Table of contents
1. Research overview (synthesis of other people's research experience on this issue)
2. Significance of the research (the value and benefits of the research)
3. Theoretical basis (theoretical principles of the research)
4. Research objectives (the specific goals to be achieved by the research)
6. Research methods (research methods in educational science)
7. Research process and measures (research process and measures on this issue)
1. Research overview (synthesis of other people's research experience on this issue)
In 2017, a seminar on engineering education in comprehensive colleges and universities was held in Shanghai. The new action plan of "new engineering" was proposed, and an in-depth discussion was carried out on the connotation and construction path of "new engineering" and a consensus was reached [1- 2]. On April 2, 2018, the General Office of the Ministry of Education issued a notice on printing and distributing the "Action Plan for Artificial Intelligence Innovation in Colleges and Universities", requiring the promotion of the construction of "new engineering" [3]. The construction of "new engineering" [4-6] has injected new blood into the construction and transformation of traditional engineering majors in colleges and universities, and provided unprecedented opportunities. It advocates the intersection and integration of disciplines to cultivate diverse and innovative talents. Liu Jianfeng et al. [7] and Lv Aiqing [8] et al. proposed that the teaching team is an important component of quality engineering in colleges and universities. It promotes the construction of disciplines and majors, deepens teaching reform, and plays an important role in the quality of personnel training through the organization of teamwork under the guidance of computational thinking. Important demonstration and leading role.
At present, the focus of computational thinking research at home and abroad has shifted to practical issues of how to promote and evaluate the development of computational thinking [9-10]. Regarding how to promote the implementation of computational thinking, although scholars agree that it should be promoted in specific teaching, there is no consensus on whether to regard computational thinking as a general subject, a subject specialty or a multidisciplinary topic in teaching [11-12]. In addition, the thinking that needs to be cultivated in higher vocational teaching is not only computational thinking, it is necessary to think about whether computational thinking is different from other thinking that students are forming? In this regard, advocates of computational thinking point out that while computational thinking shares elements with mathematics, engineering, and design thinking and draws on relevant frameworks from other ways of thinking, computational thinking extends other thinking skills in unique ways [13 -14]. Others disagree with this view, arguing that although the computing paradigm is inseparable from the support of engineering, science and mathematics, it is obviously different because of its focus on information processing [15-16]. The Carnegie Mellon Robotics Institute provides online training courses and related teaching resources to provide intensive training for pre-university students and teachers in computational thinking, analysis, and problem-solving [17-18]. Storm Robotics, an educational institution in New Jersey, offers a programming course for undergraduates that emphasizes algorithms and computational thinking through robotics projects [19]. The CS50 course in the United States is similar to the "Data Analysis" in some universities in my country. The content covers multiple branches, and the interaction between teachers and students is very strong. However, the "Introduction to Computational Thinking" course of the 9-school alliance in China is mainly based on algorithm theory (replacing the "Information Technology Fundamentals" course), and there is basically no interaction [20]. In the same "Introduction to Computational Thinking" course, Purdue University in the United States used Python language and Python library to teach computational thinking through basic programming concepts, data management concepts, simulation and visualization, and achieved good practical results [21]. Liu Guangrong et al. [22] explained the implementation process of the experimental teaching method incorporating the characteristics of computational thinking through specific examples to carry out teaching reform. Liu Qiong et al. [23] built a training model for computational thinking ability of higher vocational students based on APP Inventor. [24] analyzed the teaching status of the course "Programming Basics", compared the Python language with the C language commonly used in the course, and demonstrated the characteristics of the Python language with examples to cultivate students' computational thinking. Tang Xiaoyong et al. [25] introduced computational thinking skills into the Linux system programming course to cultivate students' computational thinking.
To sum up, the current teaching reform of higher vocational Python data analysis courses under the guidance of computational thinking mainly has the following problems:
(1) The current training mechanism and syllabus of higher vocational Python data analysis courses are too outdated, and they are not consistent with "1+X" Wait for the cutting-edge trend to connect.
(2) The existing teaching reform of Python data analysis course based on computational thinking mostly revolves around theoretical research and lacks practical cases; (
3) Less attention is paid to the mapping relationship between computational thinking and Python data analysis course knowledge points.
2. Significance of the research (the value and benefits of the research)
(1) Realize the transformation of the teaching concept for computational thinking and the reform of the research curriculum system for Python data analysis teaching reform under the guidance of computational thinking, and promote the better and faster development of the curriculum system; (2) Find out the impact of Python data in higher vocational
colleges Analyze the main factors of design ability and improve teaching quality;
(3) Establish a Python data analysis course website platform, etc., and construct and implement an online learning test system and quality courses.
3. Theoretical basis (theoretical principles of the research)
According to the characteristics of students majoring in interest majors in higher vocational colleges from the perspective of new engineering, the concept of computational thinking is introduced, based on Python data analysis, the teaching mode of higher vocational computer courses based on MOOC, SPOC, etc. is applied, and the defense guidance is provided through the teacher learning platform. Teaching reforms are carried out by means of computer programming language teaching hours and open computer experiment platforms. In order to improve students' enthusiasm for independent learning, improve the utilization rate of experimental equipment, and finally improve the practical ability of higher vocational students, and effectively serve various majors.
4. Research objectives (the specific goals to be achieved by the research)
( 1) Explore the main factors that affect the data analysis ability of students in higher vocational colleges;
(2) Build a model for the effective integration of computational thinking teaching concepts and Python data analysis courses;
(3) Establish Python data analysis course website platforms, etc., and build high-quality courses etc.
5. Research content (closely related to the specific content of the theme, organized and not necessarily elaborated)
(1) Explore the current situation of the teaching reform of Python data analysis courses in higher vocational colleges under the guidance of computational thinking; (2
) Analyze the dimensions and influencing factors of the integration of computational thinking and Python data analysis courses;
(3) Construct computational thinking The model integrated with the Python data analysis course;
(4) Optimizing the model, and the practice and application of the results.
6. Research methods (research methods in educational science)
In the research and practice of this project, it is planned to use methods such as literature method, survey method, case study, practice exploration, and experience summary method to carry out the project. The details are as follows: (1) Literature method: through CNKI, VIP,
Elsevier , Springer and other institutions retrieve relevant literature, conduct theoretical analysis on the topic, provide a theoretical basis for the reform and innovation of Python data analysis teaching under the guidance of computational thinking, and form a preliminary research framework.
(2) Survey method: Qualitative and quantitative analysis of the links and practices that affect the programming ability of information majors in higher vocational colleges, find out the main factors, and put forward a teaching reform plan based on the characteristics of students in higher vocational colleges.
(3) Case study method: conduct research on typical representatives of three types of students with strong, average, and weak Python programming ability by major, and analyze their successful experience or failure lessons for reference.
(4) Practice and exploration method: select typical teaching majors and units to carry out teaching reform practice, and modify and improve the reform plan according to the experimental results.
(5) Experience summary method: evaluate and summarize the experience of the research results of the project, and analyze the characteristics and promotion value of the research results.
7. Research process and measures (research process and measures on this issue)
( 1) January 2009-June 2009, preparation stage: collect research materials related to the topic, analyze the advantages and disadvantages of relevant research; (2)
July 2009-December 2009, early implementation stage: build data based on Python Theoretical teaching and practical teaching curriculum system of analysis courses;
(3) January 2010-June 2010, later stage of implementation: exploring the mapping relationship between computational thinking concepts and Python data analysis courses; (4) July
2010-2010 In December of 2010, the conclusion stage: organize and summarize the research results, write research papers, conclude the project report, and hold the conclusion meeting.
8. References
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