Study Partners Recommendation for Online Courses

Authors(2) :-Snehal D. Nanaware, Gyankamal J. Chhajed

Massive Open Online Courses (MOOCs) provide free learning opportunity for any learners. The online learning system can provides many courses for learners to learn any specific course as they want. The online learning system can include two types of MOOCs that is cMOOCs and xMOOCs. In cMOOCs the learning will be happen within the limited area. The cMOOCs learners can used digital platforms or social network for learning. The xMOOCs can focuses on teacher-student interaction and limited student-student interaction. The automated testing and quies compitions used to check students understanding. The study partner recommendation system can help learners to solve their problems or any difficulties by discussing with partner which encounter during learning process. The recommendation system based on behaviour of learners and topic similarities between learners.

Authors and Affiliations

Snehal D. Nanaware
Department of Computer Engineering, Vidya Pratishthan's College of Engineering, Baramati, Maharashtra, India
Gyankamal J. Chhajed
Department of Computer Engineering, Vidya Pratishthan's College of Engineering, Baramati, Maharashtra, India

MOOCs, xMOOCs, Behavior model, Topic Model, Recommendation.

  1. Bin Xu,and Dan Yang,Study Parteners Recommendation for same xMOOCs Learners,Research article, 2015.
  2. X. Chen, M. Vorvoreanu, and K. Madhavan, Mining social media data for understanding students learning experiences, IEEE Transactions on Learning Technologies, no. 3, pp. 246259, 2014.
  3. J. Jiang, C. Wilson, X. Wang et al, Understanding latent interactions in online social networks, ACM Transactions on theWeb, vol. 7, no. 4, article 18, 2013.
  4. J. Naruchitparames, M. H. Gunes, and S. J. Louis,  Friend recommendations in social networks using genetic algorithms and network topology, in Proceedings of the IEEE Congress of Evolutionary Computation (CEC 11), pp. 22072214, June 2011.
  5. F. Lu, Y. Sugano, T. Okabe, and Y. Sato, Adaptive linear regression for appearance-based gaze estimation, IEEE Trans. Pattern Anal. Mach. Intell., Oct. 2014.
  6. P. Martins and J. Batista, Accurate single view model-based head pose estimation, in Proc. IEEE Int. Conf. Autom. Face Gesture Recog., 2008.
  7. A. E. Kaufman, A. Bandopadhay, and B. D. Shaviv, An eye tracking computer user interface, in Proc. IEEE Symp. Res. Frontiers Virtual Reality, 1993.
  8. O. Jesorsky, K. J. Kirchberg, and R. W. Frischholz, Robust face detection using the Hausdor distance, in Proc. Audio Video-Based Biometric Person Authentication, 2001.
  9. J. Xiao, T. Moriyama, T. Kanade, and J. F. Cohn, Robust full-motion recovery of head by dynamic templates and re-registration techniques, Int. J. Imaging Syst. Technol., 2003.
  10. H.C. Lu, G. L. Fang, C. Wang, and Y.-W. Chen, A novel method for gaze tracking by local pattern model and support vector regressor, Signal Process., 2010
  11. S. He, Q. Yang, R.W. Lau, J.Wang, andM.-H. Yang, Visual tracking via locality sensitive histograms, in Proc. IEEE Conf. Comput. Vis. Pattern Recog., 2013.

Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 911-915
Manuscript Number : CSEIT1723338
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Snehal D. Nanaware, Gyankamal J. Chhajed, "Study Partners Recommendation for Online Courses", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.911-915, May-June-2017.
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