Design and Implementation of Student Management Information

Algorithm Based on Cloud Computing

Lu Xia

Wuhan University of Engineering Science, 430200, China

Keywords: Student Management Information, Cloud Computing, Gradient Boosting Decision Tree.

Abstract: This paper will design and implement a cloud-based student management information platform, aiming to

provide efficient and intelligent decision support for school management. In order to allow the school to

integrate students' grades, attendance and other data based on the cloud computing platform, to achieve unified

management and real-time analysis of data. In this paper, this paper first constructs a student management

informatization algorithm framework based on cloud computing and intelligent algorithms, further

strengthens and improves the framework, and finally uses the algorithm framework to carry out real-time

prediction and evaluation based on cloud computing. The results of the experiment showed that after using

the platform, the attendance rate of students increased significantly, especially those with behavioral

problems, with an average increase of 10% in academic performance and a 20% reduction in disciplinary

action. Comprehensive analysis shows that the platform can greatly improve the management efficiency of

schools and provide accurate data support for teaching and management decision-making based on effective

integration of student information and intelligent intervention. It can be seen that the design and

implementation of student management informatization algorithm based on cloud computing has reliability

and practicability.

1 INTRODUCTION

The student management information platform is a

key research field in the current education

management, and schools are basically faced with the

problem of scattered student data and inability to

obtain comprehensive information in time. In order to

solve these problems, some researchers have

proposed that centralized databases can be used to

solve the problems, but these methods are not enough

to achieve real-time monitoring and dynamic

management. Some researchers also proposed that

big data analysis technology can be used to achieve

effective problem solving by combining various

information platforms of the school. However, these

methods have poor platform integration and

insufficient prediction accuracy. In the existing

research, although some methods can achieve the

initial integration of data, they do not have enough

reliable performance in intelligent intervention and

real-time prediction. To this end, this paper uses an

intelligent algorithm based on cloud computing to

study the student management information platform,

which is based on the powerful processing power of

the cloud computing platform to fully integrate the

multi-dimensional data of students. At the same time,

this paper also applies gradient boosting decision tree

and other algorithms to achieve accurate prediction

and behavioral intervention for students'

performance. The advantage of this approach is that

the data can be updated in real time and the

management recommendations can be tailored to the

individual needs of the students. It is hoped that the

research in this paper can help schools to greatly

improve their management efficiency. At the same

time, the advantages of cloud computing are

introduced, and the design and implementation of

student management information algorithms based on

cloud computing are proposed.

2 RELATED WORKS

2.1 Service Details of Cloud Computing

Cloud computing is a framework that provides

computing resources based on the Internet, mainly

including computing power, storage capacity, and

Xia, L.

Design and Implementation of Student Management Informatization Algorithm Based on Cloud Computing.

DOI: 10.5220/0013534400004664

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 3rd International Conference on Futuristic Technology (INCOFT 2025) - Volume 1, pages 11-17

ISBN: 978-989-758-763-4

network services, and can dynamically allocate

resources based on demand (Chen, 2022). Cloud

computing is mainly Infrastructure-as-a-Service IaaS,

Platform-as-a-Service PaaS, and Software-as-a-

Service SaaS. In the student management information

platform, the cloud computing platform can provide

efficient data processing and storage capabilities, and

realize real-time analysis and processing of large-

scale data based on distributed computing and elastic

expansion. In the midst of this, the elastic scalability

of cloud computing (Han, Long, et al 2024) will be

able to dynamically adjust resources according to

changes in the amount of student data, and then keep

the platform running at its best. Using APIs and

interfaces, cloud computing can also provide a

convenient way to integrate student management

platforms (Li, 2023), which in turn enables school

administrators to obtain data in real time and make

timely decisions.

2.2 Gradient Boosting Decision Tree

Prediction Ability in Terms of

Students' Multi-Dimensional

Features

Gradient Boosting Decision Tree (GBDT) is an

ensemble learning algorithm that is based on

progressively strengthening multiple decision trees

and combining their results to form a powerful

prediction framework (Wu, 2023). Based on the

iterative optimization loss function, GBDT reduces

the error step by step, and each new tree fits the

residuals of the previous iteration to continuously

improve the prediction accuracy (Xiang, Shuai, et al

2022). In the student management information

platform, GBDT can build a prediction framework

based on the multi-dimensional characteristics of

students, such as grades, attendance, behavior records

and other characteristics, so that managers can predict

students' future performance in real time. GBDT has

excellent generalization capabilities, can handle

nonlinear relationships, and provides stable

performance in the face of large amounts of data (Xu,

Chen, et al 2022).

3 METHODS

3.1 The Overall Architecture of The

Student Management Information

System Based on Cloud Computing

In this platform, there are several functional elements

that need to be included, and each functional element

is responsible for its own part. Specifically, the data

collection function can collect students' basic

information, grades, attendance, behavior data, etc.

based on different data sources. These data sources

include student management platforms, learning

platforms, electronic attendance platforms, etc. With

this feature, the platform can automatically integrate

various data to provide the basis for subsequent

analysis and prediction (Xu, Huang, et al 2022). For

example, when a student takes a course, the platform

will automatically record their learning behavior and

grades. The data preprocessing function is mainly

responsible for cleaning and sorting the collected raw

data, such as processing missing values, filtering

abnormal data, and standardizing data formats. In this

way, the data can be consistent and complete, and

then the subsequent enhancement of the framework,

as well as optimization and prediction, will have high-

quality inputs. Feature extraction and selection

function to extract important features that have an

impact on student performance from the preprocessed

data. These characteristics may include test scores,

attendance, assignment completion, and more. Based

on the distributed processing power of the cloud

computing platform, it is possible to quickly select the

features that are most useful for the framework

prediction. For example, when analyzing student

performance, the platform automatically extracts

students' performance in different subjects and uses

them to predict their overall academic progress.

Framework hardening functional device, which

can use large-scale data on the cloud computing

platform to carry out distributed framework

hardening. The GBDT algorithm is used to enhance

the prediction framework to achieve accurate

prediction of students' future performance. The

parallel computing capability based on cloud

computing can significantly improve the hardening

efficiency of the framework and shorten the

hardening time. For example, the platform is

enhanced based on large-scale student achievement

data to predict students' next test scores. The real-time

prediction and assessment feature can make real-time

predictions of newly entered student data based on an

enhanced framework. For example, after a student

submits an assignment or exam, the platform

automatically predicts their future learning

performance and the required interventions. This

feature can also evaluate the accuracy of predictions

and the generalization ability of the framework to

provide feedback for subsequent framework

optimization. Based on this, school administrators are

able to monitor students' academic dynamics in real

INCOFT 2025 - International Conference on Futuristic Technology

time and make data-driven decisions based on them.

Security and rights management features that will

protect the security and privacy of student data on the

platform. Based on the security tool of the cloud

computing platform, this functional device can ensure

that the data is encrypted during transmission and

storage, and that only authorized users can access and

manipulate the data. If school administrators only

have access to student data that is limited to their area

of authority, they can prevent unauthorized persons

from viewing sensitive information.

3.2 Design of Student Management

Informatization Algorithm

Framework Based on Cloud

Computing

The goal of feature engineering is to extract and select

important features from a large amount of student

data that are effective in predicting student

performance and management. On the cloud

computing platform, based on the collection of

students' student status information, achievement

data, attendance records, and behavior data, feature

selection algorithms, such as mutual information

methods, can be used to determine which features can

best influence the target variables. For this, see Eq.

(1).

𝐹

selected

= 𝑎𝑟𝑔𝑚𝑎𝑥



(Info

Gain

(𝐹, 𝑌))

(1)

𝐹

selected

Represents the selected set of traits such as

student attendance, course engagement, etc., which

will have a direct impact on the student's academic

performance. 𝐹Represents a collection of all

available traits, such as a student's gender, age,

grades, etc. Info

Gain

(𝐹, 𝑌) Representation indicates

the influence of a characteristic on the target variable,

i.e., the contribution to student achievement and

management decision-making.

Decision tree initialization is the step that

provides the basic structure to the framework. On the

cloud computing platform, multiple weak learners,

that is, decision trees, can be generated in parallel.

Each tree represents a simple prediction framework.

For example, the split point of the tree can be a

student's test scores, such as math and English scores

above a certain threshold. For this, see Eq. (2).

ℎ



(𝑥)=𝑎𝑟𝑔𝑚𝑖𝑛



∑

𝐿







(𝑦



, ℎ(𝑥



))

(2)

In this formula,ℎ



(𝑥) represents 𝑚the output of

the first decision tree, which also refers to the

prediction according to the current framework.

𝐿(𝑦



, ℎ(𝑥



))

𝑥



Represents the loss function, which is used to

measure the prediction error of each decision tree.

Data characteristics that represent 𝑖the first student,

such as that student's attendance, test scores, etc.

Based on multiple iterations of optimization, the

predictive performance of the framework can be

improved. In each iteration, the newly generated

decision tree is designed to fit the residuals of the

previous tree, thereby gradually reducing the

prediction error of the overall framework. For this,

see Eq. (3).

𝑦

()

= 𝑦

(1)

+ 𝜂ℎ



(𝑥)

)

In this formula,𝑦

()

is the prediction of the

framework after the first iteration is described𝑚, such

as predicting a student's final grade. 𝜂Represents the

learning rate, which controls the degree to which each

new tree contributes to the overall prediction of the

framework. ℎ



(𝑥) represents 𝑚the output of the first

decision tree, representing the fitting of the residuals

of the previous iteration. Based on iteration, the

framework can gradually improve the accuracy of

predicting students' performance, such as future

performance and management needs, and help

schools intervene and adjust teaching and

management strategies in a timely manner.

3.3 Further Implementation of the

Cloud-Based Student Management

Information Algorithm Framework

Frame hardening is the process of adjusting frame

parameters based on large-scale data reinforcement to

more accurately reflect the characteristics of student

data. In the student management information

platform, the reinforcement data comes from years of

student performance, behavior, attendance data, etc.

Based on optimization algorithms such as gradient

descent method, the prediction error can be further

minimized, and the framework can be more accurate.

Framework optimization is a step to improve the

performance of a framework after hardening. In the

student management information platform, the

optimization framework can ensure that it can

maintain efficient prediction and management

capabilities in various data environments.

The purpose of hyperparameter optimization is to

find the optimal framework hyperparameters, such as

learning rate and depth of decision tree, so as to

improve the applicability of the framework in the

student management information platform. A cloud

computing platform-based grid search approach that

enables testing different combinations of

Design and Implementation of Student Management Informatization Algorithm Based on Cloud Computing

hyperparameters to find the configuration that works

best for the framework. See Eq. (4) for details.

𝜆

∗

= 𝑎𝑟𝑔𝑚𝑖𝑛



CV(𝐿(𝑊, 𝑋, 𝑌, 𝜆))

(4)

In this formula, 𝜆

∗

is the optimal hyperparameter

values such as learning rate and depth of the decision

tree are described. CV(𝐿(𝑊, 𝑋, 𝑌, 𝜆)) Represents the

value of the loss function under cross-validation, the

purpose of which is to measure the performance

stability of the framework in different data segments.

In the student management information platform,

optimizing hyperparameters can ensure that the

framework performs consistently in predicting

different types of student data, such as different

grades and courses, and avoids excessive prediction

errors under some sober conditions.

Constraints are also necessary. In the student

management information platform, the framework

may overfit some specific student data, so it is

necessary to limit the complexity of the framework

based on constraints. For this, see Eq. (5).

𝐿

reg

(𝑊)=𝐿(𝑊, 𝑋, 𝑌)+



||𝑊||

(5)

In this formula, the 𝛼is constraint setting

coefficient is represented and the constraint setting

strength is controlled so that the frame does not over

rely on certain features. ||𝑊||



is L2 norm represents

the weight vector, which is intended to penalize too

large frame parameters and keep the framework

stable and concise.

Constraints prevent the framework from relying

too heavily on irrelevant features, and ensure that it

can make predictions and decisions on new student

data.

Evaluating the framework with an independent

validation set is a critical step in the optimization

process to more effectively test its performance on

unseen data. Specific evaluation indicators include

accuracy, recall, etc., which can be quickly achieved

based on the distributed computing of the cloud

computing platform. See Eq. (6) for details.

Eval(𝑀)=



∑

𝕀



1

(𝑦



= 𝑦



)

(6)

In this formula, Eval(𝑀) represents the evaluation

index of the framework, which can measure the

accuracy and effect of the prediction. 𝑦



Represents

the outcome of the first student predicted by the

framework𝑖. 𝑦



Representation of actual student

performance, such as real test scores, attendance

records.

3.4 Cloud Service Interface and

Platform Integration

The task of the cloud service interface is to integrate

the functional components of the student

management information platform into the existing

school management platform, and transmit and share

data in real time based on the API interface. It will

leverage the standardized service interfaces provided

by the cloud computing platform, such as RESTful

API, SOAP API, to ensure interoperability between

platforms to enable seamless integration of real-time

prediction results. For example, when a student's

attendance data is updated, the platform automatically

invokes the cloud prediction service based on the API

to generate the student's performance prediction

results in real time and feed them back to the school

administrator. In addition, the cloud service interface

also supports real-time processing and dynamic

adjustment of data streams, ensuring that the platform

can quickly predict and update according to new

student data (Li, 2023). Based on the cloud-based

service-based architecture, the school management

platform can not only obtain real-time analysis of

student performance, but also automatically adjust

management strategies based on the feedback from

the framework. For example, when a student's

performance warning is triggered, the platform can

immediately notify the relevant teachers and parents

and take timely interventions.

4 RESULTS AND DISCUSSION

4.1 Background of the Case

In the education management of a key high school in

a city, the school faced the challenge of how to

manage students' academic and behavioral

performance more efficiently. The school has nearly

2,000 students, and their information, including

student status, grades, attendance, extracurricular

activities and other data, is stored on different

platforms, making it difficult for administrators to

obtain comprehensive information and make

reasonable decisions in a timely manner.

Table 1: Former Student Data for Platform Use.

Student ID grade Attendance Math

grades

Behavioral

roblems

Analyze it. Three

80% 72

Career information.

High

school

85% 78 not

INCOFT 2025 - International Conference on Futuristic Technology

Employment

intention.

High 90% 88 not

Development

lannin

Three 75% 68 be

Causing changes.

High

school

92% 85 not

The table shows academic and behavioural

problems, in which students with lower attendance

also perform poorly and have higher behavioural

problems. The programming content of the

information system is shown in figure 1.

Figure 1: The programming process of information

systems.

Through the same system analysis, student

information can be regulated and classified to form a

data set, which can be discussed and analyzed later. it

can be found that the attendance rate of the 6

randomly selected students is generally low,

generally only 75%~85%, which leads to their poor

academic performance, which is generally

concentrated in 68-78 points, and more than 90 points

are almost non-existent. Based on this, the school

decided to introduce a cloud-based student

management information platform, based on the

integration of various data, and the application of

intelligent algorithms for analysis and prediction, so

as to improve management efficiency and formulate

new management strategies. Before the introduction

of the platform, schools mainly relied on teachers to

manually record and manage, and the scattered and

lagging update of data seriously affected the timely

management of students, such as failing to intervene

in time with poor academic performance and

behavioral problems.

4.2 Optimization Content of

Informatization

Based on the use of this information platform, the

school integrates students' academic and behavioral

data into the cloud, and applies big data analysis

technology to provide comprehensive and real-time

student performance evaluation and personalized

management suggestions for the school.

Table 2: Data for the first semester after the use of the

platform.

Student ID Attendance Math

grades

Science

grades

Improvement in

behavioural

roblems

Analyze it. 90% 80 85 be

Career

information.

88% 82 87 not

Employment

intention.

92% 90 92 not

Developmen

lanning.

85% 78 80 be

Causing

changes.

95% 88 89 not

The table shows improvements in attendance,

academic performance, and especially for students

with behavioral problems. The data of the three

tables, it can be seen that after the use of the platform,

the attendance rate of students has increased

significantly, especially those students with low

attendance rates, such as student IDs 101 and 104,

from 80% and 75% to 90% and above. These

students' math scores also increased from 72 and 68

to 80 and 78. These students have seen significant

improvements in their academic performance based

on improved attendance. The continuity analysis of

students’ information design process is carried out to

form an effective dataset as shown in Figure 2.

Figure 2: The content and ways of changes in student

management information.

The platform effectively identifies and intervenes

in students with behavioral problems based on

behavioral management functional components, such

as student IDs 101 and 104, and under the guidance

of the platform, disciplinary actions and behavioral

problems are significantly reduced.

Design and Implementation of Student Management Informatization Algorithm Based on Cloud Computing

4.3 Analysis of the Optimization

Results of Student Information

The academic performance of such students has also

improved significantly after using the platform, such

as 80 points in mathematics and 85 points in science,

indicating that behavioral improvement is helpful for

academic improvement. In addition, in the second

semester after using the platform, the overall

academic performance of students continued to

improve. The analysis content and conditions are

shown in Table 3.

Table 3: Data for the second semester after the platform was

used.

Student ID Attendance Math

grades

English

scores

Disciplinary

actions are reduce

Analyze it. 95% 85 88 be

Career

information.

92% 85 90 not

Employment

intention.

95% 92 94 not

Development

lanning.

90% 82 85 be

Causing

changes.

98% 90 91 not

The table shows that the platform continues to

improve student attendance, academic performance,

and the number of disciplinary actions for students

with behavioral problems has also been significantly

reduced. Continuously analyze the entire information

optimization process and obtain the results shown in

Figure 3.

Figure 3: The optimization process of student information.

According to the data analysis in Figure 3, the

process of learning information is consistent with the

changing demands of cloud computing. students'

scores in mathematics with IDs 101 and 105 have

increased from 80 and 88 to 85 and 90, and their

English scores have also improved. Based on the

personalized teaching suggestions provided by the

platform, schools can develop targeted academic

plans for different students to effectively improve

their overall academic performance.

5 CONCLUSIONS

In summary, it can be seen that the design and

implementation of the student management

informatization algorithm based on cloud computing

studied in this paper has been successfully completed.

To this end, this paper constructs a corresponding

platform, which can effectively improve the

management efficiency of schools, based on cloud

computing and intelligent algorithms

Integrate and analyze students' multi-dimensional

data in real time to achieve accurate prediction of

academic and behavioral performance. The platform

not only improves student attendance and academic

performance, but also reduces the occurrence of

disciplinary problems based on intelligent behavioral

interventions. Based on the efficient data processing

capability of the cloud computing platform, the

school can also realize personalized management

strategies and update them in real time, so as to

provide strong technical support for student

management informatization. In short, the platform

can successfully solve many problems in traditional

student management, such as data fragmentation and

post-management, and greatly improve the quality of

education in schools. However, the research in this

paper still has the problem of insufficient data

comprehensiveness, and it is expected that further

improvements will be made in the future.

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Design and Implementation of Student Management Informatization Algorithm Based on Cloud Computing