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An analysis of Mobile Learning Implementation in Shinas College of Technology (SHCT)
An analysis of Mobile Learning Implementation in Shinas College of Technology (SHCT)
An analysis of Mobile Learning Implementation in Shinas College of Technology (SHCT)
An analysis of Mobile Learning Implementation in Shinas College of Technology (SHCT)
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An analysis of Mobile Learning Implementation in Shinas College of Technology (SHCT)

  1. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 06 Issue: 02, December 2017, Page No.34-37 ISSN: 2278-2400 34 An Analysis of Mobile Learning Implementation in Shinas College of Technology (SHCT) R.Madhubala1 , A.Akila2 1 Ph.D. Scholar, Department of Computer Science, Vels University, Chennai, India 2 Asst. Professor, Department of Computer Science, Vels University, Chennai, India Email: balamadh@gmail.com, akila.scs@velsuniv.ac.in Abstract - In the past decade, technology has grown exponentially, especially the speed of the Internet and mobile technology have reached its peak it seems. This technology advancement also gives its impact to all the areas especially in the education sector. Researchers have to be interested in investigating how these technologies can be exploited for educational purposes aiming to enhance learning experiences. Subsequently, this has prompt an exploration slant which is ordinarily alluded to as Mobile Learning (M- Learning) in which specialists endeavors have meant to disseminate fitting learning encounters to learners considering their own flexibility needs, the universal usage of portable advances and the accessibility of data whenever – anyplace. By and by, m-learning is still in its start and extraordinary endeavors should be done as such as to explore the potential outcomes of educational outlook change from the conventional on- estimate fits-all illuminating ways to deal with a versatile and customized discovering that can be circulated by means of portable creations. This paper, presents the suitability and need of mobile learning faciltity in Shinas College of Technology(SHCT) and also presents the framework for implemeting m- learning in SHCT. Keyword: M-Learning, Context, Adapatation, Learning through mobile, Ubiquitous Learning I. INTRODUCTION Rising progression in the field of portable innovation has prepared for mobile learning. Mobile learning alters the old learning process. Mobile learning is defined as use of handheld devices such as PDA, Mobile, Laptop and tablet technologies in teaching and learning process. Fundamental component m- leaning are learner, teacher, content, environment and assessment as shown in the figure [1]. The key benefits and characteristics of mobile leaning are as follows. Benefits of M-Learning [3][4]  Students can learn the subjects anywhere, any time and place restriction  Along with classroom based learning, students can continue his/her learning along with peers and teacher outside the class room.  Effective access of the learning contents in on- demand fashion.  Practice and sharing of knowledge through activities with help of peers and experts.  Allow to acquire more practical knowledge immediately. Figure 1: The basic elements of m-leaning [6] Encourage learners to participate more actively in the learning process by engaging them to authentic and situated learning embedded in real-life context Characteristics of Mobile Learning [5]  Ubiquitous and on Demand: Ubiquitous means, which is present everywhere. Hence, it should be accessible regardless of time and location and capable of delivering the required content at any point of need.  Bit Sized: The educational content should be relatively short in duration as it is used mostly at places having environment causing a lot of distraction.  Blended: It is very rarely used as only platform to deliver education. Normally, it complements other more resourceful modes of content delivery such as clinical and e-learning.  Can be Collaborative: Promote collaborative learning as much as possible  Types of Mobile Applications [8]  Electronic materials for mobile learning basically are the same applications which can be developed in three different ways –
  2. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 06 Issue: 02, December 2017, Page No.34-37 ISSN: 2278-2400 35  Native apps are installed on the device. Native apps must be compatible with specific platform and programming languages that are supported by this platform  Web apps are used through the browser and can support multiple Operating System but the problem is that it cannot effectively access phone hardware, like sensors which are the main component for interaction with users.  Hybrids can access the hardware and have the benefit of simpler data updates. The user can download application to the device and the application can access the API, but the content comes from the web.  The paper is structured as follows, section 2, and presents the literature review. Research issues are heighted in section 3. The proposed work is presented in section 4. Finally the conclusion. II. LITERATURE REVIEW This section presents the overview of existing effort in the arena of mobile learning and points out important findings and issues. David Parsons et.al[9], proposed a Theoretical Framework for mobile learning applications and also pointed out the key elements for mobile learning applications. Nauris et.al[10], proposed a methodology for learning content development for all kind of mobile devices and also pointed out the key elements of content development, device based issues and design process. Economides [11], presented the Framework for designers and developers of m-learning system and highlighted the importance of context aware learning and fours states of context aware learning. Zhao.ed.al [12], presents an efficient design for custom made revised contents and it proposes some algorithms to create adaptive and intelligent contents for learners and highlights the context-aware mobile learning system which can increase the learning efficiency and interest Sergio Gómez et.al [13] exhibited a setting mindful versatile and client made portable learning framework, specifically the Units of Learning portable Player (UoLmP) (a). alterations to the interconnection of the learning exercises (specifically, the learning stream) (b). alterations to the instructive assets, apparatuses and administrations that help the learning exercises. The imperative finding is alterations can encourage understudies to effectively total the learning exercises of an instructive situation. III. RESEARCH ISSUES IN M-LEARNING UI Design Mobile phones experience the hostile effects of little screens, prohibitive information strategies and restricted battery life. Hence, the interface outline for M-learning administrations must address clients' issues without over-burdening them with pointless complexity, working too gradually or consuming excessive power. Contextualized Learning Context can be information about Identities (Users), Activity, Learner Profile, Time, Localization, Environment etc. Existing mobile learning services act as passive components rather than active components that can be embedded with context awareness mechanisms and the present approaches for service discovery neglect contextual information on the surrounding environment [14] Adaptive and Personalized Learning In E-Learning, the learning content has perceived high failure rates as learners become gradually dissatisfied with subjects that do not engage them [2]. Adaptive learning solutions have been used as possible approaches to address this dissatisfaction by trying to personalize the learning experience for the student. This learner consent can help to improve learner satisfaction with the learning experience. The significant concern is lack of context modeling and cognitive procedures that allow combination of various contextual features for better personalization [14]. IV. PROPOSED WORK Before moving into the proposed work, some research survey questions are analyzed below. The questions focused on the feasibility of mobile leaning implementation in Shinas College of technology. The data samples collected from 52 students in various levels and the data are presented below. Q1. Mobile Operation System: Most of the students use android mobiles & IOS and a few of them use windows operating system. This question will help us to design the applications based on the platforms used by the users. The figure 2 show the data distribution. Figure 2: Types of Mobile (OS) 0 10 20 30 40 Types of Mobile (OS) No of Users
  3. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 06 Issue: 02, December 2017, Page No.34-37 ISSN: 2278-2400 36 Q2. Mobile Screen Size: The most attractive mobile screen size is 4 to 5 however, some students like a bigger screen (>5) from the available branded mobile models. The figure 3 shows the data distribution. Figure 3: Mobile Screen Size Q3: Internet Technology Support Currently students are using 4G technology. So, data access speed is comparatively higher than 3G and 2G technologies which were used earlier. The figure 4 shows the data distribution. Figure 4: Internet Technology Support Q4: Number of hours usage per day The data distribution figure 5 given below, shows that most of the students are spending more and more time on their mobiles. This question helps to understand the users’ interest on mobile applications Figure 5: Usage per day in hours Q5: Application Interest This question concentrates on the best way to outline the learning content with a particular aim to accomplish or achieve the understudy's advantage. The figure 6 given below shows that students are very much interested in chatting and the social media. Figure 6: Application Interest From the above analysis, learning becomes easier if we present the learning content in view of the user preference through mobile based on the curriculum. Based on the survey above and the earlier work in the arena of mobile learning, the proposed system is a hybrid app which uses the mobile and server. It must have the following components. The figure 7 shows the proposed framework for SHCT. Data Repository: The data repository consists of Learners Personal and Activities data, device profile and environment details of the mobile users. Adaptation Engine: The main work of an adaptation engine is to generate the content for the learners based on their repository and current environment, device status. The adaptation engine has two levels 1. System Centric 2. Learner Centric Context Sensing and Acquisition: This component identifies the mobile and the user environment in order to generate the required content. Service Processing Engine: This component is used to present the content to users based on the device size and other features. Figure 7: Proposed M-Learning framework for SHCT 0 10 20 30 40 50 <=5 >5 Mobile Screen Size Users 0 50 2G 3G 4G Internet Technology Support Users 0 20 40 60 < 1 hr >1hr Usage per day Users 0 20 40 60 Application Interst Users
  4. Integrated Intelligent Research (IIR) International Journal of Business Intelligents Volume: 06 Issue: 02, December 2017, Page No.34-37 ISSN: 2278-2400 37 V. CONCLUSION This paper presents the overview of m-learning and highlights its features, characteristics and m-learning. Pointed out research issues will help the new researcher to understand the domain well. Also, it presents the suitability or feasibility analysis for m- learning in SHCT. The result shows, m-learning is highly suitable for SHCT students and finally, presents the proposed framework for m-learning in SHCT. References [1] Alsaadat.K(2009), Mobile Learning and university teaching, International Conf. on Education and New Learning Technologies, Barcelona, Spain. [2] K. Frankola,(2001),Why online learners dropout, in Workforce, pp. 53-63. [3] Satyanarayanan, M., (2010). Mobile computing: the next decade. In: Proc. 1st ACM Workshop on Mobile Cloud Computing &Services: Social Networks and Beyond (MCS ’10), pp. 5:1–5:6. [4] Jeng, Y.-L., Wu, T.-T., Huang, Y.-M., Tan, Q., Yang, S.J.H., (2010). Educational Technology & Society 13 (3), 3–11. [5] Shivesh,T., and Lokendra, K.T.,(2016), Sequential Rule Mining in M-Learning Domain, IJCA, vol .134, no.3. [6] Fezile,O., Nadire,C.(2011), Basic Elements and Characteristics of Mobile Learning, Procedia social and Behavioral Sciences, pp. 937-942. Elsevier. [7] Chikh, A.(2014), Journal of King Saud University – Computer and Information Sciences, Vol. 26, p. 29–40. [8] David Parsons (2007) , A Design Requirement Framework for Mobile Learning Environment, Journal of Computers, Vol. 2. No.4 [9] Nauris Paulins, Irina Arhipova, Signe Balina (2015), Procedia Computer Science 43, pp.147- 153, Science Direct. [10]Anastasios A. Economides (2008), Context aware Mobile Learning, The Open Knowledge Society. A Computer Science and Information Systems Manifesto. Vol. 19. pp 213-220, Springer. [11]Xinyou ZHAO, Fumihiko ANMA, Toshie NINOMIYA, Toshio OKAMOTO (2008) , Personalized Adaptive Content System for Context-Aware Mobile Learning, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.8. [12]Sergio Gómeza, Panagiotis Zervasb, c,, Demetrios G. Sampsonb, c, Ramón Fabregata (2014), Journal of King Saud University – Computer and Information Sciences, Vol. 26,pp. 47–61, Elsevier. [13]Rachid Benlamri and Xiaoyun Zhang.(2014 ), Human-centric Computing and Information Sciences, pp.4-12. Springer Journal.
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