How do you plan to collect your data? Will you gather it from video comments, purchase data from large companies, or conduct surveys?
Also Where will your data be stored?
Do you intend to use public datasets for your business analysis and planning? If so, what types of public datasets are you interested in?
Why are these particular datasets valuable to your business?
Do you intend to use public datasets for your business analysis and planning? If so, what types of public datasets are you interested in?
Why are these particular datasets valuable to your business?
Data storage
Our data will be primarily stored in cloud databases (such as Google Cloud BigQuery or AWS RDS) to ensure efficient data management and secure access. In addition, we will use local servers for data backup to prevent data loss. All data storage will use encryption technology and comply with GDPR and related data privacy regulations to ensure data confidentiality and integrity.
Our data will be primarily stored in cloud databases (such as Google Cloud BigQuery or AWS RDS) to ensure efficient data management and secure access. In addition, we will use local servers for data backup to prevent data loss. All data storage will use encryption technology and comply with GDPR and related data privacy regulations to ensure data confidentiality and integrity.
I think the best approach is to conduct surveys since purchasing data from large companies would involve additional costs that we need to consider. we may can collaborate with universities to connect with students who need tutoring and those who have high grades in their courses.
@Liu what do you think about the data be stored?
In the education industry, data storage needs to support multiple data types such as student learning data, course content, behavioral analysis, etc., while ensuring security, scalability, and access speed. Cloud storage (such as AWS S3, Google Cloud) is a common choice for online education platforms. It can store large-scale video courses, interactive assignments, and learning records, while supporting machine learning models to analyze student behavior and provide personalized recommendations. Databases (SQL for student grades and NoSQL for unstructured content) can be used for real-time query and analysis to ensure efficient operation of the system. In addition, data encryption, access control, and regular backup to local or distributed storage (such as IPFS) ensure data security and compliance to meet the data storage needs of the education industry.
@kaosaier Do you intend to use public datasets for your business analysis and planning? If so, what types of public datasets are you interested in?
Why are these particular datasets valuable to your business?
Why are these particular datasets valuable to your business?
Hello Danny,
You are in the same company. You should use: "our" instead of "you".
You are in the same company. You should use: "our" instead of "you".
* Do you intend to use public datasets for my business analysis and planning? If so, what types of public datasets are you interested in?
Why are these particular datasets valuable to my business?
Why are these particular datasets valuable to my business?
Value of Datasets
These datasets are of great value to our business, mainly in the following aspects:
Optimize business decisions: Market trends and user behavior data can help us develop business strategies that are more in line with market needs and improve market competitiveness.
Improve user experience: By analyzing customer behavior data, we can optimize product design and improve user engagement and satisfaction.
Enhance predictive capabilities: Economic and industry data help us predict market changes and adjust operational strategies to reduce risks.
Support AI model training: Education and technical data can be used to train AI models to make our products more intelligent and more automated.
Taken together, these datasets can not only improve business operational efficiency, but also provide key support for innovation and growth.
These datasets are of great value to our business, mainly in the following aspects:
Optimize business decisions: Market trends and user behavior data can help us develop business strategies that are more in line with market needs and improve market competitiveness.
Improve user experience: By analyzing customer behavior data, we can optimize product design and improve user engagement and satisfaction.
Enhance predictive capabilities: Economic and industry data help us predict market changes and adjust operational strategies to reduce risks.
Support AI model training: Education and technical data can be used to train AI models to make our products more intelligent and more automated.
Taken together, these datasets can not only improve business operational efficiency, but also provide key support for innovation and growth.
Use of public datasets
Yes, we plan to use public datasets for business analysis and planning. We mainly focus on the following types of datasets:
Market trend data (such as Kaggle, government open data platforms) to analyze industry development trends and competitive landscape.
Customer behavior data (such as Google Trends and social media data) to help us understand the needs and interests of target users.
Economic and social statistics (such as World Bank or IMF data) for macro market analysis and forecasting.
Educational or technology-related datasets (such as academic paper datasets) to support AI training and product optimization.
These datasets will help improve the scientific nature of business decisions, improve the accuracy of predictive analysis, and optimize products and services.
Yes, we plan to use public datasets for business analysis and planning. We mainly focus on the following types of datasets:
Market trend data (such as Kaggle, government open data platforms) to analyze industry development trends and competitive landscape.
Customer behavior data (such as Google Trends and social media data) to help us understand the needs and interests of target users.
Economic and social statistics (such as World Bank or IMF data) for macro market analysis and forecasting.
Educational or technology-related datasets (such as academic paper datasets) to support AI training and product optimization.
These datasets will help improve the scientific nature of business decisions, improve the accuracy of predictive analysis, and optimize products and services.
@liu do you think we can change our company name
no way son of bich
why is that
because You are bingo like that
can we change our company name to a.i. bingo
of course not
Can you give me a reason than? and do you have a better idea? where is your idea come from?
@liu answer!
Data Collection
We plan to collect data through multiple channels, including user interaction logs, online surveys, social media analysis, and third-party data providers. In addition, we will also use website analysis tools (such as Google Analytics) to obtain user behavior data and collect customer feedback through the CRM system. All data collection processes will comply with data privacy and compliance requirements to ensure the legitimacy and security of the data.
We plan to collect data through multiple channels, including user interaction logs, online surveys, social media analysis, and third-party data providers. In addition, we will also use website analysis tools (such as Google Analytics) to obtain user behavior data and collect customer feedback through the CRM system. All data collection processes will comply with data privacy and compliance requirements to ensure the legitimacy and security of the data.
Hello Danny, Liu and Kaosaier,
Learning analytics is an area of research and practice that uses computational analysis of learning process data to understand and improve learning. You can think of getting some datasets as references. Do some research in this direction of learning analytics.
Learning analytics is an area of research and practice that uses computational analysis of learning process data to understand and improve learning. You can think of getting some datasets as references. Do some research in this direction of learning analytics.
1. Data Collection Plan
Description: We plan to collect data from multiple sources, including:
Course data from online learning platforms (such as Udemy, Coursera), covering course categories, prices, ratings, student registration information, etc.
Kaggle datasets, especially learning behavior data for online education, such as MOOC learning logs.
The government education database (NCES) is used to collect learning statistics for K-12 students.
A survey on user learning habits is conducted through Google Forms to collect personalized data.
Source: Udemy, Coursera, Kaggle, NCES, Google Forms
Link:
https://www.kaggle.com/datasets/samyakjhaveri/mooc-final
https://www.kaggle.com/datasets/hossaingh/udemy-courses
https://nces.ed.gov
2. Data storage
Description:
Google Cloud Storage and AWS S3 are used as cloud storage to ensure data security.
SQL database (PostgreSQL) stores structured data, such as course information, learning progress, etc.
NoSQL database (MongoDB) stores unstructured data, such as user feedback and notes.
Backup data is stored in Google Drive and AWS Glacier to ensure long-term availability.
Source: Google Cloud, AWS, MongoDB, PostgreSQL
Link:
https://aws.amazon.com/cn/s3/
https://www.mongodb.com/zh-cn
3. Public datasets planned to be used
Description:
MOOC datasets are used to analyze students' learning behavior, course completion rate, grades, etc.
Udemy course data can be used for market trend analysis to determine the most popular course categories.
NCES education statistics can be used for K-12 learning trend analysis to support B2B business.
EEG Brainwave Data is used to study adaptive learning systems and improve personalized education experience.
Source: Kaggle, NCES
Link:
https://www.kaggle.com/datasets/samyakjhaveri/mooc-final
https://www.kaggle.com/datasets/hossaingh/udemy-courses
https://nces.ed.gov
4. Value of the dataset to the business
Description:
MOOC datasets can help optimize the recommendation algorithm of the learning platform and improve students' learning participation.
Udemy course data can be used for market analysis and to formulate better course development strategies.
NCES education statistics can be used to determine the target market and formulate K-12 education product strategies.
EEG data can be used for personalized learning analysis and the development of AI adaptive learning tools.
Source: Kaggle, NCES
Link:
https://www.kaggle.com/datasets/samyakjhaveri/mooc-final
https://www.kaggle.com/datasets/hossaingh/udemy-courses
https://nces.ed.gov
Description: We plan to collect data from multiple sources, including:
Course data from online learning platforms (such as Udemy, Coursera), covering course categories, prices, ratings, student registration information, etc.
Kaggle datasets, especially learning behavior data for online education, such as MOOC learning logs.
The government education database (NCES) is used to collect learning statistics for K-12 students.
A survey on user learning habits is conducted through Google Forms to collect personalized data.
Source: Udemy, Coursera, Kaggle, NCES, Google Forms
Link:
https://www.kaggle.com/datasets/samyakjhaveri/mooc-final
https://www.kaggle.com/datasets/hossaingh/udemy-courses
https://nces.ed.gov
2. Data storage
Description:
Google Cloud Storage and AWS S3 are used as cloud storage to ensure data security.
SQL database (PostgreSQL) stores structured data, such as course information, learning progress, etc.
NoSQL database (MongoDB) stores unstructured data, such as user feedback and notes.
Backup data is stored in Google Drive and AWS Glacier to ensure long-term availability.
Source: Google Cloud, AWS, MongoDB, PostgreSQL
Link:
https://aws.amazon.com/cn/s3/
https://www.mongodb.com/zh-cn
3. Public datasets planned to be used
Description:
MOOC datasets are used to analyze students' learning behavior, course completion rate, grades, etc.
Udemy course data can be used for market trend analysis to determine the most popular course categories.
NCES education statistics can be used for K-12 learning trend analysis to support B2B business.
EEG Brainwave Data is used to study adaptive learning systems and improve personalized education experience.
Source: Kaggle, NCES
Link:
https://www.kaggle.com/datasets/samyakjhaveri/mooc-final
https://www.kaggle.com/datasets/hossaingh/udemy-courses
https://nces.ed.gov
4. Value of the dataset to the business
Description:
MOOC datasets can help optimize the recommendation algorithm of the learning platform and improve students' learning participation.
Udemy course data can be used for market analysis and to formulate better course development strategies.
NCES education statistics can be used to determine the target market and formulate K-12 education product strategies.
EEG data can be used for personalized learning analysis and the development of AI adaptive learning tools.
Source: Kaggle, NCES
Link:
https://www.kaggle.com/datasets/samyakjhaveri/mooc-final
https://www.kaggle.com/datasets/hossaingh/udemy-courses
https://nces.ed.gov
How does your approach differ from the traditional MOOC model?
Our approach differs from the traditional MOOC model in several key ways:
Personalized Learning Paths
Traditional MOOCs provide standardized course content to all learners, while our platform uses AI-driven adaptive learning techniques to tailor course recommendations, pacing, and content delivery based on individual learning progress and performance.
Real-time Feedback and AI Tutoring
Unlike traditional MOOCs that primarily rely on pre-recorded videos and quizzes, we integrate AI-powered tutors and real-time feedback mechanisms to provide immediate support and guidance to students, enhancing engagement and retention.
Interactive and Gamified Learning
While traditional MOOCs often have passive video lectures and quizzes, we incorporate interactive elements such as AI-driven simulations, gamification strategies, and real-world project-based assessments to improve student motivation and practical skills application.
Data-Driven Insights for Instructors
Our platform collects and analyzes real-time learning behavior data, enabling instructors to track student progress, identify weak areas, and refine course content dynamically. Traditional MOOCs usually provide limited instructor engagement and feedback mechanisms.
Integration of EEG Brainwave Data for Cognitive Analysis
One of our unique innovations is leveraging EEG brainwave data to assess student engagement and cognitive load during learning. This allows us to optimize content delivery, ensuring that students receive information in a way that maximizes comprehension and retention.
Supporting Data Sources
MOOC Dataset (155,000+ students, 247 courses)
Source: Kaggle
Link: https://www.kaggle.com/datasets/samyakjhaveri/mooc-final
Confused Student EEG Brainwave Data
Source: Kaggle
Link: https://www.kaggle.com/datasets/ha0han/brainwave-data
Udemy Courses Dataset (Course pricing, ratings, and enrollments)
Source: Kaggle
Link: https://www.kaggle.com/datasets/hossaingh/udemy-courses
Our approach enhances traditional MOOCs by making learning more personalized, interactive, and data-driven, ultimately improving engagement, retention, and learning outcomes.
Personalized Learning Paths
Traditional MOOCs provide standardized course content to all learners, while our platform uses AI-driven adaptive learning techniques to tailor course recommendations, pacing, and content delivery based on individual learning progress and performance.
Real-time Feedback and AI Tutoring
Unlike traditional MOOCs that primarily rely on pre-recorded videos and quizzes, we integrate AI-powered tutors and real-time feedback mechanisms to provide immediate support and guidance to students, enhancing engagement and retention.
Interactive and Gamified Learning
While traditional MOOCs often have passive video lectures and quizzes, we incorporate interactive elements such as AI-driven simulations, gamification strategies, and real-world project-based assessments to improve student motivation and practical skills application.
Data-Driven Insights for Instructors
Our platform collects and analyzes real-time learning behavior data, enabling instructors to track student progress, identify weak areas, and refine course content dynamically. Traditional MOOCs usually provide limited instructor engagement and feedback mechanisms.
Integration of EEG Brainwave Data for Cognitive Analysis
One of our unique innovations is leveraging EEG brainwave data to assess student engagement and cognitive load during learning. This allows us to optimize content delivery, ensuring that students receive information in a way that maximizes comprehension and retention.
Supporting Data Sources
MOOC Dataset (155,000+ students, 247 courses)
Source: Kaggle
Link: https://www.kaggle.com/datasets/samyakjhaveri/mooc-final
Confused Student EEG Brainwave Data
Source: Kaggle
Link: https://www.kaggle.com/datasets/ha0han/brainwave-data
Udemy Courses Dataset (Course pricing, ratings, and enrollments)
Source: Kaggle
Link: https://www.kaggle.com/datasets/hossaingh/udemy-courses
Our approach enhances traditional MOOCs by making learning more personalized, interactive, and data-driven, ultimately improving engagement, retention, and learning outcomes.
We'll build a comprehensive MOOC analytics system that combines real-time student interaction data (video viewing patterns, quiz responses, and discussion participation) with existing Kaggle MOOC datasets, which provide valuable historical patterns from platforms like edX, Coursera, and Stanford Online. Our AI system will track not just basic metrics like completion rates and assessment scores, but also analyze deeper behavioral patterns such as learning pace, preferred study times, and content revisit patterns. By incorporating institutional data and AI chatbot interactions, we'll gather qualitative feedback about learning challenges and preferences. The system will use natural language processing to analyze discussion forums and student questions, while machine learning models will identify struggling students early and suggest personalized interventions. This enriched dataset will enable adaptive learning paths, recommend prerequisite content based on knowledge gaps, and provide instructors with actionable insights about content effectiveness and student engagement.
Hey team, I really appreciate our comprehensive plan that integrates real-time student interaction data with historical Kaggle MOOC datasets to not only track completion rates and assessment scores but also analyze deeper behavioral patterns like learning pace, preferred study times, and content revisits. The incorporation of NLP to assess discussion forums and early identification of struggling students using machine learning is spot on, and I suggest we further enhance our system by integrating predictive analytics to forecast engagement trends and developing interactive dashboards for instructors to drill down into the data. These additions could refine our adaptive learning paths and intervention strategies even more. Great work, everyone—I'm excited to see where we can take this together!
— Danny
— Danny