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The Efficacy of Mobile Learning in Saudi Arabia Proposal


Introduction

Technology is the twenty-first century’s way to reshuffle the structure of the educational system on the way to academic excellence. Mobile learning is a significant subject, as it represents a new dimension of education technology, with its focus on ease of access and informality of learning. This paper proposes research on the efficacy of mobile learning within the context of Saudi Arabia. The key points of the research, namely, the problem statement, the methodology, and the limitations, are outlined. The paper provides a literature review of contemporary peer-reviewed articles and books to present the key perspectives of mobile technology studies.

Problem Statement

The concept of mobile learning refers to the educational process occurring through the medium of wireless devices. These can include smartphones, tablets, laptops, etc. Because these devices are either handheld or simply compact, m-learning is sometimes described as “knowledge in the hand” (Narayanasamy & Mohamed, 2013, p. 34). The rapid development of m-learning is perfectly explainable as the global community goes online and access to technology becomes more open. In Saudi Arabia, people acquire improved mobile devices on an annual basis, creating and sustaining a well-established mobile infrastructure that makes m-learning possible (Narayanasamy & Mohamed, 2013).

M-learning has already become a part of objective reality within and outside the educational community. On the other hand, despite the rapidity of its incorporation, m-learning as an educational practice is surrounded by some questions about its efficacy, cost-effectiveness, and ease of implementation. All of these factors are encompassed by the practical value of m-learning, specifically in the Saudi Arabian context, which constitutes the relevance of this paper.

Research Question and Objectives

About the problem of practical value, the goal of the proposed research is to ascertain if newly-emerging m-learning practices result in the improvement of academic outcomes in the Saudi Arabian context. Empirically speaking, students seem to be the most active mobile technology users and, therefore, the most likely to experience education-related changes induced by m-learning.

The research question can be formulated as follows: how effective is mobile learning for improving the academic outcomes of Saudi Arabian undergraduate students?

To answer the question, the following objectives will have to be accomplished:

  1. Review existing literature on the subject and single out the common points of concern about m-learning implementation and efficacy;
  2. Develop and design an intervention to assess the impact of mobile learning on student engagement, attitudes, and acquired competency;
  3. Implement the designed instruction and analyze the outcomes of implementation concerns.

Literature Review

A review of peer-reviewed articles and books dating back no more than five years was conducted to determine the most common issues surrounding the implementation of mobile learning. The rationale for selecting such sources was that m-learning is a relatively new phenomenon in education, and the educational community has to base the evidence on relevant data. The literature and articles reviewed about m-learning implementation are concerned with:

  1. The current status of m-learning in an educational setting;
  2. Acceptance by students as well as educators;
  3. Compliance with students’ educational needs;
  4. Impact on students’ academic performance.

Current Status of M-learning

Mobile learning is listed among the basic ideas of learning practice improvement, as it keeps the students engaged while simultaneously expanding their learning contexts (Paul & Elder, 2014; Beetham & Sharpe, 2013). Among the devices used in learning, mobile phones are reported to have the most popularity; personal digital assistants (PDA) such as tablets and similar devices are the second most popular (Wu et al., 2012). Mobile devices are used to receive assignments and turn them in, share tasks with classmates, and participate in group activities and web-conferences, etc. Naturally, students are reported to use smartphones and PDAs outside of the classroom; some of them are known to utilize their smartphones for learning purposes and self-education, e.g., reading and watching educational video materials (Zakaria, Jamal, Bisht, & Koppel, 2013). At that, social networks are used for more general purposes, such as socialization and reading the news (Bangert & Almahfud, 2014).

Saudi Arabia is reported to be the third most active smartphone user on a global scale (Badwelan, Drew, & Bahaddad, 2016). Therefore, it can be forecasted that m-learning, a practice already developing in the Saudi Arabian context, will see further massive growth in the years to follow. Indeed, although the findings are somewhat limited in terms of gender variability and sampling size, some Saudi-based studies emphasize the perceived readiness to use mobile technology for learning purposes among Arabian students (Seliaman & Al-Turki, 2012).

From these studies, it can be concluded that m-learning is a current trend in the field of education. Specifically, as the world’s third most active smartphone user, Saudi Arabia has every opportunity to integrate m-learning into its education system.

M-learning Acceptance

The success of a new technology is dependent, among other factors, on the enthusiasm of its users, which is why the assessment of acceptance levels is so important for m-learning implementation (Abachi & Muhammad, 2014). Several studies use the ‘Unified Theory of Acceptance and Use of Technology’ (UTAUT) framework to estimate the levels of acceptance (Nassuora, 201; Al-Hujran, Al-Lozi, & Al-Debei, 2014; Khan, Al-Shini, Al-khanjari, & Sarrab, 2015). Its utility can be explained by the fact that it successfully correlates the behavioral factors that are unique to each culture and the success of adoption about learning improvement promotion (Thomas, Singh, & Gaffar, 2013). Within the UTAUT model, the students’ perceptions of the effort they might spend while using m-learning, as well as the resources society will need to adopt it and how it will impact society can be singled out as the determinants of their attitudes. On the whole, the students’ prognoses can be deemed positive.

Behavioral models can be considered the second most popular when assessing the rates of m-learning acceptance. The factors determining this acceptance vary from study to study, but different bodies of evidence agree on compatibility and control as the most critical factors for the adoption to run smoothly (Chung, Chen, & Kuo, 2014; Cheon, Lee, Crooks, & Song, 2012).

Other studies assessing the acceptance factors relied on qualitative questionnaires and interviews to single out repeated motives. At that, students’ perception of the new technology’s utility, and the efficiency of their own performance when using this technology, have the most value in determining their overall attitude (Park, Nam, & Cha, 2011). Students tend to demonstrate better attitudes towards learning through iPads than engaging in computer-based interactions (Martin & Ertzberger, 2013). These conceptions are contradicted by the views of teachers who tend to regard iPads and other handheld devices as distractors and are reported to favor PCs over PDAs (Şad & Göktaş, 2013). This contradiction can be serious because frustration resulting from the lack of institutional support is deemed a critical discouragement factor in m-learning implementation (Gikas & Grant, 2013).

Thus, the acceptance of new technologies is an important issue that cannot be overlooked, even in a setting such as Saudi Arabia, where there are multiple opportunities to integrate such practices.

M-learning Compliance with Students’ Needs

The ultimate goal of any educator is to improve their classroom practice while at the same time optimizing the students’ learning experience (Carr-Chellman, 2016). At that, the US educational system is much critiqued for its desire to both standardize student outcomes and tailor-fit their learning experience individually (Ravitch, 2014; Reeves, 2011; Zimmerman & Schunk, 2014). M-learning can improve the situation in that it is individually customizable for every learner’s requirements.

Students with special needs, learning difficulties, and impairments can benefit from customizable applications that provide them with autonomy and equal access (Fernández-López, Rodríguez-Fórtiz, Rodríguez-Almendros, & Martínez-Segura, 2013). The latter is particularly important for distance learners, both within the country and from abroad, as it allows them to combine formal and informal education in a variety of contexts (Wong, 2012).

Apart from accommodating the health status of the students, m-learning is known to benefit those coming from families with less education, providing them with the learning opportunities they are unable to get at home (Lai, Luo, Zhang, Huang, & Rozelle 2015). The exposure to new dimensions of learning through mobile devices is reported to increase the students’ interest and overall positive attitude.

Some studies expressed their concerns that students can face an information overload when exposed to the multi-task regime of mobile and computer-aided learning (Chu, 2014). Importantly, these conclusions were made based on formative assessment tests that retrieved the students’ feedback on net technology usage. However, m-learning is also shown to comply with the specific needs of students based on their age and mental development, which allows for integrated learning in a game form, increasing their motivation and alleviating stress (Domínguez, Saenz-de-Navarrete, de-Marcos, Fernández-Sanz, Pagés, & Martínez-Herráiz, 2013).

The studies demonstrate the potential of m-learning as a technology that accounts for students’ needs in terms of health, background, and learning styles.

M-learning Impact on Student Performance

As a new phenomenon in education, m-learning remains understudied, although it is known to be successful when used correctly across all ages and disciplines in terms of acquired competencies and learning efficiency (Huang, Liao, Huang, & Chen 2014). For instance, an experiment involving third-grade children studying maths found a considerable improvement in the young learners’ outcomes (Kiger, Herro, & Prunty, 2014).

As an experimental technique, m-learning has been used with students in varying disciplines and settings. A meta-analysis demonstrates positive results for 15 out of 35 studies on m-learning, primarily in language learning (Burston, 2015). In an informal setting, mobile learning is shown to support the inquisitive spirit in learners, which allows for increased autonomy and, at the same time, enhances control over their learning process (Jones, Scanlon, & Clough, 2013).

Because m-learning encourages collaboration, it is possible to predict that m-learners will be more supportive in an informal setting. However, for instruction to work as desired, the learning should be personalized – as evidenced by experiments indicating better post-test performance (Hsu, Hwang, & Chang, 2013). Such an approach would also facilitate the inclusion of non-conventional thinkers and students with special requirements, whose performance with digitized concept mapping was demonstrated to be considerably more efficient than with regular text-based maps (Yen, Lee, & Chen, 2011). Because m-learning provides a unique combination of time-space contexts, inclusion and subsequent performance enhancement in learners with special needs can be predicted (Kearney, Schuck, Burden, & Aubusson, 2012).

One can conclude that m-learning is viable as an effective practice in all settings and all age groups of learners.

Proposed Methodology

In compliance with the set objectives, a review was made to assess the efficacy of m-learning in several respects. The evidence reviewed suggests that m-learning is currently gaining popularity in the field of education around the globe, and is being used with students of all ages and learning profiles. There are several models to estimate acceptance rates, but cultural and developmental factors are deemed the most significant. M-learning can optimally adhere to the needs of the students and is known to have positive results on their performance.

So far, the evidence speaks in favor of the practice. To ascertain whether the practice is applicable in Saudi Arabia, some experimental evidence will be needed. The proposed study will have a mixed-method experimental design, which will require the implementation of a developed m-learning intervention and testing both formatively and summatively to assess the student outcomes and gather their feedback qualitatively.

In this respect, two data analysis techniques are to be used by the proposed study design. Correlation analysis will be used to elicit whether students’ academic success and failure are connected with their knowledge of using mobile learning technologies and attitudes towards this method of learning. Another technique is the qualitative content analysis that is to be implemented about interviews and questionnaires. The summative content analysis seems to be the most suitable option since answers and opinions are expected to be diverse. After all, data are collected, several keywords and topics will be emphasized, and the underlying context will be identified.

As for the timeline, the following stages are proposed:

Stage Time
Planning and design of the study
  • Building a research team
  • Finding sites
  • Creating interviews and questionnaires
  • Receiving the approval of the institutional review board
3 weeks
Mobile learning implementation 12 weeks
Data collection
  • Obtaining information about students’ academic performance
  • Conducting interviews and questionnaires
4 weeks
Data analysis
  • Quantitative analysis
  • Qualitative analysis
3 weeks
Dissemination
  • Reports
  • Meetings
  • Publication
3 weeks

Limitations

The limitations of the proposed research include the small size of the sample, which may or may not skew the results in such a way as to make causality impossible to predict. Therefore, to generalize the findings for a larger population, further studies involving more participants are needed. Apart from that, time limitations may affect the result to some extent: it is not impossible that some students need more time to become accustomed to mobile learning and make use of it.

References

Abachi, H. R. & Muhammad, G. (2014). The impact of m-learning technology on students and educators. Computers in Human Behavior, 30, 491-496.

Al-Hujran, O., Al-Lozi, E., & Al-Debei, M. (2014). “Get ready to mobile learning”: Examining factors affecting college students’ behavioral intentions to use m-learning in Saudi Arabia. Jordan Journal of Business Administration 10(1), 111-128.

Badwelan, A., Drew, S., & Bahaddad, A. A. (2016). Towards acceptance m-learning approach in higher education in Saudi Arabia. International Journal of Business and Management, 11(8), 12-30.

Bangert, A. & Almahfud, M. (2014). An exploratory study of university students’ smartphone use for learning in the US and Saudi Arabia.

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Beetham, H. & Sharpe, R. (2013). Rethinking pedagogy for a digital age: Designing for 21st century learning. New York, NY: Routledge.

Burston, J. (2015). Twenty years of MALL project implementation: A meta-analysis of learning outcomes. ReCALL, 27(1), 4-20.

Carr-Chellman, A. A. (2016). Instructional design for teachers: Improving classroom practice (2nd ed.). New York, NY: Routledge.

Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054-1064.

Chu, H. (2014). Potential negative effects of mobile learning on students’ learning achievement and cognitive load – a format assessment perspective. Journal of Educational Technology & Society, 17(1), 332-344.

Chung, H., Chen, S., & Kuo, M. (2014). A study of EFL college students’ acceptance of mobile learning. Procedia – Social and Behavioral Sciences, 176, 333-339.

Domínguez, A., Saenz-de-Navarrete, J., de-Marcos, L., Fernández-Sanz, L., Pagés, Fernández-López, A., Rodríguez-Fórtiz, M. J., Rodríguez-Almendros, M. L., & Martínez-Segura, M. J. (2013). Mobile learning technology based on iOS devices to support students with special education needs. Computers & Education, 61, 77-90.

Gikas, J. & Grant, M. M. (2013). Mobile computing devices in higher education: Student perspectives on learning with cellphones, smartphones & social media. The Internet and Higher Education, 19, 18-26.

Hsu, C., Hwang, G., & Chang, C. (2013). A personalized recommendation-based mobile learning approach to improving the reading performance of EFL students. Computers & Education, 63, 327-336.

Huang, Y., Liao, Y., Huang, S., & Chen H. (2014). Jigsaw-based cooperative learning approach to improve learning outcomes for mobile situated learning. Journal of Educational Technology & Society, 17(1), 128-140.

Jones, A. C., Scanlon, E., & Clough, G. (2013). Mobile learning: Two case studies of supporting inquiry learning in informal and semiformal settings. Computers & Education, 61, 21-32.

Kearney, M., Schuck, S., Burden, K., & Aubusson, P. (2012). Viewing mobile learning from a pedagogical perspective. Research in Learning Technology, 20. Web.

Khan, A. I., Al-Shini, H., Al-khanjari, Z. A., & Sarrab, M. (2015). Mobile learning (m-learning) adoption in the Middle East: Lessons learned from the educationally advanced countries. Telematics and Informatics, 32(4), 909-920.

Kiger, D., Herro, D., & Prunty, D. (2014). Examining the influence of a mobile learning intervention on third grade math achievement. Journal of Research on Technology in Education, 45(1), 61-82.

Lai, F., Luo, R., Zhang, L., Huang, X., & Rozelle, S. (2015). Does computer-assisted learning improve learning outcomes? Evidence from a randomized experiment in migrant schools in Beijing. Economics of Education Review, 47, 34-48.

Martin, F. & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68, 76-85.

Narayanasamy, F. S. & Mohamed, J. B. K. (2013). Adaptation of mobile learning in higher educational institutions of Saudi Arabia. International Journal of Computer Applications, 69(6), 34.

Nassuora, A. B. (2012). Students acceptance of mobile learning for higher education in Saudi Arabia. American Academic & Scholarly Research Journal, 4(2), 1-6.

Park, S. Y., Nam, M., & Cha, S. (2011). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605.

Paul, R., & Elder, L. (2014). How to improve student learning: 30 practical ideas (3rd ed.). Tomales, CA: Foundation for Critical Thinking.

Ravitch, D. (2014). Reign of Error. New York, NY: Vintage Books.

Reeves, A. (2011). Where Great Teaching Begins. Alexandria, VA: ASCD.

Şad, S. N. & Göktaş, O. (2013). Preservice teachers’ perceptions about using mobile phones and laptops in education as mobile learning tools. British Journal of Educational Technology, 45(4), 606-618.

Seliaman, M. E. & Al-Turki, M. S. (2012). Mobile learning adoption in Saudi Arabia. International Journal of Computer, Electrical, Automation, Control and Information Engineering, 6(9), 1129-1131.

Thomas, T. D., Singh, L., & Gaffar, K. (2013). The utility of the UTAUT model in explaining mobile learning adoption in higher education in Guyana. International Journal of Education and Development using Information and Communication Technology, 9(3), 71-85.

Wong, L. (2012). A learner-centric view of mobile seamless learning. British Journal of Educational Technology, 43(1), 19-23.

Wu, W., Wu, Y. J., Chen, C., Kao, H., Lin., C., & Huang, S. (2012). Review of trends from mobile learning studies: A meta-analysis. Computers & Education, 59(2), 817-827.

Yen, J., Lee, C., & Chen, I. (2011). The effects of image-based concept mapping on the learning outcomes and cognitive processes of mobile learners. British Journal of Educational Technology, 43(2), 307-320.

Zakaria, N., Jamal, A., Bisht, S., & Koppel, C. (2013). Embedding a learning management system into an undergraduate medical informatics course in Saudi Arabia: Lessons learned. Medicine 2.0, 2(2), e13.

Zimmerman, B. J., & Schunk, D. H. (2014). Educational Psychology: A century of Contributions. New York, NY: Routledge.

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IvyPanda. (2020, July 31). The Efficacy of Mobile Learning in Saudi Arabia. Retrieved from https://ivypanda.com/essays/the-efficacy-of-mobile-learning-in-saudi-arabia/

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"The Efficacy of Mobile Learning in Saudi Arabia." IvyPanda, 31 July 2020, ivypanda.com/essays/the-efficacy-of-mobile-learning-in-saudi-arabia/.

1. IvyPanda. "The Efficacy of Mobile Learning in Saudi Arabia." July 31, 2020. https://ivypanda.com/essays/the-efficacy-of-mobile-learning-in-saudi-arabia/.


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IvyPanda. "The Efficacy of Mobile Learning in Saudi Arabia." July 31, 2020. https://ivypanda.com/essays/the-efficacy-of-mobile-learning-in-saudi-arabia/.

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IvyPanda. 2020. "The Efficacy of Mobile Learning in Saudi Arabia." July 31, 2020. https://ivypanda.com/essays/the-efficacy-of-mobile-learning-in-saudi-arabia/.

References

IvyPanda. (2020) 'The Efficacy of Mobile Learning in Saudi Arabia'. 31 July.

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