Research on Education Big Data for Students Academic Performance Analysis based on Machine Learning

Abstract: The application of the Internet in the field of education is becoming more and more popular, and a large amount of educational data is generated in the process. How to effectively use these data has always been a key issue in the field of educational data mining. In this work, a machine learning model based on Long Short-Term Memory Network (LSTM) was used to conduct an in-depth analysis of educational big data to evaluate student performance. The LSTM model efficiently processes time series data, allowing us to capture time-dependent and long-term trends in students' learning activities. This approach is particularly useful for analyzing student progress, engagement, and other behavioral patterns to support personalized education. In an experimental analysis, we verified the effectiveness of the deep learning method in predicting student performance by comparing the performance of different models. Strict cross-validation techniques are used to ensure the accuracy and generalization of experimental results.

• The paper demonstrates that LSTM models can effectively capture temporal dependencies in educational data, yielding higher accuracy and F1 scores.
• It employs rigorous preprocessing and K-fold cross-validation to ensure data integrity and the generalizability of its predictive analytics.
• The findings support targeted interventions by identifying at-risk students and advancing personalized learning through deep learning techniques.

Analysis of Educational Big Data for Student Performance Using LSTM Models

The paper investigates the utilization of educational big data through machine learning techniques to analyze and predict student academic performance. By employing a Long Short-Term Memory (LSTM) network, the study provides an in-depth analysis of time-dependent educational data, which is crucial for understanding and predicting student learning trajectories.

Methodological Approach

The authors leverage the strengths of LSTM models to handle the sequential nature of educational data. Such models are effective in capturing both temporal dependencies and long-term trends within the learning activities of students. The study emphasizes the importance of using cross-validation techniques to ensure the robustness and generalizability of the results obtained from these models.

Experimental Setup and Results

Utilizing the EdNet-KT1 dataset, a comprehensive large-scale education data repository, the study structures its experiments to evaluate the predictive capability of the LSTM model. The setup includes critical preprocessing steps such as data cleaning and feature selection, focusing on parameters like correctness of answers and timestamps. The results are evaluated using metrics like accuracy and F1 score, supported by K-fold cross-validation to ensure stability.

The experiments reveal that the LSTM model offers superior predictive performance in terms of accuracy and F1 scores compared to traditional methods. These outcomes reinforce the potential of deep learning models in educational analytics.

Comparative Analysis with Related Work

The paper situates its findings within the broader field of educational data mining, referencing previous work that utilizes various machine learning approaches, including deep neural networks, SVMs, and ensemble techniques. The comparative analysis showcases the efficacy of deep learning strategies, particularly when addressing complex, multi-dimensional educational datasets.

Implications and Future Directions

This research provides significant insights into the practical and theoretical implications of using machine learning in educational settings. The ability to predict student performance and identify at-risk individuals enables targeted interventions, potentially improving both educational outcomes and students’ mental well-being. The study also suggests that further exploration into algorithm optimization, data privacy, and integration of multimodal learning environments could enhance the effectiveness of these predictive models.

Conclusion

In conclusion, the paper contributes valuable evidence supporting the application of LSTM models in educational big data analytics. By demonstrating strong performance metrics and effectively capturing student behavior patterns, the study underscores the potential for machine learning techniques to transform personalized education and institutional decision-making processes. Future research should continue to explore enhancements in model accuracy, privacy considerations, and the practical deployment of these technologies in diverse learning contexts.