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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 1, 2024.
Abstract: In modern educational curriculum teaching, we should fully leverage the advantages of modern technology, especially in teaching methods, and deeply understand and apply big data technology. This article explores the design and effectiveness evaluation methods of curriculum teaching models from the perspective of big data. We utilized big data thinking and conducted research and practical exploration to compare and evaluate teaching mode design methods. In the art and design course, we adopted a blended learning model, combining MOOC and SPOC, and innovated traditional teaching methods and plans. Meanwhile, we investigated the teaching effectiveness and feasibility of this blended learning model. By extensively evaluating teaching techniques, evaluation methods, and technologies that support the learning process, we reconstructed blended learning evaluation indicators and evaluated the effectiveness of learning outcomes and processes under different teaching modes. The research results show that the blended learning model based on big data perspective can significantly improve the effectiveness of classroom teaching. In contrast, learners' self-learning ability and practical innovation ability have also been further improved.
Danjun ZHU and Gangtian LIU, “Design of Teaching Mode and Evaluation Method of Effect of Art Design Course from the Perspective of Big Data” International Journal of Advanced Computer Science and Applications(IJACSA), 15(1), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01501105
@article{ZHU2024,
title = {Design of Teaching Mode and Evaluation Method of Effect of Art Design Course from the Perspective of Big Data},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01501105},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01501105},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {1},
author = {Danjun ZHU and Gangtian LIU}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.