Collaborative Learning in Facebook: Adverse Effects of Individual Preparation

Abstract

Facebook is a social network very popular among University students for purposes of self-presentation. As social networks support the sharing of ideas, can we facilitate collaborative learning in Facebook? We designed a Facebook app that supports scripting of learners’ interaction and the construction of arguments in Facebook. In an empirical study with 128 undergraduate teacher trainees, we investigated how individual preparation and argument structuring influences collaborative learning outcomes. The results show no significant effect of argument structuring and detrimental effects of individual preparation. Learners who were asked to individually construct arguments before joining a discussion in the Facebook app learned significantly less and diverged significantly more from their learning partner in learning outcomes.

Full text

Collaborative Learning in Facebook: Adverse Effects of Individual
Preparation
Raluca Judele
Saarland University
r.judele@edutech.uni-
saarland.de
Dimitra Tsovaltzi
Saarland University
d.tsovaltzi@edutech.uni-
saarland.de
Thomas Puhl
Saarland University
t.puhl@edutech.uni-
saarland.de
Armin Weinberger
Saarland University
a.weinberger@edutech.uni-
saarland.de
Abstract
Facebook is a social network very popular among
University students for purposes of self-presentation.
As social networks support the sharing of ideas, can
we facilitate collaborative learning in Facebook? We
designed a Facebook app that supports scripting of
learners’ interaction and the construction of
arguments in Facebook. In an empirical study with 128
undergraduate teacher trainees, we investigated how
individual preparation and argument structuring
influences collaborative learning outcomes. The results
show no significant effect of argument structuring and
detrimental effects of individual preparation. Learners
who were asked to individually construct arguments
before joining a discussion in the Facebook app
learned significantly less and diverged significantly
more from their learning partner in learning outcomes.
1. The Social Networking Site Facebook
Social networking sites (SNS) attract people of all
ages all around the world. Facebook is a well-known
SNS with more than 1 billion active users in March
2013 (according to newsroom.fb.com/key-facts).
Whereas Facebook has inspired much social
psychology research on possible harmful effects [14],
there has yet been little effort to investigate Facebook
as an arena for collaborative learning. Moreover, a
huge amount of information is shared on Facebook (3.5
billion posts every week according to HubSpot). There
is concern, however, about the quality of the
information shared and about SNS’ suitability for
academic purposes. There are also contradictory
positions on the question of how much guidance is
needed in SNS where communities are informal and
self-organized.
The use of tools and supportive learning systems
for facilitating collaborative learning in computer
environments is well established among educational
scientists so far. Tools like Reason!Able [38] or
Belvedere [36] use argument diagramming to support
students in the analysis and construction of arguments.
These have been thoroughly examined by researchers
in the field of computer supported collaborative
learning (CSCL). It seems promising to use these
already known environments and their underlying
mechanisms by implementing them in highly
frequented SNS [11]. To what extent and in what
concrete ways learners can and should be guided in
SNS is still to be examined [11, 33].
This article will shortly review the main findings on
using Facebook for educational purposes. It will also
give a short outline on argumentation and scripting and
the limitations of these approaches for learning with
social media, such as Facebook.
2. Facebook as a Learning Platform
Facebook has not been designed for learning, but
for socializing and communicating. Still, Facebook
users do not consider solely the social aspects of their
network, but also the informational advantage [20]. For
this purpose and depending on certain personal traits,
different possibilities of SNS are being exploited. For
instance, there seems to be a correlation between the
type of social capital that Facebook-users expect to
find and the type of information they are searching for.
It was shown that users with greater bridging capital,
“[…] which includes the ability to access non-
redundant information and diverse perspectives,
typically through weaker ties […]” are more likely to
use Facebook for information search [20]. This depicts
a contrast to users with higher bonding capital, which
implies stronger social ties. So, participants sensibly
and intentionally use different SNS features [35].
Also, some users utilize Facebook for learning
purposes in the broader sense and for informal and
administrative aspects of academic life [33]. Learners
do post on academic concerns, but mostly remain at an
informal and superficial level of chatting, which

Facebook was designed for in the first place [33].
Students also use Facebook as an aid for adjusting to
new academic situations [30], finding out what is
important for starting life at university [24],
communicating and socializing by meeting new and
old friends, involving in social lurking [28], and
keeping in touch and reuniting with old classmates and
colleagues [7].
In contrast to the referenced surveys and studies on
Facebook being used as a portal to academia from the
students’ perspective, there is but little research about
Facebook being used as a learning platform in the
narrow sense, e.g. for discussing concepts to be
learned. For instance, Wang and colleagues have tried
to install two university courses within Facebook [39].
In this study, SNS standard functionalities were used,
i.e. putting up announcements or resources in groups.
Overall, students' acceptance of these courses was high
[39]: even when not designed for learning purposes,
SNS seem to be appreciated for distributing academic
information relevant to formal learning. Learning
outcome has not been analyzed in this study, though.
Beyond distributing information, SNS may serve
collaborative learning purposes. Social interaction is
believed to be important in collaborative learning but
not to be taken for granted [18]: social well-being and
sense of community are regarded as prerequisites of
learning through collaboration [18]. SNS like
Facebook enable communication and participation in a
community, which in turn may foster collaborative
learning. When designing SNS for learning purposes,
we may build on the sociability and high usage of SNS
like Facebook. By developing specific Facebook apps,
we can additionally implement a learning-specific
environment within this SNS that entails tools and
structures to support collaborative learning.
3. Argumentation-based CSCL
Argumentation-based Computer-Supported
Collaborative Learning (ABCSCL) has been
researched for many years showing how facilitating the
construction of arguments may lead to better online
learning. The goal of ABCSCL is to support the
sharing, constructing and representing of arguments
[27].
Beyond the goals of convincing people or winning
a debate, ABCSCL builds on the idea that learners
jointly elaborate a topic or problem by taking and
finding evidence for multiple perspectives [2, 3]. The
point of ABCSCL is to broaden and deepen views and
also to reflect upon others’ views [5]. By making an
effort to understand what the other one is saying one
has to deeply think about different arguments and
standpoints. Also, arguing is meant to lead to a better
understanding of a topic by the need to explain a
position or a certain viewpoint to another person. This
verbalization demands arrangement of one’s ideas and
leads to a better and deeper engagement with the topic.
There are notions, however, that argumentative
skills are often sparse even among adults [19].
Moreover, Facebook may hardly be associated with
academic debate yet [11, 28]. So good argumentation
may need to be instructed, scaffolded and supported for
helping learners to focus on specific content [6, 26].
ABCSCL often entails that argumentation is facilitated
with tools and structures that are implemented in an
online environment [1], so collaboration can be
enriched and improved. Many different argumentation
tools have been developed building on, e.g., argument
representations, ontologies or feedback [31]. For
example, if the goal is to help learners to get an
overview on a topic, graphs are a possible choice to
visualize argumentation, whereas matrices can outline
the missing pieces between argumentative moves [22].
Argument diagramming can help learners “construct,
reconstruct and reflect on arguments” [32] and
therefore think deeply about arguments beyond own
viewpoints.
3.1. Argumentation scripts
Collaborative learning and CSCL in particular has
often been shown to benefit from additional guidance,
particularly when learners have little prior knowledge
or experience in learning in groups [10]. Hence,
learners may have “internal collaboration scripts” at
their disposal, i.e. may possess prior procedural
knowledge of how to learn collaboratively [17], or may
be provided with external collaboration scripts that are
implemented in the instructional design of the learning
environment [16, 40]. Scripts are used for guiding
learners on how to interact with the goal of facilitating
specific social and cognitive processes for learning
[15].
Scripts can come in many forms. There can be
sentence openers that prepare the way for high
qualitative argumentation moves [32], guiding
questions which point to important conflicts in
argumentations or argument diagrams for structuring
discussions [25]. By letting the learner choose from
possible fitting openers, chances are that sound
argumentation is built up that has a stringent and
decisive structure.
Yet, the usage of scripts has certain limitations.
When given the freedom of choice of using sentence
openers or not, learners often decide to ignore the
prompts [21]. So it could be argued that the usage of
implemented scripts may be compulsory rather than
free choice. In that case, however, internal and external

scripts may collide, especially in advanced learners
resulting in overscripting and harmful effects on
learning [9, 23], e.g. disturbing interactions and
processes that otherwise would have taken place. So,
especially scripts with a low degree of freedom need to
be situated in the zone defined by complementing
internal and external scripts and provide options that
are sufficient for the task at hand, e.g. neither too many
nor too few prompt options to choose from [32].
So far, script research has repeatedly shown that
scripting fosters CSCL in a highly effective and
targeted manner, i.e. scripts can substantially support
specific processes. For instance, argumentation scripts
providing information about sound argumentation have
been shown to improve the formal quality of
argumentation as well as the acquisition of
argumentative knowledge [41].
3.2. Individual preparation
Collaborating can be supported by additional
individual phases which can serve as a preparation of
the discussion topic in general. Research has shown
that scripting collaboration by mixing both phases of
collaboration and phases of individual work on the
problem case can facilitate domain-specific and
domain-general argumentative knowledge [4, 12, 29].
Collaboration can be guided more or less strictly.
There are researchers advocating more guidance as it
supports the acquisition of knowledge especially for
complex problems [13]. But there are voices against
overburdening learners with rules and scripts, stating
that enough practice in combination with minimal
guidance leads to good results [8]. Minimal guidance,
in this context, views learners as active participants
who have to discover information or construct
strategies for developing their own concepts [13].
Individual preparation for upcoming argumentation
is one feasible approach. Learners may individually
elaborate on learning material that was presented
beforehand and hence be enabled to construct better
arguments. Learners may take time to reconsider the
information given so far and attempt to develop
personal points of view or refer to already known
information [4]. This process of considering problems
and arguments individually may raise awareness of
possible pros and cons, thus activating diverse
knowledge resources that can be contributed to the
discussion [42]. Individual preparation may also lead
to solidification of prior misconceptions by selecting
information only that is confirming the initial stance.
This would lead to nonmalleable individually
constructed knowledge, stricter hardly changeable
standpoints, and less consideration of the arguments of
the learning partner, that is, knowledge co-
construction.
3.3. Knowledge equivalence
The individually acquired knowledge can in most
cases be measured with domain-specific tests. The
answers can be awarded with points if correct and
summarized for an over-all score, which gives a
quantitative amount of knowledge to a certain well-
structured problem.
If the focus of interest lies not in the individual, but
in the group which worked together on the problem,
measuring acquired knowledge becomes different. It is
possible to sum up the over-all knowledge score for the
group or calculate the mean score for all group
members. But with this approach, the common
knowledge is diminished to a simple numerical value
which doesn’t represent the differences between the
learners. Information about deviations in knowledge is
lost, which would explain the distribution of it. For
example, one situation may be that one learner
answered ten out of ten questions correctly whereas the
learning partner gave no correct answer. Another
situation may be that each of two learners answered
five out of ten questions correctly. Mathematically the
over-all group score (= 10) and the mean (= 5) would
be the same. Still, the important fact of the completely
unequal distribution of learning would be lost without
measures of variance. Knowledge convergence
measures can take the distribution of knowledge within
a group of learners into account. The sub-concept of
“knowledge equivalence” [42] indicates the similarity
of learning partners in the extent of the individual
knowledge. Knowledge equivalence can be measured
by the variation coefficient, which “[…] is defined as
the standard deviation of a group divided by the group
mean.” [42]. This way, both group mean and
distribution via standard deviation are taken into
account and therefore more information is used.
Furthermore, by normalizing data, arithmetical artifacts
are circumvented [42].
One could hypothesize that learners who
collaborate think about and work on similar content,
which in the end leads to a higher resemblance in
knowledge. Ideas which one might not had thought of
can be distributed and knowledge is in the end shared
and more similar. Given the assumption that
collaborating leads to a more similar knowledge score,
it can be analyzed with help of the knowledge
equivalence assessment.

4. Research Questions
The goal of our study was to test whether it is
suitable to implement tools like scripts in SNS to foster
sound argumentation and foster learning in a
Facebook-group. Based on the existing research, we
expected that factors like scripting collaboration and
preparing arguments beforehand would have effects on
learning outcomes. We hypothesized that there would
be a higher learning outcome for the groups with
support. Our research questions are:
1. Is it possible to implement argumentation and
collaboration scripts in Facebook and in what way
do they impact the individual learning outcomes?
2. In what way does the implementation of an
argumentative collaboration script influence
knowledge equivalence?
5. Empirical study: Methods
Study participants (n = 128) were teacher trainees
at Saarland University. The domain was behaviorism
and the discussion topic was “Should behavioristic
principles be used in the classroom?” The task was to
discuss this topic in dyads, to reach agreement and to
sum up the most decisive arguments. We measured
participants’ knowledge differences by comparing
posttest performance between the conditions.
5.1. The Facebook app
To implement specific scripts, we modified the
Facebook interface with the help of an app. Most
Facebook apps represent games or communication
software. Few attempts have also been made to design
apps for scientific purposes. For instance, the app “Hot
Dish” has been used to change the ways people think
about or work with dilemmas of societal interest, e.g.
environmental issues [11]. So it seems feasible to
design Facebook for learning purposes and thus
enhance the already given features of the SNS.
Our app allowed dyads to compose posts and
communicate in a forum-like environment with
Facebook characteristics, such as profile photographs
of the participants, the “Like”-button and discussion
threads so that a reply would be visually linked to an
original post. Participants could write posts of variable
length (but were advised to post one argument at a
time) and were able to use the “Like”-button, a
standard feature of Facebook to indicate general social
acceptance, liking of and agreeing with other posts. In
respective experimental conditions, learners were
presented with additional information about an
argumentative structure which they were familiarized
with beforehand (see section 5.2. for more details).
Moreover, learners in these conditions had to
characterize posts by choosing the right option from a
pull-down menu.
Participants in conditions with individual
preparation time could prepare and post arguments in
their individual view of the app before starting to
discuss with their colleague. In the collaborative phase,
participants had time to send their arguments, which
instantly popped up in the collaborative view of the
app, and discuss via posts. The app also allowed the
experimenter to assign participants to different
conditions using an experimenter panel, where it was
easy to create groups. This way, it was possible to co-
ordinate the different phases on-the-fly. Data from
timestamp to ontology use was logged. The app
enables both a smooth experimental process as well as
subtle and half-automatic data storage.
5.2. Study design
Participants took part in 2-hour learning sessions on
behaviorism, which was part of their standard
curriculum. They were informed that they were going
to use Facebook with their accounts or, if they did not
have any, with accounts created for this study, to
discuss with one of their peers about the mentioned
topic.
We used a 2×2 design. The first factor was
Individual Preparation (with vs. without), the second
factor was Argument Structuring (with vs. without).
All participants received a four-page text about the
topic which they read before and which was available
to them during discussion. Conditions differed as
follows:
 Condition 1 (control group) received only the text
about behaviorism and the task explained above,
see section 5.
 Condition 2 (with Individual Preparation)
additionally allocated part of their time on task to
individually construct arguments before the
discussion.
 Condition 3 (with script “Argument Structuring”)
received the argumentation script, implemented in
the app.
 Condition 4 (combined factors) received
additionally both interventions as conditions 2 and
3 (script and individual preparation) before
discussing.
The script of Argument Structuring provided the
respective conditions with the possibility to choose
options implemented in the app, as mentioned above
(see section 5.1.): they could choose in the pull-down-
menu whether they will be using a claim or
counterclaim for then choosing if the (counter-) claim

is an argument from the field of ethics or effectiveness.
Or they chose to give a warrant for a given claim and
specify the exact definition of the warrant, be it every
day knowledge, example or research finding.
Before the intervention participants filled out an
online questionnaire on socio-demographic data and a
questionnaire about their familiarity with Facebook.
Moreover, a knowledge test on the topic was
administered before the intervention, after reading the
text and after the intervention. It consisted of 16
multiple-choice questions. Two open questions, where
participants had to list arguments for or against using
behaviorism in the classroom, were added in the
posttest graded with one point for each correct answer,
which were all averaged as learning outcome score.
To test learning outcomes, we conducted an
analysis of variances for the posttest results of the
participants, assuming random distribution of students
based on their prior domain-specific knowledge. The
learning outcome was the dependent variable with the
two conditional factors as independent variables.
To examine if there were differences in knowledge
equivalence by dyad and condition, we conducted an
analysis of variances for the knowledge tests. We
analyzed the knowledge test differences between
partners. As a basis for the calculations we took the
coefficients of variation from the differences of the
learning outcome per dyad [42].
6. Results
There were no significant differences in the socio-
demographic data such as age, gender, grade of school
certificate, course of study or year of study.
Additionally, there were no significant effects in prior
knowledge for any condition, so it can be assumed that
the prior knowledge of the participants was
comparable.
6.1. Research question 1: Individual learning
outcomes
The ANOVA showed a significant negative main
effect for the factor of individual preparation (F
(1;124) = 5.017; p = .027; partial η² = .04). The
conditions with Individual Preparation before the
collaborative phase performed worse (see table 1).
Additionally, post-hoc Helmert contrasts showed a
significant effect between the Argument-Structuring-
only-condition versus both the Individual-Preparation-
conditions with or without Argument Structuring (p =
.02). This means that scripted Argument Structuring
only leads to better results, whereas preparing
arguments individually hindered learning gains which
can arise from the learning of the argument structuring
in a profound way. As can be seen in table 1, the
groups with Argument Structuring factor only
performed better, still without significant effect.
Table 1. Means and standard deviations of
closed and open question measures of correct
answers (=learning outcome score) in the
posttest.
Individual
Preparation
M (SD)
No
Individual
Preparation
M (SD)
Overall
M (SD)
Argument
Structuring
.56 (.18)
.66 (.10)
.61 (.15)
No Argument
Structuring
.61 (.16)
.63 (.15)
.62 (.15)
Overall
.58 (.17)
.64 (.13)
.61 (.15)
6.2. Research question 2: Knowledge
equivalence
In the pretest, there were no significant differences
of knowledge equivalence between the conditions. In
the middle-test, a significant main effect for the factor
of individual preparation (F (1;60) = 6.578; p = .013;
partial η² = .10) manifested and showed that the
coefficients of variation became greater than before the
phase in which they prepared themselves for arguing
(see table 2), meaning that the differences between the
two learners in the amount of their knowledge were
bigger after reading the text compared to before
reading the text, as higher numbers stand for higher
divergence between the two learners.
Table 2. Means and standard deviations of
closed questions’ coefficients of variation for
the middle-test.
Individual
Preparation
M (SD)
No
Individual
Preparation
M (SD)
Overall
M (SD)
Argument
Structuring
.22 (.21)
.11 (.06)
.17 (.17)
No Argument
Structuring
.23 (.24)
.13 (10)
.18 (.18)
Overall
.23 (.22)
.12 (.08)
.17 (.17)
An ANOVA of the posttest-differences showed a
significant main effect for the factor of individual
preparation (F (1;60) = 4.323; p = .042; partial η² =
.07), but as can be seen tending to become smaller and
less distinct than in the middle-test. So after the
collaboration, the negative effect of the individual

preparation on the knowledge equivalence was
diminished.
Table 3. Means and standard deviations of
closed questions’ coefficients of variation for
the posttest.
Individual
Preparation
M (SD)
No
Individual
Preparation
M (SD)
Overall
M (SD)
Argument
Structuring
.30 (.32)
.12 (.09)
.22 (.25)
No Argument
Structuring
.22 (.17)
.18 (.11)
.20 (.14)
Overall
.26 (.27)
.15 (10)
.21 (.21)
7. Discussion
Although there are findings that support the benefit
of individual preparation for collaborative learning in
various settings, this was not the case in the Facebook
context. The participants with an individual preparation
phase showed lower learning outcomes. Giving
participants the chance to structure their arguments,
although not significantly, helps them by trend to
acquire more knowledge individually, but having them
individually prepare for ABCSCL eliminates this
positive effect in the SNS context. This negative
significant main effect of individual preparation
suggests that diving right into an SNS, reading existing
and composing new posts may be preferable to
preparing arguments before joining discussion in a
SNS, be it with the support of an argumentative script
or without. This finding raises the question why the
often effective individual preparation does not help in
this particular environment. One could argue here that
Facebook evokes specific expectations in the
participants related to this SNS, i.e. giving up
anonymity, high focus on representation of a public
self and informal chat [33]. Associations with private
conversation may have led participants of the study to
feel uncomfortable to reveal parts of their non-
academic and academic identity to previously
unconnected peers or the experimenters. In the context
of Facebook, expectations of being evaluated and
looked upon may be particularly salient and disturbing
when given time for individual preparation [34, 37]. If
given the time to think about their situation and
possible undesirable effects of their comments,
students tend to be in some way afraid of what they
might write or not. The situation in this study may have
been comparable: a side effect of individual
preparation might have been that participants had the
time to ponder on how and by whom they and their
arguments are going to be evaluated. This may have
prevented learners from exploring and discussing
different facets of the task, which in the end resulted in
less individual learning about the actual topic [37].
Individual preparation also reduced knowledge
equivalence. Whereas before the experiment no
significant differences between participants across
conditions could be found, which means the partners
that were randomly appointed together had comparable
knowledge about the topic, participants who
individually prepared arguments diverged more than
those who did not before collaborating with each other.
This indicates that a lot of knowledge was solidified
during individual preparation. After reading the text
and being able to prepare for the discussion, individual
differences in learning strategies may have resulted in
the different results in the test between individual
preparation and collaborative learning. In the condition
with individual preparation, these differences between
learners seem to be all the more accented: participants
with effective learning strategies might have been able
to create better arguments than the ones with
ineffective strategies, resulting in deeper and more
thorough elaboration of the topic. Hence, right after
individually preparing themselves, learners arrived at
significantly lower levels of knowledge equivalence
than unprepared learners. The actual collaboration
provides the possibility for attaining higher knowledge
equivalence as the results of this study show.
Nevertheless, we can still find a significant main effect
of individual preparation on knowledge equivalence in
the knowledge posttest, showing that the differences
cannot totally be made up by a single collaborative
session.
8. Conclusion
This study provided evidence that using
argumentative scripts and instructional environments
implemented as an app in the Social Networking Site
Facebook is a promising approach for fostering
learning. The results show that when implementing
CSCL-approaches of instruction, problems of
expectations and preparation have to be dealt with:
learning in SNS may be problematic when instruction
evokes expectations related to formal learning. In this
context, our results showed that the factor of Individual
Preparation can be a hindrance to learning, both with
respect to the individual acquisition of knowledge and
the convergence of knowledge concerning the dyad
partners. Individual preparation may, in this context,
give rise to a certain anxiety for learners of being
evaluated on the one hand.

Overall, it may be difficult to bridge formal and
informal learning with SNS without addressing the
problem of different learner expectations for formal /
informal learning contexts and resulting fears or
inhibitions. More investigation is needed to try to
channel the high effect of expectations in SNS for
productive purposeful learning. One might think along
the lines of more subtle interventions that would not
interfere with the subtle dynamics of open and
informal environments like SNS, but would still be
compelling enough to ensure that the quality of the
discussion is developing.
Future studies may analyze how participants can
be, for example, more subtly pointed to the direction of
argumentation improvement by finding out what exact
mechanisms influence the perception of learning in
SNS. Supporting learners’ argument structure seems,
by trend, to aid individual learning. Still, future work
needs to further examine interactions with other phases
of learning and instructions. Facebook apps seem to
offer a suitable platform to implement easily adjustable
features and subtle tools. Also, closely investigating
the role of learning strategies in phases of individual
preparation as this seems to be a possible influencing
factor.
Altogether the limitation of this study so far is that
to this point only quantitative data was analyzed; the
upcoming process analysis may shed more light onto
the quality of argumentation beside the quite strict
measure of knowledge descripted in this article.
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