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                <text>Given the emergence of new mobile devices (tablet, PDA and smart phone), and the growth that knows that part of the ICT, integrating these new technologies into the learning system was imposed, and a new way of learning was born. Mobile learning or "m-learning" is providing great opportunities for learners, especially to learn anytime and anywhere without limitations. There is a new generation of students who grew up using technology and who are easily related to new mobile devices. We discuss the identity of mobile learning and aim to measure the interest of students on it through a survey. This survey seeks to promote mobile learning within the student’s community for a possible adoption in Moroccan higher education. Qualitative and quantitative approaches are endorsed. The results are analyzed, conclusions and perspectives presented at the end of this paper.</text>
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                <text>Copyright (c) 2017 Youness Zidoun, Fatima-Zohra El arroum, Mohamed Talea, Rachid Dehbi</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 10 No. 4 (2016); pp. 80-84</text>
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                <text>1865-7923</text>
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                <text>Students’ Perception About Mobile Learning in Morocco: Survey Analysis</text>
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                <text>Zidoun, Youness</text>
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                <text>With the great advances in the world of mobile learning, development approaches are a very important challenge in designing an M-learning platform for instructors. The development can quickly become a handicap to the integration of certain functionalities and features if the approach is not very well thought and adapted to the pedagogical model designed for the platform. The purpose of this article is to clarify certain comparative aspects between the various methods of development of mobile learning platforms, their ordering and processing and the advantages and disadvantages of each approach. The authors present a multi-criteria analysis which can help instructors in decision-making by assisting their choice of the mobile development approach according to the most important criteria. This paper constitutes an overview of the m-learning platforms from design implementation point of view and will help researchers implement the pedagogical design model described in previous work.</text>
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                <text>10.3991/ijim.v12i3.8083</text>
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                <text>Copyright (c) 2018 YOUNESS ZIDOUN, RACHID DEHBI, MOHAMED TALEA</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 12 No. 3 (2018); pp. 38-51</text>
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                <text>1865-7923</text>
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                <text>M-learning</text>
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                <text>Multi-Criteria Analysis and Advanced Comparative Study between M-learning Development Approaches</text>
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              <elementText elementTextId="30591">
                <text>Zidoun, Youness</text>
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                <text>Dehbi, Rachid</text>
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              <elementText elementTextId="30593">
                <text>Talea, Mohamed</text>
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              <elementText elementTextId="30594">
                <text>El Arroum, Fatima-Zohra</text>
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                <text>2019-12-18</text>
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                <text>New technologies are rapidly changing mobile learning and making it difficult to control. In addition to educational factors and learning content, a modern mobile learning system must take into account the technical and personal aspects of learning, the devices and aspects related to its evolution and interoperability. Teaching on the other hand has also evolved involving more flexibility in tasks and learning stages, thus using modern technologies that offer more alternatives now. In addition, such tasks may be specific to the learning content as well as the learning context or furthermore the learner's environment.Traditionally, mobile platform design relies on the skills of a mobile developer whose knowledge allows him to design mobile applications that are useful to users. But with mobile learning, the design phase involves more than just mobile development skills. For example, if you are designing a platform for practical work, the instructors responsible for the training should be involved. However, the empirical results show that educators do not integrate technology effectively into their curricula. To enable these instructors to develop mobile learning platforms, it is important to facilitate their integration through a theoretical model that will take into account all the ingredients necessary to complete this learning and to balance them in order to ensure its efficiency. In this study authors used a thematic synthesis methodology to present a framework for mobile devices integration in learning. They focused on three models that they think are the most cited in the field of ICT (information and communication technologies) integration in learning.The five-axis framework consists of enriching the TPACK framework(Technological Pedagogical Content Knowledge model in order to more precisely address mobile learning by covering the following parts: pedagogy, content, mobile technology, learning environment and learner’s profile. It describes relatively in depth the various factors involved as well as the effective interconnection to be ensured to achieve an optimal and efficient integration of m-learning. Balancing those five parts will be a matter of plural reflection when designing or consulting on a mobile learning platform.</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/10841</text>
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                <text>10.3991/ijim.v13i12.10841</text>
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                <text>https://online-journals.org/index.php/i-jim/article/view/10841/6275</text>
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                <text>Copyright (c) 2019 YOUNESS ZIDOUN, RACHID DEHBI, MOHAMED TALEA, FATIMA-ZOHRA EL ARROUM</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 13 No. 12 (2019); pp. 152-170</text>
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              <elementText elementTextId="30605">
                <text>1865-7923</text>
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                <text>M-learning</text>
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                <text>Designing a Theoretical Integration Framework for Mobile Learning</text>
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              <elementText elementTextId="36446">
                <text>Dirin, Amir</text>
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              <elementText elementTextId="36447">
                <text>Delbiaggio, Nicolas</text>
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                <text>Kauttonen, Janne</text>
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                <text>Abstract— Computer visions and their applications have become important in contemporary life. Hence, researches on facial and object recognition have become increasingly important both from academicians and practitioners. Smart gadgets such as smartphones are nowadays capable of high processing power, memory capacity, along with high resolutions camera. Furthermore, the connectivity bandwidth and the speed of the interaction have significantly impacted the popularity of mobile object recognition applications. These developments in addition to computer vision’s algorithms advancement have transferred object’s recognitions from desktop environments to the mobile world. The aim of this paper to reveal the efficiency and accuracy of the existing open-source facial recognition algorithms in real-life settings. We use the following popular open-source algorithms for efficiency evaluations: Eigenfaces, Fisherfaces, Local Binary Pattern Histogram, the deep convolutional neural network algorithm, and OpenFace. The evaluations of the test cases indicate that among the compared facial recognition algorithms the OpenFace algorithm has the highest accuracy to identify faces. The findings of this study help the practitioner on their decision of the algorithm selections and the academician on how to improve the accuracy of the current algorithms even further.</text>
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                <text>With the rapid use of Android OS in mobile devices and related products, face recognition technology is an essential feature, so that mobile devices have a strong personal identity authentication. In this paper, we propose Android based software development framework for real-time face detection and recognition using OpenCV library, which is applicable in several mobile applications. Initially, the Gaussian smoothing and gray-scale transformation algorithm is applied to preprocess the source image. Then, the Haar-like feature matching method is used to describe the characteristics of the operator and obtain the face characteristic value. Finally, the normalization method is used to match the recognition of face database. To achieve the face recognition in the Android platform, JNI (Java Native Interface) is used to call the local Open CV. The proposed system is tested in real-time in two different brands of smart phones, and results average success rate in both devices for face detection and recognition is 95% and 80% respectively.</text>
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                <text>Copyright (c) 2020 Laxmisha Rai</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 04 (2020); pp. 103-120</text>
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                <text>1865-7923</text>
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                <text>Authentication</text>
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                <text>Software Development Framework for Real-Time Face Detection and Recognition in Mobile Devices</text>
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                <text>Lancieri, Luigi</text>
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                <text>We are in the transition to a new era where mobility extends to many aspects of our daily lives. Learning, for example, takes place throughout life and anywhere. One may wonder how the traditional orchestration of learning can be applied in a mobile context, for example, to better support students during field trips and museum visits. In this paper, we present a geographic orchestration of resources and activities associated to learning system. The objective is to consider collaboration in mobile learning scenario that depends heavily on the location of learners, their profiles and their activity logs. We also defined a software framework for the design and implementation of pedagogic scenarios of field trip. To validate our approach, we present the implementation of a mobile artifact dedicated to the support of new visitors in exploring the historic monuments of a city.</text>
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          <element elementId="47">
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                <text>Copyright (c) 2017 Nassim Dennouni, Yvan Peter, Luigi Lancieri, Zohra Slama</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 8 No. 2 (2014); pp. 35-41</text>
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                <text>1865-7923</text>
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                <text>Web technologies</text>
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                <text>To a Geographical Orchestration of Mobile  Learning Activities</text>
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                  <text>International Journal of Interactive Mobile Technologies (iJIM)</text>
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              <elementText elementTextId="33465">
                <text>Omar, Bahiyah</text>
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                <text>Internet users are increasingly becoming self-publishing consumers. Applying the Uses and Gratifications (U&amp;amp;G) perspective, this study investigated how people use TikTok in terms of consuming, participating and producing behaviors, and examined the role of personality traits and users’ motivation as predictors to this integrated usage behavior. An online survey was conducted to recruit 385 TikTok users using online network sampling technique. Our findings suggest that it was users’ motivations, not personality traits, that have significant influence on TikTok use. Results show that users’ motivations – namely archiving, self-expression, social interaction and peeking – are significant predictors to TikTok usage behaviors but differ in levels and influence. This study contributes to both the theoretical and the empirical understanding of media use in a user-generated media (UGM) context.</text>
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                <text>Copyright (c) 2020 Bahiyah Omar, Wang Dequan</text>
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                <text>International Journal of Interactive Mobile Technologies (iJIM); Vol. 14 No. 04 (2020); pp. 121-137</text>
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                <text>1865-7923</text>
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                <text>mobile video</text>
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                <text>Watch, Share or Create: The Influence of Personality Traits and User Motivation on TikTok Mobile Video Usage</text>
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