Going beyond the classroom:

Participants’ mobile devices

as research and learning tools Victoria Surtees

University of British Columbia

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Source: Rodriguez, S. (2013, July 11). Most adults have a smartphone close by, 1 in 10 use during sex. LA Times.

2 Ubiquity of mobile devices (Bachmair & Pachler, 2015)

By 2025, 5 billion people

will have smartphones

(Miller, 2012)

Mobile devices in Language research

A data collection tool Field notes, logs, recorders, digital storytelling, mapping (e.g., Beddall-Hill et al. 2011; Murthy, 2008)

A pedagogical tool (MALL, and Mobile assisted seamless learning) Vocabulary, grammar, literacy through specialized apps or activities (e.g., Burston, 2015; Kukulska-Hulme & Traxler, 2005; Kukulska-Hulme & Shield, 2008)

A site for novel forms of discourse Language of mobile posts/messages (e.g., Turner,

2009; Zappavigna, 2013)

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A sample pilot study

Context:

Study abroad, English universities in Montreal

Questions:

What social purposes do students use language for?

How often, with whom and where?

What difficulties do they encounter?

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Traditional methods:

• language contact profile

• diaries and (b)logs

• ethnography

Song, Foo & Uy, 2008

Psychology research: Mood spillover

SMS surveys, 3-4 times/day, 8 days

50 couples in Singapore

Results: 2563 reports in 8 days

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Instrument 6

SURVEY GIZMO

Procedure

Background questionnaire

Training for identifying and describing

interactions

Practice with the interface

Mobile entries submitted for 10 days

Entries monitored by researcher

Post-collection questionnaire

Group discussion concerning interactions

Reflection on collection tool

Pre-collection

training session

Mobile collection

period

Group wrap-up

session

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N = 12, multiple L1s, undergraduate exchange students

Experience with the interface

Convenience 3.50

Clarity 4.08

Ease 4.66

Speed 3.66

*On a scale of 1-5

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• Surveys took 2-4 minutes to complete

• Completed in small batches 2 or 3 times per day

• Used variety of devices

Data collected

986 total surveys

Average of 82 per participant (range: 40-114)

Reported average 70% of interaction

“I usually completed the questionnaires later. Sometimes I’m too busy to do it immediately after the interaction”.

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Unexpected findings…

Perceived self-efficacy (Bandura, 1997)

Self-regulation and assessment

“I learnt that I am improving my spoken English

and that I’m not afraid of making mistakes”

“I learned that I usually use very basic English for

communicating with friends about the same

topics.”

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Mediating Artefacts Mobile devices in situated learning (Wong, Chen, &

Jan, 2012)

“I asked to my friend the better way to write the

comments in the last survey because I didn't

know which word I could use to explain that”

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Critical incidents

Personal devices for personal details

“Talk on the phone is difficult for me.

Sometimes I think I speak like a baby talking

and it's difficult to understand if the speaker

speaks fast.”

Ethical dilemmas: Do I intervene? Do I censor?

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New ethical challenges

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Self-representation and pre-disposition to divulge

(overly) personal details (Miller, 2012, Beddall-Hill, Jabbar, Al

Shehri, 2011; Thumin, 2012).

Capturing the data of non-participants

Extracting data/storing data

Liability – battery use

Lack of familiarity for Ethics review boards

New possibilities

“What is thus significant about various tools […] is not

their abstract properties, but rather, how they

fundamentally transform human action.”

(on mediation in CALL, Warschauer, 2005, p.42)

How can we move beyond

thinking of mobile devices

as simply another

tool for research?

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References Bachmair, B., & Pachler, N. (2015). Framing ubitquitous Mobility educationally: Mobile devices and context-aware learning. In L.-H.

Wong, M. Milrad, & M. Specht (Eds.), Seamless learning in the age of mobile connectivity (pp. 57–74).

Bedall-Hill, N., Jabbar, A., & Al Shehri, S. (2011). Social mobile devices as tools for qualitative research in education: iPhones and

iPads in ethnography, interviewing, and design-based research. Journal of Research Centre for Educational Technology, 7(1), 67-89.

Burston, J. (2015). Twenty years of MALL project implementation: A meta-analysis of learning outcomes. ReCALL, 27(01), 4-20.

Buscher, M. & Urry, J. (2009). Mobile methods and the empirical. European Journal of Social Theory, 12, 99-117.

Kukulska-Hulme, A., & Traxler, J. (eds), (2005). Mobile learning: A handbook for educators and trainers. London: Routledge.

Kukulska-Hulme, A., & Shield, L. (2008). An overview of mobile assisted language learning: From content delivery to supported

collaboration and interaction. ReCALL, 20(03), 271-289.

Murthy, D. (2008). Digital ethnography: An examination of the use of new technologies for social research. Sociology, 42(5), 837-855.

Miller, G. (2012). The smartphone psychology manifesto. Perspectives on Psychological Science ,7, 221-237.

Song, Z., Foo, M. & Uy, M. (2008). Mood Spillover and Crossover Among Dual-Earner Couples: A Cell Phone Event Sampling Study.

Journal of Applied Psychology, 93(2), 443–452.

Thumim, N. (2012). Self-representation and digital culture. Palgrave Macmillan.

Turner, K. (2009). Flipping the switch: Code-switching from text speak to standard English. English Journal, 60-65.

Warschauer, M. (2005). Sociocultural perspectives on CALL. In J. Egbert & G. M. Petrie (Eds.), CALL research perspectives (pp. 41–

53). NJ: Lawrence Erlbaum.

Zappavigna, M. (2013). The language of tweets. In K. Hyland (Ed.) Discourse studies reader: Essential excepts (pp. 303-329). London:

Bloomsbury.

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Thank you!

vsurtees@gmail.com

This project was funded by:

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