Advertisement

Education and Information Technologies

, Volume 24, Issue 2, pp 995–1014 | Cite as

Design of a mobile-based learning management system for incorporating employment demands: Case context of an Australian University

  • Harpreet Singh
  • Shah Jahan MiahEmail author
Article
  • 192 Downloads

Abstract

Mobile technologies have created enormous opportunities for improving information delivery and dissemination processes among individuals. While studies of the mobile-based technologies in health and businesses have been proliferated, research on mobile applications for education are still at its emergent stage, however, for developing user-centric support to enhance individual’s involvements in learning and teaching purposes. Moreover, formal methods of learning management systems (LMS) for supporting students and academics to achieve industry demands are still yet to be developed for higher education institutes. This study develops and evaluates an innovative mobile-based technology for enhancing current approaches of LMS by linking relevant industry into learning and teaching procedure in a case context of an Australian University. The solution artefact as a model can be viewed as an industry-enabled LMS that captures and processes data from students’ teaching materials, exercises and participation contents in order to develop assistive information which is directly related to the employers’ requirements. Design science method is adopted for designing and evaluating the solution artefact that meets the key requirements of the stakeholders. It is anticipated that the developed artefact would be applicable across Australian higher education sectors for enhancing industry uptake into improving pedagogy of learning.

Keywords

Learning management systems Learning pedagogy; student centric-learning Design science research E-portfolio system 

Notes

References

  1. Ahmad, C. N. C., Ching, W. C., Yahaya, A., & Abdullah, M. F. N. L. (2015). Relationship between constructivist learning environments and educational Facility in Science Classrooms. Procedia - Social and Behavioral Sciences, 191, 1952–1957.  https://doi.org/10.1016/j.sbspro.2015.04.672.CrossRefGoogle Scholar
  2. Ajzen, I. (2006). Behavioral interventions based on the theory of planned behavior. Resource URL: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.613.1749&rep=rep1&type=pdf
  3. Al-Sadi, J., & Abu-Shawar, B. (2009). M-learning: The usage of WAP technology in E-learning. International Journal of Interactive Mobile Technologies, 3(3), 10–16.  https://doi.org/10.3991/ijim.v3i3.808.Google Scholar
  4. Au, M. H., Liang, K., Liu, J. K., Lu, R., & Ning, J. (2018). Privacy-preserving personal data operation on mobile cloud—Chances and challenges over advanced persistent threat. Future Generation Computer Systems, 79, 337–349.CrossRefGoogle Scholar
  5. Biggs, D., Hovey, N., Tyson, P. J., & MacDonald, S. (2010). Employer and employment agency attitudes towards employing individuals with mental health needs. Journal of Mental Health, 19(6), 505–516.  https://doi.org/10.3109/09638237.2010.507683.CrossRefGoogle Scholar
  6. Bimrose, J., Brown, A., & Behle, H. (2014). Understanding the link between employers and schools and the role of the National Careers Service. BIS Research Paper Number 206, Department for Business Innovation & Skills, US.Google Scholar
  7. Blanchard, R. O. B., & O'Sullivan, K. (2015). Big data risk and opportunity. Internal Auditor, 72(5), 65–67.Google Scholar
  8. Casany, M. J., Alier, M., Mayol, E., Piguillem, J., Galanis, N., García-Peñalvo, F. J., & Conde, M. Á. (2012). Moodbile: A framework to integrate m-learning applications with the LMS. Journal of Research and Practice in Information Technology, 44(2), 129–149.Google Scholar
  9. Chang, B. R., Lee, Y.-D., & Liao, P.-H. (2017). Development of multiple big data analytics platforms with rapid response. Scientific Programming, 2017, 1–13.  https://doi.org/10.1155/2017/6972461.CrossRefGoogle Scholar
  10. Chen, D. Q., Preston, D. S., & Swink, M. (2015). How the use of big data analytics affects value creation in supply chain management. Journal of Management Information Systems, 32(4), 4–39.  https://doi.org/10.1080/07421222.2015.1138364.CrossRefGoogle Scholar
  11. Chisanu, J., Sumalee, C., Issara, K., & Charuni, S. (2012). Design and develop of constructivist learning environment on learning management system. Procedia - Social and Behavioral Sciences, 46, 3426–3430.  https://doi.org/10.1016/j.sbspro.2012.06.078.CrossRefGoogle Scholar
  12. Dalsgaard, C., Sorensen, E. K., & Mathiasen, H. (2009). E-learning concepts in higher education. Paper presented at the International Conference on E-Learning in the Workplace - ICELW 2009, New York.Google Scholar
  13. Deakin, H., Wakefield, K., & Gregorius, S. (2012). An exploration of peer-to-peer teaching and learning at postgraduate level: The experience of two student-led NVivo workshops. Journal of Geography in Higher Education, 36(4), 603–612.  https://doi.org/10.1080/03098265.2012.692074.CrossRefGoogle Scholar
  14. De Leeuw, A., Valois, P., Ajzen, I., & Schmidt, P. (2015). Using the theory of planned behavior to identify key beliefs underlying pro-environmental behavior in high-school students: Implications for educational interventions. Journal of Environmental Psychology, 42, 128–138.CrossRefGoogle Scholar
  15. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137–144.  https://doi.org/10.1016/j.ijinfomgt.2014.10.007.CrossRefGoogle Scholar
  16. Green, A., Atfield, G., & Barnes, S.-A. (2015). Employer involvement and engagement: Talent Match case study theme report. In. [Sheffield]: Centre for Regional Economic and Social Research.Google Scholar
  17. Gregor, S., & Hevner, A. R. (2013). Positioning and presenting design science research for maximum impact. MIS Quarterly, 37(2), 337–356.CrossRefGoogle Scholar
  18. Hary, S., Ahmad Mursyidun, N., Andika Bagus Nur Rahma, P., & Jehad, A. H. H. (2017). Developing an LMS-based cross-platform web application for improving vocational high school students’ competitiveness in ASEAN economic community. Jurnal Pendidikan Sains, 5(3), 72–79.  https://doi.org/10.17977/jps.v5i3.9694.Google Scholar
  19. Hashem, I. A. T., Chang, V., Anuar, N. B., Adewole, K., Yaqoob, I., Gani, A., . . . Chiroma, H. (2016). The role of big data in smart city. International Journal of Information Management, 36, 748–758.  https://doi.org/10.1016/j.ijinfomgt.2016.05.002.CrossRefGoogle Scholar
  20. Hevner, A. R., March, S. T., Park, J., & Ram, S. (2004). Design science in information systems research. MIS Quarterly, 28(1), 75–105.CrossRefGoogle Scholar
  21. Hsu, T.-Y., Kuo, F.-R., Liang, H.-Y., & Lee, M.-F. (2016). A curriculum-based virtual and physical mobile learning model for elementary schools in museums. Electronic Library, 34(6), 997–1012.  https://doi.org/10.1108/EL-08-2015-0146.CrossRefGoogle Scholar
  22. Horn, J.D.V., Fierro, L., Kamdar, J., Gordon, J. Stewart, C., Bhattrai, A., et al. (2018). Democratizing data science through data science training, Biocomputing, 23, 292–303.Google Scholar
  23. Jepsen, D. M., & Rodwell, J. J. (2008). Convergent interviewing: A qualitative diagnostic technique for researchers. Management Research News, 31(9), 650–658.CrossRefGoogle Scholar
  24. Khan, S., & Yairi, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241–265.CrossRefGoogle Scholar
  25. Lefoe, G. (1998). Creating constructivist learning environments on the web: The challenge in higher education. Ascilite, 98, 453.Google Scholar
  26. Lijuan, W., Sau-ching Ha, A., & Xu, W. (2014). Teaching perspectives of Chinese teachers: Compatibility with the goals of the physical education curriculum. Journal of Teaching in Physical Education, 33(2), 213–231.CrossRefGoogle Scholar
  27. Mahmud, A., Alwi, N. H., & Sulaiman, T. (2014). A qualitative study on the perspective and teaching practice of novice lecturers in a paramedic course. Education in Medicine Journal, 6(1), e45–e49.  https://doi.org/10.5959/eimj.v6i1.194.CrossRefGoogle Scholar
  28. Mann, A., & Percy, C. (2014). Employer engagement in British secondary education: Wage earning outcomes experienced by young adults. Journal of Education and Work, 27(5), 496–523.  https://doi.org/10.1080/13639080.2013.769671.CrossRefGoogle Scholar
  29. March, S. & Smith, G. (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), 251–266.Google Scholar
  30. March, S. T., & Storey, V. C. (2008). Design science in the information systems discipline: An introduction to the special issue on design science research. MIS Quarterly, 32(4), 725–730.CrossRefGoogle Scholar
  31. McNamara, J. P., Hanigan, M. D., & White, R. R. (2016). Invited review: Experimental design, data reporting, and sharing in support of animal systems modeling research1. Journal of Dairy Science, 99, 9355–9371.  https://doi.org/10.3168/jds.2015-10303.CrossRefGoogle Scholar
  32. Miah, S. J. (2008). An ontology based design environment for rural decision support, Unpublished PhD Thesis. Brisbane, Australia: Griffith UniversityGoogle Scholar
  33. Miah, S. J. (2010). A new semantic knowledge sharing approach for e-government systems. Paper presented at the 4th IEEE International Conference on Digital Ecosystems and Technologies (DEST), IEEE Digital Library, 457–462Google Scholar
  34. Miah, S. J., McGrath, G. M. & Kerr, D. (2016). Design science research for decision support systems development: recent publication trends in the premier IS journals. Australasian Journal of Information Systems, 20, 1–14.Google Scholar
  35. Nichols, M. (2016). A comparison of two online learning systems. Journal of Open, Flexible & Distance Learning, 20(1), 19–32.Google Scholar
  36. Nunamaker Jr, J. F., Chen, M., & Purdin, T. D. M. (1990). Systems development in information systems research. Journal of Management Information Systems, 7(3), 89-–106.Google Scholar
  37. OECD (2005) OECD Economic Globalisation Indicators, OECD HANDBOOK, OECD, Paris. https://www.oecd.org/sti/ind/34964971.pdf. Accessed 23 Aug 2018.
  38. Peffers, K. E. N., Tuunanen, T., Rothenberger, M.A., & Chatterjee, S. (2008). A design science research methodology for information systems research. Journal of Management Information Systems, 24(3), 45–77.Google Scholar
  39. Protalinski, E. (2013). Flurry: Android and iOS users spend 32% of their app time playing games, 20% in the browser, 18% in Facebook. Retrieved from https://thenextweb.com/mobile/2013/04/03/flurry-android-and-ios-users-spend-32-of-their-app-time-playing-games-20-in-the-browser-18-in-facebook/.
  40. Rajpaul, K., & Acton, C. (2015). The use of smart technology to deliver efficient and effective pressure-damage education. British Journal of Nursing, 24, S4–S12.  https://doi.org/10.12968/bjon.2015.24.Sup20.S4.CrossRefGoogle Scholar
  41. Sanders, N. R. (2016). How to use big data to drive your supply chain. California Management Review, 58(3), 26–48.CrossRefGoogle Scholar
  42. Sheng, H., & Trimi, S. (2008). M-government: Technologies, Applications and Challenges. Electronic Government: An International Journal, 5(1), 1–18.Google Scholar
  43. Simões, T. M. C., Rodrigues, J. J. P. C., & de la Torre, I. (2013). Personal learning environment box (PLEBOX): A new approach to E-learning platforms. Computer Applications in Engineering Education, 21, E100–E109.  https://doi.org/10.1002/cae.20537.CrossRefGoogle Scholar
  44. Simon, H. (1996). The Sciences of Artificial, 3rd edn., Cambridge, MA: MIT Press.Google Scholar
  45. Sintef (2013). Big Data, for better or worse: 90% of world's data generated over last two years. Retrieved from www.sciencedaily.com/releases/2013/05/130522085217.htm.
  46. Struhl, S. (2015). Practical Text Analytics : Interpreting Text and Unstructured Data for Business Intelligence: London : Kogan Page, 2015.Google Scholar
  47. Thinnukool, O., Khuwuthyakorn, P., & Wientong, P. (2017). Pharmacy assistant Mobile application (PAMA): Development and reviews. International Journal of Interactive Mobile Technologies, 11(3), 178–194.  https://doi.org/10.3991/ijim.v11i3.6757.CrossRefGoogle Scholar
  48. Vaishnavi, V. K. & Kuechler, W. (2015). Design science research methods and patterns: Innovating information and communication technology, 2nd Edition. London: Chapman and Hall/CRC.Google Scholar
  49. Wei, T. T., Marthandan, G., Chong, A. Y. L., Ooi, K. B., & Arumugam, S. (2009). What drives Malaysian m‐commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370–388.Google Scholar
  50. Xerri, D., & Campbell, C. (2016). E-portfolios in teacher development: The better option? ELT Journal: English Language Teaching Journal, 70(4), 392–400.  https://doi.org/10.1093/elt/ccw032.CrossRefGoogle Scholar
  51. Yu, T. (2011). E-portfolio, a valuable job search tool for college students. Campus-Wide Information Systems, 29(1), X70–X76.  https://doi.org/10.1108/10650741211192064.CrossRefGoogle Scholar
  52. Zhao, B. A. O., & Ping, L. U. O. (2015). How college students' job search self-efficacy and clarity affect job search activities. Social Behavior and Personality: An International Journal, 43(1), 39–51.CrossRefGoogle Scholar
  53. Zhu, Z.-T., Yu, M.-H., & Riezebos, P. (2016). A research framework of smart education. Smart Learning Environments, 3(1), 4.  https://doi.org/10.1186/s40561-016-0026-2.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Business, Victoria UniversityMelbourneAustralia

Personalised recommendations