Opportunities and Obstacles for Mobile Learning in a Business School

The 2nd International Conference on Communications and Information Technology (ICCIT): Digital Information Management, Hammamet

Opportunities and Obstacles for Mobile Learning in a Business School Yaneli Cruz Telecom EM Research Center / Department of Accounting Institut Mines-Telecom / Instituto Tecnológico Autónomo de México Evry, France / Mexico City, Mexico [email protected] / [email protected]

Imed Boughzala

Saïd Assar

Telecom EM Research Center Institut Mines-Telecom Evry, France [email protected]

Telecom EM Research Center Institut Mines-Telecom Evry, France [email protected]

Virginia Tech College of Engineering require students to acquire a mobile device [4] and companies, such as Nike and Outstart, have used mobile learning programs to train their employees [5]. International academic conferences were created to discuss mobile learning issues such as MLearn, WMUTE and IADIS. Recently, UNESCO started a Mobile learning initiative, in partnership with Nokia, to grow a global community, to help national governments to take advantage of the educational opportunities and to explore how mobile technologies can be used to support teachers and their professional development [3].

Abstract—Mobile technology and mobile applications evolution have increased possibilities for mobile learning (ML). However, the lack of perceived learning value and institutional infrastructure are hindering the possibilities for ML attempts. The purpose of our study is the understanding opportunities and obstacles of mobile technologies as perceived by teachers in higher education. A questionnaire was developed based on actual research about technology adoption in higher education and was used to interview 14 teachers. Participants were asked to identify different issues associated with using mobile technology in education. In response, participants provided insights about ML perception, such as opportunities to enhance communication with students, availability for resources and immediate feedback. Finally, they identified technological, institutional, pedagogical and individual obstacles that threaten ML practices. Keywords- mobile education; teacher

learning;

I.

mobile

technology;

However, there is still much to explore and to understand about how to successfully use the mobile devices in order to get benefits from mobile learning possibilities such as availability and immediate feedback. Hence, the question raised in this paper is “What obstacles restrict the opportunities for mobile learning in Higher Education?”

higher

The aim of this study is to explore the research question and to formulate a pathway for researching ML use and adoption in Higher Education.

INTRODUCTION

Along with the evolution and popularity of telecommunications and devices, ML has emerged as an enhanced opportunity for learning that would allow learners to gain knowledge and to develop skills through teaching materials and activities available anytime and anywhere through mobile devices. With the success of mobile commerce and mobile applications, the shift to mobility in phones and computing was irreversible. Indeed, a 2009 study conducted by Morgan Stanley states that mobile Internet is growing faster and will be bigger than the desktop Internet did, due to five converging technologies and social adoption trends: 3G, social networking, video, VoIP and impressive mobile devices [1].

II.

A. Mobile Devices Mobile devices usually refer to communicators, multimedia entertainment and business processing devices designed to be transported by the human owners [6]. The basic mobile device capability is voice and text messages and capabilities can be added integrating graphic, programs or applications, mobile browsers and geographic positioning systems. Mark Wiser pioneered about mobile devices with the ubiquitous computing concept through the development of new hardware systems designed for everyday life categorized devices in tabs (small devices with three buttons and with the ability to sense position in a building), pads (family of notebook-sized devices connected with the environment) and boards (highly interactive devices with pop-up menus) [7].

Over the last years there is an increasing interest in ML around the world on going from research to initiatives with high impact on society. With the acceptance of distance education and e-learning, universities around the world launched new projects on mobile learning. The European Commission in Brussels funded the major multi-national MOBIlearn and M-Learning projects [2]. Duke University and

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BACKGROUND

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These categories are not so far from what is experienced in the market nowadays.

subset of distance learning, which is in turn a subset of flexible learning [16].

The trend toward convergence of applications, the ubiquitousness of mobile phones, and the continuing demand for smaller, more powerful devices indicates that mobile technologies are, indeed, mainstream [8]. Naismith categorize mobile devices considering two orthogonal dimensions; personal versus shared, and portable versus static outlined in Fig. 1 [9].

Although this excitement about being mobile users and therefore possible learners, ML is still in the early stages, and its theoretical underpinning have not yet matured [17]. The availability of mobile technology per se does not guarantee that its potential will be realized. The concern about how to promote acceptance of ML is unsolved since there is a lack of understanding on the factors driving ML adoption [18], [19]. The existing Learning Management System (LMS) were developed to be used on a PC with Internet access, and they are not tailored to be used on mobile devices. C. Pedagogical Considerations It is important to be clear about what exactly are ML pedagogical contributions that are new and different from previous application of technologies to learning. Activities like sharing, exploring, recording, reflecting are possible in forms of e-learning, but what may be innovative in ML is the way they are integrated in order to enrich and optimize the learning process [20]. ML can be more motivating than other kinds of distance learning. In particular, the affective forms of motivation afforded by ML are characterized as control, ownership, fun, communication, learning-in-context and continuity between contexts [21], [22].

Figure 1.

A key to ML success is the ability of educators to design and to develop pedagogically sound opportunities and environments that enhances learning [23]. Therefore, a mobile learning theory is required to develop mobile resource and to design activities that enhance learning through mobile devices.

Classification of mobile technologies

In education, teachers are expecting more advances on future devices such as being fully connected 24/7/365, Wi-Fi and 4G access, resistant, recognition applications for learning, chargeable with solar cells and hardware modules to facilitate augmented reality [10].

III.

METHOD

Fourteen professors (8 females, 6 males) from a Business School in France participated in the study. Their teaching subjects included Information Systems, Marketing, Strategy, Management, Languages. All of them have prior experience with Moodle as the Learning Management System (LMS) but only 30% of them have experienced ML at their courses. The sample included associate and assistant professors status. Three professors have taught a course using ML tools. The ML resources most used are presentations, videos, articles and blogs. In the participants' selection process, we tried to diversify subjects that are taught, and to include participants with varying level of implication in ML experimentation.

B. From E-Learning to Mobile Learning The concept of ML is evolving and there are different ML perspectives in the literature. Mobility has a variety of connotations and these will colour conceptualizations of mobile education. It may mean learning whilst traveling, driving, sitting , or walking; it may mean hands-free learning or eyefree learning [11]. Each ML definition focus on different perspective centered on technologies, mobility, context, augmenting formal education, learner, individualism, ubiquitous or e-learning [12]. Some techno-centric definition includes ‘the acquisition of any knowledge and skill through using mobile technology, anywhere, anytime, that results in an alteration in behavior’ [13]. Lan and Sie described mobile learning (m-learning) as a kind of learning model allowing learners to obtain learning materials anywhere and anytime using mobile technologies and the Internet [14]. Other researchers developed their concept considering the emphasis of social practices surrounding learning activities and the context. Vavoula and Sharples suggest learning is being mobile considering three elements; in terms of space, it happens at the workplace, at home; it may relate to work demands, self-improvement, or leisure; and it is mobile with respect to time, it happens at different times during the day, on working days or on weekends [15]. Finally, ML can be considered as a subset of e-learning. E-learning is in turn a

An exploratory questionnaire addressing the perception and use of ML was developed based on the elements that influence teachers’ adoption and integration of mobile technology within learning environments. A literature review of ML adoption and exploratory studies on ML reveled that there are particular issues that will influence teachers to adopt and to integrate mobile technology in their courses. Two general issues are included in this research in order to understand the perception and possible adoption. First, the questionnaire attempts to discover important issues regarding the opportunities for ML. Second, it identifies some obstacles or inhibitors to use ML.

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basically for interaction, for reading or for watching a video. But since 2010, they began to realize technological enhancements on devices, and that more and more students have the devices at their disposal. Others respondents indicated that traditional e-learning platforms should be suitable for ML.

A sample of 16 participants was initially targeted, they were contacted by phone or by e-mail, and the participants accepted or rejected the invitation. The questionnaire used a semi-structured interview with an open format. It was emailed to the participants prior to the interview session. Since the session used an open format, participants were able to comment as much as they wanted including further issues not specifically addressed on the questionnaire. IV.

The major institutional obstacles to academics’ use of ML include infrastructure, lack of support and institutional policies. Respondents referred to infrastructure as not complicated but not flexible. Others mentioned malfunctions including slow download times, bandwidth and connectivity issues that discourage teachers and students.

RESULTS

A. Opportunities In old days teachers were the repository of the knowledge and the students were passively absorbing it. Now the respondents face students technically well equipped and going to the Internet finding an amazing amount of resources related with their classes. Respondents indicated that like e-learning, ML can itself deliver opportunities for availability. As the materials are available anytime and anywhere, the opportunity to learn can be increased. Some respondents mentioned availability as the main opportunity with students because they value availability. For teachers, uploading electronic materials in the LMS is easier to share rather than to print and to distribute them previously considering the logistics behind it. Some respondents indicated that students appreciate reading on the screen and they prefer to have the files instead of an unmanageable book or paper. In areas like foreign languages, the accessibility for resources is quite useful because there are different formats available for students. Other respondents pointed out that some foreign students find availability as an opportunity for learning since they can record the classes and can watch the lecture anytime and anywhere on their devices. Finally, one respondent considered space as an opportunity. With ML the teacher is not restricted to a classroom with a blackboard or a whiteboard to give the class. Now they can transmit the lecture and can collaborate with students even if they are in the campus or abroad supported by their mobile devices.

Respondents also indicated that institutional policies such as annual assessment, workload, accreditation procedures and training represent an obstacle to use ML. In particular, the lack of a system reward and lack of appreciation from the institution had disabled the opportunity to adopt ML activities. There is an annual assessment that includes presential time. However, electronic resources or recorded classes are not included on the assessment and they are time consuming with much back office work behind. Furthermore, workload is higher and is not recognized, especially in financial terms. Respondents also identified some pedagogical obstacles such as information overload, skepticism from students and teachers and learning impact. Given an enormous amount of resources, students are not using mobiles for learning they use mobiles for a practical or quick search however, they can use a laptop or phone. If the professors are uploading the material for class, the likelihood for absenteeism increases. The respondents indicated that it is important to think what materials are on line and what are they going to do on a face to face activity. The class should include something special otherwise there is no motivation for attending. Personal obstacles identified were exposure, technological skills, teachers’ role and security. Electronic learning materials such as electronic lectures involve recording video and voice. Respondents expressed fearfulness about being recorded since they can lose control about the recording and could be exposed on the web. Others respondents mentioned that they do not have the adequate technical skills to use and to create electronic learning materials considering that some of them have a social science background. Some teachers upload good materials in the LMS and they are experiencing that students are no attending the class because they have all the materials on-line. Finally, the material copyrights’ and privacy issues regarding learning materials was mentioned. In old days, material copyrights was something very clear but now teaching materials, which are always copied, are vulnerable and some respondents find out not secure in terms of protecting their work.

Respondents foresee ML as a future where the students are going to be. However, some students are not relating learning activities with Smartphone usage. They look for concepts using the phone to search Wikipedia but learning practices are relatively new. Practices are changing very slowly, and only few teachers are starting to use ML. But still, the respondents expressed some doubts about ML’s future since some materials can be directly accessed with laptops. They also mentioned that ML has great opportunities for developing countries because the students do not have computers or tablet but the students in the campus have mobile devices and for the moment they have their learning environment with the minimum mobile use for education.

V.

B. Obstacles The respondents’ answers indicate that it is mainly technological, institutional, pedagogical and personal obstacles what impact on teachers’ use of m-learning activities. In regards to technological obstacles, respondents indicated that they were for a long period skeptical on ML. The size of the screen and the phone's interface were not good enough

DISCUSSION AND CONCLUSION

The study indicates availability as the key ML opportunity since the resources can be located anytime and anywhere. Mobile devices are incorporated more and more on students’ daily activities. However, some students are not relating learning activities with mobile devices usage.

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The obstacles discussed in this document include technological, institutional, pedagogical and personal issues. First, the size of the screen and interface on mobile devices were perceived as not sufficient to enhance mobile activities. Nowadays, devices are becoming more suitable with bigger screens and capabilities to interact and to read text using a better graphical interface. Second, the study revealed that major institutional obstacles to teachers’ use and integration of mobile technology include infrastructure, lack of support and institutional policies. Third, the pedagogical obstacles that influenced teachers’ use included information overload, skepticism from students and teachers and learning impact. Fourth, personal obstacles identified include exposure, technological skills, teachers’ role and security. Finally, the material copyrights’ and privacy issues regarding learning materials was exposed.

[3] [6] [7] [8]

[8]

[9] [11]

Even though mobile learning practices have been studied and implemented extensively, it seems that a pedagogical guideline is needed. Beyond being hype or trend, ML should bring educational practices that ensure collaboration and meaningful learning to be adopted at higher education. Education through mobile devices has the same challenges as traditional education pursuing motivation and long-term learning of students. Currently there are some successful practices with students promoting collaboration that can be shared. Thus, we must understand the capabilities of mobile technology and its challenges within universities to offer materials that teacher can experience in their classrooms.

[12]

[13]

[14]

[15]

There are some limitations for this study considering that the sample included different backgrounds and nationalities but it was only applied in one institution. This study will be replicated in American and Latin-American universities considering teacher, institutional and student perspectives to enhance the results.

[16] [17]

[18]

REFERENCES [1]

[2]

[4] [5]

[19]

Morgan Stanley. (2009, Dec. 15). The mobile internet report [Online]. Available: http://www.morganstanley.com/institutional/techresearch/mobile_intern et_report122009.html D. Keegan. (2005). The Incorporation of mobile learning into Mainstream Education and Training [Online]. Available: http://mlearning.noekaleidoscope.org/public/mlearn2005/www.mlearn.org.za/papersfull.html J. R. Corbeil and M. E. Valdes-Corbeil, “Are you ready for mobile learning?,” EDUCAUSE Quarterly, vol. 30, no. 2, pp. 51–58, Jun. 2007. A. Heiphetz. (2011, Nov.). mLearning: A practical approach to mobile technology for workforce training [Online]. Available: http://mcgraw-

[20]

[21] [22] [23]

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hillresearchfoundation.org/wp-content/uploads/2011/10/MobileLearning.pdf UNESCO, “Mobile learning week report,” 2011. S. Poslad, Ubiquitous computing: smart devices, environments and interactions, Chichester, U.K. : Wiley, 2009. M. Weiser, “The Computer for the 21st Century,” Scientific American, vol. 265, no. 3, p. 94, 1991. K. Peters, “m-Learning: Positioning educators for a mobile, connected future,” International Review of Research in Open & Distance Learning, vol. 8, no. 2, pp. 1–17, jul. 2007. L. Naismith, P. Lonsdale, G. Vavoula, y M. Sharples. (2004). Literature review in mobile technologies and learning [Online]. Available: http://archive.futurelab.org.uk/resources/documents/lit_reviews/Mobile_ Review.pdf T H E Journal, “Visions of mobile learning,” T H E Journal, vol. 38, no. 9, p. 28, oct. 2011. J. Traxler, “Defining, discussing, and evaluating mobile learning: The moving finger writes and having writ… ,” International Review of Research in Open & Distance Learning, vol. 8, no. 2, pp. 1–12, jul. 2007. N. O. Keskın y D. Metcalf, “The current perspectives, theories and practices of mobile learning,” Turkish Online Journal of Educational Technology, vol. 10, no. 2, pp. 202–208, abr. 2011. S. Geddes. (2004). Mobile learning in the 21st century: Benefit for learners [Online]. Available: http://knowledgetree.flexiblelearning.net.au/edition06/html/pra_simon_ geddes.html Y.-F. Lan y Y.-S. Sie, “Using RSS to support mobile learning based on media richness theory,” Computers & Education, vol. 55, no. 2, pp. 723– 732, Number 2010. M. Sharples, J. Taylor, y G. Vavoula, “A theory of learning for the Mobile Age,” in The Sage Handbook of Elearning Research, 2007, p. 221–47. T. H. Brown, “Towards a model for m-learning in Africa,” International Journal on ELearning, vol. 4, no. 3, pp. 299–315, 2005. P. B. Muyinda, “MLearning: pedagogical, technical and organisational hypes and realities,” Campus-Wide Information Systems, vol. 24, no. 2, pp. 97–104, 2007. Y. Liu, H. Li, y C. Carlsson, “Factors driving the adoption of mlearning: An empirical study,” Computers & Education, vol. 55, no. 3, pp. 1211–1219, Nov. 2010. Y. Liu, S. Han, y H. Li, “Understanding the factors driving m-learning adoption: a literature review,” Campus-Wide Information Systems, vol. 27, no. 4, pp. 210–226, Aug. 2010. D. Laurillard. (2007) Pedagogical forms of mobile learning: framing research questions [Online]. Available: http://eprints.ioe.ac.uk/627/1/Mobile_C6_Laurillard.pdf A. Jones, K. Issfroff, and E. Scanlon, “Affective factors in learning with mobile devices. In M. Sharples (Ed.),” LSRI, pp. 17–22, 2007. M. Sharples, Big Issues in Mobile Learning. University of Nottingham, 2007. M. Singh, “M-learning: A new approach to learn better,” International Journal of Education & Allied Sciences, vol. 2, no. 2, pp. 65–72, Jul. 2010.