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[PDF](+43👁️) Télécharger ERIC ED562406: Mobile Learning Using Mobile Phones pdf
The participation in mobile learning programs is conditioned by having/using mobile communication technology. Those who do not have or use such technology cannot participate in mobile learning programs. This study evaluates who are the most likely participants of mobile learning programs by examining the demographic profile and mobile phone usage patterns of those who use their mobile phones to access the internet. The results reveal that using the mobile phone to access the internet is more likely among young people and males. These users are also more likely to explore more functions of the mobile phone such as the camera, the calculator or the agenda than those who do not use the mobile phone for internet-access. [For the full proceedings see ED562140.]Télécharger gratuit ERIC ED562406: Mobile Learning Using Mobile Phones pdf
International Conference Mobile Learning 2013
MOBILE LEARNING USING MOBILE PHONES
Instituto Universitdrio de Lisboa (ISCTE-IUL), Business Research Unit, Lisboa, Portugal
Av. Forgas Armadas, 1649-026 Lisboa, Portugal
The participation in mobile learning programs is conditioned by having/using mobile communication technology. Those
who do not have or use such technology cannot participate in mobile learning programs. This study evaluates who are the
most likely participants of mobile learning programs by examining the demographic profile and mobile phone usage
patterns of those who use their mobile phones to access the internet. The results reveal that using the mobile phone to
access the internet is more likely among young people and males. These users are also more likely to explore more
functions of the mobile phone such as the camera, the calculator or the agenda than those who do not use the mobile
phone for internet-access.
Mobile learning participation, internet-capable mobile phones.
In recent years there is a growing tendency to own smartphones or internet-capable mobile phones. The
expectation is for this tendency to maintain as in 2011 the selling of smartphones surpassed the selling of
basic-feature mobile phones (IDC 2011). The penetration rate of this type of devices is however much
different across countries. In Europe, Sweden and Spain have the highest rates (35%); at an international
level Singapore is the top country in terms of smartphones penettation rate (54%) (Go-gulf 2012).
Additionally, it is difficult to find consistent information regarding penettation rates of smartphones. In the
case of Portugal, according to TNS report the penettation rate of smartphones is 9% (TNS 2012); the
Marktest’ telecommunication Barometer reports a penettation rate of 4.2% (Marktest 2012) and cross-
countries investigations report a penetration rate above 30% (Sterling 201 1).
The participation on mobile learning programs is restricted to people that possess and/or knows how to
use internet-capable mobile devices such as mobile phones, PDA’s or laptops. According to a recent report
from the mobile manufacturer Ericsson (2012), by 2015, 80% of internet accesses will be made using mobile
phones. Although this is good news for those developing and implementing mobile learning programs it is
not synonymous of full coverage of the population regarding participation in m-learning programs. In the
particular case of Portugal, despite having internet-capable mobile phones not all users use the mobile phone
to access the internet. According to TNS (2012), 40% of mobile phone users with internet-capable devices do
not use the internet access function, probably for cost reasons.
This study evaluates who are the most likely participants of mobile learning programs by examining the
demographic profile and mobile phone usage patterns of those who use their mobile phones to access the
2. DATA AND METHODS
Data comes from a mobile phone survey conducted by Marktest in 2012. The survey covered the general
Portuguese population of mobile phones users aged 15 years or older. The questionnaire included questions
regarding the use of mobile phones. The sample of numbers to contact was selected using a random digit
dialing procedure. A total of 1,500 completed interviews was accomplished.
ISBN: 978-972-8939-81-6 © 2013 IADIS
The analysis starts with a comparison between individuals who use their mobile phone to access the
internet and individuals who do not in terms of demographic characteristics. A binary logistic regression
model is subsequently estimated to identify the characteristics that strongly differentiate the two groups. The
dependent variable is the “internet access on the mobile phone” coded as 1-yes and 0-no. The independent
variables are sex, age, education level, professional situation, marital status and social class.
In a second step the analysis compares the two groups of mobile phone users in terms of mobile phone
Table 1 presents the rate of internet-access on the mobile phone by subgroups of the population in terms of
sex, age, educational level, professional status, marital status and social class. For each demographic
characteristic only the subgroup with the highest rate is presented.
Table 1. Rate of internet-access on the mobile phone by subgroups
Age: 15-24 years
Education level: university
Professional status: employed by a third party
Marital status: single
Social class: A (upper)
The overall access rate is 19%, with strong differences across subgroups. The highest percentage of
internet access is found in the upper social class - 37.3% - and in the younger’s group (aged 15-24 years) -
A binary logistic regression model was then estimated to identify which demographic characteristics most
distinguish those who use their mobile phones to access the internet and those who do not. A preliminary
analysis allowed multicollinearity to be detected among some independent variables thus excluding some
variables from entering the analysis. As a consequence the model was estimated solely with age and sex as
predictors. Both main effects and interaction effects were tested.
Table 2 presents the estimates of the model. Both age and sex have a significant effect (p<0.001). The
interaction effect was not statistically significant (p>0.1). Specifically, the likelihood of using the mobile
phone to access the internet increases as age decreases. In odds metrics for every internet-access mobile
phone user aged 65 or older (the reference category) there are 16 internet-access mobile phone users in the
15-24 years group (odds=16.8:l). Additionally, using the mobile phone to access the internet is more likely
among males than females (odds=l. 882:1).
Table 2. Binary logistic regression estimates of using the mobile phone to access the internet
Sex (ref: female)
Age (ref: 65 +)
Subsequently a comparison between internet-access mobile phones users and other users was made
regarding the use of 12 functions of the mobile phone. The outcomes reveal statistically significant
differences in 1 1 out the 12 comparisons made (Table 3).
International Conference Mobile Learning 2013
Table 3. Functions of the mobile phone used by type of mobile phone use (%)
Functions of the mobile phone used Internet-access
Make personal calls
Make professional calls
Receive professional calls
Use the alarm clock
Listen to music
Listen to the radio
Use the calculator
With the exception of “make personal calls” internet-access mobile phones users have higher percentages
of facilities’ usage than no-internet-access mobile phone users. The strongest differences can be described as
follows: among the internet-access group there are 84.9% users who use the mobile phone to take photos (vs.
41.4% among no-internet-access mobile phone users), 76.5% use the mobile phone as a calculator (vs.
38.2%) and 66.7% use the mobile phone to consult/edit the agenda (vs. 29.6%).
Accessing the internet on the mobile phone is something that will increase in the future as the ownership of
internet-capable mobile phones increases. However, for now only a small percentage of the Portuguese
population - near 20% - uses their mobile phones to access the internet; moreover, there are strong
differences across subgroups. But even in the “best rate” groups - upper social class and younger - the rate
does not reach 40%.
The idea that mobile learning is for everyone is therefore a reality not yet upon us. Not only the coverage
rate of mobile phone internet access must increase, globally and at subgroups level, but also the ability to use
the mobile devices, which tend to be, in technologic terms, increasingly complex, must improve.
This work has the financial support of Fundacao para a Ciencia e Tecnologia through the PTDC/EGE-
GES/1 16934/2010 project.
Ericsson (2012). One year or less: mobiles 2011 report. Available at: http://wp.nmc.org/horizon2011/sections/mobiles/.
Go-gulf (2012) Smartphone users around the world - statistics and facts. Available at: http://www.go-
IDC (201 1). Portugal 1CT overview, 2007 - 2015. Available at: http://www.idc.pt/
Marktest (2012) Barometro das telecomunicagdes. Available at: http://www.marktest.pt
Sterling. B. (2011) 42 major countries ranked by smartphone penetration rates . Available at: http://communities-
TNS (2012) Mobile life. Available at: http://imagensdemarca.sapo.pt/miradouro-da-atualidade/apenas-9-dos-
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