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Article

Progressive Teaching Improvement For Small Scale Learning: A Case Study in China

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School of Educational Science and Technology, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2
School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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School of Overseas Education, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Authors to whom correspondence should be addressed.
Future Internet 2020, 12(8), 137; https://doi.org/10.3390/fi12080137
Received: 16 July 2020 / Revised: 13 August 2020 / Accepted: 15 August 2020 / Published: 17 August 2020
(This article belongs to the Special Issue Computational Thinking)
Learning data feedback and analysis have been widely investigated in all aspects of education, especially for large scale remote learning scenario like Massive Open Online Courses (MOOCs) data analysis. On-site teaching and learning still remains the mainstream form for most teachers and students, and learning data analysis for such small scale scenario is rarely studied. In this work, we first develop a novel user interface to progressively collect students’ feedback after each class of a course with WeChat mini program inspired by the evaluation mechanism of most popular shopping website. Collected data are then visualized to teachers and pre-processed. We also propose a novel artificial neural network model to conduct a progressive study performance prediction. These prediction results are reported to teachers for next-class and further teaching improvement. Experimental results show that the proposed neural network model outperforms other state-of-the-art machine learning methods and reaches a precision value of 74.05% on a 3-class classifying task at the end of the term. View Full-Text
Keywords: teaching improvement; student learning feedback; small scale dataset; multi-class classification; WeChat mini program; artificial neural network (ANN) teaching improvement; student learning feedback; small scale dataset; multi-class classification; WeChat mini program; artificial neural network (ANN)
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MDPI and ACS Style

Jiang, B.; He, Y.; Chen, R.; Hao, C.; Liu, S.; Zhang, G. Progressive Teaching Improvement For Small Scale Learning: A Case Study in China. Future Internet 2020, 12, 137. https://doi.org/10.3390/fi12080137

AMA Style

Jiang B, He Y, Chen R, Hao C, Liu S, Zhang G. Progressive Teaching Improvement For Small Scale Learning: A Case Study in China. Future Internet. 2020; 12(8):137. https://doi.org/10.3390/fi12080137

Chicago/Turabian Style

Jiang, Bo, Yanbai He, Rui Chen, Chuanyan Hao, Sijiang Liu, and Gangyao Zhang. 2020. "Progressive Teaching Improvement For Small Scale Learning: A Case Study in China" Future Internet 12, no. 8: 137. https://doi.org/10.3390/fi12080137

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