Abstract
Massive teaching ability data leads to a large difference between the evaluation index and the actual index. Therefore, a classification evaluation method for innovative teachers’ teaching ability based on multi-source data fusion is proposed. Integrate innovative teachers’ teaching data feature level multi-source data to generate scene data that can accurately describe learners’ learning characteristics. The model of teachers’ practical teaching ability is constructed, the information flow expressing the constraint parameters is obtained, and the convergent solution of teaching ability evaluation is calculated. Use the analytic hierarchy process to calculate the data similarity, and use the quantitative recursive analysis method to describe the form of evaluation data. Integrate the five dimensional characteristic data of learner situation, time situation, location situation, equipment situation, event situation and learning scene, build an evaluation model based on multi-source data fusion, and achieve the classified evaluation of innovative teachers’ teaching ability. The experimental results that the maximum values of the teaching skill index, learning input index and learning harvest index of this method are 9.8, 9.6 and 9.2 respectively, which shows that the classification evaluation results are accurate.
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References
Fubing, F., Ling, S., Lei, S., et al.: Exploration of three-stage cultivation of “double-qualified” teachers’practical teaching ability under the background of “double-high plan.” Vocat. Tech. Educ. 43(02), 27–30 (2022)
Nguyen, H., Santagata, R.: Impact of computer modeling on learning and teaching systems thinking. J. Res. Sci. Teach. 58(5), 661–688 (2021)
Zhang Yong, X., Wenbin: Research on teaching ability and evaluation of teachers in local agricultural colleges and universities. Vocat. Tech. Educ. 07, 18–22 (2022)
Shiyu, Y., Liyan, L., Shuo, L.: The construction of assessment indicator system of university teachers’teaching competence. High. Educ. Explor. 12, 66–73 (2021)
Qisheng, Z., Yani, Z., Bichun, L.: The semand analyzing for the teaching ability of teachers in online courses based on the"student satisfaction model”. China Poultry 43(03), 118–120 (2021)
Hanxiao, Q., Bin, H.: Entity relation retrieval and simulation of low redundancy knowledge atlas. Comput. Simul. 39(6), 469–472 (2022)
Patjas, M., Vertanen-Greis, H., Pietarinen, P., et al.: Voice symptoms in teachers during distance teaching: a survey during the COVID-19 pandemic in Finland. Europ. Arch. Oto-Rhino-Laryngol.: Off. J. Europ. Fed. Oto-Rhino-Laryngol. Soc. (EUFOS) 278(11), 4383–4390 (2021)
Zhang, S., Deng, S.: Construction of the model of core teaching competence of ideological and political course teachers in institutions of higher education. J. Sichuan Normal Univ. (Social Sciences Edition) 48(06), 11–20 (2021)
Wu, F., Huang, .S.: Research on shared education data model based on multi-source data fusion. E-educ. Res. 41(05), 59–65+103 (2020)
Behling, F., Hennersdorf, F., Schittenhelm, J.: Epigenome-wide data collection in a case of gliofibroma. Folia Neuropathol. 59(2), 212–218 (2021)
Faraj, G., Micsik, A.: Persons, GLAM institutes and collections: an analysis of entity linking based on the COURAGE registry. Int. J. Metadata, Semantics Ontol. 15(1), 39–49 (2021)
Rašmane, A., Goldberga, A.:. The potential of IFLA LRM and RDA key entities for identification of entities in textual documents of cultural heritage: the runa collection. Catalog. Classification Quart.: CCQ, 58(5/8), 705–727 (2020)
Liu, S., Lu, M., Liu, G.: A novel distance metric: generalized relative entropy. Entropy 19(6), 269 (2017)
Zhou, Z., Qin, J., Xiang, X., Tan, Y., Liu, Q., Xiong, N.N.: News text topic clustering optimized method based on TF-IDF algorithm on spark. Comput, Mater. Continua 62(1), 217–231 (2020)
Bounabi, M., Elmoutaouakil, K., Satori, K.: A new neutrosophic TF-IDF term weighting for text mining tasks: text classification use case. Int. J. Web Inform. Syst. 17(3), 229–249 (2021)
Ganesh Karthikeyan, V., Thangaraj, P., Karthik, S.: Towards developing hybrid educational data mining model (HEDM) for efficient and accurate student performance evaluation. Soft. Comput.Comput. 24(24), 18477–18487 (2020). https://doi.org/10.1007/s00500-020-05075-4
Aknowledgement
1. This paper is the research result of the 2022 Shanxi Provincial Teacher Education Reform and Teacher Development Research Project “Research on the Path to Improve the Teaching Innovation Ability of Teachers in Private Colleges under the Background of Education Digital Transformation” (subject number: SJS2022YB067);
2. The first batch of collaborative education projects of the Ministry of Education in 2022: “Training of young teachers’ teaching innovation ability based on the” rain classroom “intelligent teaching platform” research results
3. The construction achievements of "History of Chinese and Foreign Educational Thoughts", a high-quality online open course of Xi'an Siyuan University in 2021;
4. The construction results of the school-level teaching team of Xi'an Siyuan University "Teaching team of core pedagogy curriculum group";
5. Construction achievements of the scientific research innovation team of Xi'an Siyuan University "Shaanxi regional basic education scientific research innovation team".
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Zhu, F., Fang, S. (2024). Classifying Evaluation Method of Innovative Teachers’ Teaching Ability Based on Multi Source Data Fusion. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-031-50571-3_12
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