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.
The first author and the second author contributed equally to this work.
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Acknowledgments
The corresponding author Shiliang Sun would like to thank support by the National Natural Science Foundation of China under Project 61370175, and Shanghai Knowledge Service Platform Project (No. ZF1213).
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Yin, M., Xie, X., Sun, S. (2016). Key Course Selection in Academic Warning with Sparse Regression. In: Tan, T., Li, X., Chen, X., Zhou, J., Yang, J., Cheng, H. (eds) Pattern Recognition. CCPR 2016. Communications in Computer and Information Science, vol 662. Springer, Singapore. https://doi.org/10.1007/978-981-10-3002-4_59
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DOI: https://doi.org/10.1007/978-981-10-3002-4_59
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