The Types of Learning Approaches Used by Engineering Students in Three Scenarios: An Adaptation of the R-SPQ-2F to China.

Deep learning is a type of high-level learning that has received widespread attention in research on higher education; however, learning scenarios as an important variable have been ignored to some extent in past studies. This study aimed to explore the learning state of engineering students in three learning scenarios: theoretical learning, experimental learning, and engineering practice. Samples of engineering university students in China were recruited online and offline; the students filled in the engineering Education-Study Process Questionnaire, which was revised from the R-SPQ-2F. The results of clustering analysis showed four types of learning approaches in the three scenarios: typical deep learning, typical shallow learning, deep-shallow learning, and free learning. Engineering learners in different learning scenarios tended to adopt different learning approaches and showed gender differences. Due to factors such as differences in culture and choice of learning opportunities, the deep and shallow learners demonstrated excellent learning performance, which is in sharp contrast with the "learning failure" exhibited by such students abroad.

Semi-Supervised Minimum Error Entropy Principle with Distributed Method.

The minimum error entropy principle (MEE) is an alternative of the classical least squares for its robustness to non-Gaussian noise. This paper studies the gradient descent algorithm for MEE with a semi-supervised approach and distributed method, and shows that using the additional information of unlabeled data can enhance the learning ability of the distributed MEE algorithm. Our result proves that the mean squared error of the distributed gradient descent MEE algorithm can be minimax optimal for regression if the number of local machines increases polynomially as the total datasize.