Collaborative M-Learning Adoption Model: A Case Study for Jordan

Abstract

This work investigates university students' acceptance and readiness for adopting collaborative and context-aware mobile learning services. An acceptance evaluation study was conducted to identify challenges affecting successful implementation and adoption of collaborative mlearning system. The acceptance study has focused on learning contextual factors and learners requirements available at developing countries, where Jordan was considered as the case of this research. Results have confirmed that learning style, mobile device capability and perceived ease of use are having the most positive contribution towards learners' behavior to use collaborative m-learning services. In light of the achieved results, this work provides a new user acceptance model focused toward the adoption of collaborative m-learning services. Finally, this research draws fundamental recommendations allowing for learning context adaptation and successful collaborative m-learning services implementation.

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PAPER
COLLABORATIVE M-LEARNING ADOPTION MODEL: A CASE STUDY FOR JORDAN
Collaborative M-Learning Adoption Model:
A Case Study for Jordan
http://dx.doi.org/10.3991/ijet.v9i8.3639
M.M. Alnabhan, Y. Aljaraideh
Jerah University, Jordan
Abstract—This work investigates university students’ ac-
ceptance and readiness for adopting collaborative and con-
text-aware mobile learning services. An acceptance evalua-
tion study was conducted to identify challenges affecting
successful implementation and adoption of collaborative m-
learning system. The acceptance study has focused on learn-
ing contextual factors and learners requirements available
at developing countries, where Jordan was considered as the
case of this research. Results have confirmed that learning
style, mobile device capability and perceived ease of use are
having the most positive contribution towards learners’
behavior to use collaborative m-learning services. In light of
the achieved results, this work provides a new user ac-
ceptance model focused toward the adoption of collabora-
tive m-learning services. Finally, this research draws fun-
damental recommendations allowing for learning context
adaptation and successful collaborative m-learning services
implementation.
Index Terms—Key words: mobile, learning, collaborative,
acceptance, context, adoption.
I. INTRODUCTION
The capability and accessibility of mobile technology is
rapidly increasing. This resulted for mobile devices to be
utilized in the educational context [1]. Using mobile tech-
nology improves students learning experience and per-
formance. This is achieved by providing extendable learn-
ing environments, and by motivating adaptive collabora-
tive learning outside the classroom context. Accordingly,
mobile learning (m-learning) has witnessed an increased
attention offering new features and facilities to the teach-
ing and learning process [2]. Many advantages exist from
using m-learning; this includes anywhere and anytime
learning content access, increased learning users interac-
tion, context-aware and personalized learning. In addition,
m-learning reduces accessibility barriers between learners
by allowing collaborative and shared learning to take
place.
Mobile learning becomes interactive after being im-
plemented in a collaborative environment. Collaborative
learning is more visible in m-learning considering the
mobility features offered by mobile devices. The common
definition of collaborative learning is: “a situation in
which two or more people learn or attempt to learn some-
thing together” [3]. The successful implementation of
collaborative learning depends on three main factors:
learning context description, methods of collaboration,
and level of participation. Collaborative learning is con-
ducted in different scenarios; formally (e.g. sharing course
material) or socially, where students interact and partici-
pate in a synchronized learning activity (e.g. joint problem
solving). Similarly, collaborative learning can be conduct-
ed via virtual communities, where students can interact
over available communication infrastructure and form a
social network or community.
A considerable attention has been carried out on meas-
uring determinants influencing the adoption of collabora-
tive m-learning systems. This indicates end-users ac-
ceptance and readiness of the system [4].This also in-
cludes understanding context behind implementing col-
laborative learning technologies within educational envi-
ronment. The learning context includes users’ preferences
and capabilities, technology infrastructure, learning re-
quirements, styles and patterns, and surrounding environ-
mental conditions. This work presents an integrated col-
laborative m-learning system prototype to be used for end-
user acceptance and readiness measurements. According-
ly, this research aims to achieve the following objectives:
• Determine the acceptance and readiness of students
towards using collaborative m-learning, and establish
factors influencing their acceptance level. This ob-
jective is accomplished via answering the following
research question:
! Q1: What is the perceived ease of use, perceived
usefulness, trust and behavioral intention to use col-
laborative m-learning among students at Jaresh
University, Jordan?
• Utilize personal initiatives, characteristics and pref-
erences, as well as available technology infrastruc-
ture, in order to describe learning contextual factors
affecting students’ behavioral intention to use col-
laborative m-learning.
! Q2: Is there a statistical significant difference in the
intention to use collaborative m-learning, taking into
consideration context variables, and personal initia-
tives such as learning style, mobile device capability
and internet connectivity type?.
• Identify students’ expectations towards collaborative
m-learning services and understand challenges af-
fecting successful implementation and adoption of
these services.
! Q3: What is the effect of perceived usefulness, per-
ceived ease of use, , and trust on the behavioral in-
tention to use collaborative m-learning?
• Describe a new user acceptance model used for
measuring successful adoption of collaborative m-
learning system. This model includes a set of interre-
lated variables having a positive effect towards de-
veloping the intention to use collaborative m-learning
services.
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COLLABORATIVE M-LEARNING ADOPTION MODEL: A CASE STUDY FOR JORDAN
This paper is structured as follows; section 2 describes
the literature study carried out to conduct this research
work; section 3 illustrates the proposed location-based
learning model; section 4 provides details of the evalua-
tion framework being utilized. Section 5, describes the
research instrument and study sample. Section 6 analyses
and discusses achieved results, and presents new collabo-
rative learning acceptance model. Finally, section 7 con-
cludes this work and provides important recommendations
for future m-learning systems implementations
II. LITERATURE BACKGROUND
Mobile technology is considered as the main compo-
nent facilitating the development of social and collabora-
tive learning skills. For example, Bluetooth, WI-Fi and
mobile broadband are found in most cell phones allowing
for the creation of wireless connectivity between end-
users supporting collaborative learning activities [5].
Many researchers have discussed the benefits of mobile
collaborative learning in improving and easing teaching
and learning activities [3]. In [6] advantages of utilizing
collaborative m-learning services is described within the
educational environment; this includes allowing users to
have continuous and ubiquity access to learning content,
and enhancing the interaction between students and tutors.
Moreover, [7, 8, 9] have investigated advantages of col-
laborative m-learning with comparison to non-
collaborative m-learning. It was confirmed by [10], that
collaborative learning motivates learners’ interaction and
improves learning performance. In addition, this study
indicates that utilizing m-learning offers additional infor-
mal and fixable learning environments to students. With
reference to the collaborative m-learning implementation,
[11] present an implantation framework for multimedia
content generation and interaction between learners. A
different framework was presented in [12], in which a
client-server based approach, providing substantial col-
laboration activities, such as exchange and sharing of
learning content between students and online groups dis-
cussions. In addition, a layering approach was considered
in [13], in which an adaptive collaborative m-learning
model was presented consisting of four operational layers;
mobile technology, activity, theory and design layer.
Although of the increased mobile technology advance-
ment, still users are facing some challenges in utilizing
this technology for accessing and delivering knowledge.
Hence, the successful adoption of m-learning is being
considered in several studies. Research presented in
[14,15], have determined end-users acceptance of m-
learning systems, and described variables influencing sys-
tem usage such as perceived usefulness and perceived
ease of use. In the same concern, [16,17] have described
the moderating effect of demographical variables on in-
tention to use m-learning. Factors affecting the implemen-
tation of m-learning technology are presented in [18], this
study also explores students’ readiness towards this tech-
nology. Similarly, [19] has measured students’ perception
of M-learning and confirmed its effectiveness and flexibil-
ity to increase access to learning resources. In addition,
[20] have considered students’ capability of using mobile
devices features as an important factor for adopting m-
learning. In addition, the usability of mobile technology in
supporting learning activities is measured and confirmed
in [21], taking into consideration device’s capability ad-
vances and drawbacks.
Accordingly, several research works have been con-
ducted to investigate the perceptions of utilizing m-
learning within educational environments. However, only
few studies have focused towards considering details of
learning context in the implementation and adoption of
collaborative m-learning in developing countries. This
research investigates students’ readiness and acceptance
of utilizing m-learning, taking into account contextual
factors affecting the achievement of collaborative and
interactive learning. This work has been conducted con-
sidering educational demographics available at developing
countries, in which, Jordan was the focus of the study.
III. COLLABORATIVE M-LEARNING SYSTEM
PROTOTYPE
The ubiquitous characteristics and enhanced network-
ing capabilities of mobile technology have allowed for
advanced social interactions between users, allowing them
to engage in personal learning and collaborative experi-
ences. Collaboration is coordinated and synchronous
learning activity that maintains a continuous bridge be-
tween learners for interaction and for information ex-
change. An important factor in collaborative learning is
the learning context, which is described as any infor-
mation used to define the situation and components of
learning activity. Figure (1), describes the context envi-
ronment behind collaborative m-learning, which is pre-
sented as an interaction between four environments in-
volved in the learning activity life cycle. This includes
learners’ environment, mobile technology environment,
social networking and learning resources environments.
Learning activity life cycle undergoes several steps, in-
cluding:
• Learning service request.
• Context adaptation.
• Resource and content construction.
• Learning service delivery.
Figure 1. collaborative m-learning context description
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COLLABORATIVE M-LEARNING ADOPTION MODEL: A CASE STUDY FOR JORDAN
Figure 2. Collaborative m-learning system prototype
Figure (2) presents integrated collaborative m-learning
system architecture to be used in the acceptance and read-
iness study conducted in section (4). The described archi-
tecture consists of four major components; users, commu-
nication technology, data and services. End users can be
either learners or tutors; each has different requirements
and capabilities. The mobile communication technology is
considered a major component required for successful
implementation of collaborative learning. This component
facilitates the connection between end users, and allows
for learning resources accessibility. Figure (2) shows ex-
amples of using mobile and wireless technology protocols,
services and applications (e.g. MMS, Instant Messaging,
Emails, Social Media Applications) allowing users to in-
teract together and share learning services and resources.
For example, concurrent instant messaging and interactive
social networks provides learners with increased aware-
ness of available learners allowing the capability of form-
ing group learning. Add-hoc connectivity allows end-
users to communicate and access services and resources
using mobile devices regardless of their current location
and their reference station.
Learning services can be divided into three modules;
collaborative service module, peer-to-peer service mod-
ule, and server-based module. Collaborative services are
considered interactive enabling learners to send and re-
ceive message, sharing information and resources, form-
ing learning communities and conduct group tasks. In
some cases, collaborative services require the knowledge
of learners’ location in order to search for users within the
learning group. Peer-to-peer services require two users’
peers (student – student, student – tutor) to be directly
connected sharing learning resources and experience.
Server-based services require a remote connection to the
content sever obtaining required learning resources. Noti-
fication services are considered as an example of server-
based learning services.
IV. EVALUATION FRAMEWORK
In this work, Mobile Services Acceptance Model
(MSAM) is utilized in order to evaluate the proposed col-
laborative m-leaning model in terms of users’ acceptance
and readiness. This model was presented in [22] and is
based on Technology Acceptance Model (TAM), Unified
Theory of Acceptance and Use of Technology (UTAUT),
Innovation Diffusion Theory (IDT) and Theory of Plan-
ning Behavior (TPB). The MSAM describes several fac-
tors influencing the adoption of mobile applications; this
includes personal initiatives and characteristics, context,
trust, perceived ease of use and perceived usefulness. The-
se components are described as follows:
• Personal Initiatives and Characteristics: this fac-
tor is used to define users’ motivation and capability
to utilize new applications such as mobile applica-
tions. Considering m-learning, this factors includes
the following:
! Personal characteristics such as; gender, age, ca-
pabilities and needs.
! Educational background, skills and expertise.
! Learning history, preferences and styles.
• Context: this factor refers to users’ location, state,
and available resources and surrounding physical ob-
jects. This includes information about mobile tech-
nology being utilized; network connectivity and mo-
bile device capability. Contextual information is con-
sidered crucial in determining requirements for m-
learning successful implementation.
• Trust: this factor measures the degree of user’s be-
lief on the security of a specific mobile application.
Trust can be considered as a predictor of users’ be-
havioral intention to employ m-learning systems
within the teaching and learning process.
• Perceived Usefulness (PU): this factor is used to
describe the level to which an individual believes
that utilizing the system will help improving job per-
formance. This factor is also used to measure user’s
behavior towards accepting and adoption a new sys-
tem. With reference to m-learning, this factor allows
measuring how learners will find m-learning system
useful in completing learning activates efficiently
and flexibly.
• Perceived Ease of Use (PEU): this factor describes
the level to which a user believes that using a system
would be simple and with less effort. Hence, if a user
is experiencing challenges in using a mobile applica-
tion, then he will be reluctant in using that applica-
tion [23]. Considering m-learning, if the learner is
satisfied with the simplicity of m-learning system,
this indicates a high degree of system acceptance for
usage.
• Intention to Use: this factor measures the user’s or
learner’s intention to use the mobile application.
V. RESEARCH INSTRUMENT:
An intensive questionnaire was designed taking into
consideration the acceptance evaluation model described
in the previous section. This questionnaire was adminis-
trated for evaluating end-user acceptance and readiness
towards collaborative m-learning services. The question-
naire has 33 items distributed among four domains ac-
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COLLABORATIVE M-LEARNING ADOPTION MODEL: A CASE STUDY FOR JORDAN
cording to acceptance model components. Trust has six
items, perceived usefulness has five items, perceived ease
of use has five items, context has six items, personal initi-
atives have six items, and behavioral intention with five
items. The questionnaire consists of two main question
types. A 5-point Likert scale used to describe respondents’
perception towards intended M-learning services. This
question type was focused on testing perceived ease of
use, perceived usefulness, trust and intention to use fac-
tors. The second type was open ended questions, and was
designed in order to obtain information about students’
learning styles; in which three categories were deter-
mined; interactive learning, peer-to-peer learning, and
passive learning. The open ended questions were focused
towards a set of learning scenarios corresponding to the
learning styles categories being investigated.
A. Study Sample
This study was conducted at Jerash University, Jordan.
Students in different undergraduate levels from Infor-
mation Technology (IT), Science and Education faculties
were requested to complete the questionnaire described in
the previous section. A total of 180 students have partici-
pated in the study. IT students were the largest respond-
ents group (55%) followed by (30%) of education stu-
dents and (15%) science students. Demographic character-
istic of participating students and the learning context are
shown in table (1).
As shown in Table (1), the percentage of male respond-
ents was 46%, whereas 54% of them were female. More-
over, 76% of responding students were utilizing
Smartphones and nearly 24% are using standard normal
phones. With reference to internet connectivity, the major-
ity of students (103) where accessing internet via mobile
networks, while (77) students used "Wifi" to connect to
internet. This increase in Smartphone and mobile internet
connectivity employment had a dramatic affect on the
adoption of mobile learning activities. In terms of the
learning style, the percentage of students classified as in-
teractive learners was (46%), while (33%) of students
were classified as peer-to-peer learner. Passive learners
constructed around (21%) of the sample of the study.
B. The Validity and Reliability of Instrument
In order to measure internal validity of the research in-
strument being used, a factors analysis study was con-
ducted, and results are presented in Table (2)
The validity of the instruments was measured using
SPSS data reduction techniques. Factor analysis procedure
was used with principal components analysis. High values
of KMO test (close to 1.0) generally point out that factor
analysis may considered useful with the data of this study
(KMO is .787 > 0.50). The Bartlett's test of sphericity
(Homogeneity of covariance) is 3367.7 and statistically
significant at p < 0.05. This indicates a significant rela-
tionships among the variables of the study as well as the
scale has good validity [24].
VI. RESULTS ANALYSIS AND DISCUSSION:
Results of descriptive analysis and Cronbach's Alpha
measurements are described in Table (3). This analysis
was conducted on the acceptance model factors answering
the first research question: What is the perceived ease of
use, perceived usefulness, trust and behavioral intention to
use mobile learning among students at Jaresh University?
TABLE I. .
THE SUBJECTS’ DEMOGRAPHIC CHARACTERISTICS (PERSONAL CHARAC-
TERISTIC AND CONTEXT FACTORS)
Variables
Type
Number (N)
Percentage (%)
Gender
Male 97 54%
Female 83 46 %
Specialization
Information Tech-
nology 99 55%
Education 54 30%
Science 27 15%
Mobile Device
Type
Smart Phone 149 76.2%
Normal Phone 31 23.8%
Internet Con-
nectivity
Mobile Broadband 103 57.2%
Wifi 77 42.8%
Learning Style
Interactive learning 83 46%
Peer-to-peer learn-
ing 60 33%
Passive learning 37 21%
TABLE II.
ACCEPTANCE MODEL FACTORS ANALYSIS FOR INSTRUMENT VALIDITY
Factors
Question
Factor lading
KMO
Sig
Perceived usefulness (PU)
Pu1 .753
.787 .000
Pu2 .726
Pu3 .703
Pu4 .689
Pu5 .687
Perceived ease of use
(PEOU)
Peou1 .674
Peou2 .668
Peou3 .569
Peou4 .534
Peou5 .709
Trust
T1 .701
T2 .593
T3 .711
T4 .647
T5 .532
T6 .647
Behavioral intention to
use (BIU) the Mobile
learning system
BIU1 .860
BIU2 .732
BIU3 .775
BIU4 .742
BIU5 .763
TABLE III.
DESCRIPTIVE ANALYSIS AND CRONBACH'S ALPHA VALUE
Acceptance Model Factors
Mean
SD
Alpha
Perceived usefulness (PU) 3.9 .54 .93
Perceived ease of use (PEOU) 3.7 .44 .91
Trust 3.2 .60 .86
Behavioral intention to use (BIU)
the collaborative m-learning system 3.0 .71 .91
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As shown in table 3, all means scores were ( > 3.0) of
the midpoint, ranging from 3.0 to 3.9. Hence, an overall
positive response to the model factors is indicated. The
standard deviation (SD) values showed a narrow spread
around the mean which indicate a harmony between the
members of the sample. In addition, table 3 shows that
Cranach’s alpha value is ranging from .86 to .93, indicat-
ing good scale reliability[24].
An independent sample t-test and ANOVA analysis
were demonstrated to measure the statistical differences in
the intention to use collaborative m-learning, taking into
consideration learning context variables and personal
characteristics. This analysis study was conducted in order
to answer the second research question: Is there a statisti-
cal significant difference in the intention to use collabora-
tive m-learning, taking into consideration context varia-
bles, and personal initiatives such as learning style, mobile
device capability and internet connectivity type?.
As described earlier, available resources and infrastruc-
ture are considered as a main of the learning context. Ta-
ble (4) below describes the statistical differences for inten-
tion to use collaborative m-learning considering internet
connectivity and mobile device types and capabilities.
Accordingly, results have shown no statistical differ-
ence in the mean intention to use mobile learning taking
into account internet connectivity type. This can be ex-
plained from the availability and accessibility of both in-
ternet connectivity types by learning users. However, a
statistically difference in the mean intention to use collab-
orative m-learning was measured while considering mo-
bile devices' types and capability. The favor was for
Smartphones t (178) = 3.77 which was significant at 0.05
confidence level. This was due to the capability and func-
tionality of Smartphone devices, allowing the use of ad-
vanced applications, and facilitating the accessibility of
several types of learning services. In addition, students
using Smartphones are more familiar and capable of using
mobile learning applications, comparing with standard
phone users. This result confirms with [20].
Learning styles distribution among participating stu-
dents according to their educational backgrounds or uni-
versity faculty is shown in Table (5).
The above table shows that the majority of information
technology students are classified as interactive learners
flowed by science students and education students. This
was due to the familiarity of IT students on the technical
requirements behind achieving interactive learning. Also,
the nature of courses and teaching methods employed
within applied science faculties involve using elements of
interactive learning. A one way ANOVA test study was
demonstrated in order to measure the effect of the learning
style variables on the intention to use m-learning. Table
(6) describes the statistical differences for intention to use
by participating students based on their learning style.
It was determined that there is a significant differences
in the intention to use collaborative m-learning consider-
ing the learning style types, F (5.179) = 5.32, in favor of
interactive learning. This was due to the nature of interac-
tive learning that provides students the chance to partici-
pate in the teaching and learning activities. In addition,
elements of interactive learning such as; shared learning
resources, matching learners, and competent learning en-
vironments attract students' attention.
TABLE IV.
GROUP DIFFERENCES FOR INTENTION TO USE M- LEARNING BY JORDA-
NIAN STUDENTS BASED ON INTERNET CONNECTIVITY AND PHONES' TYPE
(INDEPENDENT-SAMPLE T-TEST)
Context
variable
Type N M SD T Df
Internet
Connectivity
Mobile Broad-
band 103 3.9 .62 1.84 178
Wifi 77 3.6 .49
Mobile De-
vice Type
Smartphone 149 3.3 .55 3.77
* 178
Standard Phone 31 3.1 .53
*p<.05
TABLE V.
DISTRIBUTION OF FACULTIES’ STUDENTS ACCORDING TO THEIR LEARN-
ING STYLE
Back-
ground
Learning Style
Interac
tive
Learning % P-P
Learning % Passive
Learning %
Information
Technology 35 58.4 15 25 10 16.6
Science
25
41.6
19
31.7
16
26.7
Education
23
38.4
17
28.3
20
33.3
Total
83
46.1
51
28.3
46
25.6
TABLE VI.
GROUP DIFFERENCES FOR INTENTION TO USE MOBILE LEARNING BY
JORDANIAN STUDENT BASED ON THEIR LEARNING STYLE (ONE-WAY
ANOVA)
Variable M SD F Df
Learning Style 3.6 .59 *5.32 5.179
Interactive learning 3.3 .45
Peer-to-peer learning 3.2 .53
Passive learning 2.9 .82
*p<.05
TABLE VII.
MULTIPLE REGRESSION (MODEL SUMMARY) ANALYSIS RESULTS
Predictor B Beta t Sig R2 Contribution
PEOU .61 .54 1.7 .001 .51 51%
PU .19 .27 1.1 .000 .22 22%
Trust .12 .09 4.1 .007 .06 6%
Multiple regression analysis was used to measure the
strength of independent variables (perceived ease of use,
perceived usefulness and trust) toward dependent variable
(Behavioral intention to use (BIU) the collaborative m-
learning system. This analysis study was carried out in
order to answer the third research question: What is the
effect of perceived ease of use, perceived usefulness, and
trust on the behavioral intention to use collaborative m-
learning?. Table (7) displays results of the multiple re-
gression analysis.
The ease of use yielded (ß= 0.61, t =1.7) at significant
level (p=0.00<0.05) and predicts 51% upon intention to
use. This means that if the score of ‘ease of use’ increases
to 1 unit, the intention to use will increase .61 units. Per-
ceived usefulness as independent variable yielded values
(ß= .19, t =1.1) at significant level (p=0.00<0.05) and pre-
dicts 22% upon intention to use. This means that if the
score of ‘Perceived usefulness’ increase 1 unit the inten-
tion to use will increase .19 units. Finally, trust predicts
6% upon intention to use. Consequently, the ease of use is
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COLLABORATIVE M-LEARNING ADOPTION MODEL: A CASE STUDY FOR JORDAN
considered as the most important variable with the highest
contribution and affect to the intention to use the collabo-
rative m-learning by students at Jaresh University.
According to previously described results and analysis,
it was confirmed that learning context and personal initia-
tives have direct effect on the intention to use collabora-
tive m-learning services. A statistical significance was
measured while considering learning styles and mobile
devices capability. In which, interactive learners and
Smartphone users have shown a positive status towards
using collaborative m-learning. Results have confirmed a
significant relationship between student educational back-
grounds (personal initiatives) and learning style. IT stu-
dents considered to have the highest positive attitude to
use collaborative m-learning services. In addition, ease of
use was confirmed to have highest contribution to the in-
tention to use collaborative m-learning services, compar-
ing to other acceptance components. Learning context
understating and personal initiatives adaptation are main
imposing challenges towards adoption and successful im-
plementation of collaborative m-learning in Jordanian
Universities. A new acceptance model used for measuring
the successful implementation of collaborative m-learning
services is presented in figure (3). This model is designed
and demonstrated taking into consideration results de-
scribed earlier in this work. In which, it considers factors
having highest contribution to the intention to use collabo-
rative m-learning services. In addition the new model fo-
cuses on learning context details with direct effect to the
services usage acceptance.
The proposed model consists of two main components,
the learning context and system acceptance determinants.
The learning context consists of two interrelated parts,
learners’ requirements and technology infrastructure.
Learners’ requirements include learners’ preferences and
capability; these describe learning styles, educational
background, and learning users’ capacity. Learning con-
text also includes communication technology infrastruc-
ture and specifications; mobile device features (software
and hardware), and mobile network characteristics (type,
range, and performance). The learning context variables
are measured having positive contribution towards users’
acceptance of collaborative m-learning. The second model
component consists of technology acceptance variables;
perceived usefulness and perceived ease of use. In which,,
both variables have a direct effect to the intention to use.
VII. CONCLUSION AND RECOMMENDATIONS
This work measures university students’ acceptance
towards utilizing and adopting collaborative m-learning
services. A prototype for an integrated collaborative m-
learning model was described and utilized during the ac-
ceptance evaluation process, allowing students to under-
stand system functionality, existing context, and available
learning services. The acceptance evaluation study has
adopted Mobile Services Acceptance Model (MSAM) in
order to identify challenges affecting successful imple-
mentation of collaborative m-learning services, taking into
consideration a set of learning context variables and learn-
ers requirements available within Jordanian educational
environment, where Jerash University was considered a
the case for this study. Results have shown that both user
acceptance and learning context factors are having posi-
tive contribution towards using collaborative m-learning
services. This includes learning style, mobile device capa-
Figure 3. Proposed collaborative m-learning acceptance model
bility and services perceived ease of use. More especially,
it was confirmed that interactive learners using
Smartphone devices and coming from Information Tech-
nology backgrounds will be more constructive for adopt-
ing collaborative m-learning services. In light of the re-
sults and analysis study, this work presented a new user
acceptance model used for measuring successful imple-
mentation and adoption of collaborative m-learning sys-
tem. This model is considered distinctive because it in-
cludes detailers for a set of interrelated variables having
direct impact on improving the intention to use collabora-
tive m-learning services. Based on the previous conclu-
sions the following recommendations are presented:
• More attention to interactive learning style is re-
quired when designing and developing mobile learn-
ing systems.
• Educational institutions in developing countries have
to adopt new policies allowing the integration of col-
laborative and interactive teaching and learning
methods within the educational structure. This will
increase the awareness and readiness of collaborative
M-learning services.
• Learning context understating and adaptation is con-
sidered as the main imposing challenges towards
adoption and successful implementation of collabora-
tive m-learning in Jordanian Universities. Hence,
students' preferences, capabilities and available tech-
nological infrastructure have to be considered during
all m-learning system development stages.
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AUTHORS
M. M. Alnabhan is with the Faculty of Information
Technology, Jerah University, Jordan.
Y. Aljaraideh is with the Faculty of Education, Jerah
University, Jordan.
Submitted 11 March 2014. Published as re-submitted by the authors
26 May 2014.
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