
task behavior may be more prevalent in younger grade-levels
(e.g., self-distraction) while other types of distraction may be
more
pervasive across grade-levels (e.g. peers). Furthermore, rates of
on
and off-task behavior may vary as a function of SES. Prior work
has
found that executive function skills are typically weaker in
children
from a lower socioeconomic background (e.g., Wiebe et al.,
2011);
consequently, these children may have greater difficulty
inhibiting
distractions and thus be more likely to engage in off-task
behaviors.
Instructional design choices (i.e., average duration of an
instructional activity and instructional format) may also be
related
to off-task behavior in elementary school children. For example,
the
duration of an instructional activity may influence children's
ability
to attend to the ongoing instruction. Specifically, children may
be
better able to maintain a state of focused attention when instruc-
tional activities are shorter in duration, this may be particularly
true for younger children who are still developing the ability to
efficiently regulate their attention. This possibility is consistent
with studies suggesting that in laboratory settings the duration
of
focused attention increases gradually with development, from
approximately 4-min in 2- and 3-year-old children to over 9-min
in

5- and 6-year-old children (for review see Fisher & Kloos,
2016).
Surprisingly, this issue has not been investigated systematically
in
genuine education settings. Consequently, there is a dearth of
evidence-based guidelines that teachers can utilize to inform
their
instructional design choices. Teachers typically have
considerable
autonomy when determining how instructional time is allotted
(Rettig & Canady, 2013). At the same time, better insight into
what
the optimal durations of instructional activity might be for
main-
taining high rates of on-task behavior in elementary schools
would
be valuable information for educators.
It is also possible that some types of instructional format (e.g.,
whole-group instruction, small-group instruction, etc.) are more
likely to be associated with higher rates of off-task behavior
than
other instructional formats. There is evidence indicating that
small-
group instruction (when groups are formed on the basis of
student
ability) is more effective than whole-group instruction with
regards
to student achievement (for reviews see Lou et al., 1996; Kulik,
1992). However, the size of this effect is relatively small and
vari-
ability in effect sizes across individual studies is substantial.
For
example, Lou et al. (1996) reported that the effect size of small-
group versus whole-group instruction ranged from �1.96 to
1.52,

with an average effect size of 0.17. Thus, some researchers have
argued that the large variability in effect sizes severely limits
the
degree to which it can be concluded that small-group instruction
is
superior to whole-group instruction (Prais, 1998, 1999). The
pos-
sibility that the type of instructional format is related not only
to
achievement but also to off-task behavior has to our knowledge
been largely unexplored (see Goodman, 1990); however, some
prior research has documented higher rates of student
engagement
during teacher led activities (BTES as cited in Goodman,1990;
Good
& Beckerman, 1978; Ponitz & Rimm-Kaufman, 2011). The
present
work provides a nuanced examination of the relationship
between
off-task behavior and specific instructional formats (e.g.,
individual
work, small-group or partner work, whole-group instruction at
desks, whole-group instruction while sitting on the carpet) in
elementary school students. Obtaining evidence to evaluate this
possibility is important because it can empower teachers to
choose
instructional formats that are likely to optimize children's
attention
allocation.
Lastly, rates of on and off-task behavior as well as the types of
off-task behavior that children engage in may vary as a function
of
school type (e.g., public schools, private schools, parochial
schools).
For example, schools may have different group cultures or

norms
regarding student behavior. These shared expectations may
influ-
ence children's patterns of attention allocation.
2. The present study
Within this paper, we report the results of two studies. Study 1
investigates the temporal patterns in children's attention
allocation
[Research Question 1]. Additionally, Study 1 examines whether
student characteristics and specific instructional design choices
are
associated with patterns of attention allocation in elementary
school children, both in terms of the overall amount of on and
off-
task behavior but also the form which off-task behavior takes
[Research Questions 2 and 3 respectively]. Data for Study 1
contains
a relatively homogeneous set of schools, as all participating
schools
were part of the same public charter school organization. Study
2
investigates whether the results obtained in Study 1 regarding
the
role of student characteristics and instructional design strategies
on
children's selective sustained attention can be generalized to a
more diverse sample of schools. In Study 2, we collected data
from a
more heterogeneous sample of schools that varied in terms of
the
socioeconomic status of the student population [Research
Question
3] as well as school type (i.e., public charter schools, private
schools,

and parochial schools) [Research Question 4].
2.1. Study 1: Temporal patterns of on- and off-task behavior
across
the school year
In order to address Research Question 1, Study 1 examined
temporal patterns in children's attention allocation by collecting
observational data on children's on- and off-task behavior over
the
course of the school year. To this end, we conducted
observations at
three different time points: the beginning, middle, and end of
the
school year. For each time point the proportion of on- and off-
task
behavior was modeled in order to determine if children's
attention
patterns fluctuated or remained stable throughout the school
year.
Additionally, we examined the possibility that the prevalence of
certain types of off-task behavior may change over time. We
also
examined whether children's patterns of attention allocation
changed as a function of student characteristics [Research
Question
2] or based on instructional design strategies [Research
Question 3].
2.2. Method
2.2.1. Participants
Twenty-two classrooms participated in Study 1. Participating

3 Mind wandering, also referred to as “Stimulus independent
thought”
(Killingsworth & Gilbert, 2010, p. 932) and daydreaming (see
Smallwood, Fishman,
& Schooler, 2007), can be considered another form of off-task
behavior. However, in
the present study mind wandering was not included as a
category of off-task
behavior due to methodological concerns. This particular form
of off-task
behavior is not readily observable. Instead, mind wandering is
typically assessed
using thought sampling (Smallwood et al., 2007). However, it is
unclear whether
128e143 131
classrooms were selected from 5 charter schools located in or
near a
Northeastern medium-sized city in the United States of
America.
Five grade-levels were recruited: kindergarten through fourth-
grade.1 The distribution across the five grade-levels was as
follows:
5 kindergarten classrooms, 4 first-grade classrooms, 5 second-
grade classrooms, 2 third-grade classrooms, and 6 fourth-grade
classrooms. The average class size was 21 students (10 males,
11
females). However, due to absences the average number of
children
observed in a single observation session was 18.9 children. The
number of children observed per session ranged from 15 to 22
children.
2.2.2. Design and procedure
The observation sessions were staggered across three time pe-

riods (Time 1: October 2011eDecember 2011, Time 2: …
1 3
Eur Child Adolesc Psychiatry (2017) 26:1471–1481
https://doi.org/10.1007/s00787-017-1006-y
ORIGINAL CONTRIBUTION
Time‑on‑task effects in children with and without ADHD:
depletion of executive resources or depletion of motivation?
Tycho J. Dekkers1,2 · Joost A. Agelink van Rentergem1 ·
Alette Koole1 ·
Wery P. M. van den Wildenberg1,3 · Arne Popma2,4,5 · Anika
Bexkens6,7 ·
Reino Stoffelsen2,4 · Anouk Diekmann2,8 · Hilde M.
Huizenga1,3,9
Received: 13 June 2016 / Accepted: 17 May 2017 / Published
online: 23 May 2017
© The Author(s) 2017. This article is an open access
publication
(stop-signal reaction time) and attention (reaction time
variability and errors), was administered in 96 children
(42 ADHD, 54 TD controls; aged 9–13). To differentiate
between depletion of resources and depletion of motivation,
the SST was administered twice. Half of the participants
was reinforced during second task performance, potentially
counteracting depletion of motivation. Multilevel analyses
indicated that children with ADHD were more affected by
time-on-task than controls on two measures of inattention,

but not on inhibition. In the ADHD group, reinforcement
only improved performance on one index of attention (i.e.,
reaction time variability). The current findings suggest that
time-on-task effects in children with ADHD occur spe-
cifically in the attentional domain, and seem to originate
in both depletion of executive resources and depletion of
motivation. Clinical implications for diagnostics, psycho-
education, and intervention are discussed.
Keywords ADHD · Executive functioning · Depletion ·
Time-on-task · Reinforcement · Inhibition
Abstract Children with attention-deficit/hyperactiv-
ity disorder (ADHD) are characterized by deficits in their
executive functioning and motivation. In addition, these
children are characterized by a decline in performance as
time-on-task increases (i.e., time-on-task effects). However,
it is unknown whether these time-on-task effects should be
attributed to deficits in executive functioning or to deficits
in motivation. Some studies in typically developing (TD)
adults indicated that time-on-task effects should be inter-
preted as depletion of executive resources, but other stud-
ies suggested that they represent depletion of motivation.
We, therefore, investigated, in children with and without
ADHD, whether there were time-on-task effects on execu-
tive functions, such as inhibition and (in)attention, and
whether these were best explained by depletion of execu-
tive resources or depletion of motivation. The stop-signal
task (SST), which generates both indices of inhibition
Electronic supplementary material The online version of this
article (doi:10.1007/s00787-017-1006-y) contains
supplementary
material, which is available to authorized users.
* Tycho J. Dekkers

[email protected]
1 Department of Psychology, University of Amsterdam,
Nieuwe Achtergracht 129B, 1018 WS Amsterdam, The
Netherlands
2 Department of Forensic Psychiatry and Complex Behavioral
Disorders, Academic Center for Child and Adolescent
Psychiatry, De Bascule, Rijksstraatweg 145, 1115
AP Duivendrecht, The Netherlands
3 Amsterdam Brain and Cognition Center, University
of Amsterdam, Amsterdam, The Netherlands
4 Department of Child and Adolescent Psychiatry, VU
University Medical Center Amsterdam, Amsterdam, The
Netherlands
5 Faculty of Law, Institute of Criminal Law and Criminology,
Leiden University, Leiden, The Netherlands
6 Department of Developmental and Educational Psychology,
Leiden University, Wassenaarseweg 52, 2333 AK Leiden,
The Netherlands
7 Department of Child and Adolescent Psychiatry, GGZ
Delfland, Center for Psychiatry, Amsterdam, The Netherlands
8 Practice for Individual, Couple, and Family Therapy
and Center for Training, De Kontekst, Van Breestraat 147HS,
1071 ZL Amsterdam, The Netherlands
9 Research priority Area Yield, University of Amsterdam,
Amsterdam, The Netherlands
http://crossmark.crossref.org/dialog/?doi=10.1007/s00787-017-
1006-y&domain=pdf

https://doi.org/10.1007/s00787-017-1006-y
1472 Eur Child Adolesc Psychiatry (2017) 26:1471–1481
1 3
Introduction
Children with attention-deficit/hyperactivity disorder
(ADHD) are characterized by inattention, hyperactivity,
and/or impulsivity, which lead to problems in multiple
domains. For example, children with ADHD have more
academic problems [1] and adverse health outcomes [2],
report lower quality of life [3], and usually have one or
more comorbid psychiatric diagnoses [4]. Several models
explaining ADHD have been proposed (see [5, 6]). One
influential model is the dual pathway model, in which
ADHD is characterized by deficits in both executive and
motivational systems [7].
With regard to the executive pathway, several meta-
analyses indicate that children with ADHD are impaired
on multiple executive functions (EF) [8–11]. For example,
response inhibition, which is regarded as one of the core,
higher order executive functions [12, 13], has repeat-
edly shown to be implicated with ADHD [11, 14]. On a
more basic pre-executive level, attention is a crucial pre-
requisite of executive functioning [13], and associations
between ADHD and attentional problems are consistently
reported (ranging from problems in sustaining attention
on lab tasks to real life attention problems [9, 14]).
With respect to the motivational pathway, many empir-
ical studies as well as theoretical models suggest aberrant
motivation in children with ADHD (see [15] for an over-

6Websites, Books, Independent Studies 6 Journal Articles Summ.docx

6Websites, Books, Independent Studies 6 Journal Articles Summ.docx

Recommended

Recommended

More Related Content

Similar to 6Websites, Books, Independent Studies 6 Journal Articles Summ.docx

Similar to 6Websites, Books, Independent Studies 6 Journal Articles Summ.docx (20)

More from taishao1

More from taishao1 (20)

Recently uploaded

Recently uploaded (20)

6Websites, Books, Independent Studies 6 Journal Articles Summ.docx