Key Course Selection in Academic Warning with Sparse Regression

Abstract

Many colleges and universities are paying more attention to academic warning which warns large numbers of students who have unsatisfactory academic performance. Academic warning becomes a new part in the teaching management constitution but lacks of unified and scientific standards under the establishment of this stipulation at present. This paper solves the current setting of academic warning through well-known methods lasso and \(\ell _1\)-norm support vector regression with \(\epsilon \)-insensitive loss function which can select key courses based on the failed credits in one semester. The experiments are made on our collected academic warning datasets which are incomplete data. We impute them with one nearest neighbor method. The experimental results show that sparse regression is effective for colleges and universities to remind the students of key courses.