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Data Analysis of Blended Learning in Python Programming

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Algorithms and Architectures for Parallel Processing (ICA3PP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11336))

Abstract

The rapid emergence of blended learning has sparked a great deal of research interest in the field of educational data mining. We apply the novel educational form of blended learning in the undergraduate curriculum of python programming. With the questionnaire before curriculum is obtained to capture the basic information of undergraduate students, we design educational resources and activities for online studying and face-to-face teaching. Since the learning process of each student is captured continuously, we make teaching and learning evaluations weekly to improve current teaching methods hence arouse students’ interest of continuous learning. With analyzing data and mining knowledge received in the process of blended learning, some beneficial results are gained to promote the quality of blended learning in the undergraduate curriculum of python programming, and benefit the undergraduate students as well as higher education in the long run.

Supported by the National Nature Science Foundation of China (No. 61672329, No. 61773246), Shandong Normal University’s Educational Project for Blended Learning (No. 2016KG79, No. 2016JG54).

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Correspondence to Xiaomei Yu .

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Chu, Q., Yu, X., Jiang, Y., Wang, H. (2018). Data Analysis of Blended Learning in Python Programming. In: Vaidya, J., Li, J. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11336. Springer, Cham. https://doi.org/10.1007/978-3-030-05057-3_16

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  • DOI: https://doi.org/10.1007/978-3-030-05057-3_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05056-6

  • Online ISBN: 978-3-030-05057-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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