Abstract
To solve the problems of low evaluation accuracy and satisfaction in the complex evaluation index differences of hybrid education models in schools, this paper designs a multimodal teaching quality evaluation for hybrid education based on stepwise regression analysis. The proposed method introduces a stepwise regression analysis algorithm by constructing multimodal reliability fusion, and gradually analyzes the evaluation indicators of hybrid teaching modes based on a multiple linear regression model. By combining sensitivity and specificity, ensure the accuracy of evaluation methods and achieve teaching quality evaluation of hybrid education models. The experimental results show that for the hybrid teaching mode, the satisfaction level of the quality evaluation method using multimodal teaching is above 94.7 points, the evaluation efficiency reaches 1.1s, the evaluation accuracy reaches 97.0%, and the sensitivity and specificity reached above 0.94, which has a superior teaching quality evaluation effect. The proposed method effectively improves the accuracy of teaching quality evaluation in the hybrid joint education model, and has feasibility and effectiveness, which can provide reliable reference for schools.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data will be made available on reasonable request.
References
Gao P, Li J, Liu S (2021) An introduction to Key Technology in Artificial Intelligence and Big Data Driven e-Learning and e-Education, vol 26. Mobile Networks and Applications, pp 2123–2126. 5.
Liu S, Li Z, Zhang Y et al (2019) Introduction of key problems in long-distance learning and training. Mob Networks Appl 24(1):1–4
Sun J, Zhao X, Men F et al (2023) Research on the mixed teaching evaluation model of multi form fusion. Softw Guide 22(03):222–226
Li Y, Zhu J, Fu W (2022) Intelligent privacy protection of end user in long distance education. Mob Networks Appl 27(3):1162–1173
Zhang C, Guo Y (2021) Mountain rainfall estimation and online English teaching evaluation based on RBF neural network. Arab J Geosci 14(22):1866–1866
Segura-Egea J, Zarza-Rebollo A, Jiménez-Sánchez M et al (2020) Evaluation of undergraduate endodontic teaching in dental schools within Spain. Int Endod J 54(3):454–463
Caldwell K, Hess A, Kramer J et al (2021) Evaluating chief resident readiness for the teaching assistant role: the teaching evaluation assessment of the chief resident (TEACh-R) instrument. Am J Surg 222(6):1112–1119
Liu S, He T, Li J et al (2023) An effective learning evaluation method based on text data with real-time attribution - a case study for mathematical class with students of Junior Middle School in China. ACM Trans Asian Low-Resour Lang Inf Process 22(3):63
Yuan TQ, Xi P (2022) Quality evaluation algorithm based on multimodal audio and video fusion. J Shenyang Univ Technol 44(3):331–335
Shen XL, Zhao KL, Li SY (2021) Multi modal e-commerce waste evaluation and detection based on image text decision fusion. J Chongqing Univ Posts Telecommunications: Nat Sci Ed 33(6):1038–1046
Shuai L, Shichen H, Shuai W et al (2023) Visual tracking in complex scenes: a location fusion mechanism based on the combination of multiple visual cognition flows. Inform Fusion 96:281–296
Liu S, Gao P, Li Y et al (2023) Multi-modal fusion network with complementarity and importance for emotion recognition. Inf Sci 619:679–694
Chen J, Takashima R, Guo X et al (2021) Multimodal fusion for indoor sound source localization. Pattern Recogn 115(3):107906
Li X, Li Y, Bao H et al (2021) New development of learning input evaluation: from single dimension analysis to multimodal integration. Res Audio Visual Educ 42(10):100–107
Zhang Q, Li F (2020) Multimodal learning analysis: learning analysis towards the era of computational education. China Audio Visual Educ 8(9):7–14
Menezes D, Prata D, Secchi A et al (2021) A review on robust M-Estimators for regression analysis. Comput Chem Eng 147(8):107254
Lai L, Peng L (2021) Research on the construction of online teaching service quality evaluation index system in colleges and universities. Heilongjiang Res Higher Educ 39(6):147–154
Gao X, Li X (2022) Comparison of dimensionless methods in multivariate linear regression models. Stats Decis 38(6):5–9
Xiao X, Wu X (2021) Geometric interpretation of multicollinearity in linear regression. Stat Decis 37(21):46–51
Funding
For the purpose of assisting in the preparation of this manuscript, no funding was provided.
Author information
Authors and Affiliations
Contributions
Lei Ma contributed to Writing - Original Draft, Methodology, and Conceptualization; Hongxue Yang and Jianxing Yang are contributed to Conceptualization and Writing - Review and Editing;
Corresponding author
Ethics declarations
Conflict of interest/Competing interests
There are no declared conflicts of interest or competing interests that relate to the article’s content.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ma, L., Yang, H. & Yang, J. A Multimodal Teaching Quality Evaluation for Hybrid Education Based on Stepwise Regression Analysis. Mobile Netw Appl 28, 960–970 (2023). https://doi.org/10.1007/s11036-023-02190-y
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-023-02190-y