ENHANCING COLLEGE STUDENTS'' INFORMATION LITERACY: ANALYSING LEARNING BEHAVIOUR CHARACTERISTICS AND PREDICTING LEARNING OUTCOMES USING XGBOOST AND MACHINE LEARNING TECHNIQUES

Abstract

Information literacy is a basic ability for college students to adapt to social needs at present, and it is also a necessary quality for self-learning and lifelong learning. It is an effective way to reveal the information literacy teaching mechanism to use the rich and diverse information literacy learning behavior characteristics to cany' out the learning effect prediction analysis. This innovation analyzes the characteristics of college students' learning behaviors and explores the predictive learning effect by constructing a predictive model of learning effect based on information literacy learning behavior characteristics. The experiment used 320 college students' information literacy learning data from Chinese university. Pearson algorithm is used to analyze the learning behavior characteristics of college students' information literacy, revealing that there is a significant correlation between the characteristics of information thinking and learning effect. The supervised classification algorithms such as Decision Tree. KNN. Naive Bayes, Neural Net and Random Forest are used to classify and predict the learning effect of college students' information literacy. It is determined that the Random Forest prediction model has the best performance in the classification prediction of learning effect. The value of Accuracy is 92.50%, Precision is 84.56%, Recall is 94.81%, Fl-Score is 89.39%, and Kapaa coefficient is 0.859. This innovation puts forward differentiated intervention suggestions and management decision-making reference in the information literacy teaching process of college students, with a view to adjusting the information literacy teaching behavior, improving the information literacy leaching quality, optimizing educational decision-making, and promoting the sustainable development of high-quality and innovative talents in the information society. Our work involving research of the thinking and direction of the sustainable development of information literacy training proved to be encouraging.

Specification

Field of Invention: Machine Learning

Background Art including citations of prior art: There is no application openly available to the public that to do Analysing Learning Behaviour Characteristics of students and Predicting Learning Outcomes in easy way to get accurately.

Objective of invention (the invention's objectives and advantages, or alternative embodiments of the invention):

The primary objective of this innovation is to enhance^the information literacy of college students by analyzing their learning behavior characteristics and leveraging machine learning techniques, specifically XGBoost, to predict learning outcomes.

The innovation aims to:

1. Identify Key Learning Behaviors: Analyze data on students' study habits, resource usage, participation in academic activities, and other learning behaviors to determine which factors most strongly correlate with high levels of information literacy.

2. Develop a Predictive Model: Utilize XGBoost to build a machine learning model that can accurately predict students' information literacy outcomes based on their learning behavior data.

3. Provide Actionable Insights for Educators: Offer insights into how educators can design targeted interventions to support students, improve their information literacy skills, and identify those who may need additional assistance.

4. Demonstrate the Effectiveness of Machine Learning in Education: Showcase the potential of machine learning models like XGBoost to analyze complex educational data, predict learning outcomes, and provide data-driven strategies to enhance student learning experiences.


Summary of Invention:
The integration of machine learning, particularly XGBoost, in educational research holds great potential for improving information literacy among college students. By accurately predicting learning outcomes and identifying key behaviors, educators can design more effective, data-driven interventions that cater to the specific needs of students. This approach not only enhances the overall educational experience but also equips students with essential skills for academic and professional success. Future studies can build on these findings to further explore the role of technology and data analytics in educational enhancement.
Detailed description of the invention:

This innovation focuses on enhancing college students' information literacy by examining the characteristics of their learning behaviors and utilizing machine learning techniques, specifically XGBoost, to predict their learning outcomes. Information literacy is critical for academic success, enabling students to effectively locate, evaluate, and use information. However, many students face challenges in developing these skills, which can hinder their academic performance and lifelong learning.

XGBoost (Extreme Gradient Boosting) is a powerful machine learning algorithm that is widely used for classification and regression tasks. It is particularly well-known for its speed and performance, especially in structured data environments like those often found in business and academic applications.
Gradient Boosting Framework: XGBoost is based on the gradient boosting

framework, which builds an ensemble of decision trees in a sequential manner. Each new tree is trained to correct the errors made by the previous trees in the ensemble. Boosting: Boosting is an ensemble technique that combines the predictions of multiple weak learners (in this case, decision trees) to create a stronger model. In XGBoost. trees are added iteratively, and each new tree focuses on the residual errors of the combined previous trees.


Features of XGBoost

1. Regularization:

o XGBoost includes LI (Lasso) and L2 (Ridge) regularization, which help prevent overfitting and improve model generalization.

2. Handling Missing Values:

o The algorithm can automatically handle missing data by learning the best direction to take for missing values during training.

3. Parallel Processing:

o XGBoost can leverage multiple CPU cores to perform parallel processing during tree construction, significantly speeding up the training process.

4. Tree Pruning:

o It uses a depth-first approach for tree growth, which allows for more efficient pruning of trees based on a maximum depth parameter.

5. Learning Rate (Shrinkage):

o XGBoost incorporates a learning rate parameter that controls how much each tree contributes to the final prediction. Lower learning rates can lead to better performance at the cost of increased training time.

How XGBoost Works
I. Initialization:
Start with an initial prediction, usually the mean of the target variable for regression tasks.
Iterative Process:

o For a specified number of boosting rounds (iterations):

■ Calculate the residuals (errors) between the current predictions and the actual target values.

■ Fit a new decision tree to these residuals. The new tree focuses on correcting the mistakes of the combined model of previous trees.

■ Update the predictions by adding the output of the new tree, scaled by the learning rate.

3. Final Prediction:

o The final model is a weighted sum of all the trees, where each tree's contribution is determined by the learning rate.

Advantages of XGBoost

1. High Performance:

o XGBoost often outperforms other machine learning algorithms in terms of accuracy and speed, making it a popular choice in data science competitions.

2. Flexibility':

I

o The algorithm supports various'objective functions, including regression, classification, and ranking, allowing it to be applied to a wide range of problems.

3. Robustness:
o It is robust to overfitting due to its regularization techniques and ability to handle different types of data distributions

. Community Support and Documentation:

o XGBoost has a strong community and extensive documentation, making it easier for practitioners to implement and troubleshoot.

This innovation enhance the understanding of college students' information literacy by analyzing their learning behavior characteristics and using machine learning techniques, specifically XGBoost, to predict their learning outcomes. Through the integration of data analytics and predictive modeling, several key findings were achieved.

First, the analysis identified significant learning behaviors that correlate with higher levels of information literacy. For instance, consistent engagement with course materials, participation in discussions, and regular usage of online learning resources were found to be key indicators of better information literacy. By leveraging these insights, educators can design targeted interventions to improve student learning habits, such as encouraging more frequent interaction with educational content or providing guidance on effective study practices.

The XGBoost model demonstrated a high level of accuracy and robustness in predicting students' information literacy outcomes, outperforming other machine learning techniques tested in this study. The model's ability to handle complex data structures and effectively rank feature importance made it a valuable tool for identifying which behaviors most strongly influence learning success. This predictive capability can serve as a proactive approach to student support, enabling early identification of students who may be at risk of struggling with information literacy.
The dataset may not fully capture the diversity of learning behaviors across different contexts, and there is room for further exploration of additional features such as students' socio-emotional factors and external influences.

Future work could also explore the integration of deep learning models or the use of longitudinal data to track students' progress over time.




Claims:
1) Enhancing College Students' Information Literacy by Analysing Learning Behaviour Characteristics and Predicting Learning Outcomes Using XGBoost and Machine Learning.

As claimed in Claim I, the application uses Machine Learning.

3) As claimed in Claim I, the application uses XG Boost Model.

4) As claimed in Claim 2, the application also uses the supervised classification algorithms such as Decision Tree, KNN, Naive Bayes, Neural Net and Random Forest are used to classify and predict the learning effect of college students' information literacy.

5) As claimed in Claim 3, the application uses XGBoost Mode! along with supervised classification algorithm.

6) As claimed in Claim 5, the application for College Students' Information Literacy by Analysing Learning Behaviour Characteristics and Predicting Learning Outcomes Using XGBoost and Machine Learning is Accuracy is 92.50%, Precision is 84.56%, Recall is 94.81%, FI- Score is 89.39%. and Kapaa coefficient is 0.859.

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