Skip to main content

Advertisement

SpringerLink
Log in

A location-based privacy-preserving m-learning model to enhance distance education in Kenya

  • Published:
Journal of Computers in Education Aims and scope Submit manuscript

Abstract

Developments in mobile learning have seen the adoption of high-power, location-aware mobile gadgets in distance education. Unauthorized user’s location data collected by these devices could hamper sustainable adoption of m-learning systems. There is the need, therefore, to develop a secure location-based privacy-preserving model to evaluate learners’ behavioral intention to use location-aware mobile systems for distance education. The study employed descriptive design, and using a questionnaire, data were collected from a population enrolled for distance learning. Using SPSS version 20 and WarpPLS 5.0, data were statistically analyzed to validate or refute the intended objectives. The model would provide the university management with informed approach to consider privacy-preserving aspects in m-learning implementation. It will provide enlightened guidance to mobile-learning-application developers on the need to cater for learners’ privacy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Adams, A., & Blandford, A. (2003). Security and online learning: To protect or prohibit. Usability of online learning programs (pp. 331–359). UK: IDEA Publishing.

    Chapter  Google Scholar 

  • Bagozzi , R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94.

    Article  Google Scholar 

  • Bansal, et al. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138–150.

    Article  Google Scholar 

  • Beldad, A., de Jong, M., & Ateehoulder, M. (2010). How shall I trust the faceless and the intangible? A literature review on the antecedents of online trust. Computers in Human Behaviors, 26(5), 857–869.

  • Bettini, C., Wang, X., & Jajodia, S. (2005). Protecting privacy against location-based personal identification. In Secure data management. Volume 3674 of Lecture Notes in Computer Science (pp. 185–199). Berlin/Heidelberg: Springer.

  • Buthpitiya, S., Zhang, Y., Dey, A., & Griss, M. (2011). n-gram geo-trace modeling. In Proceedings of ninth international conference on pervasive computing. San Francisco, CA.

  • Charbaji, A., & Mikdashi, T. (2003). A path analytic study of the attitude toward e-government in Lebanon. Corporate Governance, 3(1), 76–82.

    Article  Google Scholar 

  • Chellappa, R., & Sin, R. (2005). Personalization versus privacy: An empirical examination of the online consumer’s Dilemma. Information Technology and Management, 6, 181–202.

    Article  Google Scholar 

  • Chow, W., & Angie, N. (2006). A study of trust in e-shopping before and after first-hand experience is gained. Journal of Computer Information Systems, 9(4), 125–130.

    Google Scholar 

  • Dinev, T., & Hart, P. (2006a). Internet privacy concerns and social awareness as determinants of intention to transact. International Journal of Electronic Commerce, 10(2), 7–29.

    Article  Google Scholar 

  • Dinev, T., & Hart, P. (2006b). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61–80.

  • Dinev, T., & Hu, Q. (2007). The centrality of awareness in the formation of user behavioral intention toward protective information technologies. Journal of the AIS, 8(7), 386–408.

    Google Scholar 

  • Duckham, M., & Kulik, L. (2006). Location privacy and location-aware computing. Australia: University of Melbourne.

    Google Scholar 

  • Faruq, M., & Hartini, B. (2013). The moderating effect of technology awareness on the relationship between UTAUT constructs and behavioural intention to use technology: A conceptual paper. Australian Journal of Business and Management Research, 3(02), 14–23.

    Google Scholar 

  • Glover, S., & Benbasat, I. (2011). A comprehensive model of perceived risk of e-commerce transactions. International Journal of Electronic Commerce, 15(2), 47–78.

    Article  Google Scholar 

  • GovTech. (2009). Survey raises consumer online privacy awareness. Government Technology Magazines, Government Technology.

  • Greene, S., & Kamimura, M. (2003). Ties that bind: Enhanced social awareness development through interactions with diverse peers. In Annual meeting of the association for the study of higher education. Portland: Oregon.

  • Hwang, G.-J. (2015). Mobile technology-enhanced learning. In Z. Yan (Ed.), Encyclopedia of mobile phone behavior (Vol. 1, pp. 541–548). IGI Global.

  • Hwang, G.-J. (2017, February 10). Mobile Learning research in specific disciplines. Retrieved from ouhk.edu.hk: http://www.ouhk.edu.hk/URC/Seminar_Prof%20Gwo-Jen%20Hwang.pdf

  • Hwang, G., Tsai, C., & Yang, S. (2008). Criteria, strategies and research issues of Context-aware ubiquitous learning. Educational Technology & Society, 11(1), 81–91.

  • Kambourakis, G. (2013). Security and privacy in m-learning and beyond: Challenges and state of the art. International Journal of u- and e- Service, Science and Technology, 6(3), 67–84.

  • Kim, M., & Ahn, J. (2006). Comparison of trust sources of an online market-maker in the e-marketplace: Buyer’s and seller’s perspectives. Journal of Computer Information Systems, 47(1), 84–94.

    Google Scholar 

  • Kock, N. (2015, July 20). WarpPLS 3.0 User Manual. Retrieved from http://www.scriptwarp.com/BBFB2E30-7E6A-4086-B7F8-A134A1745029/FinalDownload/DownloadId-EB35805702C247E12080F78E66A03D0E/BBFB2E30-7E6A-4086-B7F8-A134A1745029/warppls/UserManual_WarpPLS_V3_Redirect.pdf.

  • Kock, N. (2015b). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10.

    Article  Google Scholar 

  • Kukulska-Hulme, A. (2013). Aligning migration with mobility: Female immigrants using smart technologies for informal learning show the way. UNESCO Mobile Learning Symposium. Paris. Retrieved from unesco.org: http://www.unesco.org/new/fileadmin/MULTIMEDIA/HQ/ED/ICT/pdf/Kukulska-Hulme.pdf

  • Kumar, et al. (2008). Locking the door but leaving the computer vulnerable: Factors inhibiting home users’ adoption of software firewalls. Decision Support Systems, 9(1), 254–264.

    Article  Google Scholar 

  • Lee, M.-C. (2009a). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141.

    Article  Google Scholar 

  • Lee, M.-C. (2009b). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141.

    Article  Google Scholar 

  • Li, Y. (2011). Empirical studies on online information privacy concerns: Literature review. Communications of the Association for Information Systems.

  • Liao, et al. (2011). Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: An integrated model. Electronic Commerce Research and Applications, 10(6), 702–715.

    Article  Google Scholar 

  • Liu, Y., et al. (2012). A unified risk-benefit analysis framework for investigating mobile payment adoption. In: 2012 international conference on mobile business.

  • Lowry, P., Cao, J., & Everrard, A. (2011). Privacy concerns versus desire for interpersonal awareness in driving the sue of self-disclosure technologies: The case of instant messaging in two cultures. Journal of Management Information Systems, 27(4), 163–200.

  • MacCarthy, M. (2014). Student privacy: Harm and context. International Review of Information Ethics, 21, 11–24.

    Google Scholar 

  • Mason, R. (1986). Four ethical issues of the information age. MIS Quarterly, 10(1), 4–12.

    Article  Google Scholar 

  • Musau, F., & Obiria, P. (2013). Transparent computing: A new paradigm for increased user friendliness in service sharing. Kenyatta University Institutional Repository.

  • Nissenbaum, H. (2010). Privacy in context: Technology, policy, and the integrity of social life. Stanford University Press.

  • Obiria, P., Kimwele, M., & Cheruiyot, W. K. (2015a). A secure location-based privacy preserving framework for M-learning adoption to enhance distance education in Kenya: Work in progress. International Journal of Advanced Studies in Computer Science and Engineering (IJASCSE), 4(9), 1–7.

    Google Scholar 

  • Obiria, P., Kimwele, M., Cheruiyot, W., & Mwangi, G. (2015b). A location-based privacy preserving framework for M-learning adoption to enhance distance education in Kenya: Literature review. International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), 4(10), 7–14.

    Google Scholar 

  • Okazaki, S., Hairong, L., & Morikazu, H. (2009). Consumer privacy concerns and preferences for degree of regulatory control. Journal of Advertising, 38(4), 63–77.

  • Omar, K., Aláa, & Al-Nasrallah. (2011). Determinants of e-Gov adopt in Kuwait: The case of the traffic violation E-payment system (TVEPS). In The second Kuwait conference on e-Services and e-Systems. Kuwait.

  • Pan, Y., & Zinkhan, G. (2006). Exploring the impact of online privacy disclosures on onsumer trust. Journal of Retailing, 82(4), 331–338.

  • Pfitzmann, A., & Ohntopp, M. (2001). Anonymity, unobservability, and pseudonymity a proposal for terminology. In Designing privacy enhancing technologies, volume 2009 of Lecture Notes in Computer Science (pp. 1–9). Springer.

  • Podsakoff, P., MacKenzie, S., & Podsakoff, N. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903.

  • Rahman, M., Esichaikul, V., & Kamal, M. (2012). Factors influencing e-government adoption in Pakistan. Transforming Government: People, Process and Policy, 6(3), 258–282.

    Article  Google Scholar 

  • Ringle, C., Sarstedt, M., & Straub, D. (2012). Editor’s comments: A critical look at the use of PLS-SEM in MIS quarterly. MIS Quarterly, 36(1), iii–xiv.

  • Stone, M. (1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society, 36, 111–147.

    Google Scholar 

  • Tabachnick, B., & Fidell, L. (2007). Using multivariate statistics (5th ed.). Boston: Pearson Education Inc.

    Google Scholar 

  • Thietart, R. (2007). Doing management research: A comprehensive guide. Paris: Sage.

    Google Scholar 

  • Urbach, N., & Ahlemann, F. (2010). Structural equation modelling in information systems using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40.

  • Wagner, E. (2008). Realizing the benefits of mobile learning. Journal of Computing in Higher Education, 20(2), 4–14.

  • Werner, C., & Schermelleh-Engel, K. (2010). Deciding between competing models: Chi square difference tests. In Introduction to structural equation modeling with LISREL—version February 2010. Frankfurt: Goethe University.

  • Westin, A. (1967). Privacy and freedom. New York: Antheneum.

    Google Scholar 

  • Yamane, T. (1967). Statistics: An introductory analysis, 2nd Ed. New York: Harper and Row.

  • Zafar, A., Hasan, S., & Trigui, M. (2014). Towards secure m-Learning: An analysis. MAGNT Research Report (ISSN. 1444-8939) 2(5), 148–159.

  • Zhou, T. (2011). Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk. Journal of Electronic Commerce Research, 13(2).

  • Zhou, T. (2012). Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk. Journal of Electronic Commerce Research, 13(2), 135–144.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya

    Peter B. Obiria & Micheal W. Kimwele

Authors
  1. Peter B. Obiria
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Micheal W. Kimwele
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Peter B. Obiria.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Obiria, P.B., Kimwele, M.W. A location-based privacy-preserving m-learning model to enhance distance education in Kenya. J. Comput. Educ. 4, 147–169 (2017). https://doi.org/10.1007/s40692-017-0079-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40692-017-0079-4

Keywords

Search

Navigation