how useful is twitter for learning in massive communities? an analysis of two moocs

Draft – originally published in: van Treeck, T., Ebner, M. (2013) How Useful Is Twitter for Learning in Massive Communities? An Analysis of Two MOOCs. In: Twitter & Society, Weller, K., Bruns, A., Burgess,

J., Mahrt, M., Puschmann, C. (eds.), Peter Lang, p. 411-424

How Useful is Twitter for Learning in Massive Communities? An Analysis of two MOOCs

Timo van Treeck & Martin Ebner

Introduction

The use of Web technology in education has constantly increased over the last few years.

After the initial introduction of so-called learning management systems (Helic, Maurer,

& Scerbakov, 2004), a considerable shift to more interactive technologies gradually

occurred. Web 2.0, coined for the first time by O’Reilly (2010), services such as weblogs,

wikis, and podcasts have become more and more in common in today’s lectures in higher

education (Augar, Raitman, Zhou, 2004; Evans, 2007; Luca & McLoughlin, 2005). In the

last three years, social media platforms such as Facebook, Twitter, and Google+ have

attracted millions of users, including many students (Ebner, Nagler, & Schön, 2011). As a

consequence of the world becoming more and more connected, the idea of opening online

courses to anyone who is interested in them (referred to as Massive Open Online

Courses), had emerged. According to McAuley, Stewart, Siemens, and Cormier (2010), a

MOOC “integrates the connectivity of social networking, the facilitation of an

acknowledged expert in a field of study, and a collection of freely accessible online

resources” (p. 4). More importantly, in this context, MOOCs are usually spread across the

world through existing social networks, mostly Facebook and Twitter. Consequently,

these platforms are not only used before, but also during and after a lecture session.

Afterwards, educational data mining methods, or, more precisely, learning analytics



methods (Duval, 2010; Long & Siemens, 2011) can be used to analyse course data and

attempt to make predictions regarding learning outcomes.

In this chapter we concentrate on an analysis of Twitter usage surrounding a German-

language MOOC that could indicate future trends in technology-enhanced learning. Our

research focuses on the Twitter stream accompanying the course and ask how Twitter is

used and for what purposes by the heavy twitter users, by the educators / organisers /

guestspeakers in the course and if tweets from “outside” get into to the stream.

Use of Microblogging in Education

Microblogging platforms are part of an increasing number of social software tools that

feature opportunities for information management, interaction, and communication,

identity and network management (Ebner & Lorenz, 2012; Koch & Richter, 2008).

Twitter is the most frequently used and well-known microblogging platform worldwide.

Due to Twitter’s large number of users and its interactive nature (Ebner & Schiefner,

2008; McFedries, 2007;), different ideas, concepts, and educational approaches for

Twitter use in the classroom have appeared. Ebner (2012) points out six different uses of

Twitter in education:

• Enhancing interaction in mass education through the use of Twitter walls

• Discussion beyond face-to-face lectures by using a specific Twitter hashtag

• Exchanging lecture content by collecting Internet resources using a defined

Twitter hashtag



• Documentation and information retrieval, with the help of specific Web

applications that collect tweets automatically

• Enhancing academic conferences by using Twitter as an online backchannel

• Connecting with researchers, teachers, and learners with similar interests based

on Twitter’s recommendations

These uses can be combined with different methods of designing teaching and learning.

For example:

• Using Twitter as a communication channel to support different phases of think–

pair–share (Barkley, Kross, & Howell Major, 2004): Students work on a

question alone, in pairs, and then in the plenum of the lecture. By doing so, each

pair-group can share their discussion results via Twitter; some do so by

articulating their results during the lesson.

• Use a moderator instead of a Twitter Wall: At each session, a student chooses

aspects or questions from the Twitter stream he or she thinks to be of interest to

the audience of the lecture and therefore supports the offline discussion.

• Use a lead learner in sessions: Learners can be assigned one-time roles as experts

on the topic of the session; they will try to find additional resources for the

lecture on the Web and send them via Twitter.

• Use Twitter in the way a fishbowl session is constructed (Barkley et al., 2004):

Experts can get into the inner circle of the discussion by bringing forth arguments

on Twitter; the inner circle of the discussion is held within the course itself.



In the case of MOOCs, the use of Twitter follows the principles of hashtag usage. A

hashtag defined in advance by the organiser of a class has to be used within each tweet

relating to the course. A simple search can possibly help organisers, learners, and other

participants to follow up on the communication and information stream, get in touch

with others, exchange information, or simply discuss topics concerning the course. A

very important detail is the use of retweets. Boyd, Golder and Lotan (2010) mention the

social relevance of just resending or copying a tweet to followers beyond spreading

information. On the other hand, Ebner et al. (2010) points out that a massive use of

retweeting and copying can make it hard to follow the stream on a certain topic e.g. when

dealing with a conference or a topic in a MOOC which might lead to decreased attention

from readers.

As the use of twitter is in the center of the activities in the special teaching concepts of a

MOOC the following study can help to understand questions regarding teaching and

learning by analyzing the tweets of the MOOC.

Description of the Study

For the current study, two German-language MOOCs were selected that had a special

focus on e-learning. One of the online classes, which was conducted by studiumdigitale,

in cooperation with Jochen Robes (weiterbildungsblog), the Gesellschaft für Medien in

der Wissenschaft (GMW) and the Zentrum für Lehrerbildung und Schul- und

Unterrichtsforschung (ZLF) in 2011, covered the future of learning and ran over the

course of 11 weeks.i Every week, an expert gave an input talk, and participants discussed



its topic via Twitter or by writing individual blog posts about their experiences and

opinions. In 2012, the same team (together with the association eteaching.org, Institut für

Wissensmedien and MMKH) organised a second course. This time, the course followed

the outcomes of the Horizon report (Johnson, Adams, & Cummins, 2012) and its

predicted future trends in technology-enhanced learning. The course topic was trends in

e-teaching and lasted 8 weeks.ii Every one or two weeks in this course, an expert gave a

short introduction intended to help participants in further discussions. Both courses were

named OpenCourse (OPCO), followed by the year when they were conducted

(#OPCO11, #OPCO12).

In our analysis, we collected all tweets over the observation time (one week before the

respective courses started until one week after the courses ended) with twitterSTAT. This

tool, programmed at the Graz University of Technology, is able to archive tweets

containing a predefined hashtag in a database. Afterwards, an automated structural

analysis can be performed. For example, an analysis of the number of different users of

the stream, as well as the number of tweets per user, can be carried out. Furthermore,

each word of each tweet is extracted, collected, and summarised. A visualisation of the

outcomes can be generated to provide a quick overview of the archive, and the results can

be further analysed with the help of other semantic profiling tools (De Vocht, Selver,

Ebner, & Mühlburger, 2001; Softic, 2012; Thonhauser, Softic, & Ebner, 2012).

In addition to the quantitative analysis, the tweets containing #opco12 were analysed in

detail with a qualitative approach using the SOLO taxonomy (Biggs & Collis, 1982),

which has been used before to study learning-related discussions in forums. The SOLO



taxonomy has five dimensions that structure the relationships shown in communication

acts: pre-structural, unistructural, multistructural, relational, and extended abstract. This

gives an insight regarding the complexity of the communication: Do the tweets contain

information which refers to no other information (pre-structural), are the users building

simple connections to another concept or another tweet (unistructural) are they taking

more than one reference into account (multistructural), do they evaluate the relation or do

they try to think further (extended abstract). In addition, the tweets are categorised

according to the actions that were introduced by the organisers to the participants in the

MOOC. These actions were explained on the MOOC homepage: aggregate, remix,

repurpose, and feed forward. Further categories were inductively extracted from the

material: questions, answers, retweets, and retweets with comments. Additionally, we

interpreted whether the content of the tweets had some affective or evaluating aspects.

Furthermore, the tweets were classified with regard to whether they related to general

aspects regarding the format of MOOCs, the organization of the course, the different

topics of the OPCO, hints for interesting tools or techniques (for the role of new tools in

building a learning-environment in an MOOC, see van Treeck, 2012), or some kind of

self-marketing for products, papers, etc.

The first 1,000 tweets of #opco12 were categorised in this manner to determine the

communication strategies used and the documented learning through microblogging in

this course-format.



Results

The results of the analysis can be differentiated into two categories: general statistical

analysis for both MOOCs and a language-based analysis of the tweets for OPCO12.

General Statistical Analysis

Table 1 gives an overview on both courses. In the year 2012, the number of total tweets

was nearly halved, despite having the same number of users. Therefore, the quantity of

average tweets per user decreased from 10 to 7. What is remarkable is the stable

percentage of retweets (about 30%), as well as the number of tweets from the top 10

(about 33%) and top 20 users (about 50%). In other words, half of all tweets were sent by

about 6% of the participants. Finally, it can be pointed out that about 30% of the users

participated in both courses.

OPCO 2011 OPCO 2012

Total number of tweets within the

observation time

4,085 2,431

Total number of Twitter users 393 367

Tweets per user (average; rounded) 10 7

Total number of retweets 1,181 (29%) 734 (30%)

Retweets per user (average) 3 2

Top 10 most active users’ share of

all tweets

1,428 (35%) 810 (33%)



Top 20 most active users’ share of

all tweets

2,132 (52%) 1,139 (47%)

Three most frequently-mentioned

keywords

live, lernen, learning learning, mobile,

online

Three most widely-used additional

hashtags

#schulmeister,

#edublogs,

#surfingkant

#mooc, #elearning, #av

Twitter users in both courses 111

Table 1 General statistics of OPCO 2011 and OPCO 2012

Figure 1 illustrates the distribution of tweets sent by the 50 most active users (accounting

for 68% of all tweets). It shows that even among the most active users, a minority is

responsible for the majority of tweets.



Fig. 1 Visualisation of the top 50 participants of OPCO 2012

Language-based Analysis

The first 1,000 tweets with the hashtag opco12 then were analysed on the language basis

with regard to questions like: what topics can be found, did the tweets deal with

informations only or also have an affective aspect, can indicators for interactions be

found, like formulating questions and answers?

Within the first 1,000 Tweets of opco12 a structure of topics can be analysed, giving an

insight to the question on what aspects there had been the most tweets. The top ten topics

get high scores on tweetnumbers, because they were mostly repeated (not only



retweeted), without any significant change in the amount of content dealing with the

organisation of the course. This includes videos, which the organisers developed for the

course, general information about the start of #opco12, the starting time of an online

event on the topic of tablet computing, links to recordings of online events, and

information that a user has viewed them (and posted the link to the recording). Another

topic of the mostly unaltered repeated tweets was live events from other contexts which

started shortly after the online events of #opco12 and were related to the main topics of

the courses.

Probably it is very easy to find the motivations for this behaviour. The participants might

have felt the need to help other users have a good start in the course and, therefore,

shared the most relevant tweets in the beginning: When does one have to be where

online? What are the basic rules of the course? What is special in the course (e.g., is there

a possibility to repeat missed inputs because they were recorded)?

Only two categories of the most repeated tweets directly related to the topics of #opco12,

but these were unusual in that they referred to assignments that had to be performed by

the participants, such as reading certain texts before a session, or in that they promoted

apps developed by a university. The tweets that were aimed at promoting learner

activities then often where done with different approaches to the activities: informing

about the need to read (texts before live events), reminding of the activity and explaining

the importance of the activity to the learning experience in a humorous way. This kind of

support for learning seemed to be honoured by retweeting a lot.



After taking a closer look at the 10 most repeated tweets of the first 1,000 tweets of

#opco12, we found that 83 (68%) of the retweets (including the original tweet) were

related to course-organisation, while 15% involved topics from the course. These tweets

were mostly initiated by the organisers or by guest speakers in the course (7 out of 10).

When looking at the timeline of #opco12, it seems that the number of tweets on

organisational aspects decreased and became less important over time, compared to the

tweets about the topics of the course. This was the case for the first 1,000 tweets that

were analysed. In the starting week there are – as it was expected – very many tweets

about the organisation of the mooc. They reduce very much already in the second week.

To have a glimpse on the long tail of the tweets, we analysed tweets without the top 50

twitter users. The relation between tweets with organisational aspects and tweets dealing

with the topics of the course is, more or less, constant, when the top 50 Twitter users are

not included. This changes only in the starting week, where the top 50 Twitter users for

the only time sent more tweets about organisational aspects (186) than about topics (91)

of the course. In the same period the other users reduce the organisational tweets in

relation to the overall tweets of this week very much. They made only 2%, but 27% in the

week before and 25% in the week thereafter. (Table 2).

all Twitter users Without top 50

Organisation of

the course

Topics of the

course

Organisation of

the course

Topics of the

course

pre- 8 (20%) 17 (41%) 3 (27%) 7 (64%)



course

week

starting

week

186 (49%) 91 (24%) 3 (2%) 31 (19%)

second

week

48 (23%) 104 (50%) 19 (25%) 41 (55%)

Table 2: Topics of tweets from OPCO12 overall and without top 50 Twitter users,

percentage of tweets of each week

Nevertheless, the tweets that dealt with the topics of the course only made up 39% of the

analysed 1,000 tweets of the course. Some of the tweets seemed to have no connection

with the course topics and just focused on mutually interesting aspects for people

enrolled in a course about the future of learning or trends in learning. These tweets

constituted about 18% of the first 1,000 tweets of the course.

On the other hand, about 6% of the tweets were coded as questions and answers, which

represents direct interaction. These interactions were made by 51 users, of which 23 are

among the 50 most active Twitter users of the course. Questions and answers addressed

with topics of the course (47%), course organisation (31%), suggestions for tools to use

(12%), and the course format of MOOCs (2%). Looking at the interactions that cover the

topic of the course (which are about 6% of the tweets), around 40% of these questions

and answers were made by individuals who are not among the 50 most active Twitter

users of #opco12.



The tweets were also analysed for affective aspects, such as expressing pleasure on

finding or doing something, or expressing a judgement on materials or positions. At least

156 out of 1,000 tweets (16 %) were put into this category, but they were not retweeted

or answered more often than other tweets. Some of these affective aspects where

mentioned with regard to enjoying to be part of the course or the live event. User called

for other users to join in or asked who else took part. Probably users tried to support the

feeling of being a course-group by this and enhance social awareness / social integration

as motivating aspect of class participation.

One major aspect of MOOCs can be that they foster (social) serendipity (Buchem, 2011;

van Treeck, 2012), which means that they are open to unexpected irritations, information,

and discussions (e.g., from outside), because the whole conversation can be accessed by

anybody interested or following the Twitter stream of one of the participants. To find out

whether Twitter users from outside the class considered the course activity to be

interesting, we counted users that only sent one tweet—a retweet that included #opco11

or #opco12 (Table 3).

OPCO 2011 OPCO 2012

Retweets by users with

only a single tweet under

the hashtag

3% 4%

Table 3: Social serendipity: Tweets likely to come from users not participating in the

course.



The experiment to analyse the tweets using the SOLO taxonomy didn’t get any results, as

it was not possible to clearly match the tweets and the interactions of the twitter users to

the taxonomy. A conclusion may be that twitter-communication even in course-format is

very different to forum-communication where SOLO taxonomy could be used.

To summarise, the stream of the OPCO12 course was structured into broader categories

(Table 4). It shows that a major part of the tweets (70%) is directly related to the course,

nearly half of these tweets relating to topics of the course (39%) and to course

organisation (31%). But there is also a big amount of thweets that couldn’t be interpreted

as connected to the course as they just were addressing interests that users participating in

this kindof course might have.

Related to the topic of the course 39%

Related to course organisation 31%

No visible connection to course topic 17%

Related to MOOCs in general 4%

Related to software tools/platforms 4%

Self-marketing 4%

Total 99%

Table 4: Parts of #opco12 stream

Discussion

Having analyzed the two MOOC-Twitter streams (#opco11 and #opco12), the following

findings seem noteworthy:



• The number of retweets was surprisingly high and also stable in both courses.

Similar to Weller et al (2011) this microblogging characteristic (because there is

no like or sharebutton like in other social media platforms, e.g., Facebook)

constituted a considerable amount of the entire Twitter stream. About one third

of the stream was just repetition for any participant who followed the stream

permanently. On the other hand, this might be helpful to attract more users or to

allow casual participation. Nevertheless, expressing agreement and sharing

information are essential parts of social media.

• According to Table 1, only a small number of users are responsible for a large

part of the tweets. Six percent of users posted half of all the messages. There is a

long tail effect, also described by Brown and Adler (2008), in which only a few

participants engage in online activities in an interactive way. On the other hand,

it must be noted that many others seem to be reached by these tweets, regardless

of whether they are heavy Twitter users or not. Brown and Adler also point out

that, similar to a big business platform like Amazon, the long tail effect does not

only mean that few users are sending out most of the tweets; it also means that

learning 2.0 is attracting many people with only one or two tweets in the long

tail.

• As Table 1 illustrates, about one third of the participants were active in both

courses. Because both courses were on different topics, there seems to be interest

for a lot of people. Therefore, it can be stated that the concept of the MOOC is a

promising one, at least for a special target group that did take advantage of the

first MOOC and therefore joined the second.



• At the beginning of the course, many tweets concerned organisational aspects of

the MOOC (starting time, links to a live event, announcements of participation,

etc.); later, these tweets decreased in number, and more tweets about topics from

the MOOC emerged (Table 2). As in other course settings, the participants

needed some time to organise themselves and took responsibility for this by

themselves, this is indicated by the fact that the same relation between

organisational tweets and topic tweets can be found in both the top 50 Twitter

users and the less active users – after the first course week.

• Tweets from organisers or experts that asked for activities from the participants

prompted many retweets and comments from other users. It seems that the users

appreciated being active, or at least wanted to support calls for activities by

retweeting them.

• Questions and answers in the tweets were mainly sent by the top Twitter users,

but 40% of them also came from less active Twitter users. So its not only

interesting to have a look at the twitter users which sent a lot of tweets but also at

the long tail of users only sending a handful or less.

- The use of Twitter allows contact with unknown people—even people who were

not enrolled in the course. A small percentage (3- and 4% at OPCO 2011 and

OPCO 2012, respectively) of all tweets were retweets of users who did not send

other tweets. Social serendipity, through meeting unexpected people, is thus

possible.



Conclusion

In this study, the use of microblogging in education was explored and a closer look was

taken at the Twitter stream of two massive open online courses. Our cursory analysis

shows that further research is needed to better understand how social media can be

integrated into learning and teaching, it is the interpretation oft he concrete messages that

leads further here, statistical analysis can only help to explore relations A categorisation

of the tweets revealed that, although there were many similar or even identical tweets in

the Twitter streams of OPCO 2011 and 2012, there was also a big amount of tweets in

which the topics of the course where addressed and even with questions and answers

discussing them. Activity calls prompted by some tweets resulted in heavy traffic, which

also shows that interaction was appreciated in this course format. And, although only 6%

of the users posted half of the messages, the content of the organisational tweets and

tweets on course topics were the same as those from the other users. Therefore, it can be

concluded that microblogging can play a relevant role in educational contexts, especially

in open online courses, by making learning more interactive and engaging, but the

potentials of microblogging in other teaching formats (such as regular lecture-classes or

field-projects etc.) have yet to be explored.

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how useful is twitter for learning in massive communities? an analysis of two moocs

Documents

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twitter stream

analysis of twitter

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students ebner

learning outcomes

massive communities

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