This study examined the use of Twitter for vocabulary learning among 38 low proficiency students over 10 weeks. Students received two tweets with vocabulary words per day from the instructor and engaged with the words on Twitter. Pre- and post-tests showed minimal but across-the-board improvements in students' vocabulary levels. While Twitter allows for rapid sharing of concise vocabulary and access to a large corpus of words, it also lacks control over learning and privacy. The study found potentials for vocabulary growth through Twitter but recommends guidelines for its effective educational use.

1. Tweeting the Words Away: Rethinking the
Use of Twitter in Vocabulary Learning
MELTA International Conference 2013
Mr. Chuah Kee-Man
Centre for Language Studies, Universiti Malaysia Sarawak
Ms. Ch’ng Looi-Chin
Academy of Language Studies, Universiti Teknologi Mara (Samarahan Campus)

2. Page  2
Vocabulary Learning
Scholarly Citation
 Extensive repertoire of
vocabulary is capable of
expanding learners’ language
competence or mastery
(McLaughlin et al., 2010).
 Whether vocabulary should be
taught explicitly at the expense
of communicative competence
remains a heated debate(Nation,
2001; Schmitt, 2008).

3. Page  3
Vocabulary Learning
Scholarly Citation
 Vocabulary teaching in the context of higher
education is not preferred – due to limited
contact hours.
 Language educators at tertiary levels tend to
focus more on content knowledge (Pulido, 2004).
 There is a need to look at what Web 2.0 (Social
Media) tools can offer in encouraging vocabulary
learning (especially for self-access learning).

4. Page  4
Background: Twitter
Scholarly Citation
What is Twitter?
"Twitter is a micro-blogging
platform that allows people to
share posts 140 characters in
length. Each post is known as
Tweet. And a repost of it is
called Re-Tweet (RT).”
(Sagolla, 2009)

5. Page  5
Why Twitter?
Scholarly Citation
 Largely text-based and allows concise usage of
words.
 There are about 340 million tweets every day (an
extremely large corpus) – (Twitter.com Stats)
 The increasing population of active Twitter users
in Malaysia (#TwitterJaya) (Ranked third, after
Facebook and YouTube)
 Widely used as a mobile application, allowing
easy access to user posts (Tweets)

6. Page  6
Theoretical Foundations
Scholarly Citation
 Guided by the principles of Incidental Vocabulary
Learning.
 Incidental vocabulary learning can, therefore, be
defined as “learning without an intent to learn”
(Laufer & Hulstijn, 2001, p. 10)
 Schmidt (1994) - the learning of vocabulary when
the learner’s primary objective is to do something
else.
 Online user-generated contents and web resources
are providing a great opportunity for incidental
learning (Wong & Looi, 2010)

7. Page  7
The Study
Scholarly Citation
 Aims to answer the following questions:
1. How does Twitter allow incidental learning of
vocabulary among low proficiency
students?
2. What are the potential and drawbacks of
Twitter in vocabulary learning?

8. Page  8
The Study
Scholarly Citation
 Case study approach is employed.
 A total of 38 students were selected and a pre-test
was conducted to distinguish their vocabulary
level (test on 90 words on various levels as
grouped by Nation, 2001).
 They received two tweets on vocabulary (with
hashtag) from the instructor on a daily basis for
10 weeks.
 At the end of the tenth week, a post-test was
carried out (similar to pre-test but in different
order).

9. Page  9
The Study
Scholarly Citation

10. Page  10
The Study
Scholarly Citation

11. Page  11
The Study
Scholarly Citation
Example of conversations on
Twitter pertaining to the words.
Example of word search
via Hashtag.

12. Page  12
The Findings
Scholarly Citation
 Vocabulary Test Results - Pre
N Minimum Maximum Mean Std.
Deviation
2K Level 38 2.00 15.00 9.63 4.31
3K Level 38 2.00 11.00 6.34 3.60
5K Level 38 1.00 9.00 4.18 3.38
10K Level 38 0.00 5.00 2.53 1.94
University Words 38 1.00 7.00 3.68 2.12

13. Page  13
The Findings
Scholarly Citation
 Vocabulary Test Results - Post
N Minimum Maximum Mean Std.
Deviation
2K Level 38 4.00 17.00 11.13 3.38
3K Level 38 3.00 14.00 7.89 3.54
5K Level 38 2.00 9.00 5.87 1.70
10K Level 38 1.00 6.00 2.13 2.13
University Words 38 1.00 8.00 3.26 2.23

14. Page  14
The Findings
Scholarly Citation
 Vocabulary Test (Means Comparisons)
N Pre Post
2K Level 38 9.63 11.13
3K Level 38 6.34 7.89
5K Level 38 4.18 5.87
10K Level 38 2.53 2.13
University Words 38 3.68 3.26

15. Page  15
The Findings
Scholarly Citation
 The findings revealed minimal improvements in
the students’ vocabulary level.
 Although the scores from the tests did not show
a great increase, the students were able to
construct sentences using the vocabulary.
 Participants generally appreciate the extra input
of vocabulary obtained through instructor’s
Tweets.
 Incidental learning was noted through students’
use of #hashtags

16. Page  16
Potentials and Drawbacks
Scholarly Citation
1. Openness – lead to lack
of participation (privacy
concerns).
2. Overwhelming to both
teacher and learners
3. Lack of control – might
learn the word wrongly.
1. Rapid sharing –
especially via ReTweet
2. Concise usage of words
(140 characters)
3. Access to large corpus
(millions of tweets)
4. Learn while socialising
(e.g. learn with friends)
DrawbacksPotentials

17. Page  17
Recommendations
 Some recommendations for the use of Twitter to
encourage vocabulary learning
 Instructors need to be rather active to reply
students (teaching presence)
 Educate students on net-ethics.
 Use hashtags for the vocabulary (e.g.
#profound) or the activity (#WOTD) to allow easy
tracking.
 Ensure students make their Twitter profile
“protected” – cannot be viewed by the public.

18. Page  18
Conclusions
 Twitter’s roles for pedagogical use especially
vocabulary learning can be maximised.
 But educators need to formulate a clear guidance
in how to use it.
 Rethinking its drawbacks and potentials are
necessary before jumping into the bandwagon.

19. Thank you.
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