Forecasting Student Actions In A Practical Guidance Setting

Authors(2) :-D. Vamsi Kumar Reddy, S. A. Md. Noorulla Baig

Data mining is known to have a potential for anticipating client execution. Nonetheless, there are few investigations that investigate its potential for anticipating understudy conduct in a procedural preparing condition. This paper shows an aggregate understudy demonstrate, which is worked from past understudy logs. These logs are ?rstly gathered into groups. At that point an expanded machine is made for each bunch in view of the groupings of occasions found in the group logs. The primary target of this model is to foresee the activities of new understudies for enhancing the mentoring input gave by an astute coaching framework. The proposed demonstrate has been approved utilizing understudy logs gathered in a 3D virtual research center for educating biotechnology. Because of this approval, we presumed that the model can give sensibly great forecasts and can bolster mentoring input that is better adjusted to every understudy compose.

Authors and Affiliations

D. Vamsi Kumar Reddy
Department of MCA, Mother Theresa Institute of Computer Applications, Palamaner, India
S. A. Md. Noorulla Baig
Department of MCA, Mother Theresa Institute of Computer Applications, Palamaner, India

Educational Data Mining, e-learning, Procedural Training, Intelligent Tutoring Systems.

  1. C. Romero and S. Ventura, "Educational data mining: A survey from 1995 to 2005," Expert Systems with Applications, vol. 33, no. 1, pp. 135–146, 2007.
  2. C. Romero and S. Ventura, "Educational Data Mining: A Review of the State of the Art," IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 40, no. 6, pp. 601– 618, 2010.
  3. R. S. Baker, "Educational Data Mining: An Advance for Intelligent Systems in Education," Intelligent Systems, IEEE, vol. 29, no. 3, pp. 78–82, 2014.
  4. L. S. Vygotsky, Mind in society: The development of higher psychological processes. Harvard university press, 1978.
  5. J. Clemente, "Una Propuesta de Modelado del EstudianteBasadaen Ontolog?as y DiagnosticoPedagogico-Cognitivo no Monotono," Ph.D. dissertation, Universidad Politecnica de Madrid, 2011.
  6. M. Rico, J. Ram?rez, D. Riofr?o, M. Berrocal-Lobo, and A. De Antonio, "An architecture for virtual labs in engineering education," in Global Engineering Education Conference (EDUCON), 2012 IEEE, 2012, pp. 1–5.
  7. H. K. Holden and A. M. Sinatra, "A Guide to Scaffolding and Guided Instructional Strategies for ITSs," in Design Recommendations for Intelligent Tutoring Systems. Orlando: U.S. Army Research Laboratory, 2014, ch. 22, pp. 265–281.

Publication Details

Published in : Volume 4 | Issue 2 | March-April 2018
Date of Publication : 2018-03-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 141-147
Manuscript Number : CSEIT184107
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

D. Vamsi Kumar Reddy, S. A. Md. Noorulla Baig, "Forecasting Student Actions In A Practical Guidance Setting ", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 2, pp.141-147, March-April-2018.
Journal URL : http://ijsrcseit.com/CSEIT184107

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