Classroom Mobile Devices: Evaluation about Existing Applications
Fabiando Sardenberg Kuss
1
, Marcos A. Castilho
1
and Chee Kit Looi
2
1
Computer Science Department, Federal University of Parana, R. Evaristo F. Ferreira da Costa,
383-391 - Jardim das Americas, Curitiba - PR, Brazil
2
Learning Sciences & Technologies, National Institute of Education, Nanyang Technological University,
1 Nanyang Walk, Singapore
Keywords: Education, Educational Ecosystem, m-Learning.
Abstract: Mobile devices are tools that provide resources for building a seamless teaching and learning process.
Smartphones offer features such as sensors, interfaces, and wireless interfaces that are still little explored in
classrooms. The identification of programs for mobile devices adapted to a new model of use of information
technologies allows recognition on the current panorama of use of mobile devices in the modality of
classroom teaching. Smartphones in schools present a new reality about the insertion of the technologies in
the school environment in conflict with the ways of trying to use computers as an educational tool. In this
work, it was sought to identify, in a database, applications for mobile devices products that may be adequate
to support the teaching and learning process in the context of an educational ecosystem.
1 INTRODUCTION
The use of information and communication
technologies in the school environment was initially
delineated by managers and administrators. This
model of adoption of the new technologies defined
by the holders of formal power can be represented
by a tax format, the top down type. The major
challenge of this process was the definition of
strategies and procedures to leverage new
technologies to support the teaching and learning
process.
In addition to the entertainment offered by some
computers, such as personal use, these were brought
a tool to simplify the calculation, write texts as well
as create and show presentation (Turkle, 2017). The
emergence of the Internet presented the possibilities
of using this technology for the wide dissemination
of knowledge through browsers. At school, specific
spaces were made available for use of computers
and access to networks for use by students and
teachers in a supervised manner (Castilho et al.,
2007b; Vaca, 2005).
On the other hand, mobile technologies emerged
at school with students and teachers bringing their
devices into the classroom (Kraut, 2013) While the
insertion of computers into schools took place in a
more gradual and controlled fashion, mobile devices
quickly became present in schools. This new wave
of technology use, where students, teachers and
school employees become agents of insertion of
tools, has a bottom up representation.
The bottom up model of introducing new
technologies in schools poses new challenges for an
appropriate use of emerging computing and
communication technologies (ICTs). This model
tends to be preponderant in the coming years, faced
with an uncertain future of new devices resulting
from the popularization of the Internet of Things
(IoT). Wearable things, particularly smartwatches,
are already realities that offer virtually untapped
educational potentialities.
Studies evaluating the growth of mobile device
processing capacity (Sharples and Beale, 2003;
Viberg and Grönlund, 2013) with their capacities in
the educational context. These studies focused on the
equipment available at the time since the great
popularization of this type of equipment had not yet
arrived.
The reduction in the price of mobile devices
since the mid-2010 (Viberg and Grönlund, 2013)
allowed greater access to these tools for people with
different socio-economic profiles. Teachers and
students demonstrate knowledge in the use of
applications and resources of embedded systems in
their daily activities, but in the classroom there is a
496
Kuss, F., Castilho, M. and Looi, C.
Classroom Mobile Devices: Evaluation about Existing Applications.
DOI: 10.5220/0007759604960504
In Proceedings of the 11th International Conference on Computer Supported Education (CSEDU 2019), pages 496-504
ISBN: 978-989-758-367-4
Copyright
c
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
restriction on the use of new technologies (Grinols
and Rajesh, 2014).
The traditional model of teaching and learning is
still prevalent in schools around the world. Desktop
computers are less commonly used than
smartphones, while computer labs are underutilized.
This does not mean that laptops or desktops should
be abandoned as an educational tool. The integration
between different architectures advocates integrated
environments using smartphones, single board
computers, embedded technologies, cell phones
among others in an ecosystem model.
Ubiquity (Pimmer et al., 2016), context-aware
(Kalaivania and Sivakumar, 2017) and seamless
(Looi et al., 2010) provide a vision of an innovative
educational model, capable of promoting effective
improvements in the teaching and learning process.
Learning anywhere at any time, seamlessly and
seamlessly becomes a reality with proper strategies
for using mobile tools. The presence of mobile
devices adapted to the context of classrooms allows
the applicability of ubiquity and seamless concepts
in the teaching and learning process.
Although smartphones and tablets have great
potential as teaching and learning tools, little is
known about strategies for adopting them as an
instrument for improving the quality of education in
the school environment. Applications aimed at the
proper use of mobile devices in the classroom are
instruments for more intensive use of these tools in
the school context.
Modern smartphones and tablets have different
communication interfaces in data networks, are
mobile and have input and output interfaces that
provide interactivity. Applications that are able to
take advantage of these features can adjust to
context much more efficiently than older computers.
Programs developed to enhance the use of the
interfaces of networks allied together as sensors and
input and output peripherals allied with IoT tools are
decisive in the empowerment of mobile devices as
an educational tool.
The objective of this work is to evaluate
applications, declared with educational by their
developers, from the perspective of supporting the
process of teaching and learning context-aware
classroom.
2 BACKGROUND
The approach closest to an educational ecosystem is
brought about by the concepts presented in context-
aware and U-Learning studies. The notion of an
ubiquitous and individualized education refers to the
relationships between devices and people as tools of
teaching and learning. This session presents a critical
reading on the state of the art applied to these
concepts.
2.1 Ubiquitous and Context-aware
Education
Ubiquitous computing in education through the use
of mobile devices (Pimmer et al., 2016) depends on
specific hardware. Traditionally, ubiquitous
computing is characterized by the use of portable
devices, initially in the use of personal digital
assistant (pdas) and, more recently, in the use of
smartphones (Al-Emran et al., 2016). The IoT
concepts, however, suggest new opportunities for
educational environments that can hardly be solved
by software alone on tablets or smartphones.
Kalaivania and Sivakumar (2017) present works
related to concepts of context-aware and learning, in
a review of the literature. In this work the authors
emphasized approaches that involve integration
between different devices and architectures focused
on solutions of specific problems. However, none of
the papers presents a strategy focused on generic
problems through an architectural proposal about an
ecosystem approach.
In a presentation of the concepts relevant to
understanding U-Learning (Lopez et al., 2016), and
from its environment are explored various devices
and how these interact in the context of teaching and
learning.
Recent studies on contextualized and ubiquitous
education concepts highlight the relevance of IoT
(Kalaivania and Sivakumar, 2017; Yamada et al.,
2017). In the identified studies there is a concern
with the insertion of this new technology in the
context of education, but none of them presents
strategies for the use of integrative tools capable of
providing the necessary infrastructure for a truly
ubiquitous and context-aware implementation.
Knowledge of the context applied ubiquitously in
the educational context depends on a large amount
of information so that it is possible to select the
content or approach most appropriate for each
student or teacher. Forkan, Khalil, Ibaida and Zahir
(Forkan et al., 2015) assess the need to use strategies
from Big Data to a context-aware environment.
Although the work does not address educational
issues, the need for infrastructure to process context-
aware information is in line with the proposal of this
research.
Classroom Mobile Devices: Evaluation about Existing Applications
497
These uninterrupted concepts present the
importance of using different portable or fixed
technologies, online or offline, acting together for a
seamless learning experience for students. Teachers
act as facilitators and use technologies for content
delivery, management and monitoring of the
teaching and learning process (Sharples, 2015).
2.2 Top down based Projects
The top down model was used in computer projects
in schools and defines the taxing way of adopting
computers as an educational tool. This model has
been used since the adoption of the first computers
for students and teachers, but it is not suitable for the
growing use of mobile devices as a teaching and
learning tool. In this model students and teachers are
elements that receive a ready computational
infrastructure and must be trained to potentiate the
use of the available devices.
By the year 2012, this model had already been
started in about 50 countries with more than two
million devices distributed (Beuermann et al., 2015).
Data from the year 2015, however, indicate that only
two countries, Peru and Uruguay, were able to fully
implement the use of XO computers
1
in their schools
(James, 2015).
Despite its educational approach, the use of
devices proposed by On Laptop per Child project
(OLPC) did not present classroom contents or
instigated changes in pedagogical practices by the
use of laptops (Cristia et al., 2012). Sharma (2012)
highlights slightly positive results in teaching
mathematics and language negatives due to the use
of computers in public schools in Nepal.
The recent migration to other types of mobile-
based computer technologies represents an important
factor for the review of the hardware model used by
programs using the XO architecture (James, 2010).
The cost of modern mobile devices, their increasing
popularity and ease in transport and handling tend to
show that this type of equipment may be more
suitable for use in one computer mode per student
(Hockly, 2016) than laptops used by Classmate or
XO.
In Spain, a technology insertion project called
Educational Technological Network (ETN),
translated in English as Technological Educational
Network, (Berrocoso, 2008; Vaca, 2005) proposed a
model of use of technologies for incorporation into
1
A low-cost personal computer manufactured by Quanta
computer for children.
the educational system of the state of Extremadura.
This project was concerned with the creation of
infrastructure, research and support to the installed
computer park. In search of vendor independence,
the project adopted the use of free software as a
form of autonomy and control of the technologies
used (Berrocoso, 2008).
As a result of the RTE, Extremadura has 60,000
computers in elementary and secondary schools in
2016 (
́
az, 2016). However, computer use adopts
the model of computer labs with the configuration of
one computer for every five or six students. Despite
all state investment, the appropriation of the
technologies offered by RTE by teachers is still very
small (Dı
́
az, 2016).
The ecological-informative term used by
Berrocoso (2008) to describe the implementation of
RTE shows the integrated vision offered by the
project and the approach to the theme of this
research. Recent project evaluations (Dı
́
az, 2016)
point to problems related to the change in the use of
computers arising from the growing use of mobile
technologies and IoT in education.
In 2008, in Brazil, one computer-by-student
(UCA) project was created to promote access to
information technologies for students and teachers in
elementary and high school (de Casio, 2018). The
OLPC had a great influence on this project, mainly
in the tools adopted for its implementation, focusing
on the delivery of laptops to students. Another
Brazilian initiative, the UCA project (ProUCA),
sought a digital inclusion model in which the student
acts as a focal point in the dissemination of
knowledge, promoting access to communication
technologies for the family (Direne et al., 2012).
Experiments with hardware solutions such as
multi-terminal (Castilho et al., 2007a) and
multimedia TVs have demonstrated that new device-
based technologies can be an important tool for
reducing costs and improving the computing
environment. In this project, more than 40,000
computers with internet access were available in all
public schools in the state of Paraná (Castilho et al.,
2007b).
2.3 Bottom up Model based Projects
The use of mobile devices such as smartphones and
tablets assumes the role of tools to support the
teaching and learning process (Milrad et al., 2013).
Affordable prices for a large part of the world's
population (Viberg and Grönlund, 2013; Dabney et
al., 2013) favors the use of these devices as
individual tools.
CSEDU 2019 - 11th International Conference on Computer Supported Education
498
The ability to use mobile technologies in the
production and presentation of educational objects is
still limited. Looi (Looi et al., 2010) shows the
growing trend of the presence of mobile devices in
educational processes generates a new phase in
technologies applied to education. As a result of the
tendency of a greater insertion of these devices in
the school environment, a reaction contrary to their
use by students and teachers is perceived.
Low-cost boards, equipped with some form of
communication, with other devices through network
infrastructure is a new way of interacting with ITCs
(Borgia, 2014). IoT tools can provide a relevant
resource in the teaching and learning process,
allowing interaction with real-world objects, not just
simulations (Marquez et al., 2016). This type of
technology is perfectly adequate so that the
equipment brought by the students can be added as a
tool to improve the teaching and learning process
(Kuss et al., 2018).
Mobile technologies and the Internet of Things,
IoT, have been one of the most important topics on
new forms of education (Traxler and Vosloo, 2014).
While using devices such as smartphones or
smartwatches has become a part of people's lives,
using it as an educational tool does not have a
consensus on how to bring it into the classroom
(Grinols and Rajesh, 2014).
The inadequacy of the school environment to the
use of mobile devices promotes the inappropriate
use of the same in the classroom. Teachers are
concerned about how distracting these tools are for
students (Mc-Coy, 2016). However, the
administration of the insertion of the technologies,
considering that these tend to occur even more
intensely in the coming years, is the main
responsibility of the managers in the bottom up
model of insertion of technologies in the school
environment.
3 METHODOLOGY
This study evaluated information on mobile apps
that use Android OS available from the Play Store
virtual store. Data was obtained from the database in
a large mobile application service provider. This
database is updated daily and counted on January 11,
2018 with 2,879,824 applications registered.
The option to use only applications for the
Android platform, developed by Google, stems from
the popularity of the product and variety of devices
that adopt this operating system. According to the
website www.statista.com (Portal, 2019) 88% of
smartphones use Android while 11.9% are marketed
with iOS, Apple's operating system. In this work was
made the identification of general characteristics of
applications, so in spite of the restriction of the
chosen operating system, the information acquired is
sufficiently representative.
Attempts to retrieve data by directly accessing
the Play Store have been unsuccessful since there is
no provision of services for information retrieval.
The virtual store also does not have adequate search
tools, limiting the search criteria to only the use of
keywords that return a limited number of
registrations.
The data was retrieved through HTTP requests
using the curl tool triggered by a shell script on a
computer with Linux operating system, Ubuntu
18.04 distribution. The application program interface
(api) provided by the web service allows the
application of several filters for more adequate
selection of data according to the demand of the
study. Once the data were retrieved, they were
inserted into a database on the local machine to carry
out a work identifying the suitability of each of the
applications found as a tool to support education.
3.1 Inclusion and Exclusion Criteria
The query used the following criteria: description
field and title containing the word learning or the
word teaching; should be application and not game;
must be registered as family of educational
applications and have at least 500 ratings. The query
resulted in 2,411 applications that matched the
criteria you selected. A representation in the
Javascript object notation, JSON format used is
presented in the List 1:
{ "query": {
"query_params": {
"full_text_term": "learning OR teaching",
"cat_keys":["APPLICATION"],
"family_filter":["EDUCATION"],
"ratings_count_gte": 500,
"sort": "title",
"sort_order": "asc",
"include_full_text_desc": true }
Listing 1: JSON format used in the query.
An application capable of showing the data was
developed, that was more appropriate to read the
information of each of the applications, especially in
the field description. This application also allowed
the insertion of additional information for each of
the applications. The additional information entered
Classroom Mobile Devices: Evaluation about Existing Applications
499
was the classification of the software according to
table 1 and the more detailed observation mark of
the product.
The classification of applications for their
educational use was based on the work of Cherner
(Cherner et al., 2014). While reading the description
of each of the 2,411 applications, new required
classifications were identified, resulting in the
proposed classification of applications according to
table 1. In the detailed evaluation of the selected
applications those that limited access to previously
registered institutions as well as those that did not
have a description in English.
For classification criteria, descriptions of app
was the main functionality. The rating used the Skill-
Based and Content-Based Apps Apps (Cherner et al.,
2014). as categories. When none of the categories
presented in the framework allowed classification,
they were initially grouped into a generic set and
later categories were created that extend the proposal
of Cherner .
After all the process of classification and
identification of the applications, the ones that
should be analyzed in more detail were selected. The
selected products underwent a revision from their
textual description, access to the page of the
developer when it existed besides searching for more
information on search engines. If the interest for the
study was confirmed, it would then be installed on a
smartphone to detail its capabilities.
4 RESULTS
From the description of the applications this were
classified by subjects (groups) and grouped by
theme (main groups) for a better representation. The
classification activity of the applications produced
the results presented in table 1.
When discarding the products that met the
exclusion criteria, 2333 suitable study applications
were counted. Among these, those which described
some specific use in the classroom, including
teacher support activities, were defined as relevant
for a more detailed investigation
Dictionaries, e-books and attendance can be used
as a classroom support tool. Among the applications
evaluated in this research were identified 470
applications that rely on the functionality of e-books
and 433 dictionaries. These are grouped according to
table 2, where only groups with one or more
applications are displayed.
Table 1: Applications classification.
Main Group
Group
Total
Total
%
Invalidated
Not Educational
33
1.37
Not in English or
Portuguese
45
1.87
Language
Dictionary
141
5.85
Language
968
40.15
Management
tools
Class Support
42
1.74
Place Utilities
20
0.83
Management
39
1.62
Music and Sports
Music
23
0.95
Sports
5
0.21
Personal support
Self Help
14
0.58
Religion
86
3.57
Preparation and
Certification
Certification
88
3.65
College Entrance
132
5.47
Test Preparation
85
3.53
Preschool
Numeracy
15
0.62
Literacy
77
3.19
Ludic
170
7.05
Self-taught
How to
68
2.82
Memorization
16
0.66
Technology
94
3.90
Subjects
Geography
12
0.50
Science
35
1.45
Social Studies
6
0.25
Mutiple-Subjects
16
0.66
Math
63
2.61
Hight Education
Subject
29
1.16
E-Learning
Plataform
61
2.53
Video
17
0.71
Typical classroom applications are related to
presence control and timetable A search using the
key words attendance and timetable returned those
presented in the table 2. Only the major groups in
which the query obtained at least one application in
the result were shown.
CSEDU 2019 - 11th International Conference on Computer Supported Education
500
Table 2: Classroom features.
Main group
E-book
Dictionary
Tot
%
Tot
%
Tot
%
e-Learning
16
20.51
2
2.56
3
3.83
Music and
Sports
6
21.43
0
0
0
0
Subjects
40
23.26
10
5.81
0
0
Manage-
ment tools
16
15.84
0
0
26
25.74
Personal
support
13
1.3%
1
1%
0
0
Language
251
22.63
386
34.81
1
0.09
Preschool
49
8.70
2
0.76
3
1.15
Self-taught
14
7.87
8
4.49
0
0
Prep.
Certif.
48
15.74
14
4.59
1
0.33
Table 3: Data network and other devices integration.
Main Group
IoT
OffLine
Tot
%
Tot
%
E-Learning
2
2.56
20
25.62
Music and Sports
0
0
1
3.57
Subjects
0
0
15
8.52
Management tools
0
0
21
20.79
Personal support
0
0
14
14
Self-taught
0
0
26
16.25
Language
0
0
332
29.9
Preschool
1
0.38
25
9.54
Prep. and Certific.
7
2.30
64
20.98
5 DATA ANALYSIS
The application classification framework presented
by Cherner (Cherner et al., 2014). is a valid
reference but this study demonstrates the need to
update the classification. The groups listed in the list
below have their own characteristics and constitute a
large number of applications that fit appropriately in
these categories, as seen in the table1.
The data classification presented applications
dedicated to language teaching, however a small
number of applications of this category were
classified as relevant to this work. Few of the
applications in the Language group, presented, in
their description, innovative features or focus on
classroom activities. In table 4 it is noticed that
although the Language group represents 45.99\% of
the total number of applications identified, only 1 of
these presents some connectivity with key words
widely used in the literature.
Table 4: Literature influences.
Main Group
Seamless
Ubiquitous
Context-
aware
Tot
%
Tot
%
Tot
%
Subjects
2
1.61
0
0
0
0
Preschool
2
0.76
0
0
0
0
E-Learning
5
6.41
0
0
0
0
Management
Tools
7
6.93
0
0
0
0
Language
1
0.09
0
0
1
0.09
There is little connection between the
applications identified in the work with the most
relevant research in informatics in education. The
tool group that presented the best numbers was the
Management Tools with 6.93\% of applications
content the keyword seamless in the description or
title of the application. Ubiquity is a word widely
used in describing the role of mobile devices
(Kalaivania and Sivakumar, 2017) but has not been
used in any of the evaluated applications.
In relation to the integration with other devices
coming from the growing importance of IoT
(Yamada et al., 2017) this work identified little
concern of the developers with the theme as table 3.
The construction of integrated environments and
able to offer facilities in the use of ICT in
classrooms in an ecosystem model depends on
integration between devices. The bottom-up model
of insertion of technologies in school demands new
and low-cost technologies.
Although some of the applications tested in this
study use the term ecosystem in their definition none
of them presented integration capabilities with other
devices and generic infrastructure. The creation of
an integrated educational environment, based on a
basic computational infrastructure focused on all
aspects related to the educational system, differs
from other attempts to insert technologies focused
on specific aspects of teaching and learning
activities. Bonilla (2009) presents arguments that
Classroom Mobile Devices: Evaluation about Existing Applications
501
distinguish the demands of digital inclusion from the
use of ICT tools for a use as a tool to support
teaching activities.
The creation of own network infrastructure
proved to be an adequate tool to solve the
deficiencies identified by (Moreira et al., 2013) in
relation to access. Multicast-based technologies for
discovering addresses and services offered (Cirani et
al., 2014), popular technology in wireless printers
and multimedia devices, provide simplification in
building ad-hoc networks. These facilities were also
not cited or identified in any of the evaluated
applications.
5.1 Limitations
There are a number of applications that use the same
platform but have distinct content from a developer
in the Play Store virtual store that has generated
some distortion in the data. These applications offer
several of the features that have been identified in
this work and appear with 9 different application
titles. For methodological reasons each of the
applications was considered independently, but it
should be noted that in content presentation aspects
this factor may partially compromise the results.
Some of the applications that presented features
that could be better explored in this work contained
restricted access to their functionality. These
restrictions, often related to the link with an
educational institution, prevented the installed
application from running properly.
6 CONCLUSION
Providing individual computers for each student on
desktop or laptop models is still far from becoming a
viable country in poor, developing, and even wealthy
nations. However, mobile technologies have a
greater penetration in society than traditional
computers. Efforts to develop applications that can
bring educational services and usage to school are
paths to a change in the use of ICT as an educational
tool.
The change in the goal of one computer per
student has an impact on reducing the need for
financial investment by considering the difference in
the cost of acquiring mobile devices when compared
to laptops and desktops. The infrastructure to adapt
to the use of these new technologies is also reduced
and can be more functional if built properly. The
challenge of adopting solutions in a bottom-up
model of adoption of technologies in schools can be
a great opportunity for initiating various
governmental and institutional projects.
Applications developed for mobile devices do
not have a strong focus on use in the school
environment. None of the evaluated products
presented integration and connectivity
characteristics suitable for intensive use in
classrooms. Expansive aspects of computing
research in education such as ubiquity, context-
aware and seamless are not part of the approach of
mobile application developers.
There is an immediate need to migrate the
concepts applied to the model of a computer per
student to the reality resulting from the
popularization of mobile devices. The emerging
model of technology adoption resulting from the
bottom up model does not seem to have impacted
the application development environment. The
proper use of these tools is an important tool for
including technologies as a tool to improve the
quality of teaching and learning.
ACKNOWLEDGMENTS
This work was partially supported by the National
Fund for Education Development (FNDE) of the
Brazilian Ministry of Education "Research on
Dissemination and Collective Evaluation of
Educational Content for Use in Classroom"
We thank Alexandre Direne (in memorian) for
his support in the original project design, for C3SL
(Center for Scientific Computing and Free Software)
and Serpro (Federal Data Processing Service) for
which they provided, respectively, the infrastructure
and release of hours for dedication to the project.
Finally we could not fail to thank the Learning
Sciences Lab of the National Institute of Education
for its support.
REFERENCES
Al-Emran, M., Elsherif, H. M., and Shaalan, K. (2016).
Investigating attitudes towards the use of mobile
learning in higher education. Computers in Human
Behavior, 56:93–102.
Berrocoso, J. V. (2008). El software libre y las buenas
prácticas educativas con tic. Comunicación y
Pedagogı
́
a, 222(48):55.
Beuermann, D. W., Cristia, J., Cueto, S., Malamud, O.,
and Cruz-Aguayo, Y. (2015). One laptop per child at
home: Short-term impacts from a randomized
experiment in peru. American Economic Journal:
Applied Economics, 7(2):53–80.
CSEDU 2019 - 11th International Conference on Computer Supported Education
502
Bonilla, M. H. S. (2009). Inclusão digital nas escolas.
Educação, direitos humanos e inclusão social:
histórias, memórias e polı
́
ticas educacionais. João
Pessoa: Editora universitária da UFPB, 1:183–200.
Borgia, E. (2014). The internet of things vision: Key
features, applications and open issues. Computer
Communications, 54:1–31.
Castilho, M., Sunyé, M., Weingaertner, D., Bona, L.,
Silva, F., Direne, A., Garcia, L., Guedes, A., and
Carvalho, C. (2007a). Open source for knowledge and
learningmanagement: strategies beyond tools. Idea
Group Inc, pages 343–368.
Castilho, M., Sunyé, M., Weingaertner, D., de Bona, L.,
Silva, F., Carvalho, C., Garcı
́
a, L., Guedes, A., and
Direne, A. (2007b). Laboratórios de informática com
software livre para atender polı
́
ticas estaduais do
ensino escolar. In Anais do Workshop de Informática
na Escola, volume 1.
Cherner, T., Dix, J., and Lee, C. (2014). Cleaning up that
mess: A framework for classifying educational apps.
Contemporary Issues in Technology and Teacher
Education, 14(2):158–193.
Cirani, S., Davoli, L., Ferrari, G., Léone, R., Medagliani,
P., Picone, M., and Veltri, L. (2014). A scalable and
self-configuring architecture for service discovery in
the internet of things. IEEE Internet of Things Journal,
1(5):508–521.
Cristia, J., Ibarrarán, P., Cueto, S., Santiago, A., and
Severı
́
n, E. (2012). Technology and child
development: Evidence from the one laptop per child
program.
Dabney, M. H., Dean, B. C., and Rogers, T. (2013). No
sensor left behind: enriching computing education
with mobile devices. In Proceeding of the 44th ACM
technical symposium on Computer science education,
pages 627–632. ACM.
de Casio Gonçalves, Á. (2018). Computadores na sala de
aula: o projeto uca–um computador por aluno na
escola classe 102 do recanto das e EMAS - Distrito
Federal. Revista Brasileira de Aprendizagem Aberta e
a Distância, 11.
́
az, M. J. S. (2016). Recorrido de las polı
́
ticas educativas
tic en extremadura, españa [travel of educational ict
policies in extremadura, spain]. Ventana Informática,
(33).
Direne, A., da Silva, W., Silva, F., Peres, L., Kutzke, A.,
Marczal, D., Barros, G., Moura, L., and Bazzo, G.
(2012). Aprofundamento da mobilidade
tecnológicoeducacional por meio de jogos intelectivos
como facilitadores da comunicação professor-aluno
em redes virtuais de ensino. In Anais do Workshop de
Desafios da Computação Aplicada à Educação, pages
20–29.
Forkan, A., Khalil, I., Ibaida, A., and Tari, Z. (2015).
Bdcam: Big data for context-aware monitoring-a
personalized knowledge discovery framework for
assisted healthcare. IEEE transactions on cloud
computing.
Grinols, A. B. and Rajesh, R. (2014). Multitasking with
smartphones in the college classroom. Business and
Professional Communication Quarterly, 77(1): 89–95.
Hiew, F. C. and Chew, E. (2016). Seams remain in
seamless learning. On the Horizon, 24(2):145–152.
Hockly, N. (2016). One-to-one computer initiatives. ELT
Journal, page ccw077.
James, J. (2010). New technology in developing countries:
A critique of the one-laptop-per-child program. Social
Science Computer Review, 28(3):381–390.
James, J. (2015). Macroeconomic consequences of the one
laptop per child project. Journal of International
Development, 27(1):144–146.
Kalaivania, R. and Sivakumar, R. (2017). A survey on
context-aware ubiquitous learning systems.
International Journal of Control Theory and
Applications, 10:15.
Kraut, R. (2013). Policy guidelines for mobile learning.
UNESCO.
Kuss, F. S., Castilho, M. A., Peres, L. M., and Silva, F.
(2018). Aulacast: A single board computer platform to
support teaching. In Proceedings of the 10th
Intenational Conference on Computer Supported
Education - Volume 1: CSEDU,, pages 366–373.
INSTICC, SciTePress.
Looi, C.-K., Seow, P., Zhang, B., So, H.-J., Chen, W., and
Wong, L.-H. (2010). Leveraging mobile technologyfor
sustainable seamless learning: a research agenda.
British Journal of Educational Technology, 41(2):154
169.
Lopez, G. A. M., Builes, J. A. J., and Villamil, S. C. B.
(2016). Overview of u-learning. concepts,
characteristics, uses, application scenarios and topics
for research. IEEE Latin America Transactions,
14(12):4792–4798.
Marquez, J., Villanueva, J., Solarte, Z., and Garcia, A.
(2016). Iot in education: Integration of objects with
virtual academic communities. In WorldCIST (1),
pages 201–212.
McCoy, B. R. (2016). Digital distractions in the classroom
phase ii: Student classroom use of digital devices for
non-class related purposes.
Milrad, M., Wong, L.-H., Sharples, M., Hwang, G.-J.,
Looi, C.-K., and Ogata, H. (2013). Seamless learning:
An international perspective on next-generation
technology enhanced learning.
Moreira, W., Ferreira, R., Cirqueira, D., Mendes, P., and
Cerqueira, E. (2013). Socialdtn: a dtn implementation
for digital and social inclusion. In Proceedings of the
2013 ACM MobiCom workshop on Lowest cost
denominator networking for universal access, pages
25–28. ACM.
Pimmer, C., Mateescu, M., and Gröhbiel, U. (2016).
Mobile and ubiquitous learning in higher education
settings. A systematic review of empirical studies.
Computers in Human Behavior, 63:490–501.
Portal, T. S. (2019). Global market share held by the
leading smartphone operating systems in sales to end
users from 1st quarter 2009 to 2nd quarter 2018.
[Online; accessed 14-January-2019].
Classroom Mobile Devices: Evaluation about Existing Applications
503
Sharma, U. (2012). Essays on the economics of education
in developing countries. ProQuest LLC.
Sharples, M. (2015). Seamless learning despite context. In
Seamless learning in the age of mobile connectivity,
pages 41–55. Springer.
Sharples, M. and Beale, R. (2003). A technical review of
mobile computational devices. Journal of Computer
Assisted Learning, 19(3):392–395.
Traxler, J. and Vosloo, S. (2014). Introduction: The
prospects for mobile learning. Prospects, 44(1):13–28.
Turkle, S. (2017). How computers change the way we
think. In Law and Society Approaches to Cyberspace,
pages 3–7. Routledge.
Vaca, A. (2005). Extremadura and the revolution of free
software. How Open is the Future?, page 167.
Viberg, O. and Grönlund, Å. (2013). Cross-cultural
analysis of users’ attitudes toward the use of mobile
devices in second and foreign language learning in
higher education: A case from sweden and china.
Computers &Education, 69:169–180.
Wong, L.-H. (2015). A brief history of mobile seamless
learning. In Seamless learning in the age of mobile
connectivity, pages 3–40. Springer.
Yamada, M., Cuka, M., Liu, Y., Oda, T., Matsuo, K., and
Barolli, L. (2017). Evaluation of an iot-based e-
learning testbed: Performance of olsr protocol in a nlos
environment and mean-shift clustering approach
considering electroencephalogram data. International
Journal of Web Information Systems, 13(1):2–13.
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