Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students
Abstract
:1. Introduction
2. Research Framework and Target
2.1. Research Framework
2.2. Research Target and Method
3. Analysis of Big Data Courses on MOOC Platforms
3.1. Platform Dimension
3.1.1. Platform Construction
3.1.2. Resource Quantity
3.1.3. Resource Quality
3.2. Organization Dimension
3.2.1. Course Provider
3.2.2. Faculty Team
3.2.3. Learning Norms
3.3. Course Structure Dimension
3.3.1. Course Objectives
3.3.2. Teaching Design
3.3.3. Course Content
3.3.4. Teaching Organization and Implementation
3.3.5. Course Management and Evaluation
4. Suggestions
4.1. Platform
4.2. Institutional Framework
4.3. Course Construction
5. Conclusions
Author Contributions
Funding
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Foreign MOOC Platforms | Number of Courses | Domestic MOOC Platforms | Number of Courses |
---|---|---|---|
Coursera | 181 | Zhihuishu | 58 |
edX | 123 | Chinese University MOOC | 50 |
FutureLearn | 20 | XuetangX | 49 |
Udacity | 1 | Xueyin Online | 18 |
Alibaba Cloud Classroom | 12 | ||
Zhengbao Cloud Classroom | 3 | ||
Chinese MOOCs | 3 | ||
Chongqing Online Open Course Platform for Higher Education | 2 | ||
Rongyou Xuetang | 2 | ||
Zhejiang Online Open Course Sharing Platform for Higher Education | 1 | ||
CNMOOC | 1 | ||
Gaoxiaobang | 1 | ||
Total | 325 | Total | 200 |
Level | Number |
---|---|
Beginner | 137 |
Intermediate | 96 |
Advanced | 32 |
Professional | 5 |
Mixed | 55 |
Total | 325 |
Platforms | Year | Course Title | Opened by | First-Rate Universities and Disciplines or Not? | Main Creators and Those Who Are Primarily Responsible for the Course | Title of the Lecturer | Collaborating Institutions | Number of Times the Course Has Been Offered | The Number of Participants Who Have Taken the Course |
---|---|---|---|---|---|---|---|---|---|
Chinese University MOOC | 2014 | Big Data Algorithm | Harbin Institute of Technology | Yes | Wang Hongzhi | Professor | / | 5 times, providing self-study mode | 207,432 |
2016 | Biological Big Data | Fujian Agriculture and Forestry University | No | He Huaqin | Professor | Chinese Academy of Sciences and enterprises | 11 times | 35,667 | |
2016 | Playing with Data in Python | Nanjing University | Yes | Zhang Li | Associate professor | / | 13 times | 405,270 | |
2017 | Python Data Analysis and Presentation | Beijing Institute of Technology | Yes | Song Tian | Professor | / | 12 times | 492,528 | |
2017 | The Principle and Application of Big Data Technology | Xiamen University | Yes | Lin Ziyu | Associate professor | / | 10 times | 235,803 | |
2017 | Analysis and Application of Business Data | Jiangsu Vocational Institute of Commerce | No | Wu Honggui | Professor | / | 11 times | 38,156 | |
XuetangX | 2019 | Fundamentals of Big Data Systems | Tsinghua University | Yes | Wang Jianmin | Professor | / | 6 times | 94,818 |
2019 | Advanced Big Data System | Tsinghua University | Yes | Wang Zhi | Associate professor | / | 6 times | 34,393 | |
2019 | Big Data Machine Learning | Tsinghua University | Yes | Yuan Chun | Associate research fellow | / | 7 times | 45,283 | |
2019 | Big Data Machine Learning | Tsinghua University | Yes | Wu Yongwei | Professor | Alibaba Cloud | Self-study mode | 71459 | |
Rongyou Xuetang | 2017 | An Introduction to Data Science | Renmin University of China | Yes | Chao Lemen | Associate professor | / | 8 times | 34,138 |
Gaoxiao-bang | 2019 | Big Data Analysis and Processing | Chongqing University of Posts and Telecommunications | No | Wang Guoyin | Professor | / | 7 times | 3038 |
Teaching Content | Course Title | Proportion |
---|---|---|
Data acquisition and processing | Acquiring and Analyzing News and Academic Data Related to the COVID-19 Pandemic; Big Data Collection and Storage; Geographic Data Processing and Charting Techniques; Excel Data Processing and Analysis; Introduction to Big Data: Mathematical Foundations and Applications; Big Data Processing and Analysis; GNSS Measurement and Data Processing; Finance Data Processing Technology; Data-Driven Decision Making: Market Research Practice; Data Journalism; Discovering the Beauty of Data with SPSS; Introduction to Big Data Analytics and Applications; Big Data: Processing and Analysis; Data Storage and Processing; Statistics and Data Science; Statistical Analysis with R Language. | 11% |
Data analysis and mining | R Language Data Analysis; Python Data Analysis Practice; Python Foundation for Big Data Analysis; Python Data Analysis Practice; Analysis of Business Statistics; Big Data Analysis and Forecast Technology; Python Data Analysis and Application: R language Data Analysis and Mining; Big Data Analysis and Visualization; Python Language and Economic Big Data Analysis; Introduction to Data Analysis and Processing- Backup; Excel Data Processing and Analysis; Data Analysis Using Python; R Language Programming (Chinese version); SPSS Multivariate Analysis; Time and Space Big Data Analysis and Mining Actual Combat; Introduction to Big Data; Data Warehouse and Data Mining; Python Data Mining; Application of Big Data Tools; Data Mining Techniques for Big Data Analysis: Introduction to Data Mining; Data Mining. | 23% |
Data visualization | Microsoft Big Data Visualization; Python Data Analysis and Data Visualization; Data Visualization with Python; Insight into Data: Introduction to Data Analysis and Visualization; Data Analysis: Visualization and Dashboard Design; Microsoft Excel—Data Visualization, Excel Charts and Graphics; Big Data: Data Visualization; Visualizing Data with Python; Understanding and Visualizing Data with Python; Data Visualization with Tableau; Data Science: Visualization; Data Visualization and Storytelling; Information Visualization; Data Visualization—Analysis and Design. | 13% |
Big Data Management | Using HBase for Real-time Management of Your Big Data; How to Move Data to Hadoop; How to Access Data on Hadoop with Hive; Building Data Warehouse for Business Intelligence; Excel: Data Management; Big Data: Modeling and Management System; Managing Big Data with MySQL; Essentials of Database Management; Data Management, Security, and Robotic Operating System as a General Tool in IoT; Managing Big Data with R Language and Hadoop; Agile Data Science for Product Management; Non-Relational Database Technologies; Advanced Big Data Systems; Advanced Big Data Systems. | 13% |
Cloud computing and distributed platforms | Big Data Platform Core Technologies; Big Data Platform Technologies; Principles and Applications of Big Data Technology; TensorFlow Machine Learning Based on Google Cloud Platform; Introduction to Big Data and Cloud Computing; Cloud Computing and Big Data Technology; TensorFlow: Data and Deployment; Managing Big Data in Cluster and Cloud Storage; Advanced Machine Learning with TensorFlow on Google Cloud Platform; Data Engineering, Big Data, and Machine Learning on Google Cloud Platform; Cloud-based Delivery of Data Warehouse; Cloud Computing. | 16% |
Artificial intelligence | Automatically Calibrating Intersection Topology Information with Trajectory Data; From Data to Decision: Three Machine Learning Tasks, Three Structural Transformations; Big Data and Artificial Intelligence; Data Intelligence and Applications; IBM Artificial Intelligence Application Professional Certificate; Big Data, Artificial Intelligence, and Ethics; Workflow of Artificial Intelligence: Data Analysis and Hypothesis Testing; Data Ethics, Artificial Intelligence, and Responsible Innovation; Machine Learning Engineer; Master’s Program in Machine Learning and Data Science; Machine Learning Based on Big Data; Reinforcement Learning; Deep Learning Engineer; Data Science: Machine Learning; Data Science: Statistics and Machine Learning; Fundamentals of Data Science: Prediction and Machine Learning; Vertex Projects in Data Science and Machine Learning; Machine Learning with Data Science and Analytics; Applications of Mathematics in Machine Learning. | 16% |
Big data application | Agri-Big Data; Big Data Marketing and Management in Chain Enterprises; Knowledge Management and Big Data in Business; Machine Learning and Reinforcement Learning in Finance; Forestry Big Data and Artificial Intelligence; Analyzing Box Office Data with Ploly and Python; Analyzing Box Office Data with Seborn and Python; Big Data and Urban Planning; Big Data for Smart Grids; Knowledge Epidemic Map—Application of AI and Big Data in Intelligent Services for COVID-19; Acquisition and Analysis of News and Academic Data for COVID-19; Practical Application of Big Data at Tsinghua University: Rapid Construction of Data-driven Applications. | 8% |
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Sang, H.; Ma, L.; Ma, N. Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students. Information 2023, 14, 511. https://doi.org/10.3390/info14090511
Sang H, Ma L, Ma N. Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students. Information. 2023; 14(9):511. https://doi.org/10.3390/info14090511
Chicago/Turabian StyleSang, Hongfeng, Liyi Ma, and Nan Ma. 2023. "Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students" Information 14, no. 9: 511. https://doi.org/10.3390/info14090511
APA StyleSang, H., Ma, L., & Ma, N. (2023). Analysis of the Current Situation of Big Data MOOCs in the Intelligent Era Based on the Perspective of Improving the Mental Health of College Students. Information, 14(9), 511. https://doi.org/10.3390/info14090511