We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Learning Analytics and its Challenges in Education Sector: A Survey

Published on May 2015 by J Meenakumari, Jayashree M. Kudari
journal_cover_thumbnail

An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
Foundation of Computer Science USA
ICCTAC2015 - Number 2
May 2015
Authors: J Meenakumari, Jayashree M. Kudari
21a66e43-8a17-478c-ad4a-7b581a65f2d6

J Meenakumari, Jayashree M. Kudari . Learning Analytics and its Challenges in Education Sector: A Survey. An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds. ICCTAC2015, 2 (May 2015), 6-10.

@article{
author = { J Meenakumari, Jayashree M. Kudari },
title = { Learning Analytics and its Challenges in Education Sector: A Survey },
journal = { An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds },
issue_date = { May 2015 },
volume = { ICCTAC2015 },
number = { 2 },
month = { May },
year = { 2015 },
issn = 0975-8887,
pages = { 6-10 },
numpages = 5,
url = { /proceedings/icctac2015/number2/20925-2012/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%A J Meenakumari
%A Jayashree M. Kudari
%T Learning Analytics and its Challenges in Education Sector: A Survey
%J An Architectural Framework for Workload Demand Prediction in Scalable Federated Clouds
%@ 0975-8887
%V ICCTAC2015
%N 2
%P 6-10
%D 2015
%I International Journal of Computer Applications
Abstract

Analytics is a field of research and practice that aims to reveal new patterns of information through the collection of large sets of data held in previously distinct sources. Growing interest in data and analytics in education, teaching, and learning raises the priority for increased, high-quality research into the models, methods, technologies, and impact of analytics. The challenges of applying analytics on academic and ethical reliability to control over data. The other challenge is that the educational landscape is extremely turbulent at present, and key challenge is the appropriate collection, protection and use of large data sets. This paper brings out challenges of multi various pertaining to the domain by offering a big data model for higher education system.

References
  1. Beth Dietz-Uhler & Janet E. Hurn: Using Learning Analytics to Predict (and Improve) Student Success: A Faculty Perspective, Miami University ISSN: 1541-4914, Volume 12, Number 1, Spring 2013, Journal of Interactive Online Learning,www. ncolr. org/jiol.
  2. https://www. edx. org/course/data-analytics-learning-utarlingtonx-link5-10x#! 10/08/14
  3. Arnold, K. E. & Pistilli, M. D. (2012). Course Signals at Purdue: Using learning analytics to increase student success. Proceedings of the 2nd International Conference on Learning Analytics & Knowledge. New York: ACM.
  4. Shane Dawson, Perspectives on Learning Analytics: Issues and challenges. The International Journal of the First Year in Higher Education, 4(1) April, 2013
  5. Learning Analytics and Educational Data Mining: Towards Communication and Collaboration, George Siemens Technology Enhanced Knowledge Research Institute, Athabasca University, Ryan S J. D. Baker, lade, Sharon and Prinsloo, Paul (2013). Learning analytics: ethical issues and dilemmas. American.
  6. Using Learning Analytics to Predict (and Improve) Student Success: A Faculty Perspective, Beth Dietz-Uhler & Janet E. Hurn, Miami University.
  7. BIG DATA, David Bollier, Rapporteur,2010.
  8. Vincent Tinto, "Taking Student Success Seriously: Rethinking the First Year of College," in Ninth Annual Intersession Academic Affairs Forum, California State University, Fullerton, 2005.
  9. http://www. venturesity. com/blog/big-data-analytics-emerging-technology-education-training.
  10. Learning analytics: drivers, developments and challenges, Journal Article: Ferguson, Rebecca (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6) pp. 304–317.
  11. Design and Implementation of a Learning Analytics Toolkit for Teachers,Anna Lea Dyckhoff*, Dennis Zielke, Mareike Bültmann, Mohamed Amine Chatti and Ulrik Schroeder, Dyckhoff, A. L. , Zielke, D. , Bültmann, M. , Chatti, M. A. , & Schroeder, U. (2012).
  12. Romero, C. , Ventura, S. , & García, E. (2007). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368–384.
  13. Ali, L. , Hatala, M. , Gasevic, D. , & Jovanovic, J. (2012). A qualitative evaluation of evolution of a learning analytics tool. Computers & Education, 58(1), 470–489.
  14. D4. 1 Learning analytics: theoretical background, methodology and expected results, European Multiple MOOC aggregator. 2017-2013
  15. Formative Assessment and Learning Analytics, Dirk T. Tempelaar, André Heck, Hans Cuypers, Henk van der Kooij ,Evert van de Vrie.
  16. Ferguson, Rebecca (2012). Learning analytics: drivers, developments and challenges. International Journal of Technology Enhanced Learning, 4(5/6) pp. 304–317.
  17. Siemens, G. & Long, P. (2011, September/October). Penetrating the Fog: Analytics in Learning and Education. EDUCAUSE Review, 46(5), 30–32. Retrieved July 10, 2013 Retrieved from: http://net. educause. edu/ir/library/pdf/ERM1151. pdf
  18. U. S. Department of Education, Office of Educational Technology. (2012). Enhancing Teaching and Learning Through Educational Data Mining and Learning Analytics: An Issue Brief. Washington, DC. Retrieved June 10, 2013, from http://www. ed. gov/edblogs/technology/files/2012/03/edm-la-brief. pdf.
  19. Data Mining for Education Ryan S. J. D. Baker, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA.
Index Terms

Computer Science
Information Sciences

Keywords

Learning Analytics Learning Management System (lms) Educational Data Mining (edm) Big Data Special Issue Society For Learning Analytics Research.