Big data technology has become a hot research topic in the field of education, focusing on analyzing large amounts of educational data collected to improve teaching methods and enhance education quality. Among educational big data, learning analytics is particularly important, as it helps teachers understand students' learning progress and implement personalized teaching, thus promoting teaching reform. In higher education, the application of big data-based learning analytics technology can monitor students' learning processes. By analyzing students' behavioral patterns during the learning process, teachers can gain a more intuitive understanding of each student's performance. This technology provides a series of insights such as 'who is learning', 'what is being learned', and 'how well students are learning', which is crucial for ensuring educational quality.
Data collection is the first step in big data learning analytics, which involves utilizing various technical means to gather data from different sources. In the context of online education, the primary source of data is students' online behavior during the learning process. This data includes but is not limited to, video viewing patterns, discussion board participation scores, assignment scores, exam results, and forum interaction scores. These data need to be collected using appropriate tools such as web crawlers written in Python or by calling data through API interfaces.
Once the data is collected, the next step is data preprocessing. This stage involves cleaning the data, removing unreliable data points like test accounts and extreme outliers. The goal of preprocessing is to ensure the accuracy of subsequent analysis, structure the data for easy storage, and prepare it for analysis. Data analysis is the core part of learning analytics and primarily includes statistical analysis and visualization, clustering analysis, predictive analytics, association rule mining, and text mining. These methods help teachers gain deeper insights into students' behavioral patterns, learning habits, and performance trends. Statistical analysis and visualization transform data into charts and graphs for intuitive representation of students' learning progress. Clustering analysis groups students by learning habits or grades, while predictive analytics forecasts students' future performance based on historical data. Association rule mining focuses on identifying relationships between students' behaviors, and text mining analyzes content from discussion boards to understand students' learning attitudes and thought processes.
The application and development of big data in education holds great potential. With the rapid growth of global data, educational big data is gradually becoming a field of focus both domestically and internationally, offering significant value in education. In practical projects, the application of learning analytics has already shown results. For example, a research project mentioned in the article uses the 'C Programming 1' course on a MOOC platform to analyze students' learning behavior data combined with performance data to help teachers better understand students' progress and offer reasonable teaching suggestions. The application of big data in education, particularly in learning analytics on MOOC platforms, is becoming a key driver of educational reform.