Abstract: Curriculum evaluation is a key link of teaching reform,which involves the evaluation of teaching cases,test questions and classroom teaching.In order to evaluate the test questions of computing courses,this paper introduces Bloom's taxonomy,and takes the test questions of “Introduction to Computer Science” course(CS1) of Princeton University and Guilin University of Electronic Science and Technology as corpus,and the corresponding verb seed bank and noun seed bank for the cognitive process dimension and knowledge dimension of Bloom's taxonomy for CS1 are given,the positions of the two-dimensional matrix of Bloom's taxonomy that could be reached by the test questions are manually labeled,classification dataset for CS1 test questions is constructed.Machine learning technology is used,the automatic classification model TFERNIE-LR of CS1 test questions is given,which is composed of CSTFPOS-IDF algorithm,ERNIE model and LR classifier.CSTFPOS-IDF algorithm is based on TFPOS-IDF algorithm,by the weight factor of the keywords in computing discipline,CSTFPOS-IDF algorithm pays more attention to the keywords improves and generates the weight of words.At the same time,the entity knowledge enhanced pre-training model ERNIE is used to embed the word level vector of test questions,and the combined word weight and word level vector are used to generate the text vector of test questions for automatic classification.Finally,the LR classifier is used to automatically classify test questions into Bloom's taxonomy two-dimensional matrix.Experimental results show that the proposed TFERNIE-LR model has good performance,and weighted-P in the cognitive process dimension and knowledge dimension reaches 83.3% and 96.1% respectively.

Key words: Bloom's taxonomy, Curriculum evaluation, Classification dataset for CS1 test questions, Verb seed bank, Noun seed bank, Automatic classification