This paper promotes teacher-guided peer learning in education with continuous action iterated dilemma (CAID) based on team leader rotation mechanism. In previous teaching activity models, the learning communication relationship between peers was described as static, but the static relationship will hinder the learning efficiency, which does not match the real world. In addition, in view of the independence of individual students, it is necessary to establish a dynamic model which considers the complex behavior of every student. In this paper, we first propose a team leader rotation mechanism that makes sure each student has the opportunity to become a team leader, which enhances students' sense of participation and improves classroom efficiency. Next, we establish a multi-layer nonlinear student dynamic model based on continuous action iteration dilemma and involved complex and unknown nonlinear environmental factors to fit different environmental influences on different students. Also, in order to demonstrate the convergence of the proposed model, we devise the Lyapunov function as a means of mathematical proof. Through this analysis, we establish the stability of the proposed model and verify its independence from parameters, thereby enhancing its applicability in practical contexts. By incorporating the team leader rotation mechanism proposed in this paper, teachers will be able to ensure diverse student engagement to achieve information consistency, thereby ensuring the effectiveness of the classroom.
Citation: Xiangwen Yin. Promoting peer learning in education: Exploring continuous action iterated dilemma and team leader rotation mechanism in peer-led instruction[J]. Electronic Research Archive, 2023, 31(11): 6552-6563. doi: 10.3934/era.2023331
This paper promotes teacher-guided peer learning in education with continuous action iterated dilemma (CAID) based on team leader rotation mechanism. In previous teaching activity models, the learning communication relationship between peers was described as static, but the static relationship will hinder the learning efficiency, which does not match the real world. In addition, in view of the independence of individual students, it is necessary to establish a dynamic model which considers the complex behavior of every student. In this paper, we first propose a team leader rotation mechanism that makes sure each student has the opportunity to become a team leader, which enhances students' sense of participation and improves classroom efficiency. Next, we establish a multi-layer nonlinear student dynamic model based on continuous action iteration dilemma and involved complex and unknown nonlinear environmental factors to fit different environmental influences on different students. Also, in order to demonstrate the convergence of the proposed model, we devise the Lyapunov function as a means of mathematical proof. Through this analysis, we establish the stability of the proposed model and verify its independence from parameters, thereby enhancing its applicability in practical contexts. By incorporating the team leader rotation mechanism proposed in this paper, teachers will be able to ensure diverse student engagement to achieve information consistency, thereby ensuring the effectiveness of the classroom.
| [1] |
S. Latifi, O. Noroozi, E. Talaee, Peer feedback or peer feedforward? Enhancing students' argumentative peer learning processes and outcomes, Br. J. Educ. Technol., 52 (2021), 768–784. https://doi.org/10.1111/bjet.13054 doi: 10.1111/bjet.13054
|
| [2] | K. Topping, C. Buchs, D. Duran, H. V. Keer, Effective Peer Learning: From Principles to Practical Implementation, Taylor & Francis, 2017. https://doi.org/10.4324/9781315695471 |
| [3] |
A. V. Haro, O. Noroozi, H. J. A. Biemans, M. Mulder, The effects of an online learning environment with worked examples and peer feedback on students' argumentative essay writing and domain-specific knowledge acquisition in the field of biotechnology, J. Biol. Educ., 53 (2019), 390–398. https://doi.org/10.1080/00219266.2018.1472132 doi: 10.1080/00219266.2018.1472132
|
| [4] |
J. H. Chen, M. R. Chen, G. Q. Zeng, J. S. Weng, BDFL: A byzantine-fault-tolerance decentralized federated learning method for autonomous vehicle, IEEE Trans. Veh. Technol., 70 (2021), 8639–8652. https://doi.org/10.1109/TVT.2021.3102121 doi: 10.1109/TVT.2021.3102121
|
| [5] |
O. Noroozi, S. K. Banihashem, N. T. Kerman, M. P. A. Khaneh, M. Babayi, H. Ashrafi, et al., Gender differences in students' argumentative essay writing, peer review performance and uptake in online learning environments, Interact. Learn. Environ., 2022 (2022), 1–15. https://doi.org/10.1080/10494820.2022.2034887 doi: 10.1080/10494820.2022.2034887
|
| [6] |
M. L. Manning, R. Lucking, The what, why, and how of cooperative learning, Soc. Stud., 82 (1991), 120–124. https://doi.org/10.1080/00377996.1991.9958320 doi: 10.1080/00377996.1991.9958320
|
| [7] |
S. K. Banihashem, O. Noroozi, P. den Brok, H. J. A. Biemans, N. T. Kerman, Modeling teachers' and students' attitudes, emotions, and perceptions in blended education: Towards post-pandemic education, Int. J. Manage. Educ., 21 (2023), 100803. https://doi.org/10.1016/j.ijme.2023.100803 doi: 10.1016/j.ijme.2023.100803
|
| [8] | G. O'Neill, T. Mcmahon, Student-centred learning: What does it mean for students and lecturers?, Emerging Issues Pract. Univ. Learn. Teach. 1 (2005). |
| [9] | B. E. Woolnough, Effective Science Teaching. Developing Science and Technology Education, Open University Press, 1994. |
| [10] |
S. McCarthy, B. Youens, Strategies used by science student teachers for subject knowledge development: A focus on peer support, Res. Sci. Technol. Educ., 23 (2005), 149–162. https://doi.org/10.1080/02635140500266377 doi: 10.1080/02635140500266377
|
| [11] |
D. Yu, C. L. P. Chen, Automatic leader–follower persistent formation generation with minimum agent-movement in various switching topologies, IEEE Trans. Cybern., 50 (2018), 1569–1581. https://doi.org/10.1109/TCYB.2018.2865803 doi: 10.1109/TCYB.2018.2865803
|
| [12] |
D. Yu, H. Xu, C. L. P. Chen, W. Bai, Z. Wang, Dynamic coverage control based on k-means, IEEE Trans. Ind. Electron., 69 (2022), 5333–5341. https://doi.org/10.1109/TIE.2021.3080205 doi: 10.1109/TIE.2021.3080205
|
| [13] |
B. Chang, W. Tang, X. Yan, X. Tong, Z. Chen, Integrated scheduling of sensing, communication, and control for mmwave/thz communications in cellular connected uav networks, IEEE J. Sel. Areas Commun., 40 (2022), 2103–2113. https://doi.org/10.1109/JSAC.2022.3157366 doi: 10.1109/JSAC.2022.3157366
|
| [14] |
S. Mu, Y. Shen, Stochastic learning for opportunistic peer-to-peer computation offloading in IoT edge computing, China Commun., 19 (2022), 239–256. https://doi.org/10.23919/JCC.2022.07.019 doi: 10.23919/JCC.2022.07.019
|
| [15] |
X. Wang, A. Lalitha, T. Javidi, F. Koushanfar, Peer-to-peer variational federated learning over arbitrary graphs, IEEE J. Sel. Areas Inf. Theory, 3 (2022), 172–182. https://doi.org/10.1109/JSAIT.2022.3189051 doi: 10.1109/JSAIT.2022.3189051
|
| [16] |
L. Tang, K. Zhang, H. Dai, P. Zhu, Y. C. Liang, Analysis and optimization of ambiguity function in radar-communication integrated systems using MPSK-DSSS, IEEE Wireless Commun. Lett., 8 (2019), 1546–1549. https://doi.org/10.1109/LWC.2019.2926708 doi: 10.1109/LWC.2019.2926708
|
| [17] |
J. Wang, X. Jin, Y. Tang, Optimal strategy analysis for adversarial differential games, Electron. Res. Archive, 30 (10), 3692–3710. https://doi.org/10.3934/era.2022189 doi: 10.3934/era.2022189
|
| [18] | J. H. Guilmette, The Power of Peer Learning: Networks and Development Cooperation, Academic Foundation, 2007. |
| [19] | K. Boubouh, A. Boussetta, Y. Benkaouz, R. Guerraoui, Robust p2p personalized learning, in 2020 International Symposium on Reliable Distributed Systems (SRDS), (2020), 299–308. https://doi.org/10.1109/SRDS51746.2020.00037 |
| [20] | V. Zantedeschi, A. Bellet, M. Tommasi, Fully decentralized joint learning of personalized models and collaboration graphs, in International Conference on Artificial Intelligence and Statistics, (2020), 864–874. |
| [21] |
K. S. Al-Olimat, A step-by-step derivation of a generalized model coupled with questions formulation technique to teach different types of dc motors and its impact on student performance, the course, and the program, IEEE Trans. Educ., 65 (2021), 184–190. https://doi.org/10.1109/TE.2021.3108744 doi: 10.1109/TE.2021.3108744
|
| [22] |
G. Zhang, J. Lan, L. Zhang, F. He, S. Li, Filtering in pairwise markov model with student's t non-stationary noise with application to target tracking, IEEE Trans. Signal Process., 69 (2021), 1627–1641. https://doi.org/10.1109/TSP.2021.3062170 doi: 10.1109/TSP.2021.3062170
|
| [23] |
H. Dinkel, S. Wang, X. Xu, M. Wu, K. Yu, Voice activity detection in the wild: A data-driven approach using teacher-student training, IEEE/ACM Trans. Audio Speech Lang. Process., 29 (2021), 1542–1555. https://doi.org/10.1109/TASLP.2021.3073596 doi: 10.1109/TASLP.2021.3073596
|
| [24] |
P. Wan, Dynamic behavior of stochastic predator-prey system, Electron. Res. Arch., 31 (2023), 2925–2939. https://doi.org/10.3934/era.2023147 doi: 10.3934/era.2023147
|
| [25] |
D. Yu, M. Yang, Y. J. Liu, Z. Wang, C. L. P. Chen, Adaptive fuzzy tracking control for uncertain nonlinear systems with multiple actuators and sensors faults, IEEE Trans. Fuzzy Syst., 31 (2022), 104–116. https://doi.org/10.1109/TFUZZ.2022.3182746 doi: 10.1109/TFUZZ.2022.3182746
|
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Xiangwen Yin. Promoting peer learning in education: Exploring continuous action iterated dilemma and team leader rotation mechanism in peer-led instruction[J]. Electronic Research Archive, 2023, 31(11): 6552-6563. doi: 10.3934/era.2023331
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