Download The PDF File. - Dr. Jiangping Chen

What Matters?An Investigation of Student
Collaborative E-learning
Ren Ding1
School of Information Management
Wuhan University
Wuhan, P.R.China
1
[email protected]
Jiangping Chen2 & Ryan
Knudson3
Frank Braun4
College of Informatics
University of North Texas
Denton, U.S.A
2
[email protected]
3
[email protected]
University of Northern Kentucky
4
[email protected]
Abstract—This paper reports a study conducted to understand
students collaborative e-learning behavior within a team project
in a graduate-level class. The effectiveness of collaborative elearning is measured, and the factors affecting it are identified.
The study finds that cognition, information communication, IT
skill, member relationship, instructor guidance and supervision
have a significant impact on collaborative e-learning. This
research proposes a primitive model for collaborative e-learning.
It will help educators to better design collaborative e-learning
courses.
Keywords-collaborative
e-learning;
collaboration effectiveness assessment
e-learning
factors;
I.
INTRODUCTION
In this age, effective collaboration is essential, especially in
virtual environments. Currently, a growing number of
education institutions are offering Internet or online classes that
provide more flexible learning than traditional face-to-face
classrooms. One common method of teaching students to
collaborate effectively is the assignment of team projects.
However, conducting team projects in online classes poses
challenges to both students and instructors: students in a team
may have different ideas on selecting collaborative learning
methods, and the instructors have fewer opportunities to
monitor team activities and to directly advise the students [1].
A first step toward better design and supervision of
collaborative e-learning is to better understand the experiences
of students in team projects, especially by measuring their elearning effectiveness [2] and identifying methods to do so.
II. COLLABORATIVE E-LEARNING
Collaboration has been deemed among the “most prominent
21st-century skills”, and “a central piece in the education
puzzle” [3]. Collaborative e-learning is defined as
“constructing knowledge or solving problems through mutual
engagement of two or more learners in a coordinated effort
using Internet and electronic communications for their
interactions” [4]. During collaborative e-learning, each student
contributes to and learns from the project on which they are
working.
Traditionally, instructors have thought about the assessment
of student collaborative e-learning effectiveness in terms of
examination grades. Today, when the aim of e-learning is
cultivation of students’ self-directing capabilities [5], the
evaluation of collaborative e-learning needs to further consider
students’ perceptions towards their e-learning experiences.
Noble therefore proposed Cognitive-Based metrics to evaluate
effective collaborative learning [2]. Cognitive-Based metrics
are theoretically supported by the Cognition-Behavior-Product
model and the Transactive Memory model. The CognitionBehavior-Product model demonstrates that individual cognition
affects team behavior, therefore it emphasizes the importance
of measuring students’ collaboration cognition and both team
and individual collaborative learning outcomes. The
Transactive Memory model addresses the necessity of
measuring students’ communication. In this model, transactive
knowledge or memory, which appears to be a powerful
intervening variable for collaborative e-learning, is distributed
throughout the team. Swan noticed two pivotal points for the
evaluation of collaborative e-learning effectiveness: learning
goals, and both individual and group learning outcomes [6].
In our study, the expected goals for individual students in
collaborative e-learning include: (1) to increase student
knowledge about collaboration and the course content; (2) to
promote personal growth; and (3) to cultivate student interest in
collaborative learning. In the same vein, the expected goals for
teams are to increase (1) team collective capability; (2) team
learning ability; and (3) team members’ confidence in and
satisfaction with the team [7]. We will study students’
perceptions about these goals based on the actual outcomes of
the collaboration to explore the students’ learning effectiveness.
The outcomes of collaborative e-learning will be measured by
the combination of both team outcomes and individual
outcomes.
III.
AFFECTING
FACTORS
Collaborative e-learning can be influenced by many factors.
The impact of e-collaborative IT technologies on e-learning is
evident. Padilla [8] observed that students who demonstrated
proficiency in IT skills have more interest in distance learning
and usually contribute more to the team work than those who
are not as proficient. Hodgkinson [9] believed that trust and
relationship among team members are the strongest factors of
collaborative e-learning effectiveness. Haycock [10] examined
several critical success factors for student collaborative
learning effectiveness. He clustered those factors into 6
categories: factors related to the environment, factors related to
membership characteristics, factors related to process and
structure, factors related to communication, factors related to
purpose, and factors related to learning resources. Based on the
literature review, our study selected and tested ten factors, as
shown in Table 1.
TABLE 1. THE TEN FACT ORS AND THEIR EXPLANAT IONS
Factors
Collaborative e-learning
cognition (CC)
Information
communication (IC)
IT skills (IS)
Member trust (MT)
Trusting members and their knowledge
Team leadership (TL)
Guidance &
supervision (GS)
Commitment of
members (CM)
Member relationship
(MR)
Pre-collaboration
experience (PE)
Project development
process (PD)
IV.
Explanations
Understanding the importance of collaborative
e-learning and willing to collaborate
Open and frequent communication among
members
Adept at choosing and utilizing IT tools for
collaborative e-learning
Leadership of team coordinators
Guidance and supervision provided by the
course instructor
Other members’ dedication to the team project
Relationships among team members
Prior collaboration experience
H2: information communication (IC) positively affects the
outcomes of collaborative e-learning.
H3: IT skills (IS) positively affect the outcomes of
collaborative e-learning.
B. Research Procedure
A survey was designed based on the research model. The
survey instrument was adapted from an online questionnaire
(http://www.drbiz.com). Each question item was measured
using a seven-point Likert-type scale. Fifty-nine graduated
students who took a blended course (SLIS 5740: Introduction
to Digital Libraries) at the Department of Library and
Information Sciences, University of North Texas took the
survey. The class had a one-day face-to-face meeting during
the semester, with the rest of the teaching online. Blackboard
Vista (http://www.blackboard.com/) was used as the E-learning
platform. Students are required to conduct a team project for
the course. Each team has 3-4 team members and a team
coordinator nominated by the members.
The survey results were analyzed by SPSS software.
Cronbach’s alpha of team outcomes and individual outcomes is
0.84 and 0.91 respectively, and Cronbach’s alpha of affecting
factors is 0.77.
An appropriate pace of team project
development
RESEARCH MODEL
AND PROCEDURES
A. Research Model
This study aims to investigate student behavior within a
collaborative e-learning environment. The research objects are
to evaluate students’ learning effectiveness and to determine
the factors affecting their e-learning. The research model is
illustrated in Figure 1. It is established by adapting the
Cognition-Behavior-Product and Transactive Memory models.
V.
FINDINGS
A. Collaborative E-learning Assessment
The individual collaborative e-learning outcomes of the
students are positive. 84.2% students believed they learned
useful knowledge by participating in the team project. 68.4%
felt they were offered many opportunities for personal growth.
Concerning team outcomes, 82.4% students felt that their
own team demonstrated good collective capability, which
indicates that the cohesion of the team project was relatively
strong. Team projects provided a positive learning atmosphere
according to 88.0% of students, and 73.7% students felt
confident in their teams. By comparing student learning
outcomes and expected goals, the effectiveness of this elearning can be evaluated. Such evaluation reveals that most
students feel they can effectively achieve positive learning
results and reach educational goals in e-learning, shown in
Table 2.
TABLE 2. COLLABORAT IVE E-LEARNING OUT COMES
Mean
Agree and Strongly
Scale Items
(1-7)a
Agree Percentage
Individual
outcomes
Team
outcomes
Figure 1. Research Model
Ten alternative hypotheses will be tested, as labeled in Figure 1.
Three of the ten hypotheses are listed here:
H1: collaborative e-learning cognition (CC) positively affects
the outcomes of collaborative e-learning.
a
Learning useful knowledge
5.40
84.2%
Growth opportunities
4.96
68.4%
Future participant
willingness
4.28
56.1%
Collective capability
5.47
82.4%
Learning atmosphere
5.76
88.0%
Team confidence
5.09
73.7%
A seven-point Likert scale from 1=”strongly disagree” to 7=” strongly agree”
E-learning cannot be carried out without sufficient
communication channels. As Table 3 demonstrates, E-
mail is mostly used (59.4%), preferred (50.0%), and
perceived to be the most effective (37.5%) communication
channel. The discussion forum within the class site in
Blackboard was judged as the second most effective
channel (18.8%). Interestingly, traditional face-to-face
communication was considered only the third most
effective communication channel.
TABLE 3. COMMUNICAT ION CHANNEL
Communication Channels
Mostly
Mostly
Most
used
preferred
effective
Face to Face
0.0%
6.3%
15.6%
Telephone
0.0%
3.1%
6.3%
Conference calls
0.0%
9.4%
9.4%
E-mail
Discussion forum
59.4%
28.1%
50.0%
15.6%
37.5%
18.8%
Online chat
6.3%
12.5%
9.4%
Wikis (Google Docs)
6.3%
3.1%
3.1%
B. Crucial Factors
To test the ten hypotheses, correlation and multiple
regression analysis were conducted. Correlations were
measured between the ten independent variables listed in Table
1 and the dependent variable: the outcome of collaboration.
The results demonstrate that CC(r=0.433), IC(r=0.684),
GS(r=0.608), IS(r=0.787), TL(r=0.182), MT(r=0.214),
MR(r=0.701), CM(r=0.332), and PD(r=0.384) have significant
associations with the dependent variable. The coefficients
among independent variables are low (r<0.7), which indicates
little inter-correlation with multi-collinearity among
independent variables. Table 4 reveals the results of multiple
regressions.
TABLE 4. AFFECTING FACTORS
Model
Standardized
coefficient
Beta
p
-0.548*
Constant
CC
t
0.110
Hypotheses
0.059
**
0.024
H1(accepted)
**
H2(accepted)
4.230
IC
0.321
2.194
0.040
IS
0.226
5.213**
0.000
H3(accepted)
MT
0.006
0.027
0.979
H4(not accepted)
TL
0.201
1.410
0.180
H5(not accepted)
GS
0.150
2.721**
0.047
H6(accepted)
CM
0.018
0.115
0.910
H7(not accepted)
MR
0.221
4.570**
0.000
H8(accepted)
PE
-0.004
-0.021
0.984
H9(not accepted)
PD
-0.095
-0.521
0.611
H10(not accepted)
R2=0.914 Adjusted R2=0.796
F-ratio=21.195 (sig=0.000)
* P<0.10 ** P<0.05
According to the results, collaborative e-learning cognition,
information communication, IT skills, instructor guidance &
supervision, and member relationship are the crucial factors
affecting collaborative e-learning. H1, H2, H3, H6 and H8 are
accepted, while the remaining hypotheses are rejected.
VI.
DISCUSSION
A. Information Communication
Differing from other patterns of e-learning courses,
collaborative e-learning emphasizes students’ information
sharing and mutual communication within a team. As the most
important affecting factor, information communication should
be paid most attention. Open and frequent information
communication will bring about deep understanding and
interaction among student as well as abundant knowledge
sharing opportunities [11]. In an e- learning environment,
information communication activities depend on the type of
communication media and the channels through which they
are conducted. Teaching students to use appropriate
communication tools or channels will be beneficial for
pedagogy [12]. Our study suggests that E-mail is the preferred
communication channel for the majority of students, as
proposed by Padilla- Meléndez [8]; however, other types of
communication channels are also utilized by students and
shouldn’t be ignored. Educators should encourage face-to-face
meetings even in an e-learning environment. Some students
indicated that having a face-to-face meeting at the beginning
of the course would encourage learning [13]. Totally
abandoning this direct communication approach may obstruct
students’ learning effectiveness.
B. IT Skill
Web-based technology is a powerful tool for supporting
collaborative learning [14]. IT skill has proved to be an
important factor influencing collaborative e-learning outcomes.
A person who is proficient in IT skills may enjoy e-learning.
However, for those who are less skilled, it may engender more
struggles and challenges. Web2.0 has brought about a number
of IT tools supportive of collaborative learning work, such as
Google Docs, Wiki, and social networking sites. Although
these tools were not originally designed for collaborative elearning, they can be tailored by users to assist e-learning.
When observing the student communication channels, which
contain some IT tools, the students’ preference for using these
innovative tools is evident. This demonstrates that introducing
these new tools to students and cultivating their interests in
using them for course-related projects are important.
C. Member Relationship
Member relationship reflects the degree of student mutual
respect, understanding, and ability to compromise [10]. Since
an e-learning environment provides less face-to-face contact
opportunities among students, students are likely to avoid
establishing relationships with unfamiliar members, a
phenomenon called “escape” [15]. Such inactive member
connection will impair the dynamics within a collaborative
learning culture. Our study results illustrate that member
relationship has a relatively strong association(r=0.701) with
the outcomes of collaborative e-learning. Member relationship
also has associations with other factors, such as information
communication, member trust, and commitment of members.
Therefore, it seems important to devote attention to cultivating
student member relationships in an e-learning environment.
D. Instructor Guidance Supervison
Arguments are abundant about the role of educators in an
e-learning environment. Some believe that educators should
monitor students less to encourage their self-learning; while
others state that more guidance and supervision are necessary.
For instance, Woods [16] reported that online learners often
felt isolated from instructors due to a lack of connection. Our
study tests the power of instructor guidance and supervision,
and proves that it is an important factor affecting collaborative
e-learning. Suitable guidelines and oversights from the
instructor will offer a clear learning aim and rules, provide
review for team work in progress, and help create the climate
of a learning community for the students.
E. Collaborative E-learning Cognition
In the Cognition-Behavior-Product model, collaboration
behavior and outcomes are decided by collaboration cognition.
Our study confirms the important status of cognition by
proving the hypothesis that collaborative e-learning cognition
significantly influences student outcomes. Being aware of the
importance of collaborative e-learning will promote student
collaborative learning consciousness and make student
engagement in this type of course more zealous. In education
practice, instructing students in the importance of
collaborative learning and encouraging students to implement
such cognition into learning activities seems appropriate.
The results show that member trust, team leadership,
commitment of members, project development process, and
pre-collaboration experience do not significantly affect
collaborative e-learning outcomes. This is an interesting
discovery. For many students, an e-learning environment
reduces their concerns for other students - they may care more
for their individual gain [14]. That might explain in part why
member trust, team leadership and commitment of members
do not have much impact on student outcomes. Students may
desire more supervision from instructors rather than from
team leaders or fellow members. However, member trust,
team leadership, commitment of members and project
development process still have associations with collaborative
e-learning outcomes, and they might have indirect influence
on student collaborative e-learning outcomes. When educators
design and implement a collaborative e-learning course, the
five factors, including information communication, IT skill,
member relationship, instructor guidance & supervision, and
collaborative e-learning cognition should be paid closest
attention, followed by other factors, including member trust,
team leadership, commitment of members and project
development process.
VII.
CONCLUSION
This study examined the outcomes of student collaborative
e-learning and assessed student learning effectiveness. Five
factors affecting collaborative e-learning outcomes were
defined and identified. These factors are information
communication, IT skill, member relationship, instructor
guidance and supervision, and collaborative e-learning
cognition. According to the regression analysis results,
information communication is the most influential factor, and
IT skill is the second most influential factor. This study
provides important information for instructors on how to
better design and supervise students’ team projects in an elearning environment, as well as develop student motivation.
It proposes a primitive research model that could potentially
lead to guidelines for future collaborative e-learning
management.
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