This assignment requires the synthesis, evaluation, and critique of an article pertinent to your topic and taken from an academic journal.
Your Synthesis, Evaluation, and Critique of the Article must have the original article attached AND must meet the following criteria:
- Include the Synthesis, Evaluation, and Critique of the Article Outline Worksheet (right below this section)
- 2-3 pages (double-spaced) in length.
- APA-compliant
- Have a cover page and a running-head
- Include a complete APA-style reference
- Explicitly address the following questions:
- What major issue(s) are addressed in this article?
- Who were the subject(s) involved in the study?
- What was the author’s/authors’ research question/hypothesis?
- What methodology was utilized to gather data?
- What qualitative/quantitative information was presented in this study?
- What obstacles, if any, jeopardized, compromised, or impacted the study?
- Is the argument valid? well presented? convincing? Why? Why not?
- Did the article successfully answer the research question(s) it posed originally?
- Are there other considerations that could be drawn into the article’s argument(s)?
- What is/are the article’s conclusion(s)?
- What are the implications of this article on future research?
- How is this article relevant to research question you pose for the final concept paper?
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
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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).
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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
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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.
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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
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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
Program evaluation overview
Document review, online
discussion
Textbook reading, article review,
online discussion
Quizzes
Evaluation plan progress report
Final evaluation plan
Audio based learning module
design/development
Visual learning module
design/development
Personal Website development
Instructional Web based learning
module design/development
Usability testing report
Authentic tasks** Students were guided to a real
world scenario and presented with
contextualized data for weekly
discussions.
Discussion topics were ill-defined
and open to multiple
interpretations.
Students were given a week for
each discussion topic.
Students were encouraged to use a
variety of related documents and
Web resources.
Students were required to create a
course outcome (program
evaluation plan proposal) that
could be used in their own
organization.
Students were guided to design and
create instructional multimedia
materials to solve a performance
problem that they identified in their
own fields.
Students had to determine the
scope of each multimedia project to
solve the unique performance
problems they had identified.
Students were encouraged to try
different multimedia programs and
apply various design principles that
were related to their own projects.
Students were required to create a
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
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based on the constructivist learning
approach:
Moodle LMS
Online discussions
Web resources
MS-Word
MS-PowerPoint
based on the constructionist
learning approach:
Moodle LMS
Online discussions
Web resources
Multimedia design programs
Audio development tools
Visual material development tools
Instructional multimedia Web
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.
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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
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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
Quiz taking
(# of times quiz participation – quiz completion and submission)
Resource viewing
(# of visits to the Resource page)
File uploading/downloading
(# of visits to files page – file uploading and file downloadng)
Interactions with peers
Discussion viewing
(# of times discussion viewed)
Discussion posting
(# of times discussion posted – making initial posts)
Discussion responding
(# 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).
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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
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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) 193.25(69.77)
Attendance b 9.90(2.99) 10.75(2.82) 15.20(3.05) 13.50(4.47) 11.70(2.21) 9.75(4.20) 36.80(7.57) 34.00(9.15)
Time-on-task c 375.00(53.59
)
700.63(549.5
2
)
564.00(155.27
)
1919.38(1928.10
)
252.50(140.3
4
)
360.00(304.2
6)
1191.50(314.
80
)
2980.00(2713.
6
5)
Performance
Task score d 185.43(11.02) 195.00(4.47) 241.25(29.71) 283.59(21.37) 79.00(15.23) 96.43(1.66) 505.68(46.31) 575.02(26.03)
Note.
a Average number of interactions per phase
b Average number of course participations per phase (Logins)
c Time presented in minutes
d Scores ranging from 0 (minimum) to 200 (maximum) in phase 1, from 0 (minimum) to 300 (maximum) in phase 2, from 0 (minimum) to 100
(maximum) in phase 3
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RQ2: 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?
The average number of behavioral interactions with peers for the two courses was compared using the
Mann-Whitney test. Among the three phases, the average number of interactions with peers in phase 2
(weeks 4/5/6) was significantly different, as evidenced in Figure 4. In phase 2, the average number of peer
interactions in Course A (M = 126.30, Mdn = 111.50) was significantly higher than the average number of
peer interactions in Course B (M = 58.25, Mdn = 59.50), U = 7.00, z = – 2.93, p < .01, r = -.69, thus revealing
a large effect size. However, the average number of peer interactions was not significantly different between
the two courses in phases 1 and 3, nor was the total number of peer interactions between the two courses
significant.
Figure 4. Average number of behavioral interactions with peers for each phase of the semester for the two
courses.
The findings for both research questions 1 and 2 show the statistical comparisons of Moodle LMS
interactions and peer interactions between two online courses involving different types of authentic
learning tasks. To help understand the exact type of behavioral interactions and possible patterns, the
researcher visualized each student’s behavioral interaction pattern, as shown in Figures 5, 6, and 7. Each
category of students’ behavioral interactions was imported into an Excel spreadsheet with different color
themes (Figure 5).
0
20
40
60
80
100
120
140
Phase1 Phase2 Phase3
Course A
Course B
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Figure 5. Legend of color themes.
Student 1
Student 2
Student 3
Student 4
Student 5
Student 6
Student 7
Student 8
Student 9
Student 10
Figure 6. Behavioral interaction pattern for each individual student in Course A.
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Student 1
Student 2
Student 3
Student 4
Student 5
Student 6
Student 7
Student 8
Figure 7. Behavioral interaction pattern for each individual student in Course B.
Blue colors represent a student’s course exploration activities, such as course viewing and other user
viewing. Brown colors represent a student’s interactions with the Moodle LMS, and green colors represent
a student’s interactions with peers. Each square in the pattern graph represents one occurrence of the case.
Each pattern line represents the total behavioral interactions that occurred in each week. Through visual
representations of behavioral interactions, different patterns were identified in the two courses. Most of the
students in course A showed a ( ) shape of behavioral patterns, while students in Course B
showed a ( ) shape of behavioral patterns. In other words, students in Course A tend to
show more behavior interactions as they move toward the end of the semester, while students in course B
showed higher behavioral interactions in the first week of the semester and also at the end of the semester.
RQ3: 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?
Time-on-task for weekly authentic tasks for the two courses was compared using the Mann-Whitney test.
No significant differences were found in any of the three phases or for the entire semester (Figure 8).
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Figure 8. Average time-on-task (in minutes) for each phase for the two courses.
RQ4: Does online learners’ attendance differ between a course employing authentic
discussion-based learning tasks and a course employing authentic
design/development-based learning tasks?
Attendance for weekly authentic tasks for the two courses was compared using the Mann-Whitney test. No
significant differences were found in any of the three phases or for the entire semester (Figure 9).
Figure 9. Average attendance for each phase for the two courses.
RQ5: 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?
The average task score between the two courses was compared using the Mann-Whitney test. Among the
three phases compared, the average scores in phases 2 and 3 were significantly different, as displayed in
Figure 10. In phase 2, the average score in Course B (M = 283.59, Mdn = 290.00) was significantly higher
than the average score in Course A (M = 241.25, Mdn = 240.47), U = 9.00, z = -2.76, p < .01, r = -.65,
revealing a large effect size. In phase 3, the average score in Course B (M = 96.43, Mdn = 96.43) was
significantly higher than the average score in Course A (M = 79.00, Mdn = 85.00), U = 7.50, z = -2.94, p
< .01, r = -.69, indicating a large effect size. However, the task scores were not significantly different in
0
500
1000
1500
2000
2500
Phase1 Phase2 Phase3
Course A
Course B
0
2
4
6
8
10
12
14
16
Phase1 Phase2 Phase3
Course A
Course B
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phase 1. The total task scores for the entire semester for the two courses were significantly different. The
total score for Course B (M = 575.02, Mdn = 585.43) was significantly higher than the total score for Course
A (M = 505.68, Mdn = 495.47), U = 8.00, z = -2.85, p < .01, r = -.67, indicating a large effect size.
Figure 10. Average score in each phase for the two courses.
In addition to the Mann-Whitney test comparisons, a Spearman’s rank-order correlation was also run to
determine the relationship between all 18 students’ behavioral interactions, time-on-task, and performance
per week.
Table 4
Significant Correlations between Behavioral Interactions, Time-on-Task, and Performance
Weeks Correlation rs(16) p value *
Week2 Discussion viewing – Discussion response .794 .000
Week3 Discussion viewing – Discussion response .639 .004
Week4 Discussion viewing – Discussion posting .742 .000
Resource viewing – Discussion posting .632 .005
Resource viewing – Discussion viewing .631 .005
Week5 Discussion viewing – Discussion posting .599 .009
Resource viewing – Discussion posting .792 .000
Resource viewing – Discussion viewing .703 .001
Week6 File uploading – Score .650 .003
File uploading – Discussion posting .732 .001
Week7/8 File uploading – Discussion posting .622 .006
Discussion viewing – discussion response .661 .003
Note. * All correlations are significant at the 0.01 level (2-tailed).
Although no overall significant correlations were found between time-on-task and behavioral interactions,
or between performance scores and behavioral interactions, there were several noticeable patterns found
among behavioral interactions. For example, during the first half of the semester, strong positive
correlations were found between discussion reviewing and discussion response /discussion posting
0
50
100
150
200
250
300
Phase1 Phase2 Phase3
Course A
Course B
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behaviors. Then, during the second half of the semester, resource viewing, discussion posting, and file
uploading behaviors showed strong positive correlations. Especially in week 6, students scored higher when
they showed more file uploading behaviors with discussion postings.
Discussion
Time flexibility and authentic tasks are two factors that affect the success of an online learning experience.
However, the type of behavioral interactions students exhibit at different points when they are involved in
different types of authentic tasks is not well understood. Accordingly, this study attempted to analyze and
visualize the behavioral interactions of online learners at different times during the semester and compare
the occurrences of these behavioral interactions in two online courses. The study found that online students
exhibit different behavioral interactions when they are involved in two different authentic online learning
activities. Students in authentic design/development-based learning activities demonstrated more
behavioral interactions with the Moodle LMS at the beginning of the course, whereas students in authentic
discussion-based learning activities exhibited more behavioral interactions with peers during the middle of
the semester. Overall, attendance and time-on-task did not differ between the two courses. Understanding
time flexibility as the capacity to spend time-on-task at different times of the day and week (Romero &
Barberà, 2011), the results indicate that students are likely to be involved in behavioral interactions with
the Moodle LMS early in the course if given tasks require authentic design/development learning activities.
This finding could be viewed from the perspective of student time management. In other words, students
in the design/development course tried to understand the scope of the design/development projects early
in the semester so they could plan the design/development of the multimedia materials for the semester.
This notion is supported by their attendance and time-on-task (Figures 8 and 9). Although students in
Course B did not actively participate in behavioral interactions with peers in the middle of the semester,
they attended the course regularly and spent significantly more time working on given tasks compared with
students in Course A. Given the different behavioral interaction patterns found in the different authentic
online tasks, the findings support the importance of designing technological learning resources at different
points of the semester depending on the type of authentic learning tasks and on the needs of the student
(Swan & Shin, 2005).
Another important finding of this study is that the correlations between student performance and each type
of student behavioral interactions according to Spearman’s rank correlation coefficients were not
significant. The evidence offers the possibility of behavioral interactions being an intermediate variable,
suggesting that more indicators must be examined to understand factors affecting student performance in
online learning. In fact, many of the online learning analytics focus on behavioral indicators rather than on
the psychological aspects of learning, such as cognitive involvement, academic emotions, and motivation.
Therefore, we must seek ways to incorporate a different methodology to approach the online learning
experience in a holistic way. For example, the experience sampling method (ESM) combined with learning
analytics would be a good alternative method to analyze the multiple dimensions of the online learning
experience.
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Conclusion
This study analyzed the behavioral interactions of online learners and compared the differences in
behavioral interactions for two online courses each with different authentic learning tasks. Since the first
course was designed based on a constructivist approach, and the second course from a constructionist
perspective, the analysis results showed that students in each course experienced different behavioral
interactions during the semester. The findings imply that when designing an online course that involves
authentic learning tasks, instructional designers need to consider optimizing learners’ behavioral
interaction sequence to maximize their learning effectiveness. For example, interactions with peers should
be encouraged when designing an online course based on the constructive belief (Lowes, Lin, & Kinghorn,
2015). Unlike other previous studies, however, this study did not find the direct relationship between the
behavioral interactions, whether with Moodle LMS or peers, and performance scores. Previous studies such
as Davies and Graff’s (2005) also reported no relationship between discussion forum participation and final
course grades. As discussed, behavioral interactions could be an intermediate variable affected by students’
cognitive involvement and motivation, thus their psychological online learning experiences also need to be
considered when analyzing students’ Web log data. There are several limitations to this study. First, the
behavioral interaction data collected using Web logs are limited only to internal data stored in the Moodle
LMS server. External communication data, such as email correspondences or conference calls, were not
included in the data analysis. Second, although the study was conducted using two purposefully selected
courses to provide a rich description of the behavioral pattern for each individual student, future
researchers wanting to make generalizations about the findings of this study will need to increase the
number of participants. Third, this study only analyzed the behavioral patterns of online learners, and thus,
there is a need to examine how these behavior patterns are related to other learning experiences such as a
cognitive processing and affective states. This holistic approach to understanding learning experiences will
help researchers obtain a more comprehensive picture of the interactions among the cognitive processes,
affective states, and behavioral patterns. With the meticulous analysis of the individual learner’s learning
experience, we can gain deeper insight into ways to design the optimal online learning experience.
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Name:
Statement of Focus (100 points)
.
1. What area of ESE or Education do you feel YOU can change or improve? Please think of this in light of your proposed action research focus this term.
I would like to focus on increasing on-task behavior during distance learning time in gifted students diagnosed with ADHD at elementary level.
2. Why is this change particularly meaningful to YOU as an educator?
This change is particularly meaningful to me because, as an educator, I want my students to successfully engaged in academic learning time outside of the classroom setting.
3. What do other educators or professionals tell you when YOU discuss this topic with them?
Other educators agree that the classroom setting is the most successful one when it comes to knowledge acquisition because in this setting, students have less distractions than at home. Another concern that educators have in relation to this matter is that at home setting there is no scholar schedule and/or structure as in schools and also caregivers are not trained on teaching skills and most of the time responses to exercises/test can be biased by their help and/or other distractors environment related.
4. How is the desired outcome a part of YOUR educational philosophy?
The School is the ideal setting for learning acquisition for gifted students, but they can also learn in home setting if they have the appropriate resources. Applying behavioral intervention programs to keep them focused and engaged on tasks can be a method to successfully increase their academic learning time.
5. Describe the situation with your student/group of students that you want to change by implicitly focusing on: (What is the problem you would like to improve)
Who? What? When? Where? How?
I would like to increase the on-task behavior during distance learning time for gifted students at elementary level, at home setting. I will apply a behavioral intervention plan, based on the results of a preference assessment previously done according to functions of the behaviors observed.