Strategies for Optimizing the Integration of Student Management and Curriculum Education Resources in Colleges and Universities Supported by Cloud Computing Platforms

Based on the system architecture designed above, combined with the needs of student information classification management, the cloud computing technology - decision tree is constructed and its improvement to provide support for the subsequent student information classification [23-24].

Decision tree is one of the key algorithms in the field of data mining in cloud computing technology, which can classify and process a large amount of information quickly, automatically and accurately, and its internal nodes are determined by the attributes of the information and can directly extract the decision rules, so it is used as a tool for student information classification. Based on the requirements of the design system, the decision tree construction process is formulated using a top-down recursive approach, as shown below.

Inputs, training sample consisting of student information attributes (discrete values) S_a, auxiliary set consisting of candidate attributes F_b

Step 1, create decision tree node M.

Step 2, if training samples S_a belong to the same class, return M as leaf node and label training samples S_a as class A₁.

Step 3, if auxiliary set F_b is the empty set, return M as a leaf node to label it as the most common class (the class containing the largest body of information) in training sample S_a.

Step 4, randomly select an attribute in the auxiliary set F_b, mark node M as the test attribute F', divide the known values B in the test attribute to S_a, and construct a branch →F^* = B from node M.

Step 5, set up the set of samples S¹ in S_a that match the branch constructed in step 4. If S¹ is the empty set, add a leaf labeling S_a as the most common class among them, and if S¹ is the non-empty set, add a node returned by the auxiliary set.

Step 6, repeat step 1 ~ step 5, you can complete the initial construction of the decision tree.

In general, the initial construction of the decision tree structure is too complex, if it is directly applied, the cost of student information classification will be relatively large. If the number of internal nodes is too large, the number of instances contained in each node will be smaller, resulting in an increase in the probability of student information classification errors. Thus, before the decision tree is applied, it needs to be improved. Decision tree in the attribute selection process is mainly applied to the information gain, which is calculated as: (1) { G(C)=I(r1,r2,⋯,rm)*E(C)E(C)=∑j=1nSa1j+⋯+Samj|Sa|*I(Sa1j,Sa2j,⋯,Samj)⌊ I(Sa1,j,Sa2,j,⋯,Samj)=−∑i=1mPijlog2(Pij) ⌋

In Eq. (1), G(C) denotes the information gain, I(r₁,r₂,⋯,r_m) denotes the expected amount of information in the training sample in the classification, E(C) denotes the entropy value of attribute C for the classification Au, n denotes the number of training samples S_a that can be divided into subsets, and Sa1j,Sa2j,⋯,Samj denotes the subsets of training sample S_a. |S_a| represents the total amount of information in training sample S_a. I(Sa1j,Sa2j,⋯,Samj) denotes the expected amount of information in the classification of the subset of training sample S_a. P₀ represents the probability that the j th subset belongs to the i th class.

After theory and practice, it is found that the attribute selection results obtained by the information gain application have some deviation from the actual student information classification needs, resulting in a high error rate of the classification results, which affects the management effect of student information in colleges and universities. Therefore, this study improves the attribute selection basis to the information gain rate, which is calculated by the formula: (2) ζ(C)=G(C)E(C)

In equation (2), ζ(C) represents the information gain rate.

Based on the calculation results of Eq. (2), the attribute corresponding to the maximum information gain rate is extracted, which is taken as the decision attribute node, and the number of lead branch nodes is determined according to the size of its value.

The above process completes the construction and improvement of the decision tree, which is more in line with the needs of students’ information classification and lays a solid foundation for the subsequent research.

Based on the above improved decision tree and with the goal of minimizing the support and confidence, the inaccurate and infrequent decision rules are trimmed to obtain the simplest decision tree structure, and the student information to be classified is input into the trimmed decision tree, so as to obtain the results of student information classification.

The support and confidence are calculated by the formula: (3) { λ(X⇒Y)=Q(X∪Y)μ(X⇒Y)=Q(X∪Y)Q(X)

In Eq. (3), λ(X⇒Y) and μ(X⇒Y) represent the support and confidence of rule X ⇒ Y, and Q(·) represents the probability function.

When the support λ(X ⇒ Y) and confidence μ(X ⇒ Y) obtain the minimum value, the inaccurate rules can be discarded to the maximum extent and the best decision tree structure can be obtained.

It should be noted that the number of decision nodes in the process of student information classification needs to be calculated and determined according to the actual situation of the information. By applying the classification rules output from the decision nodes, the student information can be quickly and accurately classified into multiple categories, which are recorded as Z = (z₁,z₂,⋯,z_m), providing certain convenience for the subsequent management of student information.

Based on the above student information classification result Z = (z₁,z₂,⋯,z_m), the student information management functions are formulated to provide support for the stable operation of the design system.

Student information management mainly contains three functions, namely, design system user identity verification function, student information classification storage function and student information operation function. Among them, the user identity verification function of the design system mainly undertakes the task of authenticating all users entering the design system, which is the key to ensure the safe operation of the design system. User authentication is mainly based on the password, the specific rules as shown in equation (4): (4) { ηi=ΓiAllows access to the design systemηi≠ΓiDo not access the design system

In equation (4), η_t represents the user password information, and Γ_i represents the user password information saved in the design system.

The key link of the student information categorization and storage function is to make a reasonable selection of the database based on the number of student information in each category, so as to ensure the complete storage of student information and prevent the phenomenon of unstored student information. The number of student information in each category is set to γ = (γ₁,γ₂,⋯,γ_m), and the rated capacity of the database is set to ξ = (ξ₁,ξ₂,⋯,ξ_p). It should be noted that p ≥ m. The finalized student information categorization and storage scheme needs to satisfy the following conditions: (5) { ∀zi∈ξγi≤ξizi∩zj=∅

The online educational resource sharing platform not only needs to realize the sharing of resources, but also needs to carry out the storage of educational resources and guarantee the security of resource storage, so encryption algorithms are introduced to carry out the secure storage of online educational resources. In the encryption setup stage, the private key s_i for encryption of online educational resources is obtained through the private key generator [25-26]. Then the key is initialized and the corresponding public key g_i of the private key is calculated as follows.

δ denotes the public parameters of the platform. Generally, the platform will put the public key: (6) gi=siδ

g_i is published and the corresponding private key s_i is kept secretly.

All the signers send message D_i to the platform for signature, and can get the corresponding private key S_IDi and public key g_m,0. Finally, the signature is verified, if the result of the signature verification is not legal, then it proves that the user does not have the right to access the resource sharing platform. If the signature verification result is legal, then the decryption algorithm will be executed to decrypt the encrypted ciphertext, and the user receives and confirms the information. To check whether the information obtained by the signer is consistent with the original information, it needs to be verified, and the specific formula is: (7) e(gi,xgBi)=e(vi,sBi)

where e denotes the decryption algorithm, x denotes a randomly selected cryptographic hash value, and v_i denotes the signer.

When a teacher sends a resource upload request to the platform, the administrator can securely store the uploaded online educational resources to the sharing platform based on the private key.

The educational resource scheduling function based on cloud computing virtualization technology is designed and implemented to fairly and adequately allocate online educational resources in response to the learning needs of university students. When scheduling educational resources, it is necessary to stand on the perspective of cloud computing servers to calculate the target storage size of virtual machines for educational resources, and then allocate educational resources for each server based on the global perspective.

The article takes into account the performance of virtual machines and sets a target memory value Q for virtual machines, which is calculated by the formula: (8) Q={ U+CmaxF>UZCmin≤F≤CmaxZ+QmaxF<Cmin

where U denotes the size of the memory of the educational resource held by the virtual machine when it is running, F denotes the size of the memory of the educational resource held by the virtual machine when it is idle, Z denotes the difference between the value of the memory requirement of the virtual machine and the value of the target memory, Q_max denotes the maximum value of the target memory of the virtual machine, and [C_min,C_max] denotes a range of values to be taken for the value of the memory of the virtual machine.

When performing global scheduling of online educational resources, combined with the actual situation of virtual machine memory, resource scheduling needs to be performed in multiple scenarios. First, the virtual machine memory is sufficient, i.e., Q ≤ HF (HF indicates the memory of the virtual machine that can be allocated for educational resources at the moment), at this time, the storage capacity of the virtual machine fully meets the educational resources to be allocated, so there is no need to carry out resource recovery, and the educational resources will be directly allocated to the virtual machine. Second, the virtual machine memory is sufficient, i.e., Q ≤ HF + K (K indicates the amount of global recoverable memory), at this time, the free memory of the physical server can not meet the memory requirements of the cloud computing virtual machine, so it is necessary to recycle some of the available resources to meet the allocation needs of all online educational resources. Third, the virtual machine memory is insufficient, i.e., Q > HF + K, at this time, even if all the allocable memory resources of the cloud computing virtual machine are reclaimed, the scheduling demand for educational resources can not be met. It is necessary to formulate a corresponding reclaiming strategy to allocate the online educational resources to meet the memory requirements of the virtual machine.

Online educational resource sharing is an important function of the design platform, in order to accomplish resource sharing needs to use consensus algorithm - Practical Byzantine Fault Tolerant Algorithm for communication between different nodes [27]. The introduction of an integral mechanism improves the traditional PBFT algorithm, allowing for the sharing of online educational resources.

Points incentive mechanism is based on the community reward distribution rules, which assigns the corresponding proportion of points to resource uploaders and downloaders, and different points reflect whether the platform users are active or not. Assuming that the points settlement cycle of the online educational resources sharing platform is monthly, then every month the platform distributes a certain number of points P. At this time, the formula for calculating the contribution reward points that the platform distributes for college teachers and students is: (9) P1=P×(1−η) (10) P2=P×η

Where P₁ denotes the contribution reward points of college teachers, P₂ denotes the contribution reward points of college students, and η denotes the reward ratio.

In the consensus algorithm that invokes the integral mechanism, if the integral is low it will not participate in the consensus, so this improved consensus algorithm can improve the efficiency of resource sharing. When using the article design platform for online education resource sharing, each teacher and student in the university will have an initial point, and after a period of time the sharing nodes will be sorted according to the number of points {0,1,2,⋯,N–1}, so as to determine the master node, which is calculated by the formula: (11) D=εmod|N−2e−1|

Where D denotes the master node for resource sharing, ε denotes the node view number, mod denotes the residual function, and e denotes the maximum number of malicious nodes that can be tolerated in the resource sharing process.

When a client sends a resource sharing request to the master node, the request is sorted and a consistency protocol is executed based on this data, thus accomplishing node consensus, i.e., resource sharing among the nodes.

Before formally carrying out the practice of student management and curriculum education resource optimization, the performance of the college student management platform and college curriculum education resource management platform proposed in this paper is tested to guarantee the normal operation of the platform during the subsequent practice.

5.1.1

Student management platform performance testing

In order to test the student management platform proposed in this paper, the Hadoop-based student management platform and the RFID-based student management platform are compared and analyzed as a control group.In the specific testing process, the average response time of business execution is used as an evaluation index. The test results of different platforms are specifically shown in Figure 2. As can be seen from the figure, with the increase in the number of parallel requests, the response time of this paper’s platform is always smaller than that of the comparison Hadoop platform and RFID platform. When the number of parallel requests reaches the highest 6000, the response time of this paper’s platform is 1.91s, while the response time of the Hadoop platform and the RFID platform reaches 3.85s and 3.38s, respectively, and the former is faster than the latter by 1.94s and 1.47s, respectively.

Figure 2.

Response time

5.1.2

Performance testing of the curriculum education resource management platform

The main indicators of this test are throughput and clicks. This test requires 3000 user login operations to this paper’s educational resource management platform for courses to be completed within 30 minutes. The relationship between the click rate and throughput per second of this platform is shown in Figure 3. From the curve trend in the figure, the maximum click rate and throughput of this platform can reach 124 and 534550 respectively, and the two values of click rate and throughput can basically keep the same direction, which indicates that the central service of this platform can realize timely request instructions and can get relatively accurate return results.

Figure 3.

Number of clicks and throughput

This study takes the application and implementation effect of the college student management platform and college curriculum education resource management platform constructed in this paper as the research object, and randomly selects 50 students of the class of 2023 from the College of Physical Education and Health of University H in Zhejiang Province, China as the research subjects to carry out the practice of student management and curriculum education resource optimization. The practice will last for 4 months, during which this paper’s college student management platform and college curriculum education resource management platform will be applied to the subject students’ college life, and they will be deeply involved in the subject students’ curriculum teaching activities. At the end of the practice, the relevant data of the subject students will be collected, integrated, and analyzed.

5.2.2

Degree of achievement of course objectives

In this section, course objective attainment will be used as an indicator to measure the implementation effect of applying this paper’s educational resource management platform for college courses. It is known that 50 students of grade 2023 in the College of Physical Education and Health of the University of H were selected as the research subjects in this study, and 50 students of grade 2022 in the College of Physical Education and Health of the University of H will be randomly selected again in this section as a comparative sample to compare the achievement of course objectives. In the course of practice, the students of the class of 2022 always maintained the original way of acquiring educational resources and learning methods of the course. In terms of course sub-objective attainment, a full score of 10 is used, both with 7 as the preset expectation, and the course objectives contain a total of four dimensions, namely, knowledge integration, teaching ability, technology integration, and communication and cooperation, which are respectively represented by A1~A4. The course objective attainment of the students of grade 2022 and grade 2023 is specifically shown in Fig. 4. From the figure, it can be seen that on the comparison between grade 2022 and grade 2023, there is a certain difference in the achievement of course sub-objectives between the two. The mean values of the four dimensions of course goal attainment, namely, knowledge integration, teaching ability, technology integration, and communication and cooperation, were all higher than those of the students of the class of 2022, with the mean values of 7.83, 8.56, 8.86, and 9.01, respectively, after the subject students of the class of 2023 had applied this paper’s course education resource management platform. Obviously, this paper’s curriculum education resource management platform can help students to carry out the efficient integration of subject knowledge, provide a powerful aid for the preparation of classroom teachers and teaching methods, and create a good environment for students to cooperate and communicate with each other with sufficient curriculum education resources.

Figure 4.

Achievement of course objectives

5.2.3

Student practice satisfaction

Subject students’ satisfaction with student management, the provision of course educational resources, and even the overall teaching in the practice of optimizing student management and course educational resources is an important part of the test of the effectiveness of this practice of optimizing student management and course educational resources. The specific indicators of student satisfaction are shown in Table 2, which contains 11 indicators.

Table 2.

Index

Dimension	Index	Title
Teaching service	The appropriate degree of selection of textbooks	A1
	The way of examination of the course	A2
	The content and mode of teacher teaching	A3
	Teaching attitude of teachers	A4
	Professional level of teachers	A5
Education resource service	The convenience of the use of education resources	B1
	The richness of education resources	B2
Student management and support services	Student management efficiency	C1
	The integrity of the students’ training plan	C2
	The atmosphere of student management	C3
Student value perception	The degree of perception of the students’ ability to improve	C4

In this section, the quad plot method will be used to carry out the satisfaction measurement for this practice.By establishing a coordinate system with satisfaction and importance as the axes respectively, the Quadrangle Diagram methodology allows for prioritization of indicators according to their satisfaction and importance scores. All indicators are categorized into different quadrants according to the level of satisfaction and importance. Problems are analyzed and dealt with in four areas: Maintaining Performance Zone, Focused Improvement Zone, Performance Transition Zone, and Lower Value Zone. The categorization and analysis of quadrant charts can visualize the students’ attitudes towards the satisfaction and importance of each assessment indicator.The four-quadrant diagram of the subject students’ satisfaction is specifically shown in Figure 5.

Figure 5.

Quadrant

According to the figure, the first quadrant is the maintenance performance area, which is the double-high area of importance and satisfaction, that is, the indicators that students think are important and the school performs well, which needs to be maintained and continuously optimized. It can be seen that the indicators in this quadrant are “appropriate degree of textbook selection A1”, “assessment method of coursework A2”, “content and method of teacher teaching A3”, “professional level of teachers A5”, “richness of educational resources B2”, and “atmosphere of student management C3”. It is worth noting that although “the appropriateness of textbook selection A1”, “the professional level of teachers A5”, “the richness of educational resources B2” and “the atmosphere of student management C3” are in the performance zone, they are relatively close to the origin point, and there is still room for improvement.

The second quadrant is the focus area, which is the area of high importance but low satisfaction, and the indicator that is important to the students, but poor in the school, which needs to be highly emphasized and focused on improvement. From the figure, it can be seen that “the perceived degree of improvement of students’ own ability D1” is at the highest level of importance, with an importance value as high as 0.58, and is located at the lowest point of satisfaction score, with a satisfaction value of only 3.54, which indicates that in the practice of student management and curriculum and educational resources optimization, students are unable to effectively perceive their own ability, and it is difficult to grasp the learning process and learning resources optimization. This indicates that in the practice of student management and the optimization of educational resources of the curriculum, students cannot effectively perceive the improvement of their own ability, and it is difficult for them to grasp the process and direction of learning, which obviously needs to be paid attention to and make corresponding improvement and adjustment.

The third quadrant is the lower value area, which is the area of low importance and satisfaction, and also the indicators that are relatively unimportant and unsatisfactory to students, mainly including the indicator of “teachers’ teaching attitude A4”. In the short term, with limited resources, the indicators in this region can be selectively put on hold depending on the allocation of teaching resources in colleges and universities, and then gradually and steadily improved in the later stages.

The fourth quadrant is the performance transition zone, which is a zone of high satisfaction but low importance. It is the area that is relatively unimportant to students but performs well in practice. Indicators such as “ease of use of educational resources B2”, “efficiency of student management C1”, and “completeness of student training programs C1” have good satisfaction levels, and down the road the Student management and teaching in higher education can continue to be retained and transformed into strengths, just keep it up.