Optimized-Memory Map-Reduce Algorithm for Mobile Learning
The increasing accessibility of mobile technologies and devices, such as smartphones and tablet PCs, has made mobile learning (m-learning) a critical feature of modern didactics. Mobile learning is among the many computerized activities that can be performed using mobile devices. As the volume of accessible important information on university websites continues to increase, students may face difficulties in accessing important information from a large dataset. This study introduces an algorithmic framework for data reduction that is built on optimized-memory map–reduce algorithm for mobile learning. The goal of this method is to generate meaningful recommendations to a collection of students in the easiest and fastest way by using a recommender system. Through an experiment, the proposed method has demonstrated significant improvements in data size reduction up to 77 %. Such improvements are greater than those that are achieved using alternate methods.
KeywordsData reduction Mapreduce technique Mobile learning Content based recommendation
The study is supported by Project No.: RG312-14AFR from University of Malaya.
- 1.Kukulska-Hulme, A.: Mobile usability in educational contexts: what have we learnt? Int. Rev. Res. Open Distrib. Learn. 8, 1–16 (2007)Google Scholar
- 7.El-Hussein, M.O.M., Cronje, J.C.: Defining mobile learning in the higher education landscape. J. Educ. Technol. Soc. 13, 12–21 (2010)Google Scholar
- 8.Yang, X.Y., Liu, Z., Fu, Y.: MapReduce as a programming model for association rules algorithm on Hadoop. In: 2010 3rd International Conference on Information Sciences and Interaction Sciences (ICIS), pp. 99–102. IEEE (2010)Google Scholar
- 9.Cambridge University website. http://www.educ.cam.ac.uk/people/doctoralstudents/theses/
- 10.Michigan University website. http://www.lsa.umich.edu
- 11.Amazon books database. https://affiliate-program.amazon.com
- 12.Kumar, A., Kiran, M., Prathap, B.: Verification and validation of mapreduce program model for parallel k-means algorithm on hadoop cluster. In: 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), pp. 1–8. IEEE (2013)Google Scholar
- 13.Moturi, C.A., Maiyo, S.K.: Use of mapreduce for data mining and data optimization on a web portal. Int. J. Comput. Appl. 56, 39–43 (2012)Google Scholar