Abstract
During the epidemic, online teaching became the mainstream. Online teaching evaluation aims to systematically test teachers' teaching process according to certain teaching objectives and standards, and evaluate its value, advantages and disadvantages, so as to improve the quality of teaching. It is not only an important part of the teaching process, but also the basis of all effective and successful teaching. In this paper, we propose an online teaching evaluation method based on Epistemic Neural Network (ENN), which is an evolutionary intelligence method. In terms of uncertainty modeling, ENN's design innovation provides the improvement effect of geometric progression in terms of statistical quality and calculation cost. Therefore, it is very suitable for teaching evaluation, which is an evaluation process guided by a variety of uncertain factors. Specifically, this paper considers the content and grade standards of online teaching evaluation from five aspects. (1) Teachers' syllabus, teaching progress, teaching plan, courseware and other teaching documents and teaching materials; (2) Abide by teaching discipline, the implementation of teaching plan and the completion of teaching tasks; (3) Teaching attitude, teaching investment, teaching and educating people, and the comprehensive quality of teachers; (4) Whether the concepts taught in the course are accurate, the expression is clear, whether the key points are prominent and whether the difficulties are clearly explained; (5) The depth, breadth and frontier of teaching content, and the amount of classroom information. According to the above five evaluation indexes which involves the big data analysis, we train ENN to get an evaluation score that can evaluate the teacher's online teaching process. In addition, we also test the average evaluation time to verify the effectiveness.






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Osband I, Wen Z, Asghari M et al. (2021) Epistemic neural networks. arXiv preprint arXiv:2107.08924
Martin F, Ritzhaupt A, Kumar S et al (2019) Award-winning faculty online teaching practices: course design, assessment and evaluation, and facilitation. Internet High Educ 42:34–43
Ibrahim AF, Attia AS, Asma’M B et al (2021) Evaluation of the online teaching of architectural design and basic design courses case study: college of architecture at JUST, Jordan. Ain Shams Eng J 12(2):2345–2353
Jones RM (2021) Online teaching of forensic medicine and pathology during the COVID-19 pandemic: a course evaluation. J Forensic Leg Med 83:102229
Lin H, You J, Xu T (2021) Evaluation of online teaching quality: an extended linguistic MAGDM framework based on risk preferences and unknown weight information. Symmetry 13(2):192
Li M, Su Y (2020) Evaluation of online teaching quality of basic education based on artificial intelligence. Int J Emerg Technol Learn (iJET) 15(16):147–161
Hou J (2021) Online teaching quality evaluation model based on support vector machine and decision tree. J Intell Fuzzy Syst 40(2):2193–2203
Jiang L, Wang X (2020) Optimization of online teaching quality evaluation model based on hierarchical PSO-BP neural network. Complexity. https://doi.org/10.1155/2020/6647683
Lv J, Wang X, Huang M (2017) ACO-inspired ICN routing mechanism with mobility support. Appl Soft Comput 58:427–440
Janiesch C, Zschech P, Heinrich K (2021) Machine learning and deep learning. Electron Mark 31(3):685–695
Burkov A (2019) The hundred-page machine learning book. Andriy Burkov, Quebec City
Sun S, Cao Z, Zhu H et al (2019) A survey of optimization methods from a machine learning perspective. IEEE Trans Cybern 50(8):3668–3681
Haihong C, Biao H, Feng L, Wenguo C (2017) Machine learning principles and applications. University of Electronic Science and Technology Press, Chengdu, pp 2–19
Anderson JA (1995) An introduction to neural networks. MIT Press, Cambridge
He M (2015) Fundamentals of college computer. Southeast University Press, Nanjing
Greydanus S, Dzamba M, Yosinski J (2019) Hamiltonian neural networks. Adv Neural Inf Process Syst 32:1–11
What is neural network? https://www.ibm.com/cn-zh/cloud/learn/neural-networks. (2020)
Kiranyaz S, Avci O, Abdeljaber O et al (2021) 1D convolutional neural networks and applications: a survey. Mech Syst Signal Process 151:107398
Sherstinsky A (2020) Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network. Phys D 404:132306
Jospin LV, Laga H, Boussaid F et al (2022) Hands-on Bayesian neural networks—a tutorial for deep learning users. IEEE Comput Intell Mag 17(2):29–48
Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. In: Advances in neural information processing systems, Long Beach, CA, USA, pp. 6405–6416
Gal Y, Ghahramani Z (2016) Dropout as a Bayesian approximation: representing model uncertainty in deep learning. International conference on machine learning 48:1050–1059
Radford A, Kim JW, Hallacy C, Ramesh A, Goh G, Agarwal S, Sastry G, Askell A, Mishkin P, Clark J, Krueger G, Sutskever I (2021) Learning transferable visual models from natural language supervision. In: International conference on machine learning, pp. 8748–8763. PMLR
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Yao, N. Epistemic neural network based evaluation of online teaching status during epidemic period. Evol. Intel. 16, 1565–1572 (2023). https://doi.org/10.1007/s12065-022-00789-w
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DOI: https://doi.org/10.1007/s12065-022-00789-w