The week's pick
Random Articles
Reseach Article
Intelligent Adaptive Feedback Assessment System in Learning Environment
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 36 - Number 10 |
| Year of Publication: 2011 |
| Authors: G. Suvarna Kumar, P. V. G. D. Prasad Reddy, Sumit Gupta, Sandeep V. |
10.5120/4525-6363
|
G. Suvarna Kumar, P. V. G. D. Prasad Reddy, Sumit Gupta, Sandeep V. . Intelligent Adaptive Feedback Assessment System in Learning Environment. International Journal of Computer Applications. 36, 10 ( December 2011), 11-16. DOI=10.5120/4525-6363
Abstract
Effective evaluation of tutors and learning environment is a vital educational activity for the development of educational quality and enhancement of an educational institute’s improvement in terms of quality driven teaching and learning. Considering the above statement, it is mandatory to assess the learning activities and attitude of students towards learning along with changing trend from a teaching and learning perspective. In this paper an intelligent adaptive feedback-assessment (IAFA) system framework has been proposed where we provide relative feedback assessment by correlating different learning styles of students along with teacher’s resource utilization. The assessment is based on the features extracted using the techniques from the fields of computer vision and speech analysis. In audio processing the perception attitude of the students is taken into account which makes us compute relative and desirable audible features like speech rate, loudness for feedback analysis. Using video processing techniques for tracking and template matching, we compute resource utilization for teaching aids like black board, projector and student interaction and compare it with the desired values. The present algorithm has been rigorously tested with several sample classes of around 60 students with different student backgrounds i.e., non-English medium/English Medium/Native English speakers, and the performance results have been reported.
References
- GuJinling,“Design of the whole teaching quality evaluation system of higher colleges and universities[J]”. China Scienceand Technology Information .2007 (8) :240-242.
- A guide for students about Education and Teaching principles available at: Http://members.tripod.com/teaching_is_reaching/delivering_your _speech.htm
- An adaptive procedure for categorical loudness scaling,Thomas Brand and Volker Hohmann 2002 Acoustical Society of America.
- G.Seshadri and B.Yegnanarayana, “Perceived loudness of speech based on characteristics of excitation source,” J.Acoust.Soc.Am., vol. 126,no. 4,pp. 2061-2071, Oct. 2009.
- E. Shriberg, A. Stolcke, D. Hakkani-Tur, and G. Tur, “Prosody-based automatic segmentation of speech into sentences and topics,” Speech Commun., vol. 32, no.1-2, pp. 127-154,2000.
- M. Siegler, “Measuring amd compensating for the effects of speech rate in large vocabulary continuous speech recognition ,” MS Thesis, Carnegie Melon Univ., Pittsburgh, PA, USA, 1995.
- N. Morgan and E.Fosler-Lussier, “Combining multiple estimators of speaking rate,” in Proc. Int. Conf. Acoust., Speech, Signal Process., vol. 2, Seattle, Washington, USA, May 1998, pp. 729-732.
- P.Mermelstein, “Automatic segmentation of speech into syllabic units,”J.Acoust.Soc.Am., vol. 58,no. 4,pp. 880-883, 1975.
- JAIN, R. AND NAGEL, H. 1979, ?On the analysis of accumulative difference pictures from image sequences of real world scenes. IEEE Trans. PattAnaly. Mach. Intell. 1, 2, 206-214.
- William McIlhagga, “The Canny Edge Detector Revisited” in Int J Computer Vision (2011) 91:251-261.
- Templatematching-http://www.pages.drexel.edu/~weg22/templatematch.html
- Cross Correlation - http://en.wikipedia.org/wiki/Cross Correlation. EDWARD R. DOUGHERTY (1993) Mathematical Morphology in Image Processing. CRC Press.
Index Terms
Keywords