Paper Title:
Machine Learning Based Teaching Quality Evaluation
  Abstract

Teaching quality is a key metric in college teaching effect and ability evaluation. In many previous literatures, evaluation of such metric is merely depended on subjective judgment of few experts based on their experience, which leads to some false, bias or unstable results. Moreover, pure human based evaluation is expensive that is difficult to extend to large scale. With the application of information technology, much information in college teaching is recorded and stored electronically, which founds the basic of a computer-aid analysis. In this paper, we perform teaching quality evaluation within machine learning framework, focusing on learning and modeling electronic information associated with quality of teaching, to get a stable model described the substantial principles of teaching quality. Artificial Neural Network (ANN) is selected as the main model in this work. Experiment results on real data sets consisted of 4 subjects / 8 semesters show the effectiveness of the proposed method.

  Info
Periodical
Advanced Materials Research (Volumes 271-273)
Edited by
Junqiao Xiong
Pages
1451-1454
DOI
10.4028/www.scientific.net/AMR.271-273.1451
Citation
G. Zhang, J. Yin, L. L. Cheng, C. R. Wang, "Machine Learning Based Teaching Quality Evaluation", Advanced Materials Research, Vols. 271-273, pp. 1451-1454, 2011
Online since
July 2011
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Price
$32.00
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