Source: http://ijede.ca/index.php/jde/article/view/967/1633

Strengthening Deeper Learning through Virtual Teams in e-Learning: A Synthesis of Determinants and Best Practices

Abstract

Globally, e-learning is gaining popularity as its potential
contributions to economic and social development are recognised.
However, its full potential has not been realised, as most e-learning
practices merely replicate traditional existing teaching methods and
have not fully exploited the interactive and social components of peer
learning. Recently, there has been an increased focus on deeper learning
in higher educational settings, in particular, a focus on the skills
and knowledge that reinforce each other and together promote deeper
learning (Chow, 2010). In other words research shows that to be
successful all students must have access to educational opportunities
that foster deeper learning. Virtual teams (VT) are said to foster
"deeper" learning, but have not been empirically studied in the academic
sphere, and little is known about their effectiveness as a learning
mechanism in e-learning. In this paper the findings of a systemic review
and interpretive synthesis of the body of literature on e-learning and
VT are presented. The objective of the study was to identify the core
skills and knowledge from research that reinforce each other and
together promote deeper learning. The results from this study will
strengthen e-learning program planning and delivery within higher
education centres that are already engaged in e-learning, as well as
convey important best practices for learning centres at the beginning
stages of e-learning development.  Presented is an e-learning framework,
which may serve as the foundation of future empirical studies in
e-learning.

Résumé

À l'échelle mondiale, l'apprentissage en ligne gagne en popularité
puisque ses contributions éventuelles au développement économique et
social sont reconnues. Cependant, son plein potentiel n'a pas été
réalisé, car la plupart des pratiques d'apprentissage en ligne ne font
que simplement reproduire les méthodes d'enseignement traditionnelles
existantes et n’ont pas pleinement exploité les composantes interactives
et sociales de l'apprentissage par les pairs. Récemment, il y a eu une
focalisation accrue sur l’apprentissage plus approfondi dans des milieux
d'enseignement supérieur, en particulier, l'accent sur les compétences
et les connaissances qui se renforcent mutuellement et, ensemble,
favorisent un apprentissage plus approfondi (Chow, 2010). Autrement dit,
la recherche montre que pour réussir, tous les étudiants doivent avoir
accès à des possibilités éducatives qui favorisent un apprentissage plus
approfondi. Les équipes virtuelles (EV) sont dites de favoriser
l'apprentissage « plus approfondi », mais elles n'ont pas été
empiriquement étudiées dans la sphère académique, et on en sait peu sur
leur efficacité en tant que mécanisme d'apprentissage en apprentissage
en ligne. Dans cet article, les résultats d'une revue systématique et
d’une synthèse interprétative de la littérature sur l'apprentissage en
ligne et les équipes virtuelles sont présentés. L'objectif de l'étude
était d'identifier les compétences de base et les connaissances issues
de la recherche qui se renforcent mutuellement et, ensemble, favorisent
un apprentissage plus approfondi. Les résultats de cette étude
permettront de renforcer la planification de programme et la livraison
d’apprentissage en ligne dans les centres d'enseignement supérieur qui
sont déjà impliqués dans l'apprentissage en ligne, ainsi que de
transmettre d'importantes meilleures pratiques pour les centres
d'apprentissage qui en sont aux premiers stades du développement de
l'apprentissage en ligne. On présente un cadre de référence
d'apprentissage en ligne, qui peut servir de base à de futures études
empiriques en apprentissage en ligne.

Introduction

E-learning has transformed traditional ways of learning in higher education. It is defined as:

An approach to teaching and learning, representing all or part of
the educational model applied, that is based on the use of electronic
media and devices as tools for improving access to training,
communication and interaction and facilitates the adoption of new ways
of understanding and developing learning. (Sangrà, Vlachopoulos, &
Cabrera, 2012, p. 152)

Notably, e-learning encompasses some key characteristics of both
distance learning and online learning and underscores the integration of
“pedagogy, instructional technology and the Internet in teaching and
learning environments” (Carter & Salyers, 2015). Globally,
e-learning is gaining popularity as its potential contributions to
economic and social development are recognised. In Canada, e-learning’s
provision of the needed flexibility (i.e., any time, any place) is
recognized as a fundamental vehicle for fostering a lifelong learning
society (Canadian Council on Learning, 2009). According to the Contact
North 2012 report, it is estimated that between 875,000 and 950,000
registered online students at colleges and universities in Canada take a
purely online course at any one time.  In the US, in 2012, over 6.7
million students were taking at least one online course, an increase of
570,000 students over the number reported in the previous year (Allen
& Seaman, 2013).




However recent reports have revealed some countries are not
performing to expectations in their e-learning endeavours. For example
Canada is reported as trailing behind the efforts of other countries in
e-learning, with Canadian post-secondary institutions lagging behind
those in many other countries in incorporating online components into
their programs, and e-learning in workplace training is not yet a
standard feature (Canadian Council on Learning, 2009). The same report,
however, highlighted the importance of e-learning to Canadian social and
economic development and called for a coherent framework to shape
e-learning’s development in Canada, noting, among other things, the need
for concerted efforts to fill gaps in research and harness the
potential of technology to meet the needs of learners (ibid.).
This is aptly stated, as there appears to be a scarcity of research on
e-learning in Canada (Salyers, Carter, Carter, Myers, & Barrett,
2014; Kaznowska, Rogers, & Usher, 2011). A stronger understanding of
online learning is therefore essential for the future success of
education and training.




From the outset, e-learning has been hailed as offering the
“potential to enable student centred learning through the realisation of
constructivist teaching principles” (Edwards & Bone, 2012, p. 2).
However, this potential has not been realized since most studies
describe current activities in e-learning as mostly replicating or
transferring traditional existing teaching and learning approaches into
e-learning environments (Salmon, 2005). In Canada one of the major
barriers to the development of e-learning is noted as “the poor design
and quality of some early stage online courses and the low level of
student engagement these engendered” (Contact North, 2012, p. 17). In
other words, educators are striving to conceptualize how teaching and
learning can be enacted in e-learning settings whereby data,
information, knowledge, and the capacity to socially shape such data,
information and knowledge tends to define the learning experiences of
many students (Edwards & Bone, 2012). Moreover, there has been an
increased focus on deeper learning in higher educational settings, in
particular, a focus on the skills and knowledge that reinforce each
other and together promote deeper learning (Chow, 2010). Deeper
learning, as presented by the Hewlett Foundation, prepares students to
master core academic content, think critically and solve complex
problems, work collaboratively, communicate effectively, have an
academic mindset, and learn through self-direction. Nevertheless, to be
successful all students must have access to educational opportunities
that foster deeper learning.  As a result there is a growing need for a
stronger understanding of e-learning that encompasses the examination of
ways in which e-learning promotes deeper learning.




In addition, there is growing practical evidence that one of the key
factors for e-learning success is an understanding of the social
component of learning, i.e., the importance of person-to-person, and
group/team, interactions within the e-learning framework. Social aspects
of peer learning are considered to build student motivation, enhance
social connections and increase student access to feedback about their
learning (Morrison, 2006). Not surprisingly, therefore, most workplace
training and graduate teaching in e-learning environments utilize group
work. Group or team work, according to precepts of adult education,
fosters deeper learning, in a co-production of knowledge model, and also
provides skills that professional programme students require in the
workplace, where teams are the norm today and team work a required skill
set.




Virtual teams are one such example of a form of a workplace team with
potential implications for e-learning. Virtual teams are groups of
people committed to a common purpose or goal that are separated
geographically, that use a variety of communication technologies that
allow them to transcend the limits of time and distance, in order to
work together (Ale, Ahmed, & Taha,  2009; Green & Roberts, 2010;
Martins, Gilson, & Maynard, 2004). Aside from their ability to
allow highly skilled but geographically dispersed individuals to work
together, past reviews have highlighted studies that claimed other
benefits of virtual teams. For example, these benefits may include:
increased team cohesion and a greater sense of responsibility among team
members (Ale et al., 2009); increased participation among members and
reduction in the effects of status inequalities (Martins et al., 2004)
and greater opportunity for students to acquire an international
perspective through their learning (Green & Roberts, 2010).




There is a growing body of knowledge on how to develop effective
virtual teams in the professional context (Faizuniah & Chan, 2014;
Parke & Campbell, 2014; Berry, 2011).  As well, there is some
discussion in academic circles of possible relationships between
e-learning and virtual teams (Erez et al., 2013; Shea, Sherer, Quilling,
& Blewett 2011). As Hunt, Smith, & Chen, (2010) observed,
academicians need to challenge students to engage, and one way to
accomplish this is by using active collaborative teaching scenarios.
However, virtual teams have not been extensively empirically studied in
the academic sphere, and little is known about their effectiveness as a
learning mechanism in e-learning. The key question is whether virtual
teams used in the e-learning space are effective in producing better
student learning outcomes? It is useful therefore to consider what
lessons can be learned from the literature on virtual teams which can be
applied and used within e-learning environments to promote deeper
learning. In order to draw these conclusions, there is a need for an
in-depth meta-review of findings in the literature on virtual teams
concerning the impact/results from virtual teamwork, which can be useful
or transferred to general e-learning. This study therefore reviewed and
synthesised the findings in the literature on virtual teams and
e-learning published within the past decade. The objective was to
identify core skills and knowledge from the virtual team and e-learning
research that reinforce each other and together promote deeper learning;
also proposed is an e-learning framework, which may serve as the
foundation of future empirical studies in e-learning, and may contribute
to enhanced pedagogical design. The results from this study will
strengthen e-learning program planning and delivery within higher
education centres that are already engaged in e-learning, as well as
convey important best practices for learning centres at the beginning
stages of e-learning development.


This paper is organized as follows. First, we present an overview of our
knowledge synthesis methods, which includes a systematic search of the
literature and an interpretive synthesis of existing research. We then
present an analysis and discussion of our findings and our proposed
e-learning framework. The final section indicates the limitations of our
research and provides recommendations.

Methods

Our review was underscored by rigor and transparency (Mays, Pope,
& Popay, 2005) to enable the study to be replicated by others. We
conducted a systematic search and review of the literature to identify
the key determinants of effective learning in an e-learning educational
delivery model, effective virtual teams, and the additional impact of an
e-learning framework that incorporates a virtual teamwork component
within the program model. One of the key advantages of a systematic over
a narrative literature review is that it allows for the synthesis of
the research in a systematic, transparent, and reproducible manner.  In
other words, adopting a systematic review methodology helped in
counteracting bias by making explicit the values and assumptions
underpinning our review process. In addition, comparative and thematic
synthesis methods, rather than quantitative analysis, were selected to
uncover contextual issues identified in the studies and provide
educators and policy-makers with a reliable basis to formulate program
model frameworks and take evidence-informed action. We adapted an
interpretive review method, an approach that provides a useful structure
within which to conduct a synthesis of the literature. Notably, the
goal of the synthesis was not to produce an aggregation of data, but
theory grounded in the studies included in the review (Dixon-Woods, et
al., 2006).  

Study Questions

It was not possible nor desirable for us to specify in advance the precise review question, a priori definitions,
or categories under which the data will be summarised. The precise
formulation of review questions in advance of the synthesis, as
Dixon-Woods et al, (2006) noted, is successful in instances “where the
phenomenon of interest, the populations, interventions, and outcomes are
all well specified – i.e. if the aim of the review is aggregative”. For
our study the aim was to allow the definition of the phenomenon of
virtual teams and e-learning to emerge from our analysis of the
literature (Jensen & Allen, 1996). However, it should be noted that,
although at the outset we did not have a specific hypothesis that we
were going to explore, three general questions were used to frame our
project. These project review questions, which could best be described
as “tentative, fuzzy and contested” (Greenhalgh et al., 2005),
were: What drives effective e-learning? What makes virtual teams
effective? What lessons can be learned from the literature on virtual
teams which can be applied and used within e-learning environments? We
then employed a highly iterative approach to specify our review
questions, i.e., we modified the questions in response to search results
and findings from retrieved items. The multidisciplinary nature of our
research team was of great benefit to this process of refining the
questions, as it allowed a range of perspectives to be incorporated into
the process. 

Study Eligibility

Our focus was to include many different forms of evidence with the
aim of generating a comprehensive framework, thus we conducted an
interpretive synthesis (Sandelowski et al., 1997) of all types
of evidence relevant to our understanding of the mechanisms that
underlie effective e-learning and virtual team environments, and for
whom virtual teams work and in what circumstances. However, we limited
the date range to the past 10 years in adherence to the grant funding
call to focus on the state of research knowledge emerging over the past
decade. Because we sought to include only the most recent decade of
published evidence in our report, we therefore excluded studies
published prior to 2005. Non-English language materials were also
excluded because of the cost and time involved in material translation.
Thus, potential relevant studies might have been missed due to our
exclusion criteria.

Study Identification

As stated above, our research focus was to be as comprehensive as
possible in identifying studies relevant to our understanding of the
criteria that underlie effective e-learning and virtual team
environments, and for whom virtual teams work and in what circumstances.  We
therefore used purposive sampling initially to include only those
studies published within the past ten years that investigate (e-learning
OR virtual teams) AND (success* OR effective* OR best practice*) in
multidisciplinary environments. To achieve this we adopted a number of
strategies, including searching for relevant evidence in electronic
databases; reference chaining; searching grey literature websites; and
contacts with experts. During the month of May 2015 two librarians (the
co-investigator and the research assistant) developed and ran
combinations of search strategies in electronic databases: ERIC,
ABI/Inform, Business Source Complete, Web of Science, Academic Search
Premier, Science Direct, and Research Library. Appendix A presents the
combinations of search terms used in the study. We also checked the
reference list of studies retrieved from databases to ensure that we had
included all the relevant studies fitting our search criteria. In
addition, since there are numerous official reports, studies, theses,
dissertations and working papers on these topics we included relevant
materials retrieved through searching gray literature sources, including
the Canadian Research Index, ProQuest Dissertation & Theses, and
Google Scholar. Further, in May 2015 we created a research project
website. We utilized expertise within the team of policy makers and
educators participating on our website to identify relevant literature.
Our website received 435 unique visitors (740 page views) during the
months of July and August 2015. Pingbacks and referrals came from other
blogs, and social media sites, including Twitter, Facebook, Reddit,
Scoop.It, LinkedIn, and Google. Social media and website participants
suggested articles that could be included in our literature sample. We
organized the articles in RefWorks.

Study Selection

Our research team drafted a mechanism to help us eliminate studies
that were not relevant to our research. We tested the draft relevancy
criteria on a subset of fifty abstracts and discussed the differences in
interpretation among the researchers. A high level of agreement was
reached by the team of researchers (kappa = 0.80). The researchers
discussed the discrepancies and settled on final inclusion/exclusion
criteria. More importantly, the final inclusion/exclusion criteria that
we applied to all citations to determine their relevance was developed post hoc (Arkesey & O’Marlley, 2005) as researchers became more familiar with the literature. The exclusion criteria included:

• not condition of interest (E-Learning and Virtual Teams)
• not outcomes of interest (best practices, success factors, effectiveness)
• published prior to 2005
• not written in English.

All titles and abstracts of potential articles were screened by the
researchers independently and in duplicate for inclusion. The
researchers applied the inclusion and exclusion criteria to all the
retrieved citations by reading the abstracts. At this stage, the
full-text of the article was retrieved and read only in situations where
the relevance of a study was unclear from the abstract. We resolved any
conflicts by consensus. Our aim was to prioritise papers that appeared
to be relevant, rather than particular study types or research that met
specific methodological standards.

Data Extraction

We conducted a bibliometric analysis to describe the structure and
dynamics of the research literature. We developed a data classification
form to assist in systematically identifying characteristics of each
article.  We classified articles based on the following classification
scheme:

• Web of Science subject area (based on journal content specific fields of study, e.g., Business, Education, Health)
• Number of times cited
• Year of publication
• Journal and journal impact factor
• Geographic focus (i.e., did the paper have a Canadian, North American, or global/general focus?)
• Article Focus (i.e., was ELearning and VT a major focus of the paper?)
• Article type (Empirical or non-empirical)
• Study method (e.g., quantitative, qualitative, literature review, policy/management development)
• Sector (e.g., higher education, business/professional training).

A fundamental issue in reviewing qualitative and quantitative
research is the appraisal of study quality (Mays et al., 2005). Our
research team gave the articles a quality rating using two quality
rating matrices, one for empirical and one for non-empirical articles,
developed by the researchers. We used a 15-point scale for empirical
articles that included assessment of the quality of the literature
review, research questions and design, population and sampling, data
collection and capture, and analysis and results reporting (see Appendix
B). We also used a 15-point scale for non-empirical articles (see
Appendix C). Two members of the research team first rated a subset of
the articles (n = 20). A high level of agreement was reached (kappa =
0.82). The two members discussed the discrepancies and a consensus was
reached in all the cases. One member of the research team then rated the
remainder of the articles. In an effort to limit the pool of articles
to those deemed of higher quality, the research team agreed from the
outset to include only those articles that had an overall score higher
than 10/15. We thus, focussed our initial study synthesis on 110 highly
rated studies. As shown in Figure 1 most of the studies relevant to our
study were published in highly cited journals as indicated by impact
factor. For our study journal impact refers to impact factor as
calculated and published in the Thomson Reuters Journal Citation Reports and relevance was calculated by measuring the number of times that journal populated in our literature sample.


Further we identified and reviewed a number of relevant reports and
dissertations from the grey literature. It should be noted that we did
not formally rate the grey literature reports. Nevertheless, we reviewed
the reports for information that we perceived was a novel addition to
the knowledge presented in the peer-reviewed literature and would
greatly contribute to our e-learning framework as a whole.   

Analysis and Synthesis

Data handling and analysis was facilitated through the use of
Dedoose, an online qualitative analysis software that facilitates
coding, sorting, and displaying data. The complete texts of all included
studies were loaded into Dedoose and analysed following the basic
premises of Glaser and Strauss (1967)’s grounded theory and Miles and
Huberman (1994)’s data  reduction methods, methods we deem well suited
to our focussing, reinterpretation and analysis of the evidence,
primarily text-based forms of evidence (Pope at al., 2007).  The data
synthesis was conducted in several overlapping stages. In the first
stage the research assistant and the first author read the selected
studies and noted key ideas following the marginal coding process
according to Miles and Huberman (1994). In the second stage, the
researchers employed a constant comparison method to group and organize
the marginal codes conceptually, resulting in a hierarchical organized
codebook of codes and sub-codes that emerged from the data itself. The
study texts were line-by-line coded, a process that enabled the
researchers to undertake the translation of concepts from one study to
another. The use of Dedoose added to the transparency of our data
analysis.  We used Dedoose to assess inter-coder reliability. A random
selection of a third of the lines coded was assessed and a few
discrepancies were noted, mostly the discrepancies involved omissions.
All discrepancies were discussed by the researchers and a consensus
approach was used to assign the final codes. Importantly, we constantly
compared the theoretical structures we were developing against the data
in the papers. Although onerous, line-by-line coding provided key
advantages to our research, i.e., it revealed gaps and puzzles,
identified core themes, illuminated theoretical components and uncovered
potential sources of bias (Miles & Huberman, 1994). Line-by-line
coding of the texts resulted in 601 excerpts abstracted into 133
preliminary codes and subcodes. As relationships became apparent,
preliminary codes were refined and integrated into groups representing
emerging thematic areas of effective e-learning and virtual teams. As
patterns of relationships emerged the groups of thematic areas were
refined and synthesized into domains of deeper learning in e-learning.
Data saturation was reached when domain codes were densely distributed
across the literature.

Results

Overall Structure

Our systematic search of nine key databases yielded 12,802 studies in
English. Of these, 11,225 were removed on the basis of our exclusion
criteria (2,383 were duplicates, 1,051 were published before 2005, and
7,791 were deemed irrelevant by consensus) (see Figure 2). In other
words, of the 12,802 studies originally identified, 1,577 were selected
as potential relevant studies. On the basis of examining the abstracts
and full text of all these 1,577 articles during the classification
process we further eliminated 720 articles.  Our final sample included
857 studies. Of the 857 studies selected for inclusion in the synthesis,
500 were classified as empirical studies, 275 as non-empirical (e.g.,
editorials) and 22 as dissertations. Study characteristics including
first author, year, focus and subject area are detailed in Table 1 in
Appendix D, which includes a bibliography of the highly rated studies
included in our initial study synthesis.


Both e-learning and virtual teams’ investigations are published in
research journals in the following scholarly disciplines (not an
exhaustive list): education, IT, business and economics, library and
information science, communication, health, medicine, math and
statistics, pharmacy, and political science. Therefore, any claim that
either e-learning or virtual teams is a research issue confined to a
single discipline, (e.g., education or business respectively)
understates the importance of both. They are both ubiquitous topics;
topics that transcend disciplinary boundaries. 45 percent (n = 386) of
the papers in our sample appeared in education periodicals, and 22.3 (n =
191) percent appeared in business and economics periodicals (including
management, accounting, strategy, production, organizational behavior
and management information systems). Publications in IT/Computer Science
periodicals were third most frequent (148 papers, 17.3 percent). The
remaining papers appeared in periodicals in social science (3.3
percent), library and information science (2.6 percent), health science
(2.1 percent), communication (2 percent), medicine and pharmacy (1.8
percent), general interest (1 percent), math and science (1 percent),
engineering (0.6 percent), political science (0.5 percent), hospitality,
leisure sport and tourism (0.4 percent) and psychology (0.2 percent).




The process of interpreting evidence in this synthesis revealed three thematic domains of deeper learning in e-learning: contextual, behavioral, and resource. In addition, two learning theories were identified as underscoring the domains deeper learning: social constructivism theory (Vygotsky, 1978) and connectivism theory (Siemens, 2005). Further, a core process inherent in deeper learning promotion emerged: conversation.
Conversation emerged as the primary social process through which the
processes of deeper learning and effective e-learning was made possible
(see Figure 3). The following is a detailed description of the learning
theories and process revealed in this interpretive-synthesis and the
underlying themes.

Learning Theories

The synthesis revealed two learning theories that underscore the
domains of the core phenomenon of deeper learning in virtual teams and
e-learning. Table 2 presents a summary of the two learning theories; the
social constructivism theory (Vygotsky, 1978) and connectivism theory
(Siemens, 2005). 




Table 2. Learning Theories Underpinning Deeper Learning in e-Learning

Authors	Learning Theory	Main Components/Issues Raised
Vygotsky, 1978	Social constructivism	Individuals construct knowledge based on their experiences. This theory emphasizes the collaborative nature of learning. Knowledge is constructed within a social context.
Siemens, 2005	Connectivism	This theory is a product of the digital age. Learning can be achieved through networks, decision-making, collaboration, and diversity. Emphasizes the ability to connect ideas, and to find and apply knowledge when it is needed.

These two theories help explain why learners and teachers can achieve
a deeper understanding of concepts through higher levels of
communication processes. For instance, individual participants bring
their life experiences to an educational setting and those experiences
help shape how students and teachers process and interpret knowledge.
From a social constructivism perspective “learners construct knowledge
through discourse with other members of the community . . . . Learning
is produced by the team” (Savin-Baden & Major, 2004, p. 71). When
there is a collaborative environment for learning, more experiences are
shared and knowledge can be processed from different perspectives;
concepts learned by examining it from a number of different perspectives
can enhance learning.  From a connectivism theoretical basis, social
interaction within groups helps build networks, aids in decision-making,
and increases collaboration between groups that enhances the ability of
students to view concepts from diverse points of view, thereby
increasing an individual’s ability to understand and process
information.  In addition, bringing different perspectives to a learning
environment can also help in applying knowledge to a variety of
settings, and therefore can broaden that application of knowledge to
various fields.

Conversation Process

The synthesis revealed that underpinning deeper learning in virtual
teams and e-learning environments is the core phenomenon of
conversation.  Conversation is the all-embracing term that describes
socialization as well as communication processes within the learning
environment. Conversation is identified as allowing learners to
experience social presence and develop a feeling of belonging and
psychological closeness, which is crucial to the development of deeper
learning. For instance, within the e-learning literature concepts such
as collaboration, community and connectedness dominated the results
pointing to student satisfaction and success (Bolliger, Supanakorn,
& Boggs, 2010).  Among the studies included in this synthesis,
several authors cited conversation processesto describe vehicles for
effective virtual teams and e-learning. In their study, Tseng and Yeh
(2013) identified conversation process factors such as relationship
conflict and lack of communication as the most serious problems for
virtual teams’ effectiveness in collaborative learning environments.
Lin, Standing, and Liu (2008), in their triangulated study
(meta-analysis, field experiment and survey), revealed social
dimensional factors, such as developing successful social relationships,
as pre-requisite to effective task coordination in virtual teams
resulting in effective task accomplishment. Brown, and Voltz (2005)
identified a participatory design and implementation approach as the key
to effective e-learning design, “where the e-learning system is a
two-way street, allowing early and ongoing communications between
designer and users, rather than a conduit directed at the learner or
educator” (p. 8).




Notably, the synthesis revealed a change in how, through conversation
processes, “knowledge” transfer is viewed in learning environments. In
other words focus is moved from individual to social/shared learning;
from a passive to an active process; and from top-down to
learner-centered. More importantly, knowledge transfer, acquisition or
creation is not achieved by the transmission or formalization of tacit
knowledge but “through its coordination aimed at pursuing a common
objective” (Ditillo, 2004).  It is not considered as a simple transfer
of a fixed entity but as involving learners and instructors actively
inferring and constructing meaning from a process of interaction
(Hislop, 2010). In other words, learning is maximized in-context and
through interaction with others (Greenhow, Robelia & Hughes, 2009).
 Social learning strengthens the development of tacit knowledge (Tee
& Karney, 2010). Not surprising, a number of authors considered
social presence an important factor in student satisfaction and success
(Bolliger, Supanakorn, & Boggs, 2010; Swan & Shih, 2005). Shen,
Cho, Tsai, and Marra, (2013) observed students’ self-efficacy as related
to social interactions among students and between students and
instructors. According to Shen et al.:

The nature of online learning requires students to interact
actively with both instructors and classmates. Especially those students
with less experience may experience anxiety about interacting with
others and may feel social isolation if they perceive lack of support
from others. (p. 16)

Instructors are thus encouraged to create social presence and
teaching presence to foster a sense of a learning community. This may be
accomplished through: participating in discussion boards; providing
guidelines for social interaction; recognizing students' contribution to
online learning community; and, monitoring students' social interaction
processes (Shen et al., 2013). Also through engaging in conversation
students and teachers share and discuss ideas, a process that promotes
critical thinking and reflection. In addition collaborative
problem-solving promotes the externalization and internalization of
information (e.g., teaching others, or having ideas vetted and analyzed
in-context). Thus, the socialization process of learning, which can be
aptly summed up as conversation, allows for deeper learning of subject
material in online environments. Additionally, it allows for
contributions in learning that are in a way “hidden” from that found in
direct face-to-face interaction. In short, the community may contribute
in a manner that is more authentic or free from bias.




The following sections contain an overview of the three thematic
domains underlying conversation processes supporting deeper learning in
e-learning revealed in this synthesis, along with a framework that
details the three domains within an e-learning educational delivery
model.

Domains of the Core Phenomenon of Deeper Learning in e-Learning: Conversation

The synthesis identified the core phenomenon of conversation as
described within three fundamental domains:  contextual dimensions;
behavioral dimensions; and resource dimensions (see Table 3 for
details).




Table 3. Domains of the Core Phenomenon of Deeper Learning in e-Learning: Conversation

THEMATIC DOMAIN	WHAT IT ENTAILS	CHARACTERISTICS
Contextual Dimensions	Establishing or developing a shared context, an environment where learners and instructors effectively engage in conversations. Social environments are integral to effective conversation.	Individual (Self-efficacy, Motivation, Interest, Task focus / goal commitment, Tech familiarity, Learning preferences) Group dynamics (Structure / size, Task distribution, Group awareness, Trust, Leadership, Conflict, Interdependence) Course design (Pedagogy, Incentives, Expectations, Delivery method)
Behavioral Dimensions	Enabling or facilitating dynamic practices that create empowered continuous conversations. Strengthening networks of interpersonal relationships	Individual Learner (Planning, Participation, Reflection, Persistence, Communication, Task completion) Group(Social interaction, Collaboration, Discussion and feedback, Problem solving, Decision making, Task coordination) Instructor (Communication, Intervention, Information management, Setting expectations, Completing and implementing training)
Resource Dimensions	Deploying or encouraging use of  multiple tools/vehicles/supports for effective and timely conversations	Technology (Tools, Media), Time, Course content / materials, Training

These domains were distilled and organized through a deductive
process from the 133 codes identified on the basis of the highest
frequency of appearance in the literature as well as, in the
researchers’ views, the fundamental drivers for effective e-learning and
virtual teams. While these three domains are certainly interrelated and
have some overlap, the following sections highlight and describe the
domains in greater depth.

Thematic Domain 1: Contextual Dimensions

Contextual dimensions are described as elements enabling the creation
of learning environments with a shared context, an environment where
learners and instructors effectively engage in conversation. This
entails the development of a learning environment that has, as observed
by Wickersham and  McGee (2008), “a learner-centric design as opposed to
content-centric, in which the learner proceeds in a lock-step fashion
through content with little or no adaptation or deviation from a
content-driven script” (p. 74). Purposefully developing a shared context
is considered a “useful approach to facilitating online learning,
creating a strong potential to support learning processes necessary for
students to cultivate tacit knowledge” (Tee & Karney, 2010, p. 1).
Notably, the learning design considers the social context, i.e., the
learner’s context of practice, ways of learning, and experience in the
world. Social environments are integral to effective conversation and
deeper learning. Importantly, to be social, learning requires feedback
and interaction between learners and instructor. Contextual dimensions
thus include the consideration of individual, group and course design
intrinsic factors, such as learning preferences, technological
familiarity and experience; task design; task complexity; goal clarity;
and delivery methods. For instance the literature brought to light that
task design is important, as is clarity of mandate. Early and focused
goal setting and preparation are important, as are team agreements and
team regulation policies. Consistently illuminated across the findings
of the study are the notions that clear team norms, timeliness of
response, and instructor attitude support team effectiveness and a
learner-centered environment leads to greater participation, teamwork,
respect, and commitment. Courses that are designed to foster
peer-interaction, encourage collaborative and socially-negotiated
learning contribute to active learning and critical reflection that is
key to deeper learning. All in all, as Johnson, Hornik, and Salas (2008)
concluded:

Creating and maintaining a shared learning space within an
e-learning environment is important for enhancing learning, value, and
satisfaction for participants. In addition, simply exchanging
information may not create the shared social context necessary; instead
the evidence suggests that social presence is also important. (p. 364)

Thematic Domain 2: Behavioral Dimensions

Behavioral dimensions are described as factors that enable or
facilitate dynamic practices that create empowered continuous
conversation. The focus is on strengthening networks of interpersonal
relationships. Behavioral characteristics were identified at individual
learner, group and instructor levels. These include behavioral elements
such as self-reflection, individual accountability, commitment to task,
motivation, and sense of community, which are considered key to
establishing trust in virtual teams for example. In a virtual context,
trust is critical to the functioning of a team (Kim, Lee, & Kang,
2012). For instance, the synthesis revealed that the early collaborative
phase is the most important in virtual teams for establishing the
trusting relationship among its members. As Haines (2014) suggested:

Like face-to-face teams, virtual teams evolve over time. A sense of
belonging is important early in the formation of a virtual team, which
in turn builds commitment to the team’s goals. This in turn is linked
with trust in peers, which in turn is linked with performance, and
finally overall satisfaction with the team. (p. 217)

Trust is also identified as a mediating role in team performance in
e-learning. Self-regulation, team regulation, and the establishment of
team norms are also identified as key behavioral factors that drive
effective virtual teams and e-learning. As Kwon, Hong, and Laffey,
(2013) suggested “visualization of group activities relative to a group
norm enhances coordination of collaborative behavior” (p. 1273). In
addition, instructor roles that produce positive outcomes include:
fostering relationships and collaboration; fostering a collaborative
learning environment; and, promoting peer interaction, active learning
and critical reflection.  As such, behaviours which contribute to
establishing collaborative patterns, through channels of communication,
sharing and exchanging information, and building knowledge together, are
essential. Establishing a strong sense of community, high team
cohesion, has been shown to result in higher levels of motivation,
satisfaction among team members, in persistence, engagement and higher
order thinking.

Thematic Domain 3: Resource Dimensions

Resource dimensions are described as encompassing the deployment or
use of multiple tools/vehicles/supports enabling effective and timely
conversation. Resource elements include deployment and use of technology
as well as institutional and instructor supports such as training,
time, and content design. Learners and instructors need supporting and
effective communication technology to allow them to communicate
seamlessly. For instance research reveals that integrating social media
in learning management systems provides another way of communication
that allows users to easily share information. Social presence and
pedagogy grounded in the practices of interactivity and engagement leads
to student satisfaction and learning success (Carter & Salyer,
2015). In addition, training support is fundamental to success in
e-learning.  Training in how to use the technology and new media
vehicles allows for more effective and more rapid conversation.
Moreover, consideration should be given to the preparation and training
involved in in establishing cooperative patterns and behaviours. The
role of the instructor is therefore paramount with regard to content
design. As Toven-Lindsey, Rhoads, and Lozano (2015) observed
“intentional course design that facilitates structured peer interaction,
including discussion boards, wikis, and video conferencing, contributes
to active learning and critical reflection” (p. 3). The use of social
technologies and the designing of course materials and content that
create relationships and enable constructivist/connectivist learning are
mentioned by researchers as important aspects of e-learning success.

The Framework

Based on the synthesis of the knowledge in our sample of studies, we
developed a framework that details the three fundamental domains within
an e-learning educational delivery model (see Figure 3).




Underpinning the framework are the Social Constructivism and
Connectivism theories of learning. As presented in Figure 3 above, a
learning environment in which conversation drives the contextual,
behavioral and resources dimensions describing the knowledge and skills
that promote deeper learning, results in e-learning effectiveness at
individual, group and networking levels.  For example, it is evident
from the synthesis that conversation leads to effective problem-solving
competencies among students in e-learning environments and contributes
to increased positive self-evaluation on individual capabilities. In
other words students, through peer feedback and interaction between
learners and instructor, develop enhanced individual feelings of
competence. As Krause, Stark, & Mandl,  (2009) confirmed,
externalization makes students become aware of their own knowledge,
which in turn leads to greater feelings of competence. It is therefore
particularly important that, to achieve e-learning effectiveness,
e-learning instructors’ focus be expanded from enhancing individual
cognition to encouraging conversation, i.e., develop and build the
contexts, behaviors and resources that encourage conversation, knowledge
sharing and building through social interactions among students (Kwon
et al., 2013). This will result in positive outcomes for students to
successfully function in society at individual, group or network levels.
Social aspects of peer learning can contribute to student motivation,
build effective collaborative skills, enhance social connections, and
lead to increased engagement required in the workplace and lifelong
learning society.

Discussion and Conclusion

This study advances understanding of e-learning by synthesizing the
literature on effective virtual teams and e-learning practices and
proposing a framework by which a conversation driven e-learning
environment can promote deeper learning and positively influence the
learning environment and outcomes. The proposed e-learning framework
describes what is needed in developing an e-learning environment that
facilitates conversation (communication, collaboration, teamwork, and
student engagement), and promotes deeper learning, all of which
ultimately enhances the effectiveness of the learning environment and
improves individual, group and network outcomes. From this synthesis
exercise the following conclusions can therefore be drawn. The core
phenomenon that promotes deeper learning in e-learning is conversation.
In other words conversation drives the skills and knowledge that
reinforce each other and together promote deeper learning. Such
knowledge and skills are best described within the contextual,
behavioural, and resource dimensions of the e-learning environment. In
short, conversation allows learners to experience social presence and
develop a feeling of trust, belonging and psychological closeness, which
is crucial to the promotion of deeper learning. In line with social
presence, the learner-centred approach to education is identified as the
essence of ensuring students’ participation and promoting a sense of
community.




The study findings will strengthen e-learning program planning and
delivery within educational centres that are already engaged in
e-learning, as well as convey important best practices for learning
centres at the beginning stages of e-learning development. As stated
above a stronger understanding of the determinants of effective
e-learning is therefore essential for the future success of education
and training in countries like Canada where research on e-learning is
reported as lacking and is not yet a standard feature of workplace
training. The study also has broad societal implications. It has the
potential to fuel social and economic development and innovation, and to
foster lifelong learning in our society.

Limitations and Recommendations

From this interpretive-synthesis a number of important practice
implications and areas in need of further research can be derived. 
Perhaps the most significant is related to the finding that conversation
is the basic process that promotes deeper learning in e-learning. To
support deeper learning, learning centre administrators and instructors
need to encourage maximal conversation with and among students.
Nevertheless, some study limitations need to be pointed out. It is
recognized that synthesis is an interpretive endeavour and therefore
other interpretations of the data are possible. Further, our synthesis
did not include unpublished case studies or conference presentations
which could have enriched the data. Thus, despite the study rigour and
diligent attempts that have been made to gain insight and knowledge
about the fundamental knowledge and skills drawn from the virtual team
and e-learning research that reinforce each other and together promote
deeper learning, important information is still lacking. More empirical
research may be needed to substantiate the findings of this study. For
instance, empirical research is needed to support the e-learning
framework proposed in this study to evaluate its practicality and
efficacy. One study could explore, for example, individual learner
characteristics or teaching styles so as to find out if there are
specific types that are better suited to drive conversation in
e-learning environments and promote deeper learning.

---

References

Ale, E. N., Ahmed, S., & Taha, Z. (2009). Virtual teams: A literature review. Australian Journal of Basic and Applied Sciences, 3(3), 2653-2669.




Berry, G. R. (2011). Enhancing effectiveness on virtual teams. Journal of Business Communication,48(2), 186-206. doi:10.1177/0021943610397270.




Bolliger, D. U., Supanakorn, S., & Boggs, C. (2010). Impact of
podcasting on student motivation in the online learning environment. Computers & Education, 55(2), 714-722.




Brown, A. R., & Voltz, B. D. (2005). Elements of effective e-learning design. The International Review of Research in Open and Distributed Learning, 6(1), 1-10




Canadian Council on Learning. (2009). State of E-learning in Canada.
Ottawa: Canadian Council on Learning. Retrieved February 3, 2015, from
http://www.ccl-cca.ca/pdfs/E-learning/E-Learning_Report_FINAL-E.PDF




Carter, L., & Salyers, V. (2015). A model for meaningful e-learning at Canadian universities. In J. Keengwe (Ed.), Handbook of research on educational technology integration and active learning (pp. 78-113). Hershey, PA: Information Science Reference




Chow, B. (2010). The quest for deeper learning. Education Week, 30(6), 1-3




Contact North. (2012). Online learning in Canada: At a tipping point: A cross-country check-up 2012. Sudbury, Ontario, Canada, 1-30. Retrieved from this URL.




Ditillo, A. (2004). Dealing with uncertainty in knowledge-intensive
firms: The role of management control systems as knowledge integration
mechanisms. Accounting, Organizations and Society, 29(3-4), 401-421. doi:10.1016/j.aos.2003.12.001




Dixon-Woods, M., Cavers, D., Agarwal, S., Annandale, E., Arthur, A.,
Harvey, J., & ... Sutton, A. J. (2006). Conducting a critical
interpretive synthesis of the literature on access to healthcare by
vulnerable groups. BMC Medical Research Methodology, 6, 35-48. doi:10.1186/1471-2288-6-35.




Edwards, S., & Bone, J. (2012). Integrating peer assisted
learning and eLearning: Using innovative pedagogies to support learning
and teaching in higher education settings. Australian Journal of Teacher Education, 37(5), 1-12.




Erez, M., Lisak, A., Harush, R., Glikson, E., Nouri, R., &
Shokef, E. (2013). Going global: Developing management students'
cultural intelligence and global identity in culturally diverse virtual
teams. Academy of Management Learning & Education, 12(3), 330-355. doi:10.5465/amle.2012.0200




Faizuniah, P., & Chan, J. M. (2014). The mediating effect of
knowledge sharing on the relationship between trust and virtual team
effectiveness. Journal of Knowledge Management, 18(1), 92-106.




Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldine Pub. Co.




Green, D. D., & Roberts, G. E. (2010). Personnel implications of public sector virtual organizations. Public Personnel Management, 39(1), 47-57.




Greenhalgh, T., Robert, G., Macfarlane, F., Bate, P., Kyriakidou, O.,
& Peacock, R. (2005). Storylines of research in diffusion of
innovation: a meta-narrative approach to systematic review. Social Science Medicine, 61, 417-430




Greenhow, C., Robelia, B., & Hughes, J. E. (2009). Learning,
teaching, and scholarship in a digital age Web 2.0 and classroom
research: What path should we take now? Educational Researcher, 38(4), 246-259.




Haines, R. (2014). Group development in virtual teams: An experimental re-examination. Computers in Human Behavior, 39, 213-222.




Hislop, D. (2010). Knowledge management as an ephemeral management fashion? Journal of Knowledge Management, 14(6), 779-790. doi: 10.1108/13673271011084853




The William and Flora Hewlett Foundation (n.d.) Deeper learning. Retrieved from this URL.




Hunt, C. S., Smith, L. B., & Chen, M. (2010). Incorporating
collaborative technologies into university curricula: Lessons learned. Journal of Computing in Higher Education, 22(1), 24-37.




Jensen, L. A., & Allen, M. N. (1996). Meta-synthesis of qualitative findings. Qualitative Health Research, 6(4), 553-560.




Johnson, R. D., Hornik, S., & Salas, E. (2008). An empirical
examination of factors contributing to the creation of successful
e-learning environments. International Journal of Human-Computer Studies, 66(5), 356-369.




Kaznowska, E., Rogers, J., and Usher, A. (2011). The state of e-learning in Canadian universities, 2011: If students are digital natives, why don’t they like e-learning? Toronto: Higher Education Strategy Associates.




Kim, C., Lee, S., & Kang, M. (2012). I became an attractive
person in the virtual world: Users’ identification with virtual
communities and avatars. Computers in Human Behavior, 28(1), 1663–1669. doi:10.1016/j.chb.2012.04.004




Krause, U. M., Stark, R., & Mandl, H. (2009). The effects of cooperative learning and feedback on e-learning in statistics. Learning and Instruction, 19(2), 158-170.




Kwon, K., Hong, R. Y., & Laffey, J. M. (2013). The educational
impact of metacognitive group coordination in computer-supported
collaborative learning. Computers in Human Behavior, 29(4), 1271-1281.




Lin, C., Standing, C., & Liu, Y. C. (2008). A model to develop effective virtual teams. Decision Support Systems, 45(4), 1031-1045.




Martins, L. L., Gilson, L. L., & Maynard, M. T. (2004). Virtual teams: What do we know and where do we go from here? Journal of Management, 30(6), 805-835.




Mays, N., Pope, C., & Popay, J. (2005). Systematically reviewing
qualitative and quantitative evidence to inform management and
policy-making in the health field. Journal of Health Services Research & Policy, 10, S6-S20.




Miles, M. B., & Huberman, A. M. (1994). Qualitative data analysis: An expanded sourcebook. Thousand Oaks: Sage Publications.




Morrison, K. (2006). Peer assisted study sessions.Supporting quality
learning and student engagement in economics & business. Synergy, 25, 3-7. Retrieved from http://www.itl.usyd.edu.au/synergy/pdfs/2503.pdf




Parke, M. R., Campbell, E. M., & Bartol, K. M. (2014, January).
Setting the stage for virtual team development: Designing teams to
foster knowledge sharing. In Academy of Management Proceedings (Vol. 2014, No. 1, p. 17244). Academy of Management.




Pope, C., Mays, N., & Popay, J. (2007). Synthesizing qualitative and quantitative health evidence a guide to methods. New York, NY: McGraw Hill Education.




Salmon, G. (2005). Flying not flapping: A strategic framework for
e-learning and pedagogical innovation in higher education institutions. Research in Learning Technology, 13(3), 201–218




Salyers, V., Carter, L., Carter, A., Myers, S., & Barrett, P.
(2014). The search for meaningful e-learning at Canadian universities: A
multi-institutional research study. The International Review of Research in Open and Distributed Learning, 15(6). Retrieved from http://www.irrodl.org/index.php/irrodl/article/view/1713/3121




Sandelowski, M., Docherty, S., & Emden, C. (1997). Focus on
qualitative methods qualitative metasynthesis: Issues and techniques. Research in Nursing and Health, 20, 365-372.




Sangrà, A., Vlachopoulos, D., & Cabrera, N. (2012). Building an
inclusive definition of e-learning: An approach to the conceptual
framework. The International Review of Research in Open and Distributed Learning, 13(2), 145-159.




Savin-Baden, M., & Major, C. H. (2004). Foundations of problem-based learning. Maidenhead, England: Society for Research into Higher Education & Open University Press.




Siemens, G. (2005). Connectivism: A learning theory for the digital age. International journal of Instructional Technology and Distance Learning. 2(1), 1-8.




Shea, T. P., Sherer, P. D., Quilling, R. D., & Blewett, C. N.
(2011). Managing global virtual teams across classrooms, students and
faculty. Journal of Teaching in International Business, 22(4), 300-313. doi:10.1080/08975930.2011.653911




Shen, D., Cho, M. H., Tsai, C. L., & Marra, R. (2013). Unpacking
online learning experiences: Online learning self-efficacy and learning
satisfaction. The Internet and Higher Education, 19, 10-17.




Tee, M. Y., & Karney, D. (2010). Sharing and cultivating tacit knowledge in an online learning environment. International Journal of Computer-Supported Collaborative Learning, 5(4), 385-413.




Toven-Lindsey, B., Rhoads, R. A., & Lozano, J. B. (2015).
Virtually unlimited classrooms: Pedagogical practices in massive open
online courses. The Internet and Higher Education, 24, 1-12.




Tseng, H. W., & Yeh, H. T. (2013). Team members' perceptions of
online teamwork learning experiences and building teamwork trust: A
qualitative study. Computers & Education, 63, 1-9.




Vygotsky, L.S. (1978). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.


Wickersham, L. E., & McGee, P. (2008). Perceptions of satisfaction and deeper learning in an online course. Quarterly Review of Distance Education, 9(1), 73-83.





Strengthening deeper learning through virtual teams in e-learning: A synthesis of determinants and best practices | Makani | International Journal of E-Learning & Distance Education