Paper Info
Title
Preliminary results from a machine learning based approach to the assessment of student learning
Abstract
We describe a possible approach to the problem of extracting knowledge from the analysis of questionnaires through machine learning. The idea guiding our research was to investigate the existence of association rules among the topics covered in a course. The data used came from the questionnaires administered to the freshmen in electronic engineering attending the course of foundation of computer science at our university. Each questionnaire was coded into feature vectors that were classified with respect to the grade obtained by the student and analysed with C4.5. Some statistical results and hints for further work are discussed.
Year
DOI
Venue
2003
10.1109/ICALT.2003.1215156
ICALT
Keywords
Field
DocType
computer science education,educational administrative data processing,educational courses,knowledge acquisition,learning (artificial intelligence),C4.5 package,association rules,computer science course,knowledge extraction,machine learning,statistical analysis,student learning assessment
Decision tree,Feature vector,Computer science,Association rule learning,Knowledge extraction,Information engineering,Artificial intelligence,Machine learning,Knowledge acquisition,Statistical analysis,Student learning
Conference
ISBN
Citations 
PageRank 
0-7695-1967-9
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Salvatore Valenti16812.07
Alessandro Cucchiarelli222636.38