Pattern Recognition of Students' Learning Behavior Based on Deep Learning

DL is a machine learning method that simulates neural connections in the human brain by constructing deep neural networks, enabling complex pattern recognition and data analysis tasks. DL has made significant achievements in fields such as computer vision, natural language processing, and speech recognition, and has become one of the important branches of artificial intelligence. The core concept of DL is neural networks, especially Deep Neural Networks (DNN). As shown in Figure 1, a DNN consists of multiple neural network layers, each containing multiple neurons or nodes. These layers are divided into input layer, hidden layer, and output layer, with each neuron connected to the neuron in the previous layer, transmitting information and performing calculations. DL is called "depth" because networks typically contain multiple hidden layers, making them very deep.

Figure 1.

DNN structure diagram

In DL, the process of information flowing from the input layer to the output layer is called forward propagation. Each neuron multiplies its input with weights and applies an activation function, which is then passed on to the next layer. In the forward propagation process, the input data is passed through a neural network, and each neuron multiplies its input with weights and applies an activation function, which is then passed to the next layer. Forward propagation can be represented by the following formula: 1zj(l)=∑i=1nWjilail−1+bjl 2ajl=f(zjl)

Where zjl is the weighted input of the j neuron in the i layer; Wjil is the weight from the i neuron in the l – 1 layer to the j neuron in the l layer; ajl is the activation value of the j neuron in the l layer; f is an activation function, usually a nonlinear function such as ReLU, Sigmoid or Tanh.

The training goal of DNN is to minimize the loss function, which is usually used to measure the error between the prediction of the model and the actual label. Common loss functions include Mean Squared Error and Cross-Entropy Loss. Taking the cross entropy loss as an example, for the binary classification problem, it can be expressed as: 3L(y,y∧)=−1N∑i=1N[ yilog(yi∧)+(1−yi) ]

Where L is the loss function; y is the actual label; y∧ is the predicted output of the model; N is the number of samples; log is a natural logarithm.

Back propagation is the key step of DL model training. It uses gradient descent method to update the weights in the network to minimize the error between the predicted output and the actual label. This process calculates the gradient through the chain rule. Back propagation is an algorithm for training DNN, which updates the weight of the network based on gradient descent method to minimize the loss function. Back propagation calculates the gradient of each parameter to the loss through the chain rule, and then updates the parameters. Taking the weight update as an example, it can be expressed as: 4ΔWjil=−η∂L∂Wjil

Where ΔWjil is the weight update amount; η is the learning rate, which controls the step size of parameter updating; ∂L∂Wjil is the partial derivative of the loss function to the weight, which is calculated by the chain rule.

The rapid development and success of DL is partly attributed to a large amount of available data and powerful computing resources. At the same time, the DL field still faces many challenges, including over-fitting, unbalanced data, insufficient explanation and so on. Therefore, DL research continues to promote the development of new methods and technologies to solve these challenges and apply DL to a wider range of fields [24]. DL has become an important driving force in the field of artificial intelligence, providing powerful tools and methods for solving complex real-world problems.

Student ability index is a quantitative measure used to evaluate students' level and performance in different fields or skills. These indicators are of great significance in the field of education and academic research, which can help educators, schools and policy makers better understand students' abilities and needs, thus providing better educational support and improving teaching methods. Subject ability index is an index used to describe students' skills and knowledge level in a specific subject area. They can cover a wide range of disciplines, including mathematics, science, literature, history, art and so on. These indicators usually include subject-specific standards and skill requirements, which are used to evaluate whether students have reached the expected level of academic ability. One of the primary tasks of DL ability analysis is to determine students' DL ability indicators, which can help us understand students' performance and potential in DL field. This paper analyzes the students' ability indicators, as shown in Figure 2.

Figure 2.

Students' ability indicators

This paper gives a more detailed description of students' ability indicators, and the contents are as follows:

DNN Understanding: It measures students' understanding of DNN, including the understanding of network structure, hierarchical representation and activation function.

Model training and adjustment: Assess students' abilities, including data preprocessing, model training, parameter adjustment and model evaluation.

Feature engineering: measure students' ability in selecting and extracting features to optimize the performance of DL model.

Problem definition and solution: Assess students' problem definition and solution ability, including the ability to identify problems and application methods suitable for DL.

Use of DL tools: measure students' proficiency in DL tools and frameworks, such as TensorFlow and PyTorch.

Practical application: to examine students' ability to solve real-world problems, such as computer vision, natural language processing, reinforcement learning and other application fields.

Innovation and research potential: evaluate students' innovation and research potential in DL field, including achievements in publishing papers and participating in competitions.

Deep learning can be used to evaluate students' ability level in specific subject areas. For example, in mathematics education, deep learning model can analyze students' performance on mathematics topics and identify their strengths and weaknesses in different mathematical concepts and skills. This analysis can help educators better understand the subject characteristics of students and provide them with targeted educational support. Deep learning can also be used to evaluate students' problem solving and innovation ability. By analyzing students' performance in solving complex problems, putting forward innovative ideas and participating in experimental learning projects, the deep learning model can help identify students' innovative potential and problem-solving skills. This will help educators to provide more support and challenges for students with innovative talents. Deep learning technology has a wide application potential in evaluating students' ability indicators. Through deep learning analysis, educators and schools can better understand students' individual needs, and improve teaching methods to better meet students' learning needs. This will help to improve the quality of education and cultivate more competitive students.

In order to ensure consistency and reproducibility, strict methods are adopted for data collection and analysis. Specifically, the academic data from college students come from the learning management system, including test scores, learning history and learning behavior patterns. These data sources are chosen because they comprehensively and objectively reflect students' academic achievements and learning needs. Before analysis, the collected data are preprocessed to ensure the quality and suitability for deep learning analysis. This includes cleaning up to delete irrelevant or incorrect entries, formatting to maintain consistency, and standardization to facilitate comparison and analysis.

For deep learning analysis, the most advanced model tailored to deal with complex patterns in educational data is adopted. The selection of these models is based on their proven performance in similar tasks and their ability to capture the complex relationship between academic variables. The training process involves using the collected data subset to fine-tune the model parameters, taking care to avoid over-fitting.

Firstly, this study collected students' academic data through the learning management system of university education institutions, including test scores, learning history and learning behavior data. In order to ensure the representativeness of the sample, we randomly selected students from different disciplines and academic levels. In the process of data analysis, we adopt deep learning algorithms, which are based on the models and indicators previously verified in the field of education. All analyses follow strict statistical and data science standards to ensure the consistency and reliability of the results. The analysis effect diagram is designed as shown in Figure 3.

Figure 3.

Data analysis content

Analysis of students' course performance and academic performance data is one of the important indicators to evaluate students' deep learning ability. First of all, students' classroom exams, tests, homework and project scores can be counted and analyzed. This includes calculating the average score, distribution, standard deviation and other statistical indicators to understand the performance and changing trends of students in different disciplines. You can also use deep learning models, such as neural networks, to analyze students' academic performance data in order to mine potential patterns and associations, such as which disciplines or knowledge fields are students' strengths or weaknesses. To determine their performance in the DL course. By analyzing students' programming assignments, including code quality, function realization and performance evaluation, we can evaluate their DL skills and students' innovation and problem-solving ability in DL projects and laboratory reports [25]. Analyze students' behaviors on the online learning platform, including study time, course browsing and interaction, so as to understand their learning mode. Learning behavior data is the key to understand students' learning process and strategies. This includes students' activities on the online learning platform, such as click-through rate, frequency of visits, study time, frequency of participating in class discussions, etc. By analyzing these learning behavior data, students' learning enthusiasm, self-management ability and learning strategies can be evaluated. For example, frequent course visits and active participation may indicate that students have high learning motivation, while long study time may indicate that students have good self-management ability. Finally, DL-related exams and tests are used to evaluate students' theoretical knowledge and problem-solving ability. Students' self-assessment and feedback also provide important information. Students may provide self-assessment in the course, describing their learning experience, sense of accomplishment and self-cognition. These data can be used to understand students' learning motivation, goal setting and self-management ability. The deep learning model can also be used to analyze the self-assessment texts to identify the key themes and emotions, as well as the relationship with academic performance. In a word, evaluating students' deep learning ability needs to integrate a variety of data analysis methods and technologies to fully understand students' academic performance, learning behavior, proble-msolving ability, self-management and feedback. Deep learning technology has played an important role in this process, which can help educators better understand students' abilities and needs.

The exploration of learning behavior patterns necessitates rigorous experimental design and educational intervention measures to ensure their effectiveness and sustainability. This typically involves collecting academic performance, learning behavior, subject interests, and learning history data from students. By utilizing data analysis techniques such as data mining and machine learning, we can gain insights into the needs and patterns of students. Based on these data analysis results, we can design tailored learning paths and resources. Students are provided with customized educational resources, including textbooks, assignments, exercises, and assessment tools. In the context of learning behavior pattern recognition, various methods are commonly employed to estimate students' ability levels, such as Item Response Theory (IRT) or its variants. A fundamental formula in IRT is often utilized for this purpose: 5Pi(θ)=c+(1−c)⋅e(θ⋅b)1+e(θ⋅b)

In the formula, $P_i(θ)$ represents the probability of student i answering question i correctly at the ability level θ; $c$ represents the probability of guessing; $a$ represents the difficulty parameter of the question; $b$ represents a student's ability parameter.

To provide individualized learning materials or activities to students, recommendation algorithms can be used. A simple collaborative filtering recommendation formula can be: 6R(u,i)=∑υ∈N(u)(R(υ,i)⋅ω(u,υ))∑υ∈N(u)| ω(u,υ) |

In the formula, $R(u, i)$ represents the predicted interest of user u towards project i ; $N(u)$ represents a group of users similar to user u ; $R(υ, i)$ represents the interest rating of user υ towards project i ; $w(u, υ)$ represents the similarity weight between user u and user υ.

To create individualized learning paths, formulas can be used to estimate the knowledge level of students in different fields, and then select appropriate learning resources. A simple formula for estimating knowledge level can be: 7Ki=α⋅Si+(1−α)⋅Li

In the formula, $K_i$ represents the student's estimated level of knowledge in the field i ; $S_i$ represents the student's test or assignment scores in the field of i ; $L_i$ represents the student's learning history in the field of i.

Regularly monitor student progress and feedback, adjusting learning paths and teaching methods as necessary. The effectiveness of teaching methods can be evaluated through controlled experiments. The experimental design should involve an experimental group utilizing the refined teaching approaches based on deep learning, alongside a control group employing traditional educational methods. This facilitates a comparison of the effectiveness between the two methods. The educational intervention measures may include using adaptive learning systems to deliver tailored course content for each student, employing recommendation systems to suggest learning resources suited to students, and utilizing automated evaluation tools to provide timely feedback and assessment.

The implementation of DL-based teaching mode needs careful experimental design and educational intervention measures to ensure its effectiveness and sustainability.

The implementation of DL-based teaching mode usually involves the steps of collecting students' academic performance, learning behavior, subject interest and learning history data. In order to further verify the content of this paper, this chapter compares the academic achievements of the experimental group using DL-based teaching mode and the control group using traditional education methods. Students were randomly assigned to the experimental group and the control group. The experimental group used DL-based teaching mode, while the control group used traditional education methods. Collecting students' academic achievement data This paper takes the final exam results as an example to compare the academic achievements of the experimental group and the control group to determine the effect of DL-based teaching mode. The experimental results are shown in Figure 4.

Figure 4.

Comparison of final grades

In the final exam, the average score of the experimental group is slightly higher than that of the control group. The average score of the experimental group was 92, while the average score of the control group was 83. By analyzing the improvement range of grades, it is found that the students in the experimental group have a higher improvement range in the final exam, about 5%. The improvement of the control group is low, about 3%. It can be seen that the students in the experimental group are more inclined to get high scores, while the students in the control group are more scattered in different grades.

Evaluate students' learning experience and satisfaction between DL-based teaching mode and traditional teaching methods, and distribute students' satisfaction questionnaire, covering learning experience, subject interest and teaching method evaluation. The questionnaire includes the experimental group and the analysis of the questionnaire results, and the satisfaction score and learning experience of the experimental group and the control group are compared. The statistical method T test is used to determine the satisfaction, and the experimental results are shown in Figure 5.

Figure 5.

Satisfaction

The students in the experimental group showed a higher satisfaction score, with an average satisfaction score of 4.4, while the average satisfaction score of the control group was 3.9. This shows that students who use DL-based teaching mode are more satisfied with the educational experience. Students in the experimental group reported more positive learning experiences. They are more inclined to think that the course content is more relevant to their interests and needs, and feel that it is easier for them to understand and master the course materials.

Observe students' learning motivation and participation under DL-based teaching mode and traditional education methods. Observe students' participation in the classroom, including the frequency of asking questions, answering questions and participating in discussions. Record the performance of students' learning motivation, such as active participation and active questioning. Compare the learning motivation and participation data of the experimental group and the control group, and the experimental results are shown in Figure 6.

Figure 6.

Participation

The students in the experimental group have a high degree of participation in the classroom. They ask questions and answer questions more frequently, actively participate in discussions, and have more active interaction in the classroom. The students in the control group are less involved and ask and answer questions less. The classroom in the experimental group usually has a more active and interactive atmosphere, and there is more cooperation and discussion among students. However, the classroom in the control group may appear passive and traditional.

Analyze students' learning path and progress under DL-based teaching mode. Collect students' study data, including study time, test scores and study progress. Use learning analysis tools to track students' learning paths. Analyze the changes and trends of students' learning time and learning progress, and compare the experimental group with the control group. The experimental results are shown in Figure 7.

Figure 7.

Learning Time

Figure 8.

Learning progress

The students in the experimental group need longer study time, but the study time in the control group is shorter anyway. Therefore, the students in the experimental group are more inclined to study in the order suggested by the course and complete the corresponding tasks in the course. The students in the experimental group showed a better trend in learning progress. They mastered the course content faster, got higher test scores and passed the course faster. Compared with the control group, the students in the experimental group completed more learning tasks.

Track the long-term effects of DL-based teaching mode, including academic achievements. Collect students' academic achievements continuously for a period of time. Compare the long-term performance of the experimental group and the control group. Use long-term data to evaluate the stability and lasting effect of DL-based teaching mode. Analyze the changes of academic achievements in time, and the experimental results are shown in Figure 9.

Figure 9.

Long term effect tracking

The students in the experimental group have maintained high academic achievements for a long time. Their average grades remained stable for a period of time, showing long-term benefits to the DL-based teaching mode. Continuous improvement of learning experience: the students in the experimental group also continued to improve their learning experience. Their satisfaction with the course content and their interest in the subject continue to improve, which shows that the DL-based teaching mode has a lasting impact on students' learning experience.

The above experimental design can help to evaluate the effect of DL-based teaching mode. For data analysis, appropriate statistical methods can be used to determine the significance of differences and the performance of students under different conditions. In addition, regular monitoring and analysis of the results can help improve and optimize the DL-based teaching mode to meet the needs of students.

Previous studies have proved the potential of deep learning in analyzing educational data and providing individualized learning paths. However, this work is especially aimed at the different academic backgrounds and needs of students in schools. Compared with the existing literature, the method based on deep learning adopted in this study has achieved equal or better performance in predicting students' academic ability and needs. This discovery strengthens the effectiveness of deep learning in individualized teaching and shows its value as a tool for educators in university to better understand and support students.

Although the deep learning algorithm shows potential in analyzing students' academic data and individualized teaching, it also has some limitations, such as data bias and the interpretability of the algorithm. Future research can further explore how to optimize the data collection and processing process to improve the accuracy of analysis, and develop a more transparent and interpretable deep learning model to enhance the credibility of educational decision-making.