Abstract
In order to improve the quality and efficiency of sharing high-quality teaching resources and achieve ideal results, a decision tree classification algorithm based method for sharing high-quality teaching resources in college student employment guidance courses is proposed. Firstly, before resource sharing and transmission, design a security key for public information and encrypt the public information of high-quality teaching resources. Secondly, the decision tree classification algorithm is used to construct a classification standard for electronic archives, and regional partitioning is performed to extract resource sharing classification codes. At the same time, establish a database of teaching resources for employment guidance courses to store information related to high-quality teaching resources for college students’ employment guidance courses. Finally, establish a blockchain based teaching resource security sharing model to achieve information sharing of high-quality teaching resources. Experimental analysis shows that the proposed method can complete the high-quality resource sharing task of college student employment guidance course teaching within 5–8.5 ms after application, with a significant advantage in resource sharing efficiency.
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References
Ma, H.: Design and application of teaching resources sharing platform for physical education major based on internet. J. Phys. Conf. Ser. 1992(2), 022197 (2021). https://doi.org/10.1088/1742-6596/1992/2/022197
Feng, J., Zhang, W., Tsai, S.B.: Construction of a multimedia-based university ideological and political big data cloud service teaching resource sharing model. Math. Probl. Eng. 2021, 1–12 (2021). https://doi.org/10.1155/2021/9907630
Liu, H.: Application and strengthening strategies of network resources in the construction of teaching platform. J. Phys. Conf. Ser. 1915(4), 042056 (2021). https://doi.org/10.1088/1742-6596/1915/4/042056
Tucker, B.V., Kelley, M.C., Redmon, C.: A place to share teaching resources: Speech and language resource bank. J. Acoust. Soc. Am. 149(4), A147–A147 (2021)
Yao, S., Li, D., Yohannes, A., et al.: Exploration for network distance teaching and resource sharing system for higher education in epidemic situation of COVID-19 - ScienceDirect. Procedia Comput. Sci. 183, 807–813 (2021)
Rodrigues, T.C., Leitão, F.O., Thomé, K.M., Cappellesso, G.: Sharing economy practices in agri-food settlements: integration of resources, interdependence and interdefinition. J. Cleaner Prod. 294, 126357 (2021). https://doi.org/10.1016/j.jclepro.2021.126357
Syarifah, L.S.: Career guidance in preparing students with disabilities for get in to the worke in preparing students with disabilities for get in to the work. Jurnal Pendidikan Dasar 9(1), 135–145 (2021)
Ma, J., Sun, L., Zhang, T.: Thoughts on online teaching of employment guidance course for civil aviation majors under the COVID-19 epidemic--based on the teaching practice of college students enrolled in 2018 in the Civil Aviation Management Institute of China. J. Phys. Conf. Ser. 1931(1), 012001 (2021). https://doi.org/10.1088/1742-6596/1931/1/012001
Osborn, D.S., Brown, C.A., Morgan, M.J.: Expectations, experiences, and career‐related outcomes of computer‐assisted career guidance systems. J. Employ. Couns. 58(2), 74–90 (2021). https://doi.org/10.1002/joec.12158
Cheng, S., Liu, G., Ren, C., et al.: Remote resource sharing simulation of interactive experiment platform based on blockchain. Comput. Simul. 006, 039 (2022)
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Ou, M. (2024). High Quality Resources Sharing of College Students’ Career Guidance Course Teaching Based on Decision Tree Classification Algorithm. In: Yun, L., Han, J., Han, Y. (eds) Advanced Hybrid Information Processing. ADHIP 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 547. Springer, Cham. https://doi.org/10.1007/978-3-031-50543-0_26
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DOI: https://doi.org/10.1007/978-3-031-50543-0_26
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