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Public Opinion on OER and MOOC: A Sentiment Analysis of Twitter Data

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Abstract
The Open Educational Resources (OER) movement has gained significant momentum recently as a global effort culminating in the 2012 Paris OER declaration. However, the purist definition of OER has blurred since then morphing into Massive Open Online Courses (MOOC). Even though OER are a significant part of the MOOC movement, it might not be a defining one. However, this has not yet been fully verified with respect to the opinion of the general public who are the main stakeholders of both the movements. To answer this question, this paper attempts to explore the public opinion and perceptions regarding OER, MOOC and their complementary roles. A text mining approach is used to analyse raw Twitter data in the domains of OER and MOOC within a timespan of 12 months. Sentiment analysis is applied to the data to understand how public perceptions have changed during this time period. The major contribution of my paper is a chronological view of public opinion on OER and MOOC post Paris OER declaration.
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Abeywardena, I.S. (2014). Public Opinion on OER and MOOC: A Sentiment Analysis of Twitter Data.
Proceedings of the International Conference on Open and Flexible Education (ICOFE 2014), Hong Kong
SAR, China.
Public Opinion on OER and MOOC: A Sentiment Analysis of Twitter Data
Ishan Sudeera Abeywardena
School of Science and Technology, Wawasan Open University, Penang, Malaysia
ishansa@wou.edu.my
Abstract
The Open Educational Resources (OER) movement has gained significant momentum recently as a global effort culminating in
the 2012 Paris OER declaration. However, the purist definition of OER has blurred since then morphing into Massive Open
Online Courses (MOOC). Even though OER are a significant part of the MOOC movement, it might not be a defining one.
However, this has not yet been fully verified with respect to the opinion of the general public who are the main stakeholders of
both the movements. To answer this question, this paper attempts to explore the public opinion and perceptions regarding OER,
MOOC and their complementary roles. A text mining approach is used to analyse raw Twitter data in the domains of OER and
MOOC within a timespan of 12 months. Sentiment analysis is applied to the data to understand how public perceptions have
changed during this time period. The major contribution of my paper is a chronological view of public opinion on OER and
MOOC post Paris OER declaration.
Keywords: open educational resources, OER, MOOC, text mining, opinion mining, sentiment analysis
1. Introduction
Arguably, the months since the Paris Open Educational Resources (OER) Declaration (UNESCO, 2012) are the
most crucial in terms of the future direction of the whole OER movement. Many new OER initiatives have
blossomed since addressing the 10 key recommendations made to policy makers. On the other end of the spectrum,
Massive Open Online Courses (MOOC) have gained momentum as an innovative way of increasing access and
equity in education. McAuley, Stewart, Siemens, & Cormier (2010) claim that
“The large scale of the community, from several hundred to several thousand participants, maximizes the possibility
that the “long tail” effect will enable someone with even the most esoteric interests within the overall focus of the
MOOC to find people with whom to share and collaborate”.
Thus, MOOC have wide implications on how education will be perceived in the future. Although OER play a major
role in the delivery of MOOC, it might not be a defining one. As such, the two camps are partially divided when it
comes to which is the best way forward. Affirming this, Daniel (2012) argue that
“...what MOOCs will not do is address the challenge of expanding higher education in the developing world. It may
encourage universities there, both public and private, to develop online learning more deliberately, and OER from
MOOC courses may find their way, alongside OER from other sources, into the teaching of local institutions”.
This division results in confusion among the stakeholders as they are now faced with a difficult choice in terms of
organisational, institutional or national policy. In an attempt to identify the extent of this dilemma, this paper looks
at the sentiment of the public (stakeholders) with respect to OER and MOOC. To achieve this objective, a text
mining approach is used to extract data from social media for sentiment analysis.
The major contribution of this paper is a chronological view of public opinion on OER and MOOC for a timespan of
12 months post Paris OER declaration. Through this view, a roadmap can be identified for future research and
development based on public demand. This is the major advantage of the preliminary findings presented.
The remainder of the paper is divided into methodology, results, discussion and conclusion.
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2. Methodology
At present, Twitter
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is one of the largest social networking platforms in existence used by over 230 million monthly
active users (Twitter, 2013). The platform allows users to share concise (140 characters) posts in the form of
microblogs called tweets. These tweets are seen by selected individuals, groups or publically by the whole Twitter
network depending on the social status set. Following a tweet, the social interaction takes place in the form of
response tweets or as retweets where the initial tweet is shared virally. This rich interaction results in a large number
of tweets being generated on a particular topic.
It is observed that the OER and MOOC communities actively use social media such as Twitter for teaching and
learning purposes (McAuley, Stewart, Siemens, & Cormier, 2010). Furthermore, due to the unpoliced and liberal
nature of this social networking platform, frank opinions are shared by users on the benefits as well as the
shortcomings of both the ideologies. As such, Twitter was found to be the ideal data source to analyse the public
opinion regarding OER and MOOC.
The extraction of tweets was done manually using the native search mechanism of Twitter
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. The search terms ‘OER
and ‘MOOC’ were used in the search. Only the abbreviated forms were used in the search as I had identified
empirically that the terms ‘Open Educational Resources’ and ‘Massive Open Online Courseswere seldom used in
tweets. This is mainly due to the 140 character restriction of a tweet. Only the ‘Top’ tweets were extracted as these
are the best matches for a given search query. Furthermore, the use of the ‘Top’ tweets acted as a first level
cleansing of the dataset. The raw data was organised using the MS Excel 2010 spreadsheet application. A second
level cleansing of the dataset was conducted manually to remove partial, irrelevant and repeated tweets. Only
distinct tweets were used. All user details were removed to ensure anonymity and unbiased analysis. The details of
the final dataset are provided in Table 1.
Table 1 The Twitter dataset used in the study consisting of tweets which include the terms ‘OER’ and ‘MOOC’.
Search Query Tweet History Timespan No. of Distinct Tweets
OER 1/11/2012 – 31/10/2013 12 months 1209
MOOC 1/05/2013 – 31/10/2013 6 months 2823
The sentiment analysis was done using the Semantria
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software application which comes in the form of a plugin for
the MS Excel spreadsheet application. In preparation for analysis, an identity column was added to the dataset to
enable the analysis of individual tweets with respect to sentiment. A basic sentiment analysis was conducted on the
dataset using the Semantria plugin. The plugin uses a cloud based corpus of words tagged with sentiments to analyse
the dataset. Through statistical inferences, each tweet is tagged with a numerical sentiment value ranging from -1.5
to +1.5 and a polarity of (i) negative; (ii) neutral; or (iii) positive. The positivity increases with the sentiment value.
The sentiment data is then reorganised according to individual months of the tweet history to identify the public
opinion on a particular topic over a given timespan.
3. Results
The analysis of the data looks at two major aspects which are (i) the number of tweets over a given timespan; and
(ii) the public sentiment over a given timespan. Figure 1 and Figure 2 show the number of tweets on ‘OER’ and
‘MOOC’ respectively. Figure 3 shows the public opinion on ‘OER’ over a 12 month timespan whereas Figure 4
shows the public opinion on ‘MOOC’ over a 6 month timespan.
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http://www.twitter.com
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https://twitter.com/search-home
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https://semantria.com/
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Figure 1
Total tweets on ‘OER’ for a timespan of 12 months
from November 2012 to October 2013.
Figure 2
Total tweets on ‘MOOC’ for a timespan of 6 months
from May 2013 to October 2013.
Figure 3 Public opinion on ‘OER’ over a 12 month timespan.
Figure 4 Public opinion on ‘MOOC’ over a 6 month timespan.
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Figure 5 provides a comparison between the numbers of tweets on ‘OER vs. ‘MOOC’ for a 6 month timespan
between May to October 2013. Figure 6 depicts the change in public opinion on ‘OER’ vs. ‘MOOC’ for the same
timesapn.
Figure 5 Comparison between the numbers of tweets on ‘OER’ vs. ‘MOOC’ for a 6 month timespan between May to October
2013.
Figure 6 Change in public opinion on ‘OER’ vs. ‘MOOC’ for a 6 month timespan between May to October 2013.
4. Discussion
With reference to Figure 5, it can be seen that there is relatively more discussion taking place on the topic of MOOC
in comparison to OER. As shown in Table 1, the total number of distinct tweets on OER for a timespan of 12
months is only 42.8% of the total number of distinct tweets on MOOC within a time span of six months.
Furthermore, Figure 1 and Figure 2 suggest that the interest in OER is on a downward trend whereas the interest in
MOOC is on the rise. There maybe several factors contributing to this trend. Among them, the novelty of MOOC,
the involvement of the private sector, the brand names associated with the recent MOOCs delivered, the keen
interest of highly reputed conventional institutions in the concept and the large marketing budgets could be key
influences.
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Although the interest in OER seems to be declining, Figure 3 suggests that the public opinion on OER still remains
largely positive. By comparing the averages of the positive values shown in Figure 3 and Figure 4, it can be seen
that the average positive public opinion on OER is 31.19% in comparison to 25.16% on MOOC.
Considering the timespan of 6 months from May to October 2013, the change in public opinion on OER and MOOC
are highlighted in Figure 6. Even though the numbers of tweets on MOOC are considerably larger than on OER
suggesting more interest in the former, public opinion on MOOC seem to remain unchanged with respect to the
positive impact it has. In contrast, the stakeholders seem to feel more positive about OER and the benefits it has.
Furthermore, this positivity towards OER seems to be on an upward trend.
When considering the negative public opinion on OER and MOOC, Figure 6 suggests that the negativity towards
both the concepts are reducing. However, the negativity towards OER is slightly less than towards MOOC. The
more interesting trend is the change in public neutrality towards OER and MOOC. Figure 6 shows only a slight
change in neutrality towards MOOC whereas there is a decline in the public neutrality towards OER. This suggests
that the public are now forming informed opinions regarding OER whereas caution and skepticism still surrounds
the relatively new concept of MOOC.
The strength of this study is that it takes an objective look at both MOOC and OER from a public’s perspective. In
contrast to an instrument driven survey which would restrict the expression of opinion by the public to a certain
reference frame, the use of sentiment analysis takes into consideration all that the public has said and their relative
connotations. Although a considerably large number of tweets have been analysed, it can still be argued that the
sample size is not sufficient to generalise any trends. This is one of the weaknesses of the study. However, the
advantage is that this model can be replicated using a larger sample size when the data is available in the future.
5. Conclusion
The debate on Open Educational Resources (OER) vs. Massive Open Online Courses (MOOC) has been raging for
the past couple of years. Advocates of the two camps have been bombarding the public with the benefits of these
ideologies. Given that both OER and MOOC are pervasive forces which can change the education landscape of the
future, the general public (stakeholders) is in a state of confusion with respect to adopting a particular ideology for
their institution, organisation or nation. This study looks at OER and MOOC from the public’s perspective. It uses
sentiment analysis of Twitter data to approximate the public opinion on OER and MOOC in terms of the benefits
they promise.
The analysis of the twitter data suggests that there is growing interest on MOOC in comparison to OER. However,
when considering the public opinion on MOOC, it is apparent that the stakeholders haven’t yet formed strong
opinions on MOOC due to it being a relatively recent phenomenon. In contrast, the positivity towards OER is on an
upward trend. The neutrality towards OER has also decreased over a time span of 12 months post Paris OER
declaration suggesting that the public is slowly but surely forming informed opinions about the use of OER.
The contribution of this paper is a chronological view on how the interest and public opinion has changed with
respect to OER and MOOC following the 2012 Paris OER declaration. Three main trends can be identified which
need to be taken into consideration by the stakeholders when making a policy decision: (i) there is an increasing
amount of interest on MOOC; (ii) the public still hasn’t formed strong opinions regarding MOOC due to the novelty
of the ideology; and (iii) the positivity towards OER is growing.
This paper discusses the preliminary analysis of the data. It is my intention to further probe the data and identify the
reasons behind the trends identified in this paper. The next step is to potentially pinpoint certain phenomena which
would have a positive or negative impact on the public opinion with respect to MOOC and OER.
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6. Acknowledgements
I acknowledge the support provided by Dr K S Yuen, Chair of the conference organising committee; Dr K C Li,
Vice-chair of the conference organising committee; and the Open University of Hong Kong (OUHK) by extending
full sponsorship for may participation at the Inaugural International Conference on Open and Flexible Education
(ICOFE 2014).
7. References
Daniel, J. (2012). Making sense of MOOCs: Musings in a maze of myth, paradox and possibility. Journal of
Interactive Media in Education, 3.
McAuley, A., Stewart, B., Siemens, G., & Cormier, D. (2010). The MOOC Model for Digital Practice. Retrieved 12
15, 2013, from http://www.elearnspace.org/Articles/MOOC_Final.pdf
Twitter. (2013). About Twitter, Inc. Retrieved 12 15, 2013, from Twitter.com: https://about.twitter.com/company
UNESCO. (2012, June 22). 2012 PARIS OER DECLARATION. Retrieved June 13, 2013, from unesco.org:
http://www.unesco.org/new/fileadmin/MULTIMEDIA/HQ/CI/WPFD2009/English_Declaration.html
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