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Theoretical Research on College Students’ Professional Literacy Design Based on Deep Learning

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The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIOT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1282))

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Abstract

University is a critical period for cultivating college students’ professional qualities, and counselors’ guidance to college students during this period is very important. This article mainly studies the theory of professional literacy design for college students based on deep learning. Based on the status quo of college students’ professional literacy in China, this article analyzes the necessity of cultivating college students’ professional literacy, and proposes some strategies on how counselors can cultivate college students’ professional literacy, with a view to helping college students’ professional literacy improve. The research results in this article show that the average score of “influence” competency is only 2.48, and there is a huge gap between competences with a score of 4 and above, and only 29.41% of students achieve a competency of 4 or more. The average score of “interpersonal insight” competency is 3.79, which is closer to 4 competences, and the proportion of students who reach 4 or above is 79.59%. The “interpersonal competence” professionalism is in the four competence features. The large gap indicates that there is a serious imbalance in the interpersonal communication ability of local college students, that is, the “influence” competence of local college students is seriously insufficient, and the proportion of college students with organizational leadership in professional positions is low.

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Correspondence to Longquan Huang .

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Huang, L. (2021). Theoretical Research on College Students’ Professional Literacy Design Based on Deep Learning. In: MacIntyre, J., Zhao, J., Ma, X. (eds) The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy. SPIOT 2020. Advances in Intelligent Systems and Computing, vol 1282. Springer, Cham. https://doi.org/10.1007/978-3-030-62743-0_9

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