Supporting Learning Groups in Online Learning Environment
Godfrey Mayende
1,2
, Andreas Prinz
1
, Ghislain Maurice N. Isabwe
1
and Paul B. Muyinda
2
1
Department of Information and Communication Technology, University of Agder, Grimstad, Norway
2
Department of Open and Distance Learning, Makerere University, Kampala, Uganda
Keywords: Online Learning, Learning Groups, Distance Learning, Collaborative Learning.
Abstract: In this paper, we report on the initial findings on how to effectively support learning groups in online
learning environments. Based on the idea that learning groups can enhance effective learning in online
learning environments, we used qualitative research methods to study learning groups (interviews and
observation of learning group interactions in online learning environments) and their facilitators.
Preliminary results reveal that in order to have effective learning groups you need to take care of the
following online design issues: develop comprehensive study guides, train online tutors, motivate learners
through feedback, and foster high cognitive levels of interaction through questioning, rubrics, and peer
assessment. We conclude that well thought through online learning group with appropriate questioning and
feedback from facilitators and online tutors can enhance meaningful interaction and learning.
1 INTRODUCTION
The high rate of population growth in Uganda has
increased demand for higher education. The demand
is not commensurate with the number of higher
education institutions and corresponding
infrastructure in Uganda. Distance learning can cater
for the increased demand for higher education.
Distance learning is a mode of study where students
have minimal face-to-face contact with their
facilitators; the learners learn on their own, away
from the institutions, most of the time. Distance
learning in Uganda is dominated by the first
generation model which is characterised by blending
print study materials with occasional face-to-face
sessions. Learners are given hard copy self-
instructional study materials and regularly attend
two-week face-to-face sessions at the university
twice each semester. At most times, the students
study independently from their workplaces or
homes, using the print materials. Despite using this
learning model, distance learning practitioners use
learning group activities such as group assignments
to enhance collaborative and cooperative learning. In
distance learning, learning group activities can be
achieved if learners are compelled to come together
physically or some form of ICTs are used to
virtually connect group members to learn
collaboratively.
Collaborative learning hinges on the belief that
knowledge is socially constructed although each
learner has control over his/her
own learning.
Collaborative learning is underpinned by the social
constructivist learning theory (Vygotsky, 1978). The
proliferation of ICT in teaching and learning has
created new possibilities for supporting collaborative
and cooperative learning in distance learning
(Muyinda et al., 2015). Learning groups have been
preferred for propelling interaction and learning.
Vygotsky argues that a person’s learning may be
enhanced through engagement with others. Use of
computer supported collaborative learning can offer
possibilities of students’ interactions. Because many
distance learners are working adults who are not co-
located, computer supported collaborative learning
can offer possibilities for effective online learning
groups. However, motivating and sustaining
effective student interactions is not easy to achieve.
That requires planning, coordination and
implementation of curriculum, pedagogy and
technology (Stahl et al., 2006).
In cooperative online learning, learners share a
common knowledge pool for accomplishing
individual assignments (Muyinda et al., 2015).
Learning groups have been advocated for
increasing interaction in the learning process (Curtis
and Lawson, 2001). These have been widely used in
distance learning to enhance learning. They do this
390
Mayende G., Prinz A., Maurice N. Isabwe G. and Birevu Muyinda P..
Supporting Learning Groups in Online Learning Environment.
DOI: 10.5220/0005433903900396
In Proceedings of the 7th International Conference on Computer Supported Education (CSEDU-2015), pages 390-396
ISBN: 978-989-758-108-3
Copyright
c
2015 SCITEPRESS (Science and Technology Publications, Lda.)
by giving group assignments to help in the initiation
of learning groups. However, in first generation
distance learning, the difficulty of co-locating
students comes with the difficulty of determining
participation of
each group member in the group
assignment. It is common to find group assignments
contributed to by few group members and the
remaining members attaching their names on the
assignment. This hinders meaningful interaction
which is a pre-cursor for meaningful learning. Lack
of meaningful learning is the number one cause for
high failure and dropout rates in first generation
distance learning (Aguti et al., 2009). Fifth
generation distance learning is praised for
introducing virtual interaction and collaborative or
cooperative learning amongst distance learners. It is
our intention to find out how to make students more
effective in online learning groups. We want to
propose a model for effective online learning
groups. Based on this model, a human-centred
design process can be applied to develop an
interactive system that supports effective online
learning groups.
Section 2 of this paper reviews the literature
defining and analysing collaborative learning,
interaction processes in online learning groups, and
interaction analysis in online learning environments.
In section 3, we present the research directions and
our research methods. Section 4 presents the
preliminary results of our work. Finally, the paper is
summarised in section 5.
2 LITERATURE REVIEW
2.1 Collaborative Learning
Collaborative learning refers to instructional
methods that encourage students to work together to
find a common solution for a given task (Ayala and
Castillo, 2008). Collaborative learning involves joint
intellectual effort by groups of students who are
mutually searching for meanings, understanding or
solutions through negotiation (Ashley, 2009; Stahl et
al., 2006). This is what should happen in effective
learning groups. This approach is learner-centred
rather than teacher-centred; views knowledge as a
social construct, facilitated by peer interaction,
evaluation and cooperation; and learning as not only
active but interactive (Hiltz and Benbunan-Fich,
1997; Vygotsky, 1978). Anderson in his online
learning framework argues that learning can happen
through student-teacher; student-student; student-
content interactions (Anderson, 2003). Stahl et al.
(2006) also asserts that learning takes place through
student-student interactions. Ludvigsen and Mørch
(2009) found out that students effectively develop
deep learning when supported by computer
supported collaborative learning. Therefore, fourth
and fifth generation distance learning can enable
student-student interaction. Careful integration of
computer supported interaction can play a big role in
increasing interaction among distance learners using
learning groups.
Collaborative learning is based on consensus
building through interaction by group members, in
contrast to competition. This can be very helpful for
distance learners, who are typically adults.
Educational Psychologists influenced by Vygotsky
(1978) claim that students working in small groups
can share and evaluate ideas, and develop their
critical thinking (Norman, 1992; Sharan and
Shaulov, 1990; Webb and Cullian, 1983; Wells et
al., 1990). Collaborative activities are essential to
encourage information sharing, knowledge
acquisition, and skill development (Collison et al.,
2000). Different technology tools have been adopted
for collaboration in distance learning. This points to
the need to systematically integrate technology into
supporting learning groups for deep and meaningful
learning.
2.2 Interaction Processes in Online
Learning Groups
Dascalu, Bodea, Lytras, De Pablos, and Burlacu
(2014) argue that to have effective discussion groups
we need to have a friendly environment where
students feel free and comfortable enough to express
their ideas. The characteristics that bring success of
groups is categorized into personal and
organizational attributes (Hew and Cheung, 2012).
Personal attributes comprise learner’s trust, learner’s
self-awareness, learner’s motivation, learner’s
commitment, and learner’s willingness to share
experiences. Organisational attributes comprise
group size, similarity of learners’ experience (age)
or status, learners’ geographical proximity, agreed
clear aims and ground rules, flexibility to tailor a
group to learners’ needs, non-hierarchical structures,
autonomy from external authorities, planning ahead,
clarity of decision making and regular review and
feedback (Hew and Cheung, 2012). Learner’s
motivation is a key attribute in encouraging
interaction in learning groups.
Use of marks to motivate students has been
widely used in online learning environments. Marks
encourage students to contribute in online discussion
SupportingLearningGroupsinOnlineLearningEnvironment
391
forums. However, Bullen (1998); Palmer, Holt, and
Bray (2008) believe that marks do not help to
develop higher order thinking skills in Bloom’s
Taxonomy. Once a student submits the mandatory
posts or comments and is certain that s/he has scored
the required marks, s/he is not obliged to contribute
any further. Online facilitators have used guidelines
of setting number of posts as a way of encouraging
students to participate in online learning groups.
However, Murphy and Coleman (2004) found that
the quality of the discussion declined when students
were forced by the course requirement to post
messages in relation to a number of posting. The
facilitator should supplement this with feedback that
mediates learning. In learner-centred approaches the
facilitators should minimally contribute in the online
learning groups. The minimum contributions should
be strategic in assisting learning. Unfortunately,
learners would prefer the facilitator to give constant
feedback. However, Arend (2009) found out that in
forums that exhibited lower level of critical thinking,
the instructors were very active in the online
discussions, sometimes responding to nearly every
student post. Jones (2007) found out that if students
are introduced to topics that interest them, they are
more likely to be motivated to contribute in the
learning groups. Asking students to peer review one
another’s work can help increase deep interaction in
online learning environments. Peer facilitation
motivated learners to contribute in online
discussions (Hew and Cheung, 2012). This is more
common in the massive open online courses
(MOOC) where class sizes are enormous and based
on the community of practice theory as is espoused
in Wenger (1998).
2.3 Interaction Analysis in Online
Learning Environment
Quantitative methods cannot be solely depended on
in analysing the quality of interactions in online
learning groups. However, they may help in trying
to create a ground for deeper content analysis by
directing you to the specific group to look at in
detail. Fugelli, Lahn, and Mørch (2013) used both
social network analysis (SNA) and content analysis
where SNA helped them to know the peripheral and
nucleus participants in the community of practice.
During the content analysis they picked peripheral
groups and nucleus groups for further study. During
an online class environment SNA can provide a
quick understanding of the status of the learning
groups. This can help give the facilitators prompt
information on status so that the facilitator can
intervene appropriately. The facilitator’s
intervention can help to assist learning or motivate
learners to interact through questioning and
feedback. However, the introduction of interaction
analysis in analysing the quality of interactions has
seen deeper understanding of the learner’s
interactions (Jordan and Henderson, 1995).
Gunawardena, Lowe, and Anderson (1997)
developed an interaction analysis model used in
collaborative learning. This model was developed to
help in assessing the critical thinking, social and
cognitive presence, problem solving, emotion
expression and knowledge construction. Interaction
analysis can help both the learners and facilitators to
improve the quality of interactions and activities
respectively. It was developed with different phases
of knowledge construction and with more emphasis
on a qualitative approach. This can easily be
achieved through learning groups since learners can
construct their own learning. Research into
interaction analysis has revealed that teachers who
do not provoke learners into the high cognitive
levels will end at the lower levels of Bloom’s
taxonomy (Gunawardena et al., 1997).
3 RESEARCH DIRECTIONS AND
METHODS
In order to answer the overall question on how to
effectively support learning groups in online
environments, we focus on three research areas:
effectiveness of learning groups, processes of
effective learning groups and tools for supporting
effective learning groups. We want to answer the
following research questions.
What are the characteristics of an effective
learning group?
How to form effective learning groups?
How can effective learning groups be
sustained in online learning environment?
What principles can guide the creation of a
model of effective online learning groups?
How can the learning group support model
measure to the quality standards of an
effective online learning group?
What tools should be used for effective online
learning groups.
These research questions will be answered
through the following research directions.
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3.1 Effectiveness of Learning Groups
This research direction seeks to understand the
characteristics of an effective online learning group.
This can be done keeping in mind the three sub
directions: motivation, interaction sustainability, and
interaction levels. To achieve these directions we
shall seek to understand the teaching and learning
methods that the facilitator should use to have an
effective online learning group. We shall then be
able to identify the interventions which the
facilitators should do to: motivate learner’s
interactions, sustain learner’s interactions and have
high level cognitive learner’s interactions as
mentioned in Bloom’s taxonomy (L. W. Anderson et
al., 2001).
To achieve this, we shall do theoretical studies to
get comprehensive understanding on how to
measure effectiveness of learning groups. However,
we shall further collect data from online facilitators
from the University of Agder to learn the best
practices in use for effective online learning groups.
In the light of what precedes, we shall develop
guidelines to inform the quality of learning groups.
This research direction will be aimed at answering
what is an effective learning group.
3.2 Processes of Effective Online
Learning Groups
This research direction seeks to understand the
formation and operation processes of an effective
online learning group. Effective learning groups can
be influenced at both the formational and operational
level. Therefore, we shall seek to establish the
processes that inform the formation and operation of
effective online learning groups. This will guide us
in establishing the actions taken by both the learners
and facilitators to ensure an effective online learning
group. These actions can be looked at with the
following three dimensions in mind: motivation,
sustainability and level of interaction.
To alleviate this problem, we propose to
establish the actions by stakeholders that lead to
formation and operation of effective online learning
groups. We shall follow selected courses at both the
University of Agder and Makerere University with
the aim of establishing the formational and
operational processes in effective online learning
groups. We shall use the following methods of data
collection: interview the facilitators of the selected
courses, observe the learners in both face to face and
online learning groups, collect data from learners
through both interview and questionnaires, and use
interaction analysis to establish the levels of
interactions from the data interaction logs of the
online learning groups. This will guide us to get the
actions required for both facilitators and learners for
effective online learning groups. With this
information we shall then design scenarios for the
processes for formation and operation of learning
groups for both face-to-face and online. These
scenarios will then be discussed with the learners in
a focus group discussion in order to validate it and
come up with the most comprehensive scenarios.
However, we shall also engage with the facilitators
through interviews to understand their roles in the
formation and operation of learning groups. This
will be centred on the activities the facilitator gives
in a course. By comparing with existing frameworks,
theories or models, we shall be able to suggest the
most befitting characteristics for effective learning
groups, differentiating clearly effective processes by
the learners and facilitators. This research direction
will be aimed at answering two questions: how to
form effective online learning groups and how to
keep the quality of the operation of effective online
learning groups.
3.3 Tools for Supporting Effective
Online Learning Groups in
eLearning
This research direction will seek to design a model
which will inform development of ICT based tools
for supporting effective online learning groups. The
scenarios developed in the direction above will
critically be analysed to inform the development of a
model for effective online learning groups. We shall
then develop a proof of concept (POC) interactive
system to be used in the evaluation of the model.
The human-centred design process will be applied to
design an appropriate system for effective online
learning groups. This research direction will be
aimed at answering three questions: what principles
will guide the design of tools to support effective
online learning groups, how the developed model
measure to the quality standards of an effective
online learning group and what tools should be used
for effective online learning groups.
3.4 Methods
Qualitative methods were used in the data collection
and analysis. Those consist of semi-structured
interviews and tutors´ observations of students´
activities in the Learning Management System
(LMS) for earlier courses. The respondents were
SupportingLearningGroupsinOnlineLearningEnvironment
393
Course
Design
Trained
Online
Tutor
Motivation
and
Sustaining
Interaction
High levels
of
Interaction
Peer
Assessment
based
activities
Effective Online Learning Group
purposively selected from experienced online
facilitators at the University of Agder who use
learning groups in their courses. We conducted a
one-hour interview with each of the facilitators to
find out their experiences in effectively handling
online learning groups. Each interview was
transcribed immediately and informed the researcher
in the next interview. The transcriptions were then
analysed by categorising them into themes from
which empirical meaning was derived. A similar
research approach shall be adopted in the main study
at Makerere University beginning August 2015.
Preliminary results/themes from the University of
Agder are described and discussed in the next
section.
4 PRELIMINARY RESULTS AND
DISCUSSION
These are results of a study on best practices for
effective online learning groups at the University of
Agder. These results will be used in formulating the
hypothesis that guides subsequent parts of the
research. The findings fall into five categories
shown in Figure 1.
Figure 1: Salient elements in making effective learning
groups.
4.1 Course Design
The online course facilitators stressed that there is
need for comprehensive study guide and trained
online tutors in order to have an effective online
course. The necessity of trained online tutors
indicates the need for mediation of learning in online
courses. For mediation to occur there is a need to
read and give appropriate feedback of questioning
that assist learning. The study guide should include
the detailed required activities with corresponding
needed resources. These resources can range from
ICT resources, library resources, etc. The LMS
facilitators further suggested that for online tutors to
be effective each tutor should be assigned not more
than 25 learners. However, this is in contrast with
the MOOC phenomenon which emphasises that the
more knowledgeable peers will scaffold the others in
a community of practice environment (Wenger,
1998). This gives an indication about the need to
mediate, guide, scaffold and assist learning for
meaningful learning in groups. In one of our papers,
where learners were using Facebook as means to
mediate interaction and learning, learners felt that
they needed the presence of facilitator (Mayende et
al., 2014). If you chose to use tutors in a MOOC,
the cost will not be manageable since MOOCs are
free and yet online tutors have to be paid.
4.2 Trained Online Tutors
Online tutors are trained to give appropriate
feedback and questioning that assist learning groups.
Online tutor forms learning groups with five
students per group. The emphasis is put on
heterogeneous learning groups. The reason for
heterogeneous learning groups was to get different
experiential perspectives from different contexts.
This was because learners were taking a course in
global studies. However, there is need to understand
how heterogeneity affects learning. In each group
activity one student is selected by the tutor to
become the weaver of the group. A weaver is a peer
facilitator or group leader. His/her role is to direct
the discussion and summarise at the end. This can
help the group to have a sense of being together
since the peer is the one directing the discussions
and students will feel free to participate or interact.
Nevertheless, online tutors and facilitators watch
closely the interactions and can advise whenever
needed.
4.3 Motivation and Sustaining
Interactions
The online facilitators motivate learners through
allocating marks on the participation in group
activities. For LMS the number of students is
relatively small compared to MOOCs. Facilitators
give clear rubric on how marks will be assigned with
emphasis on letting the learners know the type of
interaction which will give them more marks. This is
followed during the grading where the online tutor
categorizes and reads all the contribution and awards
marks on the quality of participation. In limited
participation courses, each online tutor is allocated a
maximum of 25 students. That gives possibility to
read and grade all comments. The facilitators also
said that they motivated learners by giving feedback
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which encouraged additional participation within the
groups. However, this contrasts the MOOC where
marks do not make a lot of meaning to the learners.
Motivating learners through giving feedback in
MOOC can be very challenging since the class size
is usually enormous. However, MOOCs have seen
the use of badges to motivate the learners.
4.4 High Levels of Interaction
In order to develop high order cognitive skills
through interaction, the online tutor and facilitator
apply questioning as a method of assisting learning.
Questioning is a method that assists cognitive levels
of learning although facilitators may confuse
assessment questions with assistive questions.
Assessment questions are aimed at finding out the
ability of the learner to perform without assistance,
whereas assistive questions are used to provoke the
thinking of the learner to the level s/he would not
have attained by himself/herself (Gallimore and
Tharp, 2002). The tutors are trained in how to handle
this. That systematic questioning provokes the
learner to read deep in the literature and start giving
their own opinion based on literature. They also use
feedback that is aimed at encouraging interaction
among the students. Some examples of feedback
given by the facilitator include; “that is a wonderful
contribution”, “that is a good approach”, “fantastic
knowledge”, “reading Ethan’s contribution can
reinforce your good thought”, etc. At some point
when a particular student is not participating, the
tutor will politely ask other students to find out if
s/he has some problems. Sometimes, the tutor will
follow up the missing student with a call and/or an
email. This can be very complicated in a MOOC
environment because there are very many learners.
4.5 Peer Assessment based Activities
The MOOC facilitator emphasised the use of peer
assessment as a way of motivating learners to
contribute in learning groups. The MOOC course
unit was facilitated by five facilitators and observers.
The course setting involves group work and each
group is restricted to a maximum of 5 members.
Unlike in the limited participation online courses,
groups in MOOC are created by the learners
themselves. In every module students do a group
assignment and submit as a group submission. After
that, each student is supposed to submit an
individual assignment from his/her context.
However, the students are encouraged to interact
with one another during the making of the individual
assignment. At the end of the module each student is
required to peer assess five individual assignments.
That means each student’s work is peer assessed five
times. Because of the large number of students the
facilitator is not able to effectively apply questioning
and feedback as a way of assisting learning.
However, he is able to check on some groups.
5 SUMMARY
Online learning groups can help foster meaningful
learning. This is supported by the literature on
collaborative learning and we discussed how it can
work effectively. We have presented preliminary
findings on the best practices for effective online
learning groups from the University of Agder. The
main elements to be considered include course
design, the availability of trained online tutors,
learners´ motivation and sustaining interaction,
development of high levels of interaction, and peer
assessment based activities. It was found that there is
need to provide a comprehensive study guide and
online tutors with a ratio of 25 learners per tutor.
Effective learning groups can be achieved with
appropriate intervention from the facilitators through
questioning and feedback to assist learning in the
online learning environment. This shows that
scaffolding and guidance are propellers to
meaningful learning within online learning groups.
However, there should be a mechanism to
automatically inform online facilitators whenever
the learning groups are in critical states that need
intervention.
ACKNOWLEDGEMENTS
The work reported in this paper was financed by
DELP project which is funded by the NORAD and
partial funding from ADILA Project.
Acknowledgements also go to the University of
Agder and Makerere University who are in research
partnership.
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