Research on Interdisciplinary Talent Cultivation Mechanism of Environmental Design Specialty Supported by Cloud Computing in the Context of New Liberal Arts

To build an interdisciplinary integrated curriculum system and practice platform, the first task is to set up interdisciplinary course modules, introduce literature, history, sociology and other multidisciplinary contents [31], and closely integrate them with environmental design professional knowledge. The curriculum design needs to take into account the characteristics of each discipline and practical application scenarios, realize the deep combination of theory and practice, and construct a systematic and complete curriculum system. Each course module should be designed and taught by experts in the relevant fields to ensure the professional depth of the content and help students to deeply understand the connection and interaction between different disciplines in the learning process. At the same time, teaching resources need to be integrated and optimized to create a shared resource library covering e-books, journal databases, case studies and project materials, which can be freely accessed and used by teachers and students. The resource library needs to be updated regularly to keep the content cutting-edge and practical.

Practical teaching methods and means are rich and varied, and their core purpose is to comprehensively improve students’ practical ability, innovation ability, and quality. Specifically, a case teaching method can be introduced to promote students’ better understanding of the application of theoretical knowledge in practical work through in-depth analysis and discussion of actual cases. In this process, teachers should carefully select representative typical cases and guide students to deeply participate in the teaching process through group discussions, writing case analysis reports and other forms, so as to cultivate their analytical and problem-solving abilities. At the same time, project-driven teaching can also be introduced through the introduction of real projects into the classroom, so that students can learn and apply knowledge in the process of project implementation. These projects should contain multiple stages of tasks, such as demand analysis, program design and implementation evaluation, etc., to ensure that students can gain practical experience in a comprehensive and systematic way.

The evaluation system reform, as the cornerstone of teaching mode reform, should result in the transformation from single grade assessment to multi-dimensional comprehensive evaluation. To this end, it is necessary to build a process evaluation system to comprehensively assess students’ performance in the learning process, covering classroom participation, assignment completion, and project practice. At the same time, combined with summative evaluation, students’ learning effectiveness and ability development should be measured comprehensively, and students’ self-assessment and mutual assessment mechanism should be introduced to enhance the diversity and objectivity of evaluation. In terms of teaching method innovation, a flipped classroom model can be introduced to encourage students to utilize online resources to learn basic knowledge independently before class, while focusing on discussion, practice and problem solving in class, so as to enhance the interactivity and effectiveness of classroom teaching.

Practice teaching is an important way to refine students’ practical skills, therefore, it is necessary to establish on- and off-campus practice bases and deep cooperation with enterprises, institutions and communities to provide students with rich and diverse practice opportunities and resources. In the process of practice, students will experience the actual working environment and master professional skills and workflow, so as to improve their practical ability and professionalism. Additionally, students should be encouraged to actively participate in interdisciplinary and cross-sectoral project cooperation, and collaborate with experts and peers from different fields to complete design tasks. These projects should closely relate to the needs of society and the theme of cultural heritage, so that students can propose innovative design solutions while deeply exploring social issues and cultural backgrounds.

The cloud-based professional teaching resource base is a specialized teaching data resource platform that can be expanded as needed. It not only has a huge storage space, but also has the maintenance and security of a professional technical team. Users can use a variety of terminal devices to access the resources they need, and they can also share them with other users. It is because cloud computing is cross-device, cross-platform, open, and sharing, so it provides the possibility for large-scale data resources to be shared in the cloud. The cloud computing environment is a change in the technical environment for professional teaching resource libraries, and the internal and external aspects of the resource libraries have to change accordingly in order to realize the transformation from traditional services to cloud services [32]. 1)

The cloud layer of the resource library cloud service model. Cloud computing’s excellent infrastructure clusters make it the preferred choice for large-scale storage of professional teaching resource libraries. To build a professional teaching resource base using cloud computing, it is necessary to establish infrastructure devices, cloud platforms, server-side applications, and so on. in the cloud. These devices and systems in the cloud layer are centrally managed through the cloud management platform and provide services to the user.

Fuzzy comprehensive evaluation method is a method of comprehensive evaluation and decision-making using fuzzy set theory [34]. 1)

Evaluation indicator set F, describing the indicators or guidelines for comprehensive evaluation of various influences on the quality of interdisciplinary talent training, is denoted as: (1)F=(f1,f2,Λ,fn)

where f₁, f₂, Λ, f_n are each evaluation indicator or criterion, and n is the number of evaluation indicators.

The set of evaluation indicators can also be a collection of multilevel recursive order structure.

Different weights W should be given to different evaluation indicators, denoted as: (2)W=(w1,w2,Λ,wn) 2)

Evaluation scale set E, which describes the scale on which each evaluation indicator is evaluated, is denoted: (3)E=(e1,e2,Λ,em)

where m is the number of evaluation scales in the set of evaluation scales.

The grading of the evaluation scales can be done in a hierarchical way or in a fractional way, for example: (4)E=(Excellent, Good, General, Difference)

Or: (5)E=(0.9,0.7,0.5,0.3) 3)

The degree of affiliation r_ij^k that describes the extent to which it is possible to make an e_i evaluation scale with the evaluation indicators of f_i for influencing the quality A_K of interdisciplinary personnel training. The degrees of affiliation of all evaluation indicators for A_k form an affiliation matrix Rk˜ , which is a fuzzy relationship matrix, denoted as: (6)Rk˜=[ r11k r12k Λ r1jk Λ r1mk r21k r22k Λ r2jk Λ r2mk M M M M r11k ri2k Λ rijk Λ rimk M M M M rn1k rn2k Λ rnjk Λ rnmk]

In Matrix Rk˜ , element rijk can be calculated based on the evaluation results made by the experts participating in the evaluation, i.e.: (7)rijk=dijkd

Where d represents the number of experts who participated in the evaluation, and dijk represents the number of experts who made e_i evaluation scales for the i th evaluation indicator fi of A_k. Obviously, ∑j=1mrijk=1 .

1)

Invite experts from relevant fields to form an evaluation team.

Determine the set of evaluation scales for each evaluation indicator E: (9)E=(e1,e2,Λ,em) 3)

Determine the weights of the evaluation indicators by methods such as hierarchical analysis, or based on the experience of experts W: (10)W=(w1,w2,Λ,wn)

E.g., for E = (excellent, good, fair, poor), if Sk=(0.26,0.16,0.21,0.37) , then combined judgment l = 4, ∑i=13si=0.63>12∑i=14si=0.5

Conclusion: A_k is rated at level (l − 1) = 3, i.e., “medium”.

The research combines the requirements related to interdisciplinary teaching of new liberal arts and environmental design majors, and the talent quality evaluation index system constructed is shown in Table 1. The system for evaluating talent cultivation quality is divided into two levels. The first level contains three indicators of cultivation objectives, talent demand, and curriculum system. Cultivation objectives include five secondary indicators of learning ability, problem-solving ability, practical ability, teamwork ability, and practicing ability. Talent demand includes 5 secondary indicators: professional ability, communication and cooperation ability, management ability, ideological and political literacy, and research and promotion ability. The curriculum system includes five secondary indicators: knowledge of basic disciplines, professional and technical knowledge, knowledge of related disciplines, instrumental knowledge, and knowledge of humanities and social sciences.

Through the combination of hierarchical analysis and fuzzy comprehensive evaluation method, the fuzzy judgment matrix is formed after the two-by-two comparison of the indicators of each level. This fuzzy evaluation method is based on the theory of fuzzy mathematics and involves transforming qualitative indicators into quantitative evaluation. If a certain index cannot be expressed by exact numbers, but can only be expressed as “excellent”, “good”, “average” and “poor”, the fuzzy evaluation method can be used to solve the quantitative problem of the decisive index. At the same time, in order to reduce the random errors caused by subjective judgment, this paper adopts the cluster decision-making method, hiring 10 experts from the national model schools (including backbone schools), autonomous region model schools, higher vocational colleges and universities with special characteristics, as well as cooperative units, to carry out the scoring. Finally, these experts compare the importance of each factor in the evaluation at all levels and derive the values of each element in the fuzzy judgment matrix.

Ten invited experts compared the importance of each factor at each level of evaluation two by two, and the results were used to establish the judgment matrix distribution weights for AHP. In order to obtain the quantitative judgment matrix, the 1-9 scale method was used. The issue of the relative importance of the first-level indicator factors and the second-level indicator factors is further examined through the expert consultation method, and finally the A-B and B-C judgment matrices can be derived. The weights of the B-level indicators relative to the A-level and the weights of the C-level indicators relative to the B-level are calculated in turn, and the consistency test is carried out, and the summarized results are shown in Table 2. The consistency test is passed if the CR of each judgment matrix is less than 0.1.

Based on the relative weights of the lower level indicators relative to each level of A and B, the relative weights of the C level indicators relative to the A level (RW_i), i.e., the weight coefficients, are calculated. The specific results are shown in Table 3. Learning ability (C1) has the highest weight coefficient in the quality of interdisciplinary talent cultivation, reaching 0.106, indicating that learning ability has the greatest impact on the quality of talent cultivation.

In this paper, the fuzzy comprehensive evaluation method was used to analyze the quality of interdisciplinary talent cultivation based on cloud computing technology in the environmental design program of School A. In establishing the rubric set, the quality of talent cultivation was categorized into four grades, i.e., excellent, good, fair, and poor, and the rubric set was determined by the questionnaire method.

In the research, we distributed questionnaires to teachers, students, and enterprise-related personnel, with a total of 550 participants. These evaluators scored the quality of talent cultivation, and based on the questionnaire opinions, we finally obtained the comment sets of the corresponding indicators as shown in Table 4. Taking the determination of “learning ability (C1)” as an example, the results of the 550 valid questionnaires show that 275, 165, 55 and 55 participants respectively consider “learning ability” as “excellent”, “good”, “fair” and “poor”, so their respective affiliation degrees are 0.5, 0.3, 0.1 and 0.1, respectively. The evaluation matrix of “learning ability (C1)” is [0.5, 0.3, 0.1, 0.1].

The fuzzy comprehensive evaluation value of each index in different evaluation levels is shown in Table 5. The fuzzy comprehensive evaluation conclusion is (0.466,0.373,0.127,0.038), according to the principle of maximum affiliation, the quality of interdisciplinary talent cultivation based on cloud computing technology in the environmental design program of school A is “excellent”. It shows that the cloud computing-based professional teaching resource library designed in this paper can be used in talent cultivation to improve students’ professional level.