Study on the Integrated Effectiveness of Information Technology-based Educational Management

With the rapid development of information technology (IT), the field of education has gradually begun to emphasize and widely apply IT to enhance the efficiency and quality of education management. Information technology not only furnishes more accurate and timely data support for education management, but also opens up a vast arena for innovation in teaching, learning, and assessment [1-4].

The application of information technology in education management includes teaching management system, informationized learning platform, data analysis, real-time monitoring and teaching resource sharing. Information technology plays a pivotal role in enabling educational institutions to manage data effectively and enhance the scientific rigor and precision of their decision-making processes, as evidenced by the data management policies at Ankang University and Taizhou University, as well as the application of big data in intelligent educational data analysis and decision support. By utilizing the school management information system, education administrators gain insight into students’ and teachers’ fundamental information, as well as curriculum arrangements and other pertinent details.capturing activities and other relevant data in real time, thereby enabling more effective overall planning and management of the school. For example, education administrators can monitor students’ academic performance and attendance through education management information systems, so as to detect problems and take appropriate measures in time [9-12]. Information technology for staff information management can further streamline the management process, enhancing both convenience and efficiency. SchooSchools can establish a staff information management system that encompasses teachers’ basic information, salary accounting, attendance management, and various other functions. This system facilitates centralized management of staff information and allows for quick searches [13-16]. By doing so, school administrators can gain a better understanding of teachers’ work, conduct timely performance evaluations, implement rewards and punishments, and ultimately enhance teachers’ motivation and the overall management level of the school [17-19].

The article discusses the current status of the application of information technology in education management, including teachers’ class preparation management, webpage teaching and research activities management, informationized classroom teaching management, and students’ online homework and tutoring management. An educational management evaluation system was established to analyze the current status of its application in School A, thereby facilitating the subsequent teaching endeavors discussed in this article.xperiments under information technology. Using the Analytic Hierarchy Process to assign weights to evaluation indicators, and combining fuzzy comprehensive evaluation, classify the level of information technology education management. Finally, evaluate the comprehensive effects of information technology education management through teaching experiments and teaching and research activities.

2

Study on the development of educational management under information technology

2.1

Higher education management

Colleges and universities, as unique social organizations, focus on nurturing well-rounded, highly educated talents, with their core functions encompassing teaching, scientific research, and management. Scientific management is crucial for achieving both teaching and research goals, and it is even more vital for fulfilling educational objectives. The ultimate goal of college education managements and universities aims to enhance the quality of various educational components and their integration, thereby fostering socially responsible talents with enriched spirits, noble characters, independent thoughts, and benevolent dispositions. College education management serves as a crucial method for harmonizing the interplay between internal and external elements within colleges and universities, as well as for the rational allocation of resources. Utilize limited resources more effectively and ensure their compatibility with the environment, thereby enhancing the achievement of educational goals in school operations.

2.2

General analysis of information-based education management

Information-based education management in colleges and universities represents an essential aspect of education modernization, characterized by science, timeliness, interactivity, differentiation, and a shift in power dynamics, thereby offering unparalleled advantages compared to traditional education management models.

2.2.1

Types of informatization of education management

Information technology is an important force in the evolution of college education management from traditional scientific management to cultural management. As college information platforms are constructed and educational information technology is extensively utilized on campus, the trend of informatization in college education management becomes evident, characterized by diversification, complexity, and dynamism. From various perspectives, the informatization of college education management exhibits diverse traits.pes. For instance, based on the categorization of collection tasks, college education management informatization encompasses four primary areas: student education management informatization, teacher education management informatization, comprehensive education management informatization, and third-party application informatization.

2.2.2

Characteristics of information-based education management

Traditional college education management suffers from a lack of humanity, a monotonous format, inadequate feedback, and numerous other shortcomings, all of which contradict the development requirements of modernizing education management. College information technology education management holds the potential to effectively address these issues.

1)

Scientific

The core of information technology education management lies in anticipating trends, overcoming traditional management limitations, and addressing overall shortcomings. Through comprehensive consideration, we aim to gain insight into the complexity of teachers’ and students’ behavior, uncovering behavioral laws amidst chaotic data, thereby enhancing the scientific nature of education management.

2)

Timeliness

On campuses with extensive network coverage, data and information on teachers’ and students’ activities abound, creating an unprecedented flood of numbers. This information, however, does not always align with the essence of the phenomena observed. Among the vast amounts of data, both abnormal and regular information consistently emerge. Abnormal information can be managed by employing data technology to establish tolerance levels and critical points.mit to start the alarm system, and ultimately play a role in preventing problems before they occur.

3)

Differentiation

Teaching students according to their aptitude, personalized management and diversified talent cultivation have always been the ideals of education, and the objects of education management in colleges and universities have differences. In the era of small data, college education administrators faced significant challenges in detecting micro-knowledge, but with the advent of the information technology era, these tasks have become more manageable. Informatized education and teaching resources can be tailored to meet the individual needs of students, enhancing their learning motivation and improving educational quality. Suit the personality characteristics of the training programs and course lists, so that students can overcome time and space limitations to access high-quality education and teaching resources.

4)

Interactivity

Information-driven education management in colleges and universities transcends the one-way street of traditional educational oversight, fostering a dynamic interplay between educators and learners, thereby catalyzing a synergistic effect. Interaction effect also refers to the phenomenon that two or more individuals influence each other through interaction and thus unite to produce incremental force. On the digital teaching platform, university instructors and students can engage in real-time interaction, seamlessly.olve questions and answer questions, preach and teach, and teachers can monitor the students’ speed and learning progress in real time.

5)

Integration

The integration of information technology in colleges and universities includes the integration of internal and external resources of colleges and universities. Integrating resources is essential to maximizing their utilization value. Colleges and universities can well realize the integration of resources through information technology. By constructing an informatization platform, we can achieve data sharing and enhance data openness and circulation.

3

Construction of an evaluation system for information-based education management

3.1

Hierarchical Analysis

Hierarchical analysis (AHP), is a multilevel, multi-objective decision analysis method [20-21]. There are mainly the following steps: 1)

Analyze and decompose the elements contained in a complex problem and their interconnections to establish a recursive hierarchy.

2)

Compare the elements in the same level to form a judgment matrix, and the range of values of the elements in the matrix adopts the interval scale of [1,9].

3)

According to the judgment matrix, the weight of each element in the level can be calculated by a specific calculation method, reflecting the influence size of the element on the level.

4)

Conduct consistency test on the judgment matrix, if it is satisfied, then determine the weight values of all elements and sort them, otherwise return to step 2 to modify the coefficients of the judgment matrix and then solve the problem.

In hierarchical analysis, when the matrix order is above 3 (including 3-order matrices), a consistency test is required, and the consistency index is derived by calculating the stochastic consistency index $R I$ and the consistency index $C I$ . Table 1 gives the values of $R I$ corresponding to matrices within the 9th order.

Table 1.

Random consistency index

N	1	2	3	4	5	6	7	8	9
RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.46

The first-order and second-order judgment matrices are regarded as having full consistency in the hierarchical analysis method, which only requires the division of importance between the indicators. When $N \geq 3$ , the judgment matrix consistency index $C I$ is calculated as: 1 $C I = \frac{λ_{\max} - n}{n - 1}$ where $n$ is the order of the matrix and $λ_{max}$ is the largest eigenroot under the current matrix. Consistency ratio $C R$ is a measure of matrix consistency and represents the ratio of matrix consistency index $C I$ to random consistency index $R I$ : 2 $C R = \frac{C I}{R I}$

It is generally accepted that the matrix is considered consistent if $C R$ is less than 0.1. Otherwise, the weights in the matrix need to be readjusted or the matrix needs to be reconstructed until the consistency requirement is satisfied. If the judgment matrix has full consistency, this means that the consistency index is equal to 0. So the following equation can be obtained: 3 $\sum_{j = 1}^{n} a_{i j} ω_{j} = \sum_{j = 1}^{n} (ω_{i} / ω_{j}) ω_{j} = n ω_{i}, (i = 1, \dots, n)$ 4 $\sum_{i = 1}^{n} |\sum_{j = 1}^{n} (a_{i j} w_{j}) - n w_{i}| = 0$

The value on the left side of Equation (3) reflects the consistency of the weight value $ω_{i}$ with the judgment matrix $A$ , the smaller the value is, the higher the consistency of the weight value with $A$ . When Equation (3) is satisfied, the matrix $A$ has full consistency. So the consistency test problem can be converted to the following nonlinear programming problem: 5 $\begin{array}{l} \min C I F (n) = \sum_{i = 1}^{n} |\sum_{j = 1}^{n} (a_{i j} ω_{j}) - n ω_{i}| / n \\ s . t . \sum_{j = 1}^{n} ω_{j} = 1 ω_{j} > 0, j = 1, 2, \dots, n \end{array}$ $$\matrix{ {\min CIF(n) = \mathop \sum \limits_{i = 1}^n \left| {\mathop \sum \limits_{j = 1}^n \left( {{a_{ij}}{\omega _j}} \right) - n{\omega _i}} \right|/n} \hfill \cr {s.t.\mathop \sum \limits_{j = 1}^n {\omega _j} = 1{\omega _j} > 0,j = 1,2, \cdots ,n} \hfill \cr } $$

Where $\min C I F (n)$ is the consistency index, $n$ is the number of elements of the matrix, $a_{i j}$ is the element of the $i$ th row and $j$ th column of the matrix; $ω_{j}$ and $ω_{i}$ are the values of the weights of the $j$ th and $i$ th elements, which can be used as optimization variables in the subsequent work. The basic procedure of the operation is to solve for the eigenvalues and the weights of the indicators corresponding to the eigenvectors.

3.2

Fuzzy integrated evaluation method

The fuzzy comprehensive evaluation method [22-23], rooted in fuzzy geometry, addresses challenges posed by high ambiguity and difficult-to-quantify issues, thereby enabling the establishment of an evaluation model. Assessing the comprehensive effectiveness of information technology-based education management necessitates a multi-level, multi-angle evaluation, with each factor’s influence on outcomes being subject to subjective variations. Therefore, this paper adopts the fuzzy comprehensive evaluation method to address these complexities.sive evaluation method, and the specific steps include:

1)

The factor set $U = \{U_{1}, U_{2}, U_{3}\}$ is the set of factors for evaluating the comprehensive effectiveness of educational management, and the secondary indicators under each level are the sub-criteria level.

2)

Distinguish the evaluation set into five levels: $V = \{V_{1}, V_{2}, V_{3}, V_{4}, V_{5}\}$ , representing excellent, good, average, poor and very poor, respectively.

3)

The weight vector of the evaluation indicators can be expressed as $W = (a_{1}, a_{2}, \dots, a_{n})$ , and the weight vector needs to satisfy the normalization, which is usually analyzed and processed by the sum-product method.

4)

Determine the degree of affiliation of each factor in the evaluation set $V$ to each evaluation level $(R | u_{i})$ , the degree of affiliation is the degree of belonging of a certain indicator to a certain evaluation level, thus constituting a fuzzy evaluation matrix R: 6 $R = [\begin{matrix} r_{11} & r_{12} & \dots & r_{1 n} \\ r_{21} & r_{22} & \dots & r_{2 n} \\ \dots & \dots & \dots & \dots \\ r_{m 1} & r_{m 2} & \dots & r_{m n} \end{matrix}]$ $$R = \left[ {\matrix{ {{r_{11}}} & {{r_{12}}} & \cdots & {{r_{1n}}} \cr {{r_{21}}} & {{r_{22}}} & \cdots & {{r_{2n}}} \cr \cdots & \cdots & \cdots & \cdots \cr {{r_{m1}}} & {{r_{m2}}} & \cdots & {{r_{mn}}} \cr } } \right]$$

5)

The fuzzy synthesis factor is used to synthesize the weight vector $W$ with the fuzzy relationship matrix $R$ , and the fuzzy comprehensive evaluation result $B$ of the evaluated thing can be obtained: 7 $\begin{array}{l} B & = W * R = (\begin{array}{l} a_{1}, & a_{2} & \dots, & a_{n} \end{array}) [\begin{matrix} r_{11} & r_{12} & \dots & r_{1 n} \\ r_{21} & r_{22} & \dots & r_{2 n} \\ r_{m 1} & r_{m 2} & \dots & r_{m n} \end{matrix}] \\ = (\begin{array}{l} b_{1}, & b_{2}, & \dots, & b_{n} \end{array}) \end{array}$ $$\matrix{ B \hfill & { = W*R = \left( {\matrix{ {{a_1},} \hfill & {{a_2}} \hfill & { \cdots ,} \hfill & {{a_n}} \hfill \cr } } \right)\left[ {\matrix{ {{r_{11}}} & {{r_{12}}} & \ldots & {{r_{1n}}} \cr {{r_{21}}} & {{r_{22}}} & \ldots & {{r_{2n}}} \cr {{r_{m1}}} & {{r_{m2}}} & \ldots & {{r_{mn}}} \cr } } \right]} \hfill \cr {} \hfill & { = \left( {\matrix{ {{b_1},} \hfill & {{b_2},} \hfill & { \cdots ,} \hfill & {{b_n}} \hfill \cr } } \right)} \hfill \cr } $$

3.3

Weighting of evaluation indicators

The study initially constructed the evaluation indexes of information technology-based educational management based on relevant literature review and offline expert consultation, and adopted AHP hierarchical analysis to calculate the index weights of the evaluation index system of information technology-based educational management, and realized the allocation of the index weights with the help of Yaanp Hierarchical Analysis software. Figure 1 shows the model of comprehensive effectiveness evaluation index system of education management based on information technology (three-level indexes are omitted).

First of all, based on the evaluation index system of information technology-based education management and the relationship between the indicators, a hierarchical model is constructed. After that, the judgment matrix of evaluation indexes is derived, the consultation questionnaire of index weights is prepared, and experts are invited to assign scores. Finally, the weights of the indicator system are calculated according to the results of the experts’ scores.

After two rounds of expert consultation, the indicators at all levels of the evaluation index system for informatized education management and the weights of the indicators were determined. There are 8 first-level indicators, 22 second-level indicators and 46 third-level indicators in the evaluation index system. The specific evaluation index system and comprehensive weights are detailed in Table 2, which encompasses a range of aspects such as information awareness, information capability, and information technology operation skills, as outlined in the educational informatization evaluation framework.

Table 2.

Information education management evaluation index and weight

Primary index	Secondary index	Tertiary index	Number	Weight
Education concept (0.223)	Development goal(0.166)	The relevance of the information management target	Q1	0.166
Education concept (0.223)	Development concern(0.057)	Confidence development concerns	Q2	0.057
Information education facilities (0.129)	Information teaching scene(0.115)	Regional information experience center	Q3	0.031
		Degree of school information	Q4	0.057
		Functional classroom construction in schools	Q5	0.027
	New information network(0.014)	Regional education	Q6	0.007
		Campus coverage	Q7	0.004
		New network technology application	Q8	0.003
Education resources (0.127)	Resource construction(0.052)	Regional feature digital resources	Q9	0.023
	Resource construction(0.052)	The school’s digital resources	Q10	0.029
	Resource sharing(0.031)	Information education public service platform access degree	Q11	0.013
		Regional education resource public service platform	Q12	0.006
		Student network learning space	Q13	0.012
	Resource application(0.044)	Informationization education resource utilization	Q14	0.013
		Information resource usage of teachers	Q15	0.011
		Resource dynamic adjustment mechanism	Q16	0.004
		Resource quality evaluation mechanism	Q17	0.016
Information teaching application (0.136)	Teaching model(0.068)	Information technology supports the diversity of classroom teaching	Q18	0.043
		Three classroom promotion applications	Q19	0.009
		Open online tutoring	Q20	0.016
	Teaching research(0.027)	Wisdom teaching	Q21	0.011
		Regional level information education class	Q22	0.008
		Information teaching case	Q23	0.008
	Teaching evaluation(0.041)	Informational difference	Q24	0.014
		Information learning assessment	Q25	0.019
		Online assignment	Q26	0.008
Student number literacy(0.15)	Leadership of principal(0.043)	Principal digital leadership training	Q27	0.043
	Teacher information quality(0.057)	Training of teachers’ information teaching ability	Q28	0.057
	Student information quality(0.05)	Digital literacy promotion courses are available	Q29	0.031
	Student information quality(0.05)	Digital literacy promotion activities	Q30	0.019
Education management (0.075)	Governance data(0.027)	Regional education and seniority comprehensive platform	Q31	0.014
	Governance data(0.027)	Regional education services comprehensive platform	Q32	0.013
	Multiple participation(0.017)	The introduction of scientific research institutions and other forces	Q33	0.017
	Management distribution(0.031)	Regional education governance business	Q34	0.009
	Management distribution(0.031)	Education management business	Q35	0.022
Education guarantee (0.083)	Organizational security(0.024)	The demonstration area creates work leaders	Q36	0.013
		The demonstration zone creates the expert instructor	Q37	0.005
		Quantity of information and part-time personnel	Q38	0.006
	Institutional security(0.02)	Information education development plan	Q39	0.007
		Information education action plan	Q40	0.01
		The education test system of informationization	Q41	0.003
	Investment guarantee(0.023)	Construction funds for demonstration zones	Q42	0.023
	Safety guarantee(0.016)	Network security management and emergency protection mechanism	Q43	0.007
	Safety guarantee(0.016)	Network security training	Q44	0.009
Information education characteristics (0.077)	Characteristic pattern(0.051)	The regional level of informationization education development characteristics pattern	Q45	0.051
Information education characteristics (0.077)	Feature results(0.026)	Innovative achievements in teaching, teaching, managing and social services in new technologies	Q46	0.026

3.4

Example Analysis of Basic Education Management Informatization in School A

This section evaluates the development of informatization of education management in School A, effectively verifying the validity, accuracy and scientificity of the research index system. On the basis of the weight allocation of the evaluation indexes above, five experts were invited to conduct a comprehensive evaluation of the informatization of educational management in School A. Using the fuzzy judgment algorithm, the school’s informatization of educational management was further categorized into distinct gradesorithm. Table 3 shows the grade division interval based on fuzzy judgment.

Table 3.

Classification interval based on fuzzy judgment

Score	[90,100)	[80,90)	[70,80)	[60,70)
Grade	Excellence (I)	Good (II)	General (III)	Worse (IV)

After post data processing, the average weighted ratings for the first-level indicators of the statistical evaluation system, which include Education concept, Information education facilities, Education resources, Information teaching application, Student number literacy, Education management, Education guarantee, and Information education characteristics, are calculated.

The average weighted ratings of each level of indicators are shown in Figure 2. It is clear from the figure that for indicator 3 (Education resources) and indicator 5 (Student number literacy) in the informationization of education management in school A, the experts’ evaluation results are lower than the other indicators. For Indicator 6 (Education management), the average weighted rating after counting was the highest, with five experts giving a rating of 97 or more. Combining the evaluation results of the 5 experts, the average scores of Level 1 Indicators 1-8 ranged from 84.172 to 97.654, and the overall informatization education management rating of School A was 92.62, which is a good performance.

According to the grading interval based on fuzzy comprehensive judgment, the grading results of each level of indicators are shown in Table 4. As can be seen from the table, indicator 3 (Education resources) and indicator 5 (Student number literacy) are “Good”, and the rest of the indicators as well as the comprehensive informatization education management in school A are “Excellence”.

Table 4.

Division of Grades by Level

Primary indicator	Weighted score						Grade
Primary indicator	Expert 1	Expert 2	Expert 3	Expert 4	Expert 5	Average	Grade
1	92.64	93.81	96.02	93.94	96.47	94.576	I
2	95.78	97.38	96.1	96.86	96.33	96.49	I
3	83.11	86.73	84.67	85.89	80.46	84.172	II
4	96.46	95.47	95.48	96.87	98.37	96.53	I
5	85.11	89.46	87.02	84.64	86.19	86.484	II
6	97.94	96.54	98.73	97.68	97.38	97.654	I
7	94.63	95.73	94.92	94.2	94.57	94.81	I
8	91.83	92.87	95.19	91.32	89.69	92.18	I
Average	92.62						I

Generally speaking, the informatization environment construction in School A has been largely completed. The school leadership and teachers possess a certain level of understanding regarding educational informatization. The supporting informatization plans and systems are progressively being refined, and the organization is undergoing rapid development. The informatization resources construction is relatively comprehensive, and the informatization literacy of teachers and students has seen improvement. Furthermore, network-based teaching activities are being actively implemented.nd learning have begun to be widely used. The comprehensive effectiveness of education management can be evaluated through the application of information technology in schools, as demonstrated by the case of this school.

4

Use of information technology in education management

Information technology (IT) encompasses technologies used for acquiring, processing, transmitting, and utilizing information. Its application in education comprises two main aspects: learning about IT and leveraging IT to enhance learning.

This paper focuses on the use of information technology to promote teaching, i.e., using information technology to support classroom teaching, develop educational resources, optimize the educational process, transform learning styles, present teaching content, improve teaching efficiency, and cultivate students’ information literacy. Figure 3 shows the nature of information technology education management.

4.1

Management of teachers’ teaching preparation

The process of lesson preparation in teaching management informatization

1)

Make a detailed plan for lesson preparation, divide it into different units of lesson preparation according to the teaching content, and one or two teachers will be responsible for the preparation of a chapter.

2)

Based on the teaching content of each unit, proceed with the collection of resources. The scope of collection is not limited, it can be lesson plans, courseware, or excellent teaching materials in the network.

3)

Categorize the collected resources and establish a resource library for each lesson preparation group.

4)

Leverage the resource library to design lesson plans.

5)

Uploading lesson plans. The teaching design plan is uploaded to the campus curriculum plan library in accordance with the teaching plan, all curriculum plans are shared and can be modified and improved by the teaching and research group teachers based on actual conditions.

6)

Reflection and exchange. The teacher conducts teaching reflection after the lesson and improves the online course plan accordingly.

4.2

Management of webpage teaching and research activities

Based on the school network, information technology is utilized to establish subject-specific websites for the school, enabling the management of teaching and research activities through these web pages.

1)

Management of regular information webpage of teaching and research activities

In the management of the teaching process, All static information, including the “teaching and research work plan”, “semester teaching work plan”, and “teaching schedule”, which are essential for teaching and research activities, must be submitted in electronic format to the school teaching office at the semester’s outset and archived on the school’s website.

2)

Web-based management of teaching and research activities

Weekly teaching and research activities, along with their detailed contents, can be disclosed and advertised throughout the school via a dedicated webpage, allowing administrators to keep abreast of both the activity specifics and participant engagement at any given moment. Upon publication, the webpage will document the activities sequentially and archive them on the school’s website, providing a realistic and detailed record of all events. Furthermore, summarizing the educational and research endeavors becomes effortless. Research activities carried out by each teaching and research group and classroom team during the semester.

4.3

Information-based classroom teaching management

Classroom teaching, as highlighted in recent educational research, stands out as the most pivotal and challenging aspect of the teaching process, directly influencing student learning outcomes and requiring sophisticated management strategies. Under the umbrella of information technology, network teaching places the student at the core of the learning process. Knowledge acquisition is facilitated through the construction of meaning in specific contexts, individual inquiry, and the collaborative efforts of peers, which includes communication and the strategic use of information. School administrators in the management of network teaching should fully understand the auxiliary role of the network platform for classroom teaching, thoroughly explore the diverse functionalities of the platform to complement and elevate teaching outcomes that are challenging to attain in traditional classroom settings, and in the process of using it, actively guide students to adapt to the platform, leveraging its interactive capabilities and resource-sharing features to aid in the teaching process.

4.4

Online Assignment and Tutoring Management

Modern information technology fulfills teaching needs by establishing appropriate subject question banks. Teachers can organize topics based on student levels and send them via email. Students then complete the assignments and return them to teachers via email. Teachers correct assignments promptly and return them to students. Students can also submit their assignments via email.

4.5

Example study of the effectiveness of education management based on information technology

4.5.1

Analysis of classroom effectiveness in information-based education management

This section takes English teaching as an example to study the efficacy of English education management under information technology. Before conducting the teaching experiment, two classes with no significant difference in English proficiency in the freshman year of a university were selected for the teaching comparison experiment. The experimental class (T) implemented modern information technology in English teaching, while the control class (CK) ***parative analyses from various studies, such as one conducted at Tonghua Normal University, have shown that while information technology can offer new strategies and resources, its effectiveness may vary and can sometimes be less effective than traditional methods. Lasted for 12 weeks.

During the teaching practice period, 15 lessons each of the experimental class and the control class were randomly selected, and the classroom effects of the experimental class and the control class were observed, statistically and comparatively analyzed, covering the surface of teacher-student communication (the number of collective replies and the number of student-initiated replies to questions), the mode and degree of teacher-student communication (the quality of students’ replies and the teacher’s way of reasoning and answering), so as to test the classroom effects of the English language under the information technology teaching.

1)

Facets of teacher-student communication

Across 15 randomly selected lessons in both the control and experimental classes, we counted the number of collective and individual student responses to questions. The statistical outcomes reflecting teacher-student communication in the classroom are presented in Figure 4. According to the data in the figure, it can be seen that the average number of unanswered questions in the control class is 7.6 and the average number of unanswered questions in the experimental class is 1.6, and the average number of unanswered questions in the experimental class is lower than that in the control class. The average number of collective responses to questions in both classes was around 13. In addition, the average number of times students volunteered to answer questions was 2.1 in the control class and 7.5 in the experimental class, highlighting a higher level of engagement in the experimental class. From this, it is clear that the The experimental class demonstrated superior classroom dynamics and teacher-student communication compared to the control class, as evidenced by the facets of communication frequency, engagement, and skill development.

2)

Manner and degree of teacher-student communication

In 15 randomly selected lessons in the control and experimental classes, the quality of students’ responses and the teacher’s way of answering questions were counted, and Table 5 depicts the results of the manner and degree of teacher-student communication in the classroom. As can be seen from the table, the p-value for the extent of students’ answers to the questions posed by the teacher is 0.000 for the control and experimental classes in terms of unresolvable, tentative and comprehensible statements, i.e., there is a highly significant difference between the two classes.

To some extent, the quality of students’ answers influences the teacher’s reasoning approach. The mean number of repetitive teacher responses in the experimental class was lower than that of the control class with P=0.000, i.e., there was a highly significant difference in the repetitive teacher responses in the control and experimental classes. In the experimental class, the average number of teachers’ encouraging comments or follow-up questions was 13.21, higher than that in the control class. The non-response of teachers in both the control and experimental classes showed no significant difference, suggesting that teachers in the experimental class employed a more effective response method. Therefore, it is evident that the classroom effect in the experimental class surpasses that of the control class, particularly in terms of the manner and extent of teacher-student communication.

Table 5.

Analysis of the way and degree of communication

Type	Degree of communication	Class	Mean value	Standard deviation	P value
Student answer certificate	Unsolvable	CK	3.35	0.688	0.000
	Unsolvable	T	1.71	0.633	0.000
	Preliminary solution	CK	7.78	1.105	0.000
	Preliminary solution	T	14.07	0.963	0.000
	Understandable answer	CK	2.28	0.984	0.000
	Understandable answer	T	3.71	0.925	0.000
Teacher solution	Repetitive response	CK	5.00	1.025	0.000
	Repetitive response	T	2.14	0.645	0.000
	Encourage evaluation	CK	6.64	0.827	0.000
	Encourage evaluation	T	13.21	0.663	0.000
	Not answering	CK	0.50	0.753	0.926
	Not answering	T	0.57	0.652	0.926

4.5.2

Evaluation of Information Technology Effectiveness Based on Student Achievement

In this section, EXCEL and SPSS were used to analyze the results of the pre- and postexperimental classes to evaluate the role of information technology in influencing students’ performance. Table 6 shows the statistical results of the grades of the two classes before and after the experiment. The distribution of English scores for two classes, as depicted in Figure 5, aligns with the findings from a sample survey report on English scores, indicating a normal distribution with most students scoring within the passing range.

Table 6.

Presents the comparative analysis of student performance in two classes before and after the educational experiment, showcasing the impact of the intervention on average scores, pass rates, and other key indicators.

	Class	N	Mean value	Standard deviation	P value
Pre-test	CK	50	65.12	7.592	0.854
Pre-test	T	50	64.96	6.498	0.854
Post-test	CK	50	69.50	9.496	0.000
Post-test	T	50	77.72	9.613	0.000

Combined with the charts, it can be seen that before the experiment, the average scores of the control class and the experimental class were 65.12 and 64.96, respectively, with P=0.854>0.05, which indicates that there is no significant difference between the two classes in the pre-test scores of the teaching experiment.Following a 12-week teaching experiment, the post-test average scores revealed a significant improvement in the experimental class, with an average score of 77.72, compared to the control class’s average score of 69.50., and the post-test of the experimental class was higher, The P-value is 0.000, much lower than the significance level of 0.001, indicating a highly significant difference between the post-test scores of the control class and the experimental class. From the graph, it can be seen that the post-test scores of the experimental class are distributed between 60 and 90 points. And there is a significant increase in the number of students with high scores (more than 80) in the experimental class compared to the control class, which makes the gap between the scores of the two classes widen. It shows that informatization teaching has a promoting effect on students’ academic performance.

5

Conclusion

This study analyzed the use of information technology in education management, then constructed an evaluation model of information technology education management based on hierarchical analysis, combined with a fuzzy comprehensive judgment algorithm, to make a preliminary exploration of the information technology education management system of School A. It also evaluates the comprehensive effectiveness of IT in educational management in School A, focusing on the educational management outcomes.ss in educational management as well as teachers’ teaching and research activities. The conclusions of the experiment are as follows:

The first-level evaluation indexes of School A’s informatization education management ranged from 84.172 to 97.654, and the overall level of informatization education management was 92.62, which was at the “Excellence” level. Information technology can optimize the effectiveness of education management in the classroom in terms of the surface of teacher-student communication as well as the way and degree of teacher-student communication. At the same time, information technology has a certain effect on enhancing the quality and efficiency of education managementect on the improvement of students’ performance, and the back side of the experimental class is 8.22 points higher than that of the control class, and there is a significant difference. In conclusion, informatization technology can improve the comprehensive effectiveness of educational management.

Lingua:: Inglese

Frequenza di pubblicazione:: 1 volte all'anno
Argomenti della rivista:: Scienze biologiche, Scienze della vita, altro, Matematica, Matematica applicata, Matematica generale, Fisica, Fisica, altro

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Study on the Integrated Effectiveness of Information Technology-based Educational Management

Ye Zhang

Pubblicato online: 24 set 2025

Ricevuto: 31 gen 2025

Accettato: 09 mag 2025

DOI: https://doi.org/10.2478/amns-2025-0983

Parole chiaveInformation Technology, Hierarchical Analysis, Fuzzy Comprehensive Judgment, Educational Management

© 2025 Ye Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Information Technology, Hierarchical Analysis, Fuzzy Comprehensive Judgment, Educational Management