Research on the Path of Innovative Reform of Finance Curriculum in Colleges and Universities under the Background of Digital Economy

In the era of digital economy, the financial industry is facing the development trend of digitization, intelligence, and borderlessness, and the demand for financial talents has also changed dramatically. On the one hand, financial institutions have higher and higher requirements for technical background, especially for knowledge and skills in the fields of big data analysis, artificial intelligence and blockchain [1–3]. On the other hand, innovation and interdisciplinarity have become the key words in the financial industry, and talents with innovative thinking and interdisciplinary vision are more competitive in the financial market [4–5]. Therefore, only composite, innovative, internationalized and applied talents can be compatible with the rapidly changing digital economy era, which is bound to have an important impact on the curriculum teaching of finance majors [6–8].

At present, most of the universities and colleges financial discipline talent training is based on traditional finance, financial discipline curriculum and talent training programs lag behind the development status quo of the financial industry, and it is difficult to adapt to the era of digital economy [9–10]. At present, most colleges and universities are not adapted to the digital economy era in terms of the cultivation method and curriculum of finance, although some colleges and universities have attempted the corresponding curriculum teaching reform, but there are still problems such as the lack of systematic setting of the curriculum system, the lack of case study and students’ practical skills operation of the curriculum content, and the low degree of digitization of the operation of the teaching system [11–14], which leads to the fact that the students cultivated are still difficult to meet the digital economy era. The students trained are still difficult to meet the development needs of financial talents for the development of digital finance. Therefore, it is imperative to explore the curriculum teaching reform of finance majors under the background of digital finance [15–16].

Literature [17] points out that higher vocational colleges and universities need to systematically analyze the requirements of the digital economy on the ability of professionals, in order to improve the quality of talent training and professional teaching reform by exploring the factors that constrain the training of talents at the present time, and proposing countermeasures. Literature [18] based on the analysis of the teaching status quo of finance majors in a university, reveals its deficiencies in teaching mode, curriculum system and other aspects, and puts forward the optimization of the teaching system, strengthen the cooperation between schools and enterprises and other effective measures, aiming at providing references for the teaching reform of financial accounting majors in colleges and universities. Literature [19] combines the changes in the demand side of economic talents, analyzes the deficiencies of the talent cultivation mode in colleges and universities from the supply side of education, and explores the reform path of the talent cultivation mode of the integration of industry and education based on the integration of industry and education in terms of the construction of the knowledge sharing platform and the linkage design of the curricular system. Literature [20] reveals that the development of digital economy puts forward new requirements on the content and teaching methods of accounting courses in colleges and universities. It indicates that improving the adaptability and teaching quality of accounting talent cultivation in colleges and universities requires colleges and universities to take initiatives such as industry-teaching integration, improving teaching mode, and improving infrastructure. Literature [21] discussed the teaching design of financial management theory class based on OBE theory, through changing the traditional linear teaching design ideas, carrying out the teaching reform of financial management ethics class, and combining electronic information technology and other means in order to cultivate talents with excellent financial management ability. Literature [22] takes Shandong University of Commerce and Industry as the research object, and elaborates on the ways of teaching reform and innovation in finance and economics colleges and universities from the aspects of innovative teaching mode and strengthening entrepreneurship education.

The innovative design of the curriculum system should take into account the number of courses in the system, the content, credits, and forward and backward articulation. This paper applies the DEMATEL-ISM method to construct a reachability matrix to study the correlation relationship among the courses in the financial course system of colleges and universities, and design the blended teaching strategy accordingly. It analyzes the rationality of course design and then establishes a multilayer recursive order explanatory structure model, which provides conditions for reasonable adjustments of teaching content and progress. In addition, the explanatory structure model can make the course systematic and structured, easy to highlight important and difficult points, and achieve a rational arrangement of course content design. The strategy is applied to the finance major of a university to explore the practical effects of the blended teaching strategy designed in this paper.

2

DEMATEL-ISM modeling framework

Combined with the theoretical basis of DEMATEL-ISM, the steps of model construction in this paper are as follows: 1)

Based on the identified influencing factors, which are as follows: X1 Linear Algebra, X₂ Advanced Mathematics, X₃ Western economics, X₄ Econometrics, X₅ Statistics, X₆ Finance, X₇ Corporate Finance, X₈ Probability and Statistics, X₉ Public Finance, X₁₀ Time series analysis, X₁₁ Bank Accounting, X₁₂ Political Economy, X₁₃ Insurance, X₁₄ Finance Data Analysis, X₁₅ Financial Engineering. Therefore, the set of factors influencing is F={X₁,X₂,X₃ …,X₁₅}

2)

Direct influence matrix A

Based on the relationship between different courses in finance in higher education, with a_ij referring to the relationship between X_i(i = 1,2,3, …,15) and X_j(j = 1,2,3, …,15), the factors are scored on a scale of 0 to 3 in the traditional DEMATEL method to form a direct influence matrix [23]. However, due to the large number of factors included in this study, the traditional 0 to 3 scale is not sufficient to differentiate the effects. Therefore, this study used a 0 to 5 scale to design the rubric, where the scores for impacts of 1, 2, 3, 4, and 5 represent no impact, very weak impact, weak impact, strong impact, and very strong impact, respectively. Direct Impact Matrix of Influencing Factors A : 1 $A = ⌊ \frac{1}{m} \sum_{k = 1}^{\dots} a_{i j}^{k} (k = 1, 2, 3 \dots m) ⌋ n * m$ where $a_{i j}^{k}$ is the ranking of the degree of direct influence on column a_j based on the influence factor a_i given by the k nd expert, and m is the number of experts involved in the evaluation. In Eq. (1), averaging is used to synthesize the evaluation results of several experts, thus eliminating the individual cognitive differences of each expert.

3)

Normalized direct influence matrix B

The normalized influence matrix (B) is obtained by normalizing the direct influence factor matrix (A) with Eq: 2 $B = (\frac{a_{i j}}{\max \sum_{}^{n}}) n * m$ where a_ij represents the value of the direct ind row and j rd column.

4)

Combined impact matrix C

The combined impact matrix C is determined by adding up between direct and indirect impacts: 3 $C = B {(I - B)}^{- 1}$

5)

Influence f, Influenced e, Center M, Cause N

Influence f indicates the combined influence value of each row element on other elements. Influenced degree e indicates the combined influence value of each column element on other elements [24]. The higher the centrality M the higher the importance of the influencing element, and the causality N indicates the degree of causality. According to the positivity and negativity of the centrality degree M, the causality can be judged, and the positive value indicates that it is the cause factor, and the opposite is the result factor. The formula is as follows. 4 $f_{i} = \sum_{.. e} t_{i j} (i = 1, 2, \dots, n)$ 5 $e_{i} = \sum t_{i j} (i = 1, 2, \dots, n)$ 6 $M_{i} = f_{i} + e_{i}$ 7 $N_{i} = f_{i} - e_{i}$

6)

Overall Impact Matrix D

The overall matrix of impact factors D can be expressed by the combined impact matrix C plus the unit matrix E with the following formula. 8 $D = C + E$ where E is the unit matrix.

7)

Reachability matrix K

The reachability matrix can be used to indicate whether a path exists between two nodes. If a path exists between influences X_i and X_j, then K_ij = 1 and vice versa is calculated as K_ij = 0. K_ij The calculation formula is shown below: 9 $K_{i j} = {\begin{array}{l} 1, d_{i j} \geq λ \\ 0, d_{i j} < λ \end{array}$

Reasonable threshold λ setting can simplify the hierarchy of influencing factors, and thresholds are usually set in two ways. One, it is determined based on the empirical value method. Based on experts in the field and previous research, for systems with fewer influencing factors, there is usually no need to simplify the system structure, and the threshold value is usually set to 0. Based on the correlation between the influencing factors, the threshold value can be determined according to the actual situation. Secondly, the inter-value is determined through the measurement research method, which is usually based on a high degree of uncertainty and a large amount of preliminary data and more complete data.

8)

Reachable set R_i, the set of antecedents and intersection

Reachable set R_i is the set of influences corresponding to row i of reachable matrix K is equal to 1; the set of antecedent terms S_i shows that F_i can reach the set of other influences with the following formula: 10 ${\begin{array}{l} R_{i = {f_{j} / f_{j} \in F, k_{i j} = 1}} \\ S_{i = {f_{j} / f_{j} \in F, k_{i j} = 1}} \end{array}$

9)

Multi-level step-structural modeling

In the multilevel step-structure model, the main task is to decompose the hierarchy, i.e., to determine the position of each influencing factor in the hierarchy and to model the explanatory structure of the influencing factors.

3

Rationalization of curriculum design

3.1

Empirical analysis of curriculum

Combined with the actual situation, an empirical analysis has been carried out on the selected 15 major courses in finance. The curriculum system involves important courses such as public courses, professional foundation courses, compulsory professional courses, and professional elective courses, among others. Using the DEMATEL method, the influence relationship between the above courses and the status and role in the whole curriculum system is analyzed.

Firstly, the direct influence matrix between the courses is determined. Using a five-level scale from 1 to 5, the direct influence matrix between courses is comprehensively determined by consulting a number of instructors and calculating the correlation coefficients of the course grades of 75 students in the class of 2022. Next, the normalized direct influence matrix between courses was calculated, and then the influence and influenced degrees of the courses were calculated, and finally the centrality and cause degrees of the courses were determined. In the original training program, each course credit is 4 points, and the course credits are reformulated according to the cause-effect diagram.

Table 1 shows the influence, influenced, centrality, and cause degrees of the effects of college finance courses.

Table 1.

The extent of influence enter and reason of each course

Course name	Influence degree	Influence degree	Center degree	Reason degree
Linear Algebra X1	1.131	-0.012	1.147	1.168
Advanced Mathematics X2	0.739	-0.032	0.725	0.72
Western economics X3	0.397	0.158	0.621	0.234
Econometrics X4	0.365	0.634	1.016	-0.291
Statistics X5	1.206	0.233	1.453	0.91
Finance X6	0.193	0.58	0.798	-0.405
Corporate Finance X7	0.06	0.157	0.233	-0.153
Probability and Statistics X8	0.88	0.142	1.033	0.731
Public Finance X9	0.019	0.366	0.428	-0.376
Time series analysis X10	0.246	0.266	0.593	-0.029
Bank Accounting X11	0.046	0.265	0.349	-0.201
Political Economy X12	0.134	0.614	0.792	-0.524
Insurance X13	0.068	0.494	0.549	-0.451
Finance Data Analysis X14	0.043	0.291	0.39	-0.237
Financial Engineering X15	0.288	0.378	0.726	-0.12

Accordingly, a cause-effect diagram is drawn. The cause-effect diagram is shown in Figure 1, and relevant suggestions are made for the design of the curriculum system and the implementation of the teaching program according to the cause-effect diagram. 1)

The centrality index reflects the importance of each course in the curriculum system, from left to right, indicating that the courses are becoming more and more important. Accordingly, Statistics, Linear Algebra, Probability and Statistics, Econometrics, etc. are very important courses for finance majors (I, IV regional part), which should be keyed to guarantee in terms of credits and arrangement of appointed teachers. The credits of the revised teaching plan are arranged as follows: 8 credits of Linear Algebra, 5 credits of Probability and Statistics, and 3 credits each of Statistics and Econometrics, and professors and doctors of high level are hired to teach the courses.

2)

The degree of reason indicator reflects the degree of association of each course in the curriculum system. The degree of reason indicator is greater than zero, indicating that the course should be taken first (I, II regional part), otherwise it should be taken later (III, IV regional part). Accordingly, the types of public courses, specialized basic courses, and specialized elective courses, and the before-and-after arrangement and articulation relationship of the four academic year courses can be determined. Accordingly, the first courses include Linear Algebra, Advanced Mathematics, Probability and Statistics, Statistics, and Western economics, and the subsequent courses include Time series analysis, Corporate Finance, Financial Engineering, Bank Accounting, Finance Data Analysis, Econometrics, Public Finance, Finance, Insurance, and Political Economy.

3.2

Integration of the DEMATEL-ISM method to determine the reachability matrix

In this calculation process, the unit matrix is used to represent the initial relationship strength. In order to eliminate the relationships with less influence, this paper sets the threshold value. When the relationship strength is less than the threshold, it is set to 0. When the relationship strength is greater than or equal to the threshold, it is set to 1. This forms a reachability matrix and determines the appropriate threshold size. Based on experience, this paper sets different thresholds, such as 0.15, 0.18, 0.20, and 0.24, and observes their effects. According to the degree of decay of the node degree scatter plot and the size of the node degree under different thresholds, it is finally determined that λ is 0.20, thus obtaining the reachability matrix F for teaching courses in finance, as shown in Table 2.

Table 2.

The finance specialized course teaching accessibility matrix

Aourse	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10	X11	X12	X13	X14	X15
X1	1	0	1	0	1	0	0	1	0	0	0	0	0	0	0
X2	0	1	0	0	0	0	0	0	0	0	0	0	0	0	0
X3	0	0	1	0	0	0	0	0	0	0	0	0	0	0	0
X4	0	0	0	1	0	0	0	0	0	0	1	0	1	0	1
X5	0	0	0	0	1	0	0	0	0	0	0	0	0	0	0
X6	0	0	0	0	0	1	0	0	0	0	0	1	0	0	0
X7	0	0	0	0	0	0	1	0	0	0	0	1	0	0	0
X8	0	0	0	0	0	0	0	1	0	0	0	1	0	0	0
X9	0	0	0	0	0	0	0	0	1	0	0	0	0	1	0
X10	0	0	0	0	0	0	0	0	0	1	0	0	0	0	0
X11	1	0	1	0	0	0	0	0	0	0	1	0	0	0	0
X12	0	0	0	0	0	0	0	0	0	0	0	1	0	0	0
X13	0	0	0	0	0	0	1	0	0	0	0	0	1	0	0
X14	0	0	0	0	0	0	0	0	0	0	0	0	0	1	0
X15	0	1	0	0	0	1	0	0	0	0	0	0	0	0	1

3.3

Modeling multilayer recursive explanatory structures

3.3.1

Analyze the hierarchy of objectives underlying the reachability matrix

Based on the reachability matrix, we can get the antecedent set X (A_i) and the reachable set Y (A_i), which is the set of columns whose rows correspond to columns with a value of 1 in the matrix, and the set of rows whose columns correspond to rows with a value of 1 in the antecedent set X (A_i) matrix F. On this basis, the tier in which each element is located is determined, and when Y (A_i) = Y (A_i) ∩ X (A_i), the corresponding influencing factor Ai is the bottom influencing factor and the rows and columns corresponding to the influencing factor are deleted [25]. The same method is then used to find the rank distribution of the remaining elements. Through many iterations, it has been concluded that the influencing factors are characterized by a multi-layer recursive order structure, which is specifically divided into five levels, as shown in Table 3.

Table 3.

Influence factor hierarchy

Hierarchy	Influencing factor
1	X1, X2, X3, X5
2	X4, X6, X7, X8, X11, X13, X14
3	X12
4	X10
5	X9, X15

3.3.2

Multi-Level Progressive Interpretive Structural Models

By analyzing Table 3 and based on the results of the DEMATEL-ISM method, we constructed a sequence diagram of the multilevel hierarchical interpretation structure of “finance course innovation”, as shown in Figure 2. According to the course standards and teaching objectives, for online learners, students need to master the first level of learning objectives first, in order to lay a good foundation for the learning of high-level content; for teachers’ teaching, teachers need to teach according to the necessity of each teaching objective at the same level, and adjust the content and progress of teaching; for the design of the course, the content of the course needs to be set up in accordance with the level of the learning objectives, and improve the rationality of the design of the course content. For curriculum design, the curriculum content should be structured according to the level of learning objectives, from shallow to deep, to improve the rationality and logic of the curriculum content design.

The first tier is X1, X2, X3, X4, X5, the second tier is and X4, X6, X7, X8, X11, X13, and X14. The third tier is X12, the fourth tier is X10, and finally the fifth tier is X9 and X15.

Figure 2 Multi-layer recursive interpretation structure model of financial teaching

Teachers can use the explanatory structure model of the basic objectives to rationally adjust the content and progress of teaching, in addition, this explanatory structure model can make the course systematic and structured, easy to highlight the important and difficult points, and realize the rational arrangement of course content design.

4

Blended learning strategies for the finance profession

4.1

Talent cultivation is the fundamental task of higher education

Talent cultivation is the fundamental task of higher education, and it is the starting point and landing point of all work in colleges and universities. The nurturing function of the blended teaching system for finance courses is complex. On the one hand, the talents required by the national economic development strategy should have the ability of sustainable development and innovative thinking, which stems from the in-depth understanding of multidisciplinary knowledge and the integration of cross-disciplinary knowledge; on the other hand, the complexity is also reflected in the dual attributes of biological and social nature of human beings, and human beings form their cognition, emotion and ambition in social interaction, which is a process of both active adaptation to the environment and continuous transformation of the environment. Therefore, the mission of talent training should focus on cultivating complex talents with an innovative spirit, practical ability, and an international vision. With the shift of economic development hotspot, the center of gravity of social demand for economic talents is gradually shifting from economics and international economics and trade to finance major. Whether the curriculum reform of finance is smooth or not is one of the keys to the construction of the discipline, and the teaching quality of the core courses of the specialty will directly affect the students’ acquisition of financial knowledge and their competence in related professional positions. It is imperative to closely integrate classroom teaching, laboratory teaching and practical training to create a teaching practice simulation environment for students that is relatively close to reality, as well as to provide teachers with a variety of auxiliary teaching tools. Therefore, the direction of professional construction should focus on making full use of “online + offline” teaching resources to build a blended teaching system based on competence training.

4.2

Enhancing capacity to serve regional development

Enhancing the ability to serve regional development is an important challenge for China in building world-class universities. In order to give full play to the role of academic resources of universities in supporting regional development strategies and stimulating regional economic vitality, the “Double First Class” construction project aims to guide universities to shift from the pure pursuit of credits, the number of papers and rankings in the past to the realization of the value of serving the country and leading the society. Therefore, universities constructing world-class universities should implement the latest requirements of national and local government policies, integrate new education and teaching concepts in the classroom, improve teaching methods, optimize curriculum design, enhance student experience, etc., and improve the construction of blended teaching system, so as to realize the integration of knowledge transfer and regional development.

4.3

Enriching blended learning diversified teaching methods.

The pre-course session should focus on pre-study of basic knowledge, and resources such as high-quality online course resources from outside the school and documentaries containing important background knowledge can be introduced to help students sort out and master basic knowledge, so as to facilitate teachers’ teaching according to the students’ needs. The online platform should be set up with a Q&A area and a discussion area, where students can ask teachers questions and teachers can organize student interactions in the discussion area to enhance the fun of the learning process and stimulate students’ interest in learning. Teachers in the class session according to the students’ pre-course preparation and feedback, combined with the efficiency of offline communication, reasonable setting of the course explanation time, focus on solving the learning difficulties, and consolidate the students’ basic knowledge of the subject. At the same time, teachers can use Internet teaching resources to enhance classroom interactivity, set up case studies and discussion tasks, and guide students to deepen their understanding of knowledge through flipped classrooms and group discussions. Through the financial investment simulation training, students can deeply understand the characteristics of financial instruments, pricing methods, trading rules and investment strategies, master the financial trading technology and portfolio theory for risk management, establish the financial risk responsibility and awareness of the rule of law, and abide by the professional ethics of the financial industry and laws and regulations, to achieve the goal of the construction of “Curriculum Civics”.

In order to measure the effectiveness of teaching, the post-course session should be designed with a combination of common and personalized assessment mechanisms. The common assessment is for all students, focusing on timeliness and aiming at assessing students’ mastery of knowledge points; the personalized assessment guides students to set up long-term investment concepts, construct investment portfolios, and effectively control and diversify risks according to students’ risk preferences and tolerance abilities. In addition, attention should also be paid to the construction of learning ecology, the creation of an extracurricular learning environment conducive to the continuous development of professional competence, and the encouragement of students to take part in finance-related qualification exams and disciplinary competitions, so as to apply what they have learned in practice.

5

Analysis of self-evaluation by finance students

In order to reflect more intuitively the effect of applying the blended teaching strategy designed in this paper in a finance course, a one-semester teaching experiment was conducted in a finance major of a university. The experimental and control classes had the same conditions except for whether they used the blended teaching strategy or not.

Self-assessment forms were distributed to the control and experimental classes before the teaching experiment. The self-evaluation forms were categorized into four main dimensions: student interest, course participation, problem-solving ability, and teamwork ability. The self-evaluation forms were distributed in an anonymous way using online mode, 103 self-evaluation forms were collected before and after the experiment, and the total number of valid questionnaires that could be counted in the statistics after screening was 100, of which the total number of valid questionnaires in the experimental class was 50, and the total number of valid questionnaires in the control class was 50, and the questionnaire results were adopted at a rate of 97.08%, which is in line with the basic conditions of the research in this paper.

In the following, we will explore the practical effects of the blended teaching strategy designed in this paper from three perspectives: descriptive statistics, independent sample t-test, and paired sample t-test.

5.1

Descriptive statistics

Table 4 shows the descriptive statistics of the pre-test for the experimental class. The descriptive statistics of the pre-test of the control class are presented in Table 5. Through the results of the students’ self-assessment form research, it was found that before the teaching experiment, the mean values of the experimental class and the control class in the dimension of learning interest were 3.266 and 3.353 respectively, in the dimension of course participation were 3.331 and 3.334 respectively, in the dimension of problem solving ability were 3.073 and 3.208 respectively, and in the ability to work in a team, the mean values were 3.225 and 3.418.

Table 4.

Pre-experimental descriptive statistics

	N	Min	Max	Mean	Standard deviation
Study interest	50	1.5	4.75	3.266	0.57
Classroom participation	50	1.75	5	3.331	0.616
Problem-solving ability	50	1.5	4.5	3.073	0.633
Team ability	50	2	4.75	3.225	0.755
Effective case number (listed)	50

Table 5.

Pre-analysis of the comparison class

	N	Min	Max	Mean	Standard deviation
Study interest	50	1.75	4.5	3.353	0.616
Classroom participation	50	1.5	4.75	3.334	0.632
Problem-solving ability	50	2	5	3.208	0.542
Team ability	50	1.5	5	3.418	0.58
Effective case number (listed)	50

Overall, there is not much difference between the two classes in the four dimensions of learning interest, degree of course participation, problem-solving ability, and teamwork ability. Moreover, the average scores of the four dimensions are about 3, which is rated as average, so it can be seen that, through this research, the two classes had average learning interest, average level of course participation, average problem solving ability and average teamwork ability before the teaching experiment.

5.2

Independent samples t-test

Before the teaching experiment, the self-evaluation scale results data of the two classes were subjected to an independent sample t-test, and the results of the side-by-side comparison are shown in Table 6, the independent sample t-test results of Sig of the experimental class and the control class students’ learning interest, course participation, problem solving ability and teamwork ability are all greater than 0.05, which indicates that before the teaching experiment, there is no significant difference between the two classes in the four dimensions, and the two classes learning situations are similar, and the subsequent experimental data are valid.

Table 6.

Self-evaluation Scale (pre-test) independent sample T-test

		Levin variance equivalence test		Average equivalent t test
		F	Sig.	T	Freedom	Sig.(double tail)	MD	SED	Confidence interval
		F	Sig.	T	Freedom	Sig.(double tail)	MD	SED	down	up
Study interest	Assumed equal variance	0.607	0.441	8.784	88.721	0	1.035	0.11	0.778	1.264
	Unassuming equal variance			8.76	85.374	-0.018	1.035	0.113	0.78	1.268
Classroom participation	Assumed equal variance	1.049	0.306	9.048	88.74	-0.014	1.064	0.112	0.817	1.316
	Unassuming equal variance			-0.043	88.407	0.985	-0.023	0.136	-0.299	0.279
Problemsolving ability	Assumed equal variance	3.035	0.089	-0.384	88.736	0.721	-0.054	0.129	-0.358	0.228
	Unassuming equal variance			-0.387	87.112	0.717	-0.056	0.128	-0.349	0.227
Team ability	Assumed equal variance	0.818	0.368	-0.388	88.731	0.721	-0.064	0.136	-0.381	0.239
	Unassuming equal variance			-0.379	88.453	0.72	-0.064	0.136	-0.366	0.234

After the teaching experiment, the self-evaluation scale data of the control class and the experimental class were subjected to independent samples T-test, and the results of their cross-sectional comparison are shown in Table 7. The results of the independent samples t-test for the two classes in terms of students’ interest in learning, course participation, problem solving ability and teamwork ability Sig are all less than 0.05, indicating that there is a significant difference between the self-assessment scales of the two classes before and after the experiment, which proves that the blended teaching strategy designed in this paper can indeed improve students’ interest in learning, course participation, and cultivate problem solving ability and teamwork ability.

Table 7.

Self-evaluation Scale (post-test) independent sample T-test

		Levin variance equivalence test		Average equivalent t test
		F	Sig.	T	Freedom	Sig.(double tail)	MD	SED	Confidence interval
		F	Sig.	T	Freedom	Sig.(double tail)	MD	SED	down	up
Study interest	Assumed equal variance	0.602	0.438	8.787	90.488	0	1.027	0.12	0.78	1.272
	Unassuming equal variance	--	--	8.768	87.055	0	1.035	0.117	0.789	1.272
Classroom participation	Assumed equal variance	1.035	0.313	9.037	90.491	0	1.069	0.103	0.822	1.298
	Unassuming equal variance	--	--	9.015	80.751	0	1.064	0.103	0.816	1.311
Problem-solving ability	Assumed equal variance	-0.011	0.952	9.726	90.498	0	1.157	0.112	0.921	1.398
	Unassuming equal variance	--	--	9.702	88.342	0	1.158	0.109	0.917	1.391
Team ability	Assumed equal variance	1.989	0.156	8.629	90.499	0	1.052	0.112	0.811	1.292
	Unassuming equal variance			8.609	81.229	0	1.05	0.111	0.804	1.292

5.3

Paired samples t-test

The before and after survey results of the experimental class on the four dimensions were compared longitudinally and a paired-sample t-test was conducted, and the results of the survey are shown in Table 8, which shows that the paired-sample t-test results of the experimental class students’ attitudes toward learning, their participation in the course, their problem-solving ability, and their ability to work in a team are all Sig less than 0.05, which proves that the experimental class students’ changes in the four dimensions have been significant, as follows: 1)

Stimulate students’ interest in learning. Under the blended teaching strategy designed in this paper, the teacher introduces learned knowledge through problematic situations to better mobilize students’ desire for inquiry and learning.

2)

Improve students’ participation in the course. In the classroom around the problem first independent thinking, and then to solve the problem by group cooperation, each student has the opportunity to express their own ideas, which can effectively improve the degree of student participation.

3)

Improve students’ problem-solving ability. Students in the blended teaching strategies designed in this paper, group cooperative learning brainstorming approach helps students to disperse thinking, teaching activities around the classroom center problem, giving students full exploration and problem solving time, students in the step-by-step approach to the solution of the problem in the process of cultivating a certain degree of self-confidence, but also cultivate the problem-solving ideas and thinking habits.

4)

Cultivate teamwork skills. Students in the group cooperative learning process, students need to communicate with group members, and in the limited time to carry out an effective division of labor to improve the efficiency of problem solving, which requires students to be able to clarify the strengths and weaknesses of the group members, to avoid the shortcomings to achieve the optimal division of labor, in order to effectively solve the problem, in the subtle cultivation of the students’ teamwork awareness and teamwork ability.

Table 8.

T-test of paired samples before and after experiment in experimental class

Pairing	Pair difference					t	Freedom	Sig.(double tail)
	Mean	SD	SD Mean	Confidence interval
	Mean	SD	SD Mean	Down	up
Study interest	-1.061	0.578	0.068	-1.225	-0.884	-12.3	44	0.000
Classroom participation	-1.096	0.629	0.086	-1.301	-0.923	-11.822	44	0.000
Problem-solving ability	-1.266	0.68	0.1	-1.474	-1.063	-12.668	44	0.000
Team ability	-1.149	0.631	0.092	-1.344	-0.97	-12.295	44	0.000

The self-evaluation scale data of the control class before and after the teaching experiment were compared longitudinally, and the test results are shown in Table 9, the students’ paired-sample t-test results Sig on the four dimensions are all greater than 0.05, indicating that there is no significant difference between before and after the experiment, which proves that without the use of the blended teaching strategy designed herein can not be in the interest of learning, the degree of course participation, problem solving ability and teamwork ability to get Improvement.

Table 9.

T-test of paired samples before and after the control class experiment

Pairing	Pair difference					t	Freedom	Sig.(double tail)
	Mean	SD	SD Mean	Confidence interval
	Mean	SD	SD Mean	Down	up
Study interest	-0.036	0.081	0.008	-0.045	0	-1.388	43	0.179
Classroom participation	-0.028	0.042	0.007	-0.028	-0.006	-1.465	43	0.158
Problem-solving ability	-0.051	0.174	0.009	-0.111	0.014	-1.447	43	0.162
Team ability	-0.038	0.155	0.005	-0.077	0.005	-1.258	43	0.247

6

Conclusion

This paper uses the DEMATEL-ISM model to analyze the hierarchical structure and degree of influence of each course factor in the teaching system of finance in colleges and universities, and accordingly proposes a blended teaching strategy for finance majors. The DEMATEL-ISM analysis reveals that Statistics, Linear Algebra, Probability and Statistics, Econometrics, etc., are very important courses in finance majors, and they should be given priority in terms of credits and arranging for the appointment of teachers. Should be emphasized in terms of credits and faculty arrangement. The credits of the revised teaching plan are arranged as follows: 8 credits of Linear Algebra, 5 credits of Probability and Statistics, and 3 credits each of Statistics and Econometrics, and professors and doctors of higher level are hired to teach the courses. Prior courses include Linear Algebra, Advanced Mathematics, Probability and Statistics, Statistics, and Western economics, and the subsequent courses include Time series analysis, Corporate Finance, Financial Engineering, Bank Accounting, Finance Data Analysis, Econometrics, Public Finance, Finance, Insurance, and Political Economy. Teachers can use the explanatory structure model of the basic objectives to rationally adjust the content and progress of teaching, in addition, the explanatory structure model can make the course systematic, structured, easy to highlight the important and difficult points, and realize the rational arrangement of course content design. Applying the strategy to finance majors in a university for a semester teaching experiment, there is a significant difference between the self-evaluation scales of the two classes before and after the experiment, which proves that the blended teaching strategy designed in this paper can indeed improve students’ interest in learning, their participation in the course, and cultivate problem-solving ability and teamwork ability.

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Research on the Path of Innovative Reform of Finance Curriculum in Colleges and Universities under the Background of Digital Economy

Tiantian Wan

Published Online: Mar 19, 2025

Received: Nov 03, 2024

Accepted: Feb 08, 2025

DOI: https://doi.org/10.2478/amns-2025-0435

KeywordsDEMATEL, ISM, Reachability matrix, Curriculum innovation

© 2025 Tiantian Wan, published by Sciendo

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

Keywords
DEMATEL, ISM, Reachability matrix, Curriculum innovation

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