Analysis of Learning Behavior Characteristics and Prediction of Learning Effect for Improving College Students’ Information Literacy Based on Machine Learning.

1. Base paper Title: Analysis of Learning Behavior Characteristics and Prediction of Learning
Effect for Improving College Students’ Information Literacy Based on Machine Learning
Modified Title: Machine learning-based analysis of learning behaviour characteristics and
learning effect prediction for raising college students' information literacy
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 carry out the learning effect prediction
analysis. This paper 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%, F1-Score is 89.39%,
and Kapaa coefficient is 0.859. This paper 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 teaching 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.
Existing System
With the rapid development of information technology represented by computer,
network technology and communication technology, computers and the Internet have been

2. widely used in various fields of society. Information plays an increasingly important role in the
development of human society and increasingly becomes one of the most active and decisive
factors in all fields of society. Information literacy, critical thinking and creativity are the core
skills that college students must master in the 21st century [1]. In the information age,
information literacy is an important part of college students’ core literacy. Information literacy
is a kind of adaptability to the information society. The information literacy of college students
is directly related to the sustainable development of future talents and the cultivation of
innovative talents [2], [3].Information literacy is a part of cultural literacy and overall quality.
Cultivating college students’ information literacy has already become an important issue facing
contemporary higher education. Information literacy includes the basic knowledge and skills
of information and information technology, the ability to use information technology to learn,
cooperate, communicate and solve problems, as well as information awareness and social
ethics. At present, information literacy education has received the attention of people from all
walks of life. The education departments and libraries in the United States, the United
Kingdom, Australia and other countries have carried out information literacy education to
different degrees.In 2022, the Ministry of Education and other four departments of China
jointly issued the ‘‘key points of improving the digital literacy and skills of the whole people
in 2022’’. Students’ information literacy and digital literacy are expected to be further
improved in the next few years [4].In recent years, due to the influence of online teaching and
hybrid teaching, and the development of artificial intelligence technology, information literacy
has also received more and more research attention. Many colleges and universities at home
and abroad have opened information literacy courses through various ways to carry out targeted
information literacy education.
Drawback in Existing System
 Data Quality and Bias:
Challenge: The quality of input data is crucial for machine learning models. If the
data used for training is biased or incomplete, the model may generate inaccurate
predictions or reinforce existing biases.
Mitigation: Ensure that the training data is diverse, representative, and free from bias.
Regularly update and validate the dataset to account for changes in educational
practices and demographics.

3.  Interpretability:
Challenge: Some machine learning models, especially complex ones like deep neural
networks, can be difficult to interpret. Understanding why a model makes a specific
prediction is crucial for acceptance and improvement.
Mitigation: Choose interpretable models when possible, or use techniques such as
feature importance analysis to gain insights into the model's decision-making process.
 Lack of Causation:
Challenge: Machine learning models often identify correlations but may not establish
causation. Understanding the cause-and-effect relationships is crucial for designing
effective interventions.
Mitigation: Combine machine learning results with domain expertise to interpret
findings. Use experiments or quasi-experimental designs to explore causal
relationships.
 Ethical Considerations:
Challenge: Machine learning models may inadvertently perpetuate or exacerbate
existing inequalities. Biased predictions may lead to unequal opportunities for different
student groups.
Mitigation: Regularly audit and assess the model for biases. Incorporate fairness
metrics into the model evaluation process and take corrective actions to address any
identified issues.
Proposed System
 Data Collection:
Gather diverse and representative data on students' learning behaviors, including
engagement with learning materials, interaction with online resources, assessment
performance, and other relevant factors.
Ensure ethical considerations and privacy regulations are adhered to in data
collection.

4.  Machine Learning Models:
Select appropriate machine learning algorithms for the task, considering the nature of
the data and the prediction goals.
Experiment with various models such as decision trees, random forests, support
vector machines, or neural networks to find the most effective solution.
 Predictive Analytics:
Develop a system that can predict students' learning effects based on their behavior
characteristics.
Integrate real-time prediction capabilities to adapt to changes in learning behaviors.
 Feedback Mechanism:
Establish a feedback loop to provide insights to educators, allowing them to
understand the factors influencing information literacy.
Implement a user-friendly interface for educators to interpret predictions and take
informed actions.
Algorithm
 Feature Selection:
Identify relevant features that represent learning behaviors (e.g., time spent on tasks,
frequency of engagement, participation in discussions).
Include variables related to information literacy, such as research skills, critical
thinking, and ability to evaluate sources.
 Model Evaluation:
Evaluate models on the testing dataset using metrics like accuracy, precision, recall,
and F1 score.
Analyze the confusion matrix to understand model performance in different areas.
 Implementation:
Develop a system or platform that integrates the machine learning model to provide
real-time feedback on students' information literacy.

5. Ensure the system is user-friendly and aligns with educational goals.
Advantages
 Personalized Learning Paths:
Machine learning algorithms can analyze individual learning behaviors to identify
strengths and weaknesses, enabling the creation of personalized learning paths for
students.
 Adaptive Curriculum Design:
Analysis of learning behavior can inform the design of adaptive curricula that cater
to the diverse needs and learning styles of students, promoting better engagement and
understanding.
 Increased Student Engagement:
Personalized learning paths and interventions tailored to individual needs can lead to
increased student engagement, motivation, and satisfaction with the learning process.
 Competitive Advantage:
Institutions adopting machine learning for educational improvement gain a
competitive advantage by staying at the forefront of innovative and data-driven
teaching methodologies.
Software Specification
 Processor : I3 core processor
 Ram : 4 GB
 Hard disk : 500 GB
Software Specification
 Operating System : Windows 10 /11
 Frond End : Python
 Back End : Mysql Server
 IDE Tools : Pycharm