Research on Flipped Classroom of Big Data Course Based on Graphic Design MOOC

• Main Author: Yanqi Wang
• Format: Article
• Language: English
• Published: Hindawi-Wiley 2021-01-01
• Series: Wireless Communications and Mobile Computing
• Online Access: http://dx.doi.org/10.1155/2021/4042459

With the rapid development of the Internet, traditional teaching models can no longer meet the needs of talent training in colleges and universities, and reform is imperative. With the advent of the era of big data, the emergence of a large number of rich and diverse teaching resources, MOOC (Massive Online Open Course), microclasses, flipped classrooms, and other teaching models on the Internet has provided reform thinking and directions for teaching reform. This model divides the entire teaching design into two major modules: SPOC (Small Private Online Course) platform teaching activity design and flipped classroom teaching activity design, and applies this model to the actual teaching of open education, designing detailed teaching activity plans, in a real teaching situation. This study uses questionnaire surveys and interview surveys to investigate the basic personal situation of course learners, learning expectations, course participation, learning experience, and learning effects. It is planned to use the questionnaire star platform to issue and return questionnaires and use EXCEL and SPSS software to analyze the data and perform analysis and processing, combined with in-depth interviews with learners and professors for comprehensive analysis, so as to obtain the most true views of students and teachers on this model. In this process, we collect a variety of data from the SPOC platform and the flipped classroom platform, including feedback from students studying on the SPOC platform before class, observation of students’ learning attitudes in flipped classrooms to display of students’ results after class, and academic performance, summarize experience based on the analysis results, and optimize the teaching design plan. In classification algorithms, support vector machines (SVM) are widely used due to their advantages such as less overfitting and inconspicuous dimensionality of feature vectors. The traditional SVM algorithm is not suitable for processing large-scale data sets due to factors such as high time complexity and long training time. In order to solve these shortcomings, parallelizing the SVM algorithm to process large-scale data sets is an effective solution. On the basis of comparison, a SPOC-based flipped classroom teaching design model was constructed, and empirical application was carried out in the Open University, in order to promote the sustainable development of open education.