Classroom Teaching Quality Evaluation based on Neuro-Fuzzy ID3 Algorithm
Hongxia Jin, Heping Yao · 2008
Based on large amounts of information concerning classroom teaching quality collected in daily teaching and management, Neuro-FDT is introduced to the research on the undergraduate classroom teaching quality so as to find potential and valuable teaching information, fuzzy decision trees are powerful, top-down, hierarchical search methodology to extract human interpretable classification rules. However, they are very poor in classification accuracy. Neural networks-fuzzy decision tree improves FDT's classification accuracy and extracts more accuracy human interpretable classification rules. The fuzzy rules enable a decision-maker to decide the optimal teaching quality.