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The Impact of Online Learning Activities on Student Learning Outcome in Blended Learning Course

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The aim of the study is to determine the impact of online learning activities on learning outcomes of students who participated in the blended learning course, focusing specifically on skill-based courses. The learning outcomes or results of a learner are usually measured by scores, knowledge or skills gained in the course. In blended learning courses, the learning outcomes can be assessed according to many criteria. In this study, interactive activities such as teacher–student interaction, student–student interaction, student–content interaction and student–technology interaction are considered. Undergraduate students participated in the blended learning course in which formative assessment was used to evaluate student learning outcomes by the combination of different learning activities through a learning management system. The quantitative results obtained by using regression analysis of data from the system showed that the students who effectively interacted with learning activities in the course have better results. Quantitative analytical results indicated that student–student interaction has a greater impact on student learning outcomes. These learning activities are used for interactive activities as suggestions for teachers to design and implement learning activities for blended learning courses.
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Journal of Information & Knowledge Management (JIKM)
Full version at: https://doi.org/10.1142/S021964921750040X
The impact of online learning activities on student learning outcome in blended learning course
Author: Viet Anh Nguyen
Affiliation: VNU University of Engineering and Technology, Hanoi, Vietnam
Email: vietanh@vnu.edu.vn
Abstract
The aim of the study was to determine the impact of online learning activities to the learning outcomes of students who
participated in the blended learning course. Interactive activities are considered, in this study, include teacher - student
interaction, student - student interaction, student - content interaction, and student - technology interaction. The
undergraduate student participated in the blended learning course which using formative assessment to evaluate student
learning outcomes by the combination of different learning activities through a learning management system. The
quantitative results obtained when implementing learning analytics data from the system through using regression analysis
showed that the students interact effectively with learning activities in the course have better results. Quantitative analytical
results indicate that student student interaction has a greater impact on student learning outcomes. These learning
activities used for interactive activities as suggestions for teachers to design and implement learning activities for blended
learning courses.
Keyword learning outcomes, online learning activities, learning analytics, blended learning
1. Introduction
One of the goals of information technology application in education reform is to improve the student learning outcomes.
The results of student learning are a criteria evaluation accomplishing academic goals of the learner. Up to now, there are
two often ways to evaluate student performances: summative assessment and formative assessment. The formerly estimated
results through scores at the end of the course or evaluation forms, the later estimated results in the process student learning,
to consider many aspects. When implementing the blended learning course, a question always arises for teachers is how to
design learning activities for learners to get the best results? Not easy to have a template for every course which applied to
the different learning objects. Recent studies have focused on studying and understanding the relationship between factors
affecting learning outcomes, especially in blended learning environments (Ellis, Pardo, & Han, 2016). Zachris (Zacharis, 2015)
conducted a data analysis of learners participating in the LMS-system Moodle, by observing that 29 variables and found out
reading and posting messages, content creation contribution, quiz efforts and the number of files viewed are four factors
which are 52% affect the academic performance of students. Aspects covered include pedagogical (Lou, Bernard, & Abrami,
2006; Means, Toyama, Murphy, Bakia, & Jones, 2009), learning content, learning tools, the motivation of students, teacher
quality, student satisfaction (Abdous & Yen, 2010). Garrison (Garrison & Cleveland-Innes, 2005) studied the effect of the
acquired ability to the learning outcomes, given that people have a good feeling about the interaction with the instructor and
other students tend to achieve the higher academic achievement than students without a good feeling about the interaction.
Russo (Russo & Benson, 2005) showed that the correlation between the perception of students with interactive participation
and scores. Morris (Morris, Finnegan, & Wu, 2005) when analyzing the correlation between online learning activities to
learning outcomes, says regularly interacting with the course content, to spend more time participating in seminars
Comments that affect learning outcomes.
Journal of Information & Knowledge Management (JIKM)
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When participating in the blended learning course, students use most of the time through the online activities or interacting
with the LMS system, so the study results often depend on many different factors. The previous studies have shown
interactive activities is one of the factors that affect learning outcomes of students (Kayode & Teng, 2014; Kent, Laslo, &
Rafaeli, 2016; Wei, Peng, & Chou, 2015). However, to clarify interactive activities that most significantly affect the results of
student learning is still important questions to find the answer.
The objective of the study was to determine the specific interactive activities and the extent of its impact on student learning
outcomes in the blended learning course. In addition to, we proposed assessment model and forecast results of learners
through the learning activities and suggested approaches in the course design help students obtain the best results.
2. Related literature
2.1. The impact of online interaction on student learning outcome
Chou (Chou, Peng, & Chang, 2010) has defined active interaction in online learning activities including the types of
interaction: the learner -self, learner- learner, learner - instructor, learner -content, and learner interface. The learning
activities in the course is a combination of forms of interaction between the subjects involved in the teaching and learning
activities include: student-content, student-instructor, and student-student interaction (Gradel & Edson, 2010). Popular LMS
systems currently provide essential tools that allow interactive activities in the course, such as forums, message, online
forms of assignments, exercises in wiki format, virtual classroom, etc. These tools also assist teachers in tracking and
monitoring the student learning process, such as status submitted assignments reports, the frequency of access statistics,
activity logs on the system. There have been many studies propose solutions to make interactive activities effectively
support the learning process of students. Evans and colleagues (Evans & Sabry, 2003) implemented three interactive activities:
the pace control, self-assessment, interactive simulation of his research and time of using the system is a factor affecting
student results. The results of their study showed that students with better results and need less time learning when
interacting more with the system. However, the research no conducted with other interactive forms. Similarly, according to
research results (Damianov, Kupczynski, & Calafiore, 2009), there is a positive influence in the direction of time spent online
and the results calculated by the scores of students, especially students in the group above average. Contrary to the judgment
of Eom (Eom, Wen, & Ashill, 2006) showed that there was no relationship between other forms of interaction to the learning
outcomes of students. Early research found out interactive activities online in the blended learning course have an impact on
student learning outcomes.
In this study, we examine the influence of interactive forms of student - teacher interaction, student - student interaction,
student - content interaction, and student - technology interaction to learning outcomes. There are some reasons: i) there is a
variety of interactive activities but can classify into four groups of above mention interaction, based on the participants. ii)
LMS systems support tools and mean to implement the relevant operation effects mentioned above. iii) Clarifying the
impact of interactive form to student performance based on previous studies have shown these types of interactions
mentioned above can affect the student learning outcomes.
2.1.1 The student-teacher interaction
Student - teacher interaction is a key activity in the traditional teaching method when the teachers play a central role. With
blended learning environment, learners play the central role, interaction between teacher and students become more flexible
in many different forms. Kang and colleagues (Kang & Im, 2013), said that the interactive activities between teachers and
students have an impact on learning outcomes of students when implementing learning activities such as learning assistance,
and social intimacy, communication and instructional Q & A, instructor presence, Instructional support. Liu (Liu, 2016)
Journal of Information & Knowledge Management (JIKM)
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suggested using video blogging class to assist students in achieving good results for some kind of special courses for the
oral training course.
2.1.2 The student - student interaction
Blended learning environment allows students to have more favorable conditions of time, space to perform the interactive
operation. With the supported technology, the forms of interaction between the students in the course are increasingly
diverse and more efficient. The previous studies have shown that this kind of interaction student - student that affect
learning outcomes. The online learning activities different to be tested to determine whether the effects of this interaction.
Dawson and colleagues (Dawson, E, & Tan, 2008) indicate that interaction via discussion forums is 80% of interaction in
online learning environments. However, studies have not mentioned the influence of activities through the forum on
learning outcomes. Schrire (Schrire, 2006) suggests that students obtain better academic results when participating in
discussions with each other rather than proceed with the teacher. Song (Song & SW, 2011) examined the interaction through
discussion measured by the number of postings and log-in with academic results and showed no correlation between the
number of scores posted to results. Besides, in this study, the authors implemented only in the asynchronous interactive
type.
Similarly, Macfadyen (Macfadyen & Dawson, 2010) constructed regression model that results showed a tight correlation
between the study results to the number of forum posts, the number of completed assignments. Kent (Kent et al., 2016)
analyzed the quantitative data based on the number of post and view of the 231 students in online discussion activities.
Considering the role of teamwork, Mitchell (Mitchell & Honore, 2007) noted that working groups have a positive impact on
learning outcomes of students. Consider factors influenced by social networks, Sparrowe (Sparrowe, Liden, Wayne, & Kraimer,
2001) suggests that social networks have a direct impact on the final learning outcomes of learners. However, Kayode
(Kayode & Teng, 2014) review the impact of the interaction on learning outcomes, with interactive activities including
reading the contents of the blog, interacting with other learners, and engaging in the blog context with 342 students
participated in the experiment. The results showed that this form of interaction between the students together no significant
impact on student learning outcomes.
2.1.3. The student - content interaction
With the support tools, learning content design is increasingly diverse in forms and ways to communicate the sense of
excitement generated for learners to learn. Moallem (Moallem, 2003) stated that “it became clear that developing an online
course that encourages student exploration and reflection required much more thinking, time, and effort than had been
predicted.” (p.99). Anderson (Anderson, 2003) also stated that “Content, having only volition ascribed to it by humans, is
the most flexible of actors, “willing” to undertake any combination and quantity of interaction” (p. 3). Lee (Lee & Bonk,
2016; Sim & Hew, 2010) shows that the impact of experience using blogs to the learning outcomes of students. Yang (Yang,
Quadir, Chen, & Miao, 2016) developed Col framework model proposed by Garrison and colleagues (Garrison & Vaughan, 2008)
develop blog content course, and online presence shows there impact on academic performance. Similarly, video blog in the
course content is also used to improve the efficiency of learning (Liu, 2016). Asterhan (Asterhan & Hever, 2015) showed a
positive effect on the content reads to the learning outcomes, which are also shown in the study by Ramos (Ramos & Yudko,
2008) when they analyzed correlation keep the number of pages viewed, discussion posts, discussion reads to the learning
outcomes of students. Nandi (Nandi, Hamilton, Harland, & Warburton, 2011) also showed that the number of posts increases in
the time students have to submit assignments or take exams, students have better academic results time more online during
the course.
Journal of Information & Knowledge Management (JIKM)
Full version at: https://doi.org/10.1142/S021964921750040X
2.1.4. The student technology interaction
LMS systems to help design and develop the course in the form of blended or online learning easier and more convenient.
Through providing learning activities such as lessons, forum, quiz, wikis, surveys help students easily interact with the
learning environment. Steel and colleagues (Steel, Keppell, Gerbic, & Housego, 2010) showed that the relationship between the
frequency of the LMS system access (via counting the number of clicks) affect student scores. Wei and colleagues (Wei et
al., 2015) have examined the impact of the interaction via the LMS tools. Data of 381 undergraduate students through
analysis of the results of assignments form (online discussion, exam, group project) and the data access (access time, the
number of posts, the time to read the document), the research results show that the relative activity this can affect their
academic performance. Notice that the LMS system or technological factors play a major role in promoting the interactive
learning activities. Nick Z. Zacharis (Zacharis, 2015) have demonstrated the Wiki edit learning activities, content creation
contribution, mail messages read, and assignments submitted quiz engagement affect 10% to 27% of the learning outcomes
of students in the blended learning courses when considering 29 online Activities.
2.2. LMS supports the implementation of learning activities enhance learning efficiency
Using the LMS system to support the process of teaching and learning in the form of e-learning, blended learning in which
focused on learner is being applied and widely deployed in the higher education establishments. Through the LMS system,
students easy access to in rich course materials and lectures are presented in such as documents, presentations, pdf files,
audio, and video, links. Besides the learning content, the LMS system also provides tools to design learning activities to
support interaction. We shall classify the types of learning activities that LMS systems currently popular Blackboard,
Moodle, and Sakai is supporting activities to bind to interact in Table 1.
Table 1. Learning activities that popular LMS supports
Interaction type
Learning activities
Examples
Student - Teacher
interaction
Virtual Classroom via Adobe
Connect integration or
BigblueButton integration,
Open Meetings integration,
Quiz,
(Liu, 2016); (Arslan, 2014); (Beatty & Gerace, 2009);
(Joksimović, Gašević, Loughin, Kovanović, & Hatala, 2015);
(Çardak & Selvi, 2016);(McBrien, Jones, & Cheng, 2009)
Student - Student
interaction
Discussion Forum, Chat, Wiki,
Workshop, Group assessment,
Messages, Blog
(Nandi et al., 2011); (Grant, 2016); (Joksimović et al.,
2015); (Çardak & Selvi, 2016); (Richardson,
2010);(Tambouris, Zotou, & Tarabanis, 2014) (Dawley,
Un, Klinger, Berger, & Schmidt, 2007)
Student Content
interaction
Lesson, Assignment, Glossary,
Quiz, SCORM Package,
Survey, Links
(Crampton, Ragusa, & Cavanagh, 2012);(Joksimović et
al., 2015); (Çardak & Selvi, 2016)
2.3 Research questions
Studies on the impact of interactive activities in blended learning course to learning outcomes showed that the types of
interactions are affecting the results of the learners. However, up to now, there are not many studies clearly identify the
degree of impact of interactive forms of learning results. Besides determining the impact factors, the design guidelines,
deploy interactive activities to best effectively to the learning outcomes have not been many results. In order to fill the gap,
the research is conducted to answer two questions:
Journal of Information & Knowledge Management (JIKM)
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1. Determine the level of the impact of different types of interaction to learning outcomes in blended learning courses
with variety learning activities?
2. Where forms of interaction in groups have a significant role in the student - student interaction that impact learning
results of the team or not?
3. Methods
3.1. Sample
68 undergraduate students participating in two courses that had the same subject. In each class, the students were divided
into groups to perform group activities, the student can self-selection and the formation of groups, each having from 4 to 6
members.
3.2. Design and Procedure
Test course length three credits and implemented within fifteen weeks. In each week, students must attend two classes in the
form of face-to-face (F2F), other learning activities to be applied in the online and hands-on lab. We designed two learning
activities types of interaction: interaction between teacher and students, the interaction among the students via LMS
Moodle.
3.2.1. Interactive learning activities between teacher and students
Online learning
Beside the teaching activities in the form of F2F in the classroom, teachers also spent a certain amount of time for online
teaching activities. In the model, we used BigBlueButton tool (Zafar et al., 2003) which was integrated with Moodle LMS
systems to implement the online course. For this activity, interactive activities between teacher and students focused on
answering question issues related to projects, assignments. In working time, students could use the "raise my hand" function
to express their opinions directly. In the experiment course, the online teaching activities focused on guiding students to use
frameworks for developing web applications.
Online question & answer system
In the teaching process, teachers need to understand the student’s feedback about the learning section. During the period of
an online learning section, the teacher cannot answer all the questions of the students; so many students who want to ask or
demand the response will not be answered. In addition, to fully collect honest feedbacks from students is not easy because
not all of their problems will be responded to the teacher if their identification is clear. So that we built a tool that allows
students to ask questions while they are participating in an online learning section. With this software, students easily ask
questions; submit their feedback in an anonymous state to their teacher who is teaching online. The issue is shown in public
for all students who are participating in the online learning session; the members can vote for interesting or the most
concerned questions. The teacher does not need to answer all the questions; he can select to answer the most concerned
questions.
3.2.2. Interactive learning activities among students
The implemented interactive learning activities among students in the course were: Group exercise, Wiki document, peer
group assessment, forums.
Group assignment activities
The students selected the other members of the class to form working groups at the beginning of the course. As for students
who could not form the working groups themselves, teachers randomly assigned students into different groups. These
groups remained stable until the end of the course. The teacher announced the content and requirements of group exercises
for teams to select to conduct during the course progress. In our experiment, each group consisted of 5 to 6 students; each
group must do at least three group exercises to complete the course.
Peer group assessment activities
Exercises are implemented by groups. Each group of 5 to 6 members needs to select a project; its execution time is
Journal of Information & Knowledge Management (JIKM)
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approximately four to five weeks. Project performance evaluation is conducted through three phases: (1) The members of
the group perform a self-assessment based on their level of contribution to the group work, the members of the group will
carry out the self-classification levels A, B, C, D respectively. (2) The groups will discuss and conclude a final assessment
for each member of the group, with the consensus of the whole group through the minutes of the discussions, with each
member’s certification.
The result of peer assessment is considered as valid if each student of the group has different levels of evaluation. It can be
understood that it is not acceptable if a group has the same assessment level at A, B, C or D for its members. This is quite
natural because almost there are not equal contributions of all members for the group work. (3) Results of group exercises
are reported offline in the class, where teacher evaluates their project performance by giving scores with the criteria outlined
in the project requirements. The students of the other groups will fill out a rating list of the groups after hearing all
presentations.
Wiki activities
This activity is implemented by groups. The groups write a document to study about the specific content of the course.
Members of the group work together to build the document that will be evaluated by the teacher and other groups in the
class. This activity uses the Moodle workshop tool allowing students to participate online and to see the process of doing
their group’s exercise from the start until the finish. In the experimental course, we asked groups of students to write a
studying document about a framework for developing Web-based applications.
Forum activities
Each course has a forum for students to exchange related matters of the course. It is also a place where the official
information is exchanged between students and teachers and among students on course issues. The level of forum
participation is a criterion to assess the student’s attendance level.
Course Grading
Giving scores did the evaluation of student’s completion of the course. Currently, according to the regulations of the
university, student assessment results by the score at the end of the course consist of two parts. Part one, the learning
outcomes of students making learning activities including individual implementation exercises, group exercises, tests, and
exams. Part two, the attendance of the students involved in learning activities: there is full participation of learning activities
online and offline deployed or not? Students were evaluated according to a scale of 10, in which the proportion of
component scores was calculated by learning activities such as: attending to LMS, assignments, projects, mid-term exam,
and the final exam. In particular, the necessary activities required students to attend were two of the three individual
assignments, two of the three projects, 01 mid-term exams, and the final exam. We build weighting of evaluation based on
two principles. 1) The scientific training committee of university regulations weight for a final exam, it is from 50% to 70%
of total grade. 2) The teacher can himself construct the weight for the remaining points, which are from 30% to 50% of total
points. In this study, we propose the weight of final exam is 55%. With 45% of evaluation for other activities, we evaluate
students through the implementation of learning activities: personal assessment, peer group assessment, wiki projects.
3.3. Instrumentation
Students need to participate in learning activities designed for each week and take part in the final exam period prescribed
by the subject. The activities including the following main activities: 1) Access the data resource which updates by teacher
each week. 2) Join forums discuss the subject. 3) Take individual exercise per week (experiment course includes 03
individual assignments). 4) Participate in a project team to perform in teams to evaluate cross-use tool workshop. 5)
Contract materials in wiki project. 6) Test between semesters in the form online through the LMS system. 7) Join some
online lessons via video conference. 8) FAQ during face to face class through online Q & A system.
Data were collected from actual Reported from the database of system logs. The data attributes are adopted from previous
Journal of Information & Knowledge Management (JIKM)
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studies (Joksimović et al., 2015; Wei et al., 2015; Zacharis, 2015) including data attributes related to interactive activities such as
student -teacher, student - student, student - content, and student - technology. In our experiment, an interaction is measured
by one view or post of the students on LMS system.
Analyzing results, the interaction data is collected after the end of the course. It includes the file number of the view for six
weeks, the number of interactions with five exercises which include three individual assignments and two group exercises,
one wiki activity, one workshop activity, one forum to discuss, and one online test.
To examine the relationship between interaction in group activities and the group learning outcomes, we examine a
correlation between the total number of interactive activities through active engagement in the form of groups including
wikis, workshops, and quizzes impact group learning outcomes.
4. Results
4.1. The correlation between interactive activities and learning outcomes
A correlation matrix in Table 2 showed that the listed explanatory variables are correlated to the academic performance of
students. Correlation coefficients strongly affect the learning outcomes of these activities is the interaction between students
with students through workshops learning activity (r = 2.85, p <0.05). Other interactive activities are also quite tight
correlation, for example, student -student interaction in the form of wikis and discussion forum (r = 0:32, p <0.01). The
related explanatory variables correlate with academic results are included in the model to consider the impact of interactive
activities to learning outcomes.
Table 2. Correlation coefficients between learning result and learning activities
R1
SCF
SCA
wiki
workshop
forum
quiz
R1
1
.176
.014
.085
.285*
.105
-.159
STF
.604*
*
.212
.273*
.080
.276*
.029
SCF
1
.154
.215
.099
.157
-.138
STA
.171
.097
.155
-.052
-.111
SCA
1
.137
.049
.166
.111
wiki
1
.216
.328**
.005
workshop
1
.213
-.121
forum
1
.234
*. Correlation is significant at the 0.05 level (2-tailed).
**. Correlation is significant at the 0.01 level (2-tailed).
4.2. Regression models predicting learning outcomes related to interactive activities
Results correlation coefficient between student - content interaction through assignment activities on results is the lowest (r
= 0.014, p = 0.05), should be removed from the model.
4.2.1. The relationship between student-teacher interaction and learning outcomes
The results of the correlation matrix between the student-teacher interaction through view/post course materials (STF
variable) and learning outcomes (R1 variable) showed in Table 3. The value of R2 in the cubic model (R2 = 0.33), the model
log (R2 = 0.33) was the highest of ability to explain two most powerful model for the relationship between the STF and R1.
Here we choose the form of a log-linear model for relations between the STF and R1.
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Table 3. Model Summary and Parameter Estimates for STF variable
Dependent Variable: R1
Equation
Model Summary
Parameter Estimates
R
Square
F
df1
df2
Sig.
Constant
b1
b2
b3
Linear
.025
1.658
1
66
.202
6.369
.073
Logarithmic
.033
2.248
1
66
.139
6.124
.444
Quadratic
.025
.826
2
65
.442
6.310
.095
-.001
Cubic
.033
.735
3
64
.535
5.804
.394
-.042
.001
Power
.035
2.424
1
66
.124
5.661
.088
Exponential
.028
1.887
1
66
.174
5.928
.015
The independent variable is STF.
Similarly, based on our review model student-teacher interaction, a relationship between assignment activities (STA
variable) and learning outcomes (R1 variable) showed in Table 4. The cubic model was selected to explain the relationship
between the STA and R1 by R2 in the cubic model (R2 = 0.08) shows the ability to explain the relationship between the STA
and R1.
!
Table 4. Model Summary and Parameter Estimates for STA variable
Dependent Variable: R1
Equation
Model Summary
Parameter Estimates
F
df1
df2
Sig.
Constant
b1
b2
b3
Linear
.156
1
66
.694
7.020
-.042
Logarithmic
.042
1
66
.839
6.587
.115
Quadratic
.283
2
65
.754
6.160
.261
-.024
Cubic
1.846
3
64
.148
1.419
3.025
-.497
.024
Power
.258
1
66
.613
5.883
.054
Exponential
.023
1
66
.880
6.560
-.003
The independent variable is STA.
4.2.2. The relationship between student - content interaction and learning outcomes
Table 5 showed correlation matrix results between student-content interaction through activities view/post course materials
(SCF variable) and study results. The value of R2 in the quadratic model (R2 = 0.068) and the cubic model (R2 = 0.082) are
the highest of ability to explain the most powerful two models for the relationship between SCF and R1. The quadratic
model is chosen to consider the relationship between the STF and R1.
Table 5.Model Summary and Parameter Estimates for SCF variable
Dependent Variable: R1
Equation
Model Summary
Parameter Estimates
Journal of Information & Knowledge Management (JIKM)
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R
Square
F
df1
df2
Sig.
Constant
b1
b2
b3
Linear
.015
1.026
1
66
.315
6.483
.103
Logarithmic
.045
3.089
1
66
.083
6.337
.552
Quadratic
.068
2.371
2
65
.101
5.790
.555
-.046
Cubic
.082
1.910
3
64
.137
5.090
1.263
-.203
.008
Power
.057
3.953
1
66
.051
5.860
.118
Exponential
.024
1.637
1
66
.205
6.001
.025
The independent variable is SCF.
4.2.3. The relationship between student - student interaction and learning outcomes
Table 6, 7, and 8 showed results correlation coefficient matrix between student- student interaction activities to learning
results (R1) through wiki activity (wiki variable), the workshop activity (workshop variable), and discussion activity (forum
variable). The linear model was selected to explain the relationship between the workshop activity (R2 = 0.079) and
discussion (R2 = 0.011) with the study results. A log-linear model was chosen to explain to select the relationship between a
wiki activity (R2 = 0.054).
Table 6. Model Summary and Parameter Estimates for Wiki variable
Dependent Variable: R1
Equation
Model Summary
Parameter Estimates
R
Square
F
df1
df2
Sig.
Constant
b1
b2
b3
Linear
.007
.441
1
66
.509
6.679
.012
Logarithmic
.054
3.797
1
66
.056
6.215
.379
Quadratic
.016
.514
2
65
.600
6.530
.042
.000
Cubic
.125
3.051
3
64
.035
5.876
.262
-.011
9.387E-
005
Power
.067
4.776
1
66
.032
5.715
.081
Exponential
.012
.769
1
66
.384
6.283
.003
The independent variable is wiki.
Table 7.Model Summary and Parameter Estimates for Workshop variable
Dependent Variable: R1
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Equation
Model Summary
Parameter Estimates
R
Square
F
df1
df2
Sig.
Constant
b1
b2
b3
Linear
.079
5.690
1
66
.020
5.888
.057
Logarithmic
.070
4.943
1
66
.030
5.305
.590
Quadratic
.081
2.871
2
65
.064
6.053
.028
.001
Cubic
.081
1.887
3
64
.141
6.005
.043
.000
1.739E-
005
Power
.077
5.519
1
66
.022
4.792
.119
Exponential
.081
5.787
1
66
.019
5.428
.011
The independent variable is workshop.
Table 8.Model Summary and Parameter Estimates for Forum variable.
Dependent Variable: R1
Equation
Model Summary
Parameter Estimates
R
Squ
are
F
df1
df2
Sig.
Constant
b1
b2
b3
Linear
.011
.741
1
66
.392
6.637
.004
Logarithmic
.001
.055
1
66
.815
6.685
.035
Quadratic
.011
.365
2
65
.696
6.639
.003
8.584E-007
Cubic
.015
.316
3
64
.814
6.542
.014
.000
4.239E-
007
Power
.001
.044
1
66
.834
6.341
.006
Exponential
.011
.724
1
66
.398
6.274
.001
The independent variable is forum.
4.2.4. Regression model predicting learning outcomes related to interactive activities
A multiple regression model included the variables likely explanations for the study results include: Log (STF), Quadratic
(SCF), Cubic (STA), Log (Wiki), Workshop, Forum. Sig value on Table 9 allows us to be able to trust regression model
included the variables mentioned above can explain the results of student learning.
Table 9. Coefficientsa of variables in regression model between R1 and factors
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std. Error
Beta
Tolerance
VIF
(Constant)
5.291
.632
8.378
.000
log_stf
.848
.778
.151
1.091
.280
.741
1.349
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qua_scf
-.004
.012
-.048
-.320
.750
.629
1.590
cub_sta
-.001
.001
-.095
-.700
.487
.759
1.317
log_wiki
.711
.494
.190
1.439
.155
.809
1.236
forum
-.002
.004
-.059
-.444
.658
.800
1.249
workshop
.049
.025
.241
1.926
.059
.903
1.107
a. Dependent Variable: R1
From the results in Table 9, can use the general model to predict the results of student learning as: R1 = 5.291+ 0848 * log
(STF) + 0711 * log (wiki) + 0049 * workshop - 0001 * cubic (STA) - 0.002 * forum - 0.004 * quadrics (SCF).
4.3. The correlation between the active interactive group and learning results
The results considering the relationship between interactive activities in groups to study results in Table 10. It surprisingly
not reflect the interaction between the activities of the group for learning outcomes (as a group) while the small R2 value (R2
= 0.04).
Table 10. Coefficientsa of interaction variable and group result
Model
Unstandardized
Coefficients
Standardized
Coefficients
t
Sig.
Collinearity
Statistics
B
Std. Error
Beta
Tolerance
VIF
1
(Constant)
8.362
.786
10.64
6
.000
INTERACTION
-.003
.003
-.211
-.747
.469
1.000
1.000
a. Dependent Variable: R2
5. Discussion
5.1. Answers to research questions
This study evaluated the impact of the interaction through the learning activities in blended learning course to student
learning outcomes. The learning activities are implemented based learning activities tools which popular LMS systems
currently supported. Answering to questions determine the impact of the interaction types to learning outcomes in blended
learning courses varied deploy collective action, results showed quantitative analysis; interactive activities are affecting the
academic performance of students. The statistical results can indicate that the impact of the kind of interactive on learning
results can be ranked from high to low level of student-student, student-teacher, student-content. In which the influence of
student activities - student interaction have a significant impact on learning outcomes (0.71) in the linear regression model
to predict student results. Obviously, learning outcomes of students in blended learning environment depends on many
factors, so interactive online elements account for only a small percentage, this represents a multiple regression model in
predicting the outcome. Additionally regression model is not a linear.
The student - teacher interaction affects learning outcome is not significant when the regression model of the student-
teacher variables is not linear models that are log model. Obviously, the interaction between students and teachers
increasingly changing both manner and frequency in blended learning. It poses a challenge in the design of the course, the
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environment and tools to support interaction between students and teacher.
Student - content interaction without much impact on learning outcomes (lower proportion of regression model 0004).
Results are consistent with the results of recent studies (Joksimović et al., 2015; Wei et al., 2015; Zacharis, 2015) where those
results also showed the impact of the student - content interaction is lower than other types of interactions. In contrast to the
statement of Kayode (Kayode & Teng, 2014), the research results show that this kind of interaction between student - content
has the greatest impact on learning outcomes. It can be explained by the learning activities in each trial was designed and
built differently, there is no uniformity in the type and frequency of use of interactive activities during key deployment
learn.
Answers to study questions whether interactive group forms that affect the significant in this kind of interaction or not? The
results showed that not lead to a conclusion that interactive in a group has a significant role in group results. When
considering the relationship between perceived team learning and group project scores, Turel (Türel, 2016) also showed no
relation between them.
5.2. Implication
These research findings showed that online learning activities blended learning course that impacts on student learning
outcomes. Each type of interaction with different levels of impact on learning outcomes through learning activities is carried
out in the course. It also poses a challenge for the design and implementation of learning activities blended learning course
when the interaction depends on the learning activities are designed without an available prototype. In this study, we have
carried out to implement the common learning activities to demonstrate the types of interactions are the most popular:
student- teacher, student-content, and student-student. Assuming that, if the course does not have some learning activities,
such as wikis, forums, the results of assessing the impact of learning activities to learning outcomes will be different. It is a
research question need to looking for possible answers. According to findings, student-student interaction, has the greatest
impact on learning outcomes, this can be considered as suggestions for the construction of the course activities support more
interactive styles. When analyzing the types of interaction in distance education, Bernard (Bernard et al., 2009) also pointed
out that student - student interaction is the highest percentages in other interactions. Those findings also showed that
student-teacher and student-content interaction not significantly impact on learning results. It revealed that student-centered
in the blended learning model, lecturer role as orientation and guidance instead of imparting knowledge in the traditional
teaching model.
Together with supporting diversified assessment types, teachers should have enough formative assessments to ensure that
students maintain their concentration during the course. Student interacted with the system less and only accessed it when
required. In our opinion, to maintain the interaction with learning activities, it is necessary to provide enough assessments in
courses.
The results of data analysis collected from LMS based on learning analytics method (Phillips, Maor, Preston, & Cumming-
Potvin, 2012) when analyzing the impact of interaction types to the student learning outcomes, although need further
research, can be used as a critical approach. One of the difficulties encountered when approaching this method is popular
LMS systems today, yet provides many learning analytics tools. It is a challenge for the development of LMS systems,
where learning analytics more becomes an important role.
5.3. Limitation
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There are some limitations in this research, firstly, the number of samples collected is limited. In addition, learning activities
deployed mainly use the LMS available tools. Secondly, besides the number and frequency of learning activities much less
implemented. On the other hand, this study only focused on the common types of interactions. Also, the classification of
learning activities to interactive forms in this research is being conducted qualitative.
6. Conclusion
There are many factors affect the student learning outcomes in the blended learning course. Find out the specific factors and
considering the extent of its impact on learning outcomes is an intricate problem. This paper conducted to examine how
interactive types and interactive factors impact on student learning outcomes. Research has developed the chain of online
learning activities reflect the operational interaction between actors: student - teacher, student - content, student - student
and student - technology. The findings indicate that the online learning activities in the blended learning model that affect
student learning outcomes, in which the student - student interaction is the significant impact on student results. Based on
analysis of these factors, this study proposes a model to assess the incidence of the learning outcomes based on interactive
learning through learning activities.
The limitations of the study pose a challenge in the future research. Whether we can develop a model that predict academic
performance of students through interactive activities? In other words, whether the course with variety online interactive
activities that support learners better or not? Learning analytics has a genuinely efficient method of evaluating the learning
process of students and provide information-oriented faculty, the course designed to build or not? The role of the LMS
system in the collection and analysis of objective data of students participating in the model instead of the survey polls
subjective? Despite limited results, this research can be viewed as a suggestion in the construction of model evaluation,
forecasting learning outcomes-based learning activities in blended learning.
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Due to the current education trend, many students, including traditional-age, non-traditional, returning, evening, and adult students, move from traditional on-campus study to distance learning and online education. The current COVID-19 pandemic offers opportunities for these colleges and universities to expand their channel to international students who cannot come on-campus due to the recommendation of social distancing and the self-quarantine policy. However, it is important to capture the students' comments and opinions, particularly international students who are looking for the living experience in an overseas country. With the tools of qualitative inductive survey and interview sessions, the researcher collected 63 valid data from the Chinese international students. This study provided the blueprint for school leadership, department heads, policymakers, faculty members, and students who are interested in reforming the current curriculum and instruction.
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This study aims to understand the satisfaction and experience of programme-seeking students in a community college in the United States. In order to improve the satisfaction, experience, and teaching and learning procedures of distance learning courses and programmes, it is important to understand the students' feedback and ideas. Based on the case study methodology, the researcher collected data from 1,857 inductive surveys and 11 focus group activities. This research allowed the researcher to gain knowledge and understanding about students' satisfaction, experience, and potential enrolment in degree programmes during and after the COVID-19 pandemic. More importantly, the results provide recommendations to school leaders, instructors, government leaders, and policymakers about current and future college and university development regarding changes in teaching and learning behaviours.
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Evaluating the quality of student experiences of learning in a blended environment requires the careful consideration of many aspects that can contribute to learning outcomes. In this study, university students in first year engineering were required to collaborate and inquire in a blended course design over a semester-long course. This study investigates their approaches to inquiry and online learning technologies as they collaborated both in class and online. The results identify sub-groups within the population sample (n > 200) which reported qualitatively different experiences of how they approached inquiry and used the online learning technologies. The results also measure aspects of their collaborations which help to explain why some students were more successful than others. The outcomes of the study have important implications for teaching and course design and the effective evaluation of blended experiences of university student learning.
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Epistemic and technical shifts in how individuals relate to one another have, however, raised some concerns in the higher education sector, including how the mechanism for social interaction and communication affects learning outcomes. Questions regarding the effects such changes have on the educational experience of students are but one example of issues facing contemporary consumers of knowledge in societies, where specialised expert knowledge replaces traditions to grant or deny power and authority (Giddens 1991). Alongside, macro level social changes are relentless micro level, everyday shifts that determine our technical proficiency. In the case of education, the focus on technical solutions can translate into poorly designed or executed resources that waste both educators’ and students’ time, the latter of which might have been better spent on traditional learning tasks, such as textbook reading. Conversely, educators who are less enthusiastic about educational technologies may feel that their expertise and authority are threatened by a lack of technical command in an increasingly technical environment. Given the ever-evolving plethora of educational technology platforms, tools and resources (“bells and whistles”) presented to the academic community, it is easy to lose sight of core educational goals. A specific educational goal that we investigate in this article is the enhancement of learning environments to facilitate students’ acquisition of substantive and tacit knowledge. Student resource use and grades were entered into cohort-specific SPSS data files and analysed separately by subject to produce descriptive statistics and correlations. T-tests were conducted with significantly correlated (p = < 0.05) variables using the independent sample t-test with Levene's test for equality of variances, where equal variances were assumed. After tests for normality confirmed that the samples were normally distributed, two-tailed independent groups t-tests were conducted to test the means between the two subjects related to resource use and final grades. Findings revealed a significant difference in the final grades between SOC students who received at least a pass (M = 9.40, SD = 4.59) and those who failed (M = 5.24, SD = 4.72) related to the access of virtual lectures t(133) = 5.22, p=0.00. In other words, the more virtual lectures accessed, the higher SOC students’ final grades. These significant relationships are evident in Figure 2, 3 and 5.
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Integration of technology into many areas of the language learning classroom is increasingly becoming a reality, and peer review of student writing is one area in particular which has shown to benefit from these technology enhancements. This study explored the ability of students to autonomously complete a suite of technology enhanced (TE) training, practice and actual asynchronous peer review activities using only a learning management system (LMS). In addition, the study gauged differences in student perceptions and attitudes when undertaking this process in a completely online versus blended mode of study. Results suggest students can successfully carry out TE training, practice and asynchronous peer review activities autonomously through an LMS completely online, but they exhibit more favorable attitudes and motivation when this process is conducted in a blended mode within a computer laboratory.
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The main purpose of this study is to examine relationships between the social abilities, perceived team learning, and the performances of students in a blended learning setting. The participants, 82 undergraduate students, worked in small teams on a research method task over one semester. The instruments used for this study included a five-factor social ability scale and a one-dimensional perceived collaborative learning scale. The results showed moderate significant relationships between students' perceived team learning scores and students' peer social presence scores as well as weaker relationships between team learning and two social ability subscales, written communication skills and instructor social presence. There appears to be an important effect of peer social presence that is linked to learning and performance. Using a blended learning model may have an important impact on increasing social interaction and learning with a team learning approach thereby helping students undertake comprehensive tasks and increase student learning.
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Within the scope of this research, efforts were exerted to increase teacher candidates' interaction ways through action research in a blended teaching profession course in higher education level. Teacher candidates participated in various blended learning activities during a semester-long course, and the problems related to learners' participation in blended learning activities and their interactions during learning process were solved via action decisions. Blended learning activities were developed according to Felder-Silverman Learning Style Model and the lessons learned from previous two pilot studies. In order to conduct the activities; face-to-face and synchronous virtual classroom sessions were combined with asynchronous from discussions and blog. At the beginning of the course, teacher candidates chosed activity sets according to learning style model and throughout a semester-long course, they participated in the activities. Throughout the action research process, the ways of interactions between students, students and instructor, and students and the content were examined as well as the levels of learning accomplished by students during the learning process. This study has revealed indicators pointing to an increase both in students’ interactions and levels of learning during the blended learning process.
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This exploratory study investigates whether blending a class video blog into face-to-face instruction may enhance university students' actual learning performance and affective outcome at the same time. Research as to the effects of such a pedagogical approach remains less studied in the extant literature. This yearlong investigation collects multiple data sources from 42 university freshmen in an experimental group (EG, N = 21) and a control group (CG, N = 21). Results indicate that the EG statistically outperforms the CG in oral proficiency development after the interventions. While there is no significant difference between the two groups in terms of overall and outside-class willingness to communicate in the target language, it appears that the CG perceived more in-class willingness at the end of this study. Qualitative data sources reveal the EG's positive attitude toward joining this shared blog platform and several concerns raised by some of these learners during this learning process.
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The increased use of online discussions in learning environments both formal and informal, positions the construct of interactivity as central to learning. Interactivity in learning communities' online discourse is viewed in this study as a socio-constructivist process. It is the network of interactions among content items and participants which drives a collective knowledge construction process. Conceptualizing interactivity in the literature is still unclear and not enough is known about its role in knowledge construction and about its relationship to learning outcomes. In addition, assessing learning outcomes using analytics has not matured fully and is still subject to intense development. This study thus sets out to investigate the role of interactivity as a process of knowledge construction within online discussions, and in particular, its association with learning outcomes, as measured by formal assessment tasks. We present significant positive correlations between various interactivity measures, taken from various learning communities, and a set of well-known learning assessments. We suggest that patterns of interactivity among learners can be measured, and teach us, not just about group dynamics and collaboration, but also about the actual individual learning process.
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The role of discussion forums is an essential part of online courses in tertiary education. Activities in discussion forums help learners to share and gain knowledge from each other.. In fully online courses, discussion forums are often the only medium of interaction. However, merely setting up discussion forums does not ensure that learners interact with each other actively and investigation into the type of participation is required to ensure quality participation. This paper provides a general overview of how fully online students participate in discussion forums and the correlation between their activity online and achievement in terms of grades. The main benefit of this research is that it provides a benchmark for the trend of participation expected of the fully online introductory information technology and programming students. Investigating the participation and the factors behind online behaviour can provide guidelines for continual development of online learning systems.. The results of the data analysis reveal that a high number of students are not accessing or posting in the discussion board. Results also show that there is a correlation between activity of students' in online forums and the grades they achieve. Discussion about the findings of data analysis and the lessons learned from this research are presented in this paper.
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This study examines the social network of the learner relationships and online interactions in a graduate course using weblogs for writing and sharing weekly reflective journals during a 16-week semester. The social network data of the learner relationships were gathered twice by measuring learners' perceived emotional closeness with other learners. In terms of the online interactions among the learners, the numbers of replies that individual learners had posted to and received from others' postings were respectively calculated and analyzed. The findings from these measures indicated that the social network patterns and values as measured by peer relationships were noticeably changed at the end of the semester, when compared to that at the beginning. The impact of blogging activities on such changes was supported by correlational analysis between the peer relationships in the social network and online interactions through the learner blogs.