Research on a Sustainable Teaching Model Based on the OBE Concept and the TSEM Framework

Abstract

This paper reports the results of a study on the implementation of a sustainable teaching model based on the OBE (Outcome-Based Education) concept and the TSEM (Teach, Study, Evaluate, and Manage) framework in computer science and technology at NingboTech University, China. In the context of digital education, the OBE concept and the TSEM framework are integrated to explore sustainable teaching and learning models based on “artificial intelligence and education”. Based on the core concept of engineering professional education accreditation, the course is designed by using the PCCM (Professional Competency Correlation Matrix) method to build a model based on big data analysis, deepen the classroom teaching reform of “artificial intelligence and education”, and explore the integrated digital sustainable teaching mode of “teaching, learning, evaluation, and management”. The aim of this study is to explore the effectiveness of the teaching model based on OBE and the TSEM framework on students’ sustainable development. The results show that students deepen their learning in computer science while enhancing their own learning initiative, teamwork skills, innovation skills, and awareness of sustainable development. Research shows that our teaching model plays an important role in the development of student sustainable education, enhancing student engineering practice and innovation capabilities and cultivating applied innovative talents. The efficacy of the teaching model based on the OBE concept and the TSEM framework for improving students’ competence in sustainable education warrants further investigation.

1. Introduction

In recent years, the COVID-19 pandemic has had a significant impact on all sectors. The pandemic’s impact has not yet subsided. While the education sector’s response to the pandemic’s impact has been relatively timely and reasonable, it has had some impact on the education model in higher education [1,2]. The COVID-19 pandemic affected hundreds of millions of students worldwide. The findings demonstrate how, with strong government support and international cooperation, some countries are transforming the challenges posed by the rapid digitization of education into opportunities [3,4]. The COVID-19 pandemic has also had a significant impact on enrollment. Student mobility has been severely harmed, with serious consequences for international students. Due to pandemic control, offline classes are not possible in many countries and must be held via remote video [5,6,7,8,9]. The complicated effects of the COVID-19 pandemic on university budgets have increased inequality in higher education. The COVID-19 pandemic has caused global reductions in campus maintenance and services, as well as campus closures. Government and external funding are critical to the survival of higher education. Students and higher education institutions are currently concerned about adapting teaching and learning models to the new environment [10,11,12,13]. Employers are looking for job seekers with higher skills in such an uncertain economic climate. In the digital age, higher demands are placed on students’ abilities in all areas. Because of the significant decrease in job opportunities, the transition from higher education to the labor market has become more difficult [14,15,16,17,18].

The new epidemic’s impact on the university education model is far-reaching, encompassing both economic and cultural aspects. The educational model has had to adapt to the new era. This research incorporates the concept of OBE (Outcome-Based education), also known as competency-based education, goal-based education, and needs-based education. The OBE concept is a sophisticated approach to developing a curriculum system that is outcome-oriented, student-directed, and uses a reverse-thinking approach. OBE has become the mainstream philosophy of educational reform in the United States, the United Kingdom, Canada, and other countries. OBE denotes that the ultimate goal of instructional design and delivery is the learning outcomes that students achieve as a result of the educational process [19,20,21].

Because we are studying computer and science technology, the primary teaching tools include artificial intelligence. Artificial intelligence’s impact on education is systematic, multifaceted, and multi-layered. From a demand standpoint, the continued maturation and widespread application of AI technology will have a direct impact on various industries and give rise to new sectors, resulting in new requirements for talent training and educational reform [22,23]. On the supply side, the use of AI technology in education will enable more diverse opportunities, richer content, more flexible forms of learning, and more convenient access to education.

According to the reform of undergraduate education and teaching, the quality of talent training will be vastly improved. We will raise the standard of education, create a new engineering and liberal arts talent training system, and investigate the development of an output-oriented integration of recruitment, training, and employment. The TSEM-based framework is proposed for the study of educational models based on the OBE concept [19,21,24]. The course modules are designed based on big data analysis and the PCCM (Professional Competency Correlation Matrix). Based on the core concept of engineering professional education accreditation, the classroom teaching reform of “artificial intelligence and education” is deepened, and the integrated digital teaching mode of “teaching, learning, evaluation, and management” is explored [25]. This teaching model investigates teaching methods that incorporate artificial intelligence, such as combining theoretical and practical knowledge, improving students’ engineering practice and innovation abilities, and cultivating applied and innovative talents [22,23]. We evaluate online courses and analysis reports generated by the system, as well as feedback on evaluation results obtained through big data mining and analysis [26]. The system automatically generates relevant tables for teaching data analysis, such as student learning analysis, teacher teaching analysis, course activity analysis, classroom interaction analysis, performance and attendance analysis, and resource usage analysis. Attendance reminders, absenteeism reminders, and academic alerts are examples of intelligent management. In comparison, students have made significant gains in practical skills, innovation, and achievement.

2. Literature Review

Education should be people-directed, follow objective laws of educational development, correctly handle the interrelationship between its own development and economic and social development, and build a harmonious development mechanism. This will allow education to maintain the vitality of sustainable development and cultivate talents with sustainable development capacity [27]. The Computer Science and Data Engineering School courses, which are characterized by their great difficulty, many knowledge points, and strong practicality, will be taken as an example. According to the preliminary research analysis, the following points are first put forward as teaching problems.

• Inadequate competency and goal-directed instructional design.

Teaching is lacking in terms of systematic planning for knowledge transfer, competence development, learning methods, and quality development, as well as competence and goal-oriented teaching design.

2.

Many aspects of practical courses are restricted.

Theory must be combined with practice, but most schools are unable to allocate enough practical hours for students to practice continuously due to teaching conditions. This reduces students’ motivation over time, and the theoretical knowledge just taught cannot be verified or mastered in greater depth over time, while students’ hands-on skills remain unchanged.

Most universities are now committed to building laboratories and increasing hardware investment, but the number of students is increasing each year. Practical classes are now held in small groups, equipment is reused, and hardware equipment is under significant strain. Because the pace of iteration in computing subjects is so rapid, hardware updates cannot keep up, limiting the development of students’ hands-on practical skills.

The small number of computer science laboratories, which must cater to the college’s thousands of students from various majors, are under great pedagogical pressure. The equipment is subject to maintenance every semester, and special maintenance funds are required. Maintenance is generally implemented at fixed times to avoid delaying basic practical teaching. Professional equipment is large and inconvenient to mail, requiring professional staff to visit and making experimental hardware maintenance costly and time-consuming.

3.

There is no teaching evaluation system.

The lack of a scientific and effective teaching evaluation system makes pedagogical improvement impossible. The first aspect of this is directed at teachers, the majority of whom are simply delivering lessons without access to real-time feedback on the results of their teaching process. The second aspect is for students who are passive listeners and lack a proper standard for determining whether knowledge has been truly mastered.

4.

There is a scarcity of scientific management tools for teaching and learning.

Teachers should focus not only on teaching but also on learning. Because not all students in higher education can study continuously and conscientiously, we need to develop effective and intelligent teaching management tools in the teaching process to improve the efficiency of teaching throughout the process [28].

Let us begin by understanding the teaching model based on the above analysis of the current teaching situation. The teaching model is a stable structural form of the process of carrying out teaching and learning activities in a specific environment guided by specific educational ideas, teaching theories, and learning theories. It also includes a set of methodological systems for carrying out teaching and learning activities and a stable framework and procedure for carrying out teaching and learning activities based on specific teaching theories. The teaching model is the concretization of teaching theory and is directly oriented and guided by teaching practice. It is the bridge between teaching theory and teaching practice [29].

Many successful projects are based on the concept of outcome-based education. For example, the program at University Kebangsaan Malaysia emphasizes student learning outcomes and practical applications, with an emphasis on active student participation and inquiry. The program implements a variety of learning tasks and projects designed to help students develop their competencies and skills [30].

For the sustainable development of education, it is essential to design teaching and learning models with accreditation in engineering education at their core. Student-centering, outcome-based education, and continuous quality improvement are the three accreditation concepts for professional engineering education. The three central concepts of professional accreditation in engineering education are consistent with the new vision of educational quality [31,32,33]. The concept of student-directed accreditation reflects the new vision of quality in education’s emphasis on the educational process, while the core concept of outcome- or result-oriented instructional design reflects the educational outcomes to be focused on, and the core concept of a quality assurance mechanism for continuous improvement corresponds to the value-added concept in the new vision of quality. The three concepts for engineering education professional accreditation address the new needs of modern engineering education personnel training while also strengthening education training that integrates engineering technology with society, the humanities, law, and the environment. Accreditation standards for engineering education majors are a type of education quality assurance mechanism guided by the new education quality concept.

3. Materials and Methods

The question to be answered is whether a teaching model based on the OBE concept and the TSEM framework enables students’ competencies to be developed in a sustainable manner. The study’s hypothesis contends that using the OBE concept in conjunction with the TSEM framework to teach university students will allow students to develop in a sustainable manner. This is because the OBE concept focuses on student learning outcomes, whereas the TSEM framework emphasizes the development of sustainability awareness and competencies, which when combined can promote students’ holistic development in a variety of areas.

Our study sample was drawn from the 2019–2022 undergraduate students of the School of Computer and Data Engineering, NingboTech University. The participants were aged between 18 and 22 years old, with 219 boys and 40 girls. There were 140 students in the computer science group and 119 in the non-computer science group. The two groups are our experimental group and the control group. The experimental group was trained using our teaching model, while the control group was trained using traditional methods. Our students had to be in a computer-related field. This is very important as it relates to the way our teaching model is set up.

As shown in Figure 1, this paper proposes a sustainable teaching model based on the TSEM framework for teaching curriculum design reform, which consists of teaching, studying, evaluating, and managing. Below, we go over the content and approach in greater detail.

3.1. Course Design for Teaching

The three accreditation concepts for engineering education are student-directed, outcomes-based, and continuous improvement. We adhere to a student-directed accreditation philosophy: educational objectives are centered on student development; teaching content is designed to focus on the development of students’ abilities; teachers and educational resources should meet student learning outcomes; and assessment should focus on the evaluation of student learning outcomes [34]. OBE (outcome-based education) has become the mainstream philosophy of educational reform in the United States, the United Kingdom, Canada, and other countries. OBE denotes that the ultimate goal of instructional design and delivery is the learning outcomes that students achieve as a result of the educational process. The quality assurance mechanism is continuous improvement. We must establish ongoing evaluation mechanisms and strive for continuous improvement. Training objectives, graduation requirements, and instructional sessions are all assessed. Each teacher is held accountable for their ongoing development. Students’ performance demonstrates the effectiveness of continuous improvement.

The PCCM (Professional Competence Correlation Matrix) method is used for the teaching design of professional module courses, based on the core concept of engineering professional education accreditation [25]. As shown in

Table 1. Correlation matrix between curriculum and graduation competencies.

Graduate Competence Requirements	Graduation Competency Requirement Indicator Points	Course	Weighting
1 Engineering knowledge: Ability to solve complex engineering problems in computing applications using mathematics, natural sciences, engineering fundamentals, and computing expertise.	1.1 Understand professional presentations of computational problems and give a professional presentation of a specific computational problem while explaining the underlying principles involved.	Calculus (A) I	0.15
		Calculus (A) II	0.15
		Linear Algebra (A)	0.2
		University Physics I (B)	0.15
		University Physics II (B)	0.15
		Discrete Mathematics (A)	0.2
	1.2 Be able to select or create appropriate descriptive models for computational systems and their computational processes and solve them.	Linear Algebra (A)	0.1
		University Physics I (B)	0.1
		University Physics II (B)	0.1
		Discrete Mathematics (A)	0.15
		Numerical calculations and optimization methods	0.1
		C Programming (A)	0.15
		Data structure (A)	0.1
		Fundamentals of computer circuits and experiments	0.1
		Principles of database systems	0.1
	1.3 Ability to reason, analyze, and draw conclusions based on the design of a computational system and the model created.	Discrete Mathematics (A)	0.4
		Computer Composition Principles and Experiments	0.3
		Fundamentals of computer circuits and experiments	0.3
	1.4 Ability to solve complex engineering problems in computer applications using specialist knowledge and methods, including analysis and improvement.	Introduction to Computer Science and Technology	0.3
		Computer Networking Principles and Experiments (A)	0.4
		Introduction to Algorithms	0.3
2 Analysis of the problem: To reach valid conclusions, you must be able to apply fundamental principles of mathematics, as well as natural and engineering sciences, to identify, represent, and analyze complex engineering problems in computer applications using literature research.	2.1 Be able to identify and determine the key aspects of complex engineering problems in computer applications using fundamental principles from mathematics, natural science, and computer science.	Calculus (A) I	0.1
		Calculus (A) II	0.1
		Probability Theory and Mathematical Statistics	0.15
		Discrete Mathematics (A)	0.1
		Computer Composition Principles and Experiments	0.15
		Operating systems and experiments	0.15
		Object Oriented Programming (JAVA)	0.15
		Introduction to Software Engineering	0.1
	2.2 Be able to choose or create a model for representing key aspects of complex engineering problems in computer applications, as well as analyze the key influencing factors.	Principles of database systems	0.15
		Database course design	0.05
		Data structure (A)	0.15
		Data Structures Course Design	0.05
		Introduction to Algorithms	0.15
		Object Oriented Programming (JAVA)	0.1
		Computer Networking Principles and Experiments (A)	0.1
		Operating systems and experiments	0.15
		Fundamentals of computer circuits and experiments	0.1
	2.3 Be able to analyze and research multiple alternative solutions to complex engineering problems in computer applications, drawing valid conclusions based on key influencing factors and the literature.	C Language Course Design	0.2
		Numerical calculations and optimization methods	0.1
		Integrated Practice in Computer Science	0.2
		Engineering Practical Training in Computer Science	0.2
		Thesis	0.3
3 Design development solutions: The ability to design solutions to complex engineering problems in computer applications such as the Internet and the Internet of Things, to design hardware and software systems, modular components, or algorithmic processes that meet specific needs, and to demonstrate a sense of innovation in the design process while taking social, health, safety, legal, cultural, and environmental factors into consideration.	3.1 Understanding of the multiple factors that influence design objectives and technical solutions, as well as knowledge of basic design and development methods and techniques for the entire cycle and process of computer application design and development.	Introduction to Software Engineering	1
	3.2 Design and development of software modules or algorithmic processes to meet specific requirements for complex engineering problem-solving in computer applications.	C Programming (A)	0.2
		C Language Course Design	0.15
		Data structure (A)	0.15
		Data Structures Course Design	0.05
		Introduction to Algorithms	0.15
		Principles of database systems	0.2
		Fundamentals of computer circuits and experiments	0.1
	3.3 Be able to design computer hardware and software systems for complex engineering problem solutions in computer applications, while keeping in mind social, health, safety, legal, cultural, and environmental constraints.	Database course design	0.1
		Integrated Practice in Computer Science	0.2
		Engineering Practical Training in Computer Science	0.3
		Thesis	0.4
4 Research: The ability to investigate complex engineering problems in computer applications such as the Internet and the Internet of Things using scientific principles and methods based on the discipline of computing, including designing experiments, analyzing and interpreting data, and synthesizing information to reach reasonable and valid conclusions.	4.1 Capable of designing feasible experimental solutions to complex engineering problems in computing applications using specialist theory, literature research, or related methods.	Computer Composition Principles and Experiments	0.5
		C Language Course Design	0.5
	4.2 Be able to design feasible experimental solutions to complex engineering problems in computing applications based on specialist theory, literature research, or related methods.	Computer Circuit Fundamentals Experiment	0.2
		Computer Composition Principles and Experiments	0.3
		Computer Networking Principles and Experiments (A)	0.3
		University Physics Experiments B	0.2
	4.3 Ability to analyze and interpret experimental results, as well as synthesize relevant information, in order to draw reasonable and valid conclusions and present them in a standardized manner.	Computer Circuit Fundamentals Experiment	0.2
		Computer Composition Principles and Experiments	0.2
		Computer Networking Principles and Experiments (A)	0.2
		Operating systems and experiments	0.2
		Probability Theory and Mathematical Statistics	0.1
		University Physics Experiments B	0.1
5 Using cutting-edge technology: Be able to develop, select, and apply appropriate techniques, resources, modern engineering tools, and information technology tools for complex engineering problems in the field of computer applications such as the Internet and the Internet of Things, including complex engineering problem prediction and simulation, and understand their limitations.	5.1 Knowledge of the performance and applicability of commonly used hardware and software environments, development tools, and simulation software, as well as their suitability for development and analysis, as well as the ability to correctly use them.	Operating systems and experiments	0.4
		Computer Networking Principles and Experiments (A)	0.4
		Computer Studies Cognitive Placement	0.2
	5.2 Be able to develop or select appropriate techniques, resources, and tools to simulate, predict, analyze, or design specific objects in computer applications to meet the practical needs of solving complex engineering problems, as well as analyze strengths and limitations.	Integrated Practice in Computer Science	0.3
		Engineering Practical Training in Computer Science	0.3
		Thesis	0.4
6 Engineering and Society: Be able to conduct sound analysis based on relevant background knowledge in the field of computer engineering, and assess the social, health, safety, legal, and cultural implications of computer engineering practices and solutions to complex engineering problems, as well as understand the responsibilities involved.	6.1 Understand technical standards, intellectual property protection, laws and regulations, and industrial policies pertaining to computer application areas, as well as the impact of various social cultures on computer-related engineering activities.	Introduction to Computer Science and Technology	0.5
		Engineering Ethics	0.5
	6.2 Understand and be able to analyze and evaluate the social, health, safety, legal, and cultural implications of specific computer engineering practices and solutions to complex engineering problems.	National Security Education for University Students	0.2
		General Theory of Labor	0.1
		Outline of Modern Chinese History	0.1
		Engineering Practical Training in Computer Science	0.2
		Thesis	0.2
		Innovation and Entrepreneurship Classes	0.2
7 Environment and Sustainable Development: Understand and evaluate the environmental and socially sustainable impacts of engineering practices in the field of computer engineering that address complex engineering problems.	7.1 Implement the scientific concept of development, understand the concept and connotation of environmental protection and sustainable social development, and be familiar with environmental protection and sustainable development guidelines, policies, laws, and regulations in the computer industry.	Ethics and the Rule of Law	0.2
		Situation and Policy I, II	0.2
		Science and Exploration	0.3
		Ecology and Life Care	0.3
	7.2 Understand the relationship between information technology and environmental protection, correctly understand and evaluate the impact of complex engineering problems in computer applications on environmental protection and sustainable social development, and evaluate the potential damage and hazards to humans and the environment during the product cycle.	Situation and Policy I, II	0.3
		Engineering Practical Training in Computer Science	0.3
		Ecology and Life Care	0.4
8 Professional Standards: Literacy in the humanities and social sciences, social responsibility, and the ability to understand and comply with engineering ethics and codes of practice, as well as fulfill responsibilities in engineering practice, are all required.	8.1 Develop a proper perspective on life, values, and the world, comprehend the relationship between the individual and society, and comprehend China’s national conditions as well as the current development of China’s information industry.	Ethics and the Rule of Law	0.15
		Basic Principles of Marxism	0.15
		Introduction to Mao Zedong Thought and the Theoretical System of Socialism with Chinese Characteristics	0.15
		Mental Health Education for University Students	0.1
		Art and Aesthetics A	0.1
		Civilization and Society	0.1
		Literature, History, and Philosophy	0.1
		Introduction to Computer Science and Technology	0.15
	8.2 Understand and be able to consciously follow the engineering professional ethics and codes of honesty and fairness (truthful reflection of learning outcomes, no concealment of problems, no exaggeration or falsification of results) and integrity (compliance with the law, no cheating, respect for intellectual property) in computer engineering practice.	Outline of Modern Chinese History	0.2
		Physical Fitness Test for University Students I–II	0.2
		Career planning for university students	0.3
		Career guidance for university students	0.3
	8.3 Understand the social responsibility of computer engineers for public safety and health, as well as environmental protection, and be able to exercise this responsibility consciously in engineering practice.	Ethics and the Rule of Law	0.15
		National Security Education for University Students	0.2
		Basic Principles of Marxism	0.15
		Introduction to Mao Zedong Thought and the Theoretical System of Socialism with Chinese Characteristics	0.15
		Situation and Policy I, II	0.15
		Engineering Ethics	0.2
9 Individuals and teams: A team player who can work collaboratively in a multidisciplinary context and assume the roles of individual, team member, and leader.	9.1 A sense of teamwork, the ability to listen to other members’ suggestions and opinions, and a clear understanding of their responsibilities and duties as members of a multidisciplinary team.	General Theory of Labor	0.2
		PE I–IV	0.2
		University English I–IV	0.2
		Engineering Practical Training in Computer Science	0.4
	9.2 Ability to work independently on individual tasks while also collaborating effectively with other team members in a multidisciplinary context; organize, coordinate, and direct teamwork.	Military skills	0.2
		Military doctrine	0.2
		PE I–IV	0.2
		University Physics Experiments	0.2
		Engineering Practical Training in Computer Science	0.2
10 Communication: Ability to communicate and interact effectively with industry peers and the public on complex engineering issues in computing applications, including writing reports and design briefs, presenting statements, articulating or responding to instructions, and communicating and interacting in a cross-cultural context, with an international perspective.	10.1 Write reports and design briefs, present statements, articulate or respond to instructions on complex engineering issues in computing applications, and communicate effectively with industry peers and the public.	Integrated Practice in Computer Science	0.15
		Engineering Practical Training in Computer Science	0.2
		Thesis	0.4
		Introduction to Software Engineering	0.25
	10.2 Have an international perspective, be aware of international trends in computer applications and relevant technological hotspots, and be able to express their opinions.	Introduction to Computer Science and Technology	0.4
		Thesis	0.6
	10.3 Good listening, speaking, reading, and writing skills in a foreign language, as well as the ability to communicate in a basic cross-cultural context on computer-related issues.	University English I–IV	0.4
		Data structure (A)	0.3
		Operating systems and experiments	0.3
11 Project Management: Understand and master the principles of engineering management and economic decision-making methods, as well as be familiar with computerized engineering project management methods and tools, and be able to apply them in a multidisciplinary environment.	11.1 Understand the basic principles of engineering management and economic decision-making in computer applications, as well as the corresponding engineering management and economic decision-making methods.	Introduction to Software Engineering	0.5
		Innovation and Entrepreneurship Classes	0.5
	11.2 Understand the cost components of the entire life cycle and process of information systems engineering and products, as well as be able to apply engineering management and economic decision-making methods in a multidisciplinary environment in the design and development of solutions to complex engineering problems in computer applications.	Engineering Practical Training in Computer Science	0.3
		Innovation and Entrepreneurship Classes	0.4
		Thesis	0.3
12 Lifelong Learning: A sense of self-direction and lifelong learning, as well as the ability to learn and adapt to the rapid development of computer application technology.	12.1 Recognize the ever-changing nature of computer technology and the need for self-directed and lifelong learning.	Computer Studies Cognitive Placement	0.2
		Career planning for university students	0.2
		Career guidance for university students	0.3
		Innovation and Entrepreneurship Classes	0.3
	12.2 The ability to learn independently and to broaden their knowledge and abilities in a variety of ways, such as understanding, summarizing, and asking questions.	University English I–IV	0.2
		Introduction to Computer Science and Technology	0.3
		Thesis	0.5

, we have established 12 major and 31 sub-competency indicators based on the graduation competencies. The correlation matrix between curriculum and graduation competencies reflects the degree to which different courses are associated with certain graduation competencies.

Table 1 displays a matrix of correlations between selected course systems and graduation competencies, i.e., the sub-competency values that can be obtained through a specific course.

C stands for course, I stands for course serial number, and j stands for sub-competency serial number. The competency weights assigned to each course differ, and we assume that a specific competency sub-item can achieve a full competency value of 1 by taking multiple courses, i.e., ${\displaystyle \sum }_{i=1}^n{C}_{ij}$ = 1. The table shows the percentage of courses taken to achieve a certain level of competence.

3.2. Artificial Intelligence and Teaching

AI will undoubtedly trigger a series of changes and innovations in the education model, teaching methods, teaching content, evaluation methods, education governance, and the teaching force, helping to reorganize and reengineer the education process, promote the evolution of education ecology, promote educational equity, and improve educational quality [35]. Finally, technology is aimed at the learner or student. The application of technology is ultimately about student development and about students being better equipped for and happier in the society of the future. Educational goals must be considered at this point. One goal is to produce people who are competent and will live happily in society.

In an effort to create a smart education system, we are exploring a new model of “AI and education” using our comprehensive technical facilities and modern information platform, allowing technology to empower educational development and artificial intelligence to help students grow. The deep integration of artificial intelligence and education will result in significant changes in future education that will make education and teaching more accurate and effective, including not only scientific material knowledge, specialized teaching, and personalized development supported by data, but also scaled-up teaching and learning based on material. For effective teaching and learning, we must prioritize core literacy-oriented talent development; students’ souls and well-being; personalized, diverse, and adaptive learning; and human–computer collaboration.

Teachers can import students into the database prior to the lesson to create new teaching classes through the system and integrate excellent teaching resources into the system platform. PPT, course handouts, course software, and other materials are included in the course content. Students can view the documents by downloading them prior to the lesson, allowing them to fully prepare for the lesson and any subsequent lessons.

Teachers using the platform’s question database system have the ability to add and delete questions. Database questions come in a variety of formats, including multiple-choice, fill-in-the-blank, judgment, short answer, and lab questions. Teachers can assign homework to students using question sets that can be selected in the database system based on the various knowledge points. Students are given assignments and a deadline by which to complete and submit them. As part of their regular work, the teacher receives and corrects the assignments.

Computer classes emphasize the integration of theory and practice, as well as the need to apply theoretical knowledge in the real world. We developed a virtual simulation system for online experimental teaching based on MXGragh (a JavaScript drawing component) for artificial intelligence and teaching based on Logisim software ideas to address the characteristics of computer classes that require strong hands-on practical skills, using the computer composition principles class as an example. This virtual simulation system is one of our provincial projects. The system has controls for inputs, outputs, and links, with or without gates. Figure 2 depicts an XOR Gate simulation circuit with and without gates, where the green line indicates a positive input signal and the black line indicates a negative input signal. In digital logic, an XOR Gate is a type of logic gate circuit. The output has a high level of 1 if the levels of the two inputs differ, and a low level of 0 if the levels of the two inputs are the same. It is clear that when the leftmost two square inputs are both 1, the rightmost output is 0. This validates the XOR Gate circuit we designed. Students can combine these controls to build more complex logic circuits, as well as circuits of their own design. In the Principles of Computer Composition practical course, students can go beyond the lab box and implement circuits that they could not do on their own, such as adders, subtractors, multipliers, and so on, to complete logic circuits. Students can verify the knowledge covered in the theoretical lessons by changing the values of the control inputs and outputs, which not only deepens their understanding of the course knowledge but also improves their hands-on skills.

For teachers and students, the artificial-intelligence-integrated education platform includes an interactive message board module. Teachers can post course-related discussion topics on this board, and students can leave messages relating to difficult points in class, boosting students’ enthusiasm and learning initiatives.

3.3. Teaching Evaluation

Teaching evaluation is the activity of making value judgments on the teaching process and results in accordance with the teaching objectives and serves as the basis for teaching decisions. It is also the process of making judgments on the real or potential value of teaching activities. The process of studying the value of teachers’ teaching and students’ learning is known as teaching evaluation. In general, teaching evaluation encompasses the evaluation of teachers, students, teaching contents, teaching methods and means, teaching environment, and teaching management factors in the teaching process. It primarily involves the evaluation of students’ learning effects and teachers’ teaching work processes. On the one hand, big data mining and analysis are used to analyze the teaching of online course teachers, as well as teaching evaluations, system-generated evaluation analysis reports, and feedback from evaluation results. On the other hand, teaching big data analysis such as students’ learning effect analysis, course activity analysis, classroom interaction analysis, grade attendance analysis, and resource usage analysis is carried out. The grade weights are assigned based on the degree of graduation achievement, and the system then generates the necessary tables or analysis data automatically [36].

Using the course Data Structures as an example, we describe the teaching and learning evaluation method below. Data Structures is a comprehensive foundation course in computing that focuses on basic algorithm theory, data logical structure, storage structures, and algorithms based on these structures and data organization and processing techniques. Students studying this course will gain basic programming skills, prepare efficient and reliable programs, and gain a more integrated ability to solve problems by studying the initial conversion from the abstraction of real-life problems to the realm of information, and then to the organization and processing of data in the computer. The study of subsequent courses and the design of system programs is built on a solid theoretical and practical foundation.

Course objectives differ depending on the course. To denote a specific course objective, we use the first letter of the word Course Object plus a serial number. We set the course objectives based on the characteristics of the course and its contribution to the graduation requirements.

CO1: Learn the fundamental logical structure, storage structure, and data element operations in non-numerical information processing. This will improve students’ ability to think abstractly, abstract real-world problems into problems that computers can represent, and build and solve models.

CO2: Understanding the benefits and drawbacks of various forms of data organization, as well as the basic evaluation indicators of algorithms, and being able to select the most appropriate method among various types of data organization and processing methods for the same problem. Students will learn how to analyze and solve problems.

CO3: Master the fundamental algorithms of common data processing and be able to implement a list of operations in a specific programming language for previously represented data objects, or be able to write efficient algorithms and debug them in response to practical problems using appropriate data structures.

CO4: Reading ability in English textbooks, English literature, and English titles relevant to course content.

The corresponding weightings of the above course objectives and graduation competency indicator points are shown in

Table 2. Corresponding weighting of course objectives and graduation competency indicator points.

Serial Number	Indicator Points for Graduation Competency Requirement	CO1	CO2	CO3	CO4
1	Graduate Competence Requirements 1.2	0.1
2	Graduate Competence Requirements 2.2		0.15
3	Graduate Competence Requirements 3.2			0.2
4	Graduate Competence Requirements 10.3				0.3

.

Relevant graduation competency indicator points:

This course supports the graduation competency matrix requirements 1, 2, 3, and 10.

• This course supports graduation competency requirement 1.2 of the professional development plan: the ability to select or build appropriate descriptive models and solve them for computational systems and their computational processes. Account for a support weighting factor of 0.1 for this indicator point.
• This course supports graduation competency requirement 2.2 of the professional development plan: to be able to select or develop a model for the analysis of key influencing factors on key aspects of complex engineering problems in computer applications. Account for a support weighting factor of 0.15 for this indicator point.
• This course supports graduation competency requirement 3.2 of the professional development plan: to be able to design and develop software modules or algorithmic processes that meet specific needs for complex engineering problem solving in the area of computing applications. Account for a weighting factor of 0.2 in support of this indicator point.
• This course supports graduation competency requirement 10.3 of the professional development plan: good listening, speaking, reading, and writing skills in a foreign language; and the ability to communicate and exchange basic information on computer professional issues in an intercultural context. It accounts for a supporting weighting factor of 0.3 for this indicator point.

The relationship between the evaluation assessment points supporting the course objectives is shown in

Table 3. The relationship between the course objectives and the support of the assessment line items.

Course Objectives	Appraisal Breakdown	Weighting Factor for Support	Percentage of Achievement	Record Files	Specific Evaluation Content
CO1	Assignment grades	0.08	8	8-course platform assignments	Abstract modeling using basic data structures and the case for model solving
	Quiz	0.08	8	8-course platform quizzes	Abstract modeling using basic data structures and the case for model solving
	Final Results	0.22	22	Final paper (some judgement questions, multiple-choice questions, and a comprehensive modeling solution question)	Abstract modeling using basic data structures and the case for model solving
CO2	Assignment grades	0.12	12	Course platform assignment 2 questions on algorithm analysis	The ability to select the most appropriate method among the various types of data organization and processing methods for the same problem
	Final Results	0.10	10	Final paper (some objective questions on model and algorithm analysis; algorithm analysis questions)	The ability to analyze the performance of algorithms; the ability to select the most appropriate method among the various types of data organization and processing methods for the same problem
CO3	Experimental Results	0.12	12	System Platform Experiment	Writing of programs to implement operations; selection of data structures to implement efficient algorithms
	Midterm Results	0.12	12	System Platform Testing	The ability to write programs to implement operations; the selection of data structures to implement efficient algorithms
	Final Results	0.8	8	Final Paper (Program Fill-in-the-Blank)	Refinement of the operation code to implement the operation
CO4	Classroom performance grades	0.08	8	Classroom Performance Record	Classroom reading of English textbooks or English topics or English textbooks to answer questions
Total		1	100

.

Assessment evaluation scoring criteria:

• CO1 target assignments consist of multiple-choice and judgment questions. Questions are automatically judged by an artificial intelligence system. The final grade is expressed as a percentage: (score for correct questions/total score for all questions) × 100.
• The CO1 quiz consists of multiple-choice and judgement questions. The questions are automatically judged by an artificial intelligence system. The final score is expressed as a percentage: (score for correct questions/total score for all questions) × 100.
• The algorithmic analysis assignments are subjective and each worth 50 points, for a total of 100 points for the two assignments.
• The experimental session consists of function and programming questions. The questions are automatically judged by an artificial intelligence system. The final score is expressed in percentages: (total score for all questions/total score for all questions) × 100.
• The midterm exam consists of function questions and programming questions. The questions are automatically judged by an artificial intelligence system. Grades are expressed in percentages.
• The final exam is marked according to the reference answers and marking criteria, and the final grade is marked on a percentage basis.
• The classroom performance grade assesses students’ ability to read and comprehend English, and this ability is judged by answering questions in class and thus graded.

The final result is a quantitative evaluation of the achievement of the course objectives, as shown in Equation (1).

$$\mathrm{COjReach}\%=\frac{\sum \mathrm{Score}\mathrm{corresponding}\mathrm{to}\mathrm{COj}\mathrm{in}\mathrm{the}\mathrm{item}\times \mathrm{Support}\mathrm{weighting}\mathrm{factors}\mathrm{for}\mathrm{items}}{\mathrm{Total}\mathrm{percentage}\mathrm{share}\mathrm{of}\mathrm{COj}}\times 100\%$$

(1)

3.4. Teaching Management

An important link between education and teaching, teaching management is the process of using management science and pedagogy principles and methods to fully realize the management functions of planning, organizing, coordinating, and controlling, in order to coordinate all elements of the teaching process, make it run smoothly, and improve its effectiveness. Process management is a two-way activity that consists of teacher teaching and student learning in accordance with certain social requirements and the goals of teaching and learning as well as the physical and mental development of the student. The elements of the process are the teacher, the student, the content, and the method of instruction. The teacher is the most important factor in the teaching and learning process, the student is the most important subjective factor, and the content and means are the most important objective factors. The teaching process consists of five basic components: lesson preparation, teaching, extracurricular tutorials, homework correction, and evaluation of results. Pre-learning, listening, revision and consolidation, examination, mastery, and application are the five basic components of student learning. The process of deciding the sequence of teaching work according to the curriculum focused on student learning, establishing the corresponding methods, and achieving the teaching objectives through measures such as planning, entertaining, checking, and summarizing is referred to as teaching process management. The planned and organized management of the school’s teaching and learning activities is referred to as operations management. It is an essential component of school teaching management and determines the level of school teaching management [22].

Teaching quality monitoring and management are separated from teaching process monitoring. The process of organizing teaching activities in accordance with the requirements of cultivation objectives and conducting quality control on all stages and links of the teaching process is known as teaching quality management. The primary responsibility of the school’s teaching administration is to improve the quality of teaching and learning. This may involve understanding and monitoring the process and situation of teaching according to the requirements of the curriculum for teaching, obtaining information and data reflecting the quality of teaching, discovering problems in teaching, analyzing the causes of the problems, making suggestions to correct the problems, promoting the quality of teaching, improving students’ learning and professional development [22].

The smart teaching management platform manages talent training and curriculum construction perfectly. This means constructing a curriculum that reflects knowledge and competence requirements with a solid curriculum system, a talent training system, and the facilitation of MOOC construction [37]. Talent development focuses on students’ personalized development and implementing a full credit system with large class enrollment, a flexible academic system, a major and minor system, a double major and double degree system, and students’ independent construction of knowledge systems through information technology. Students will be able to choose a course of study after considering their future career path and understanding the core competencies required. Students can see their current progress (module completion, course completion) and access course selection advice and credit alerts online. The platform allows users to define core competencies for each profession, link them to courses, and automatically generate a radar map of the courses and core competencies. The professional radar chart allows for the automatic delivery of student academic alerts via email or app at specific times (before course selection, before mid-term assessments, and before degree applications each semester).

4. Results and Discussion

The impact of research on sustainable teaching models based on the OBE concept and the TSEM framework on subject competition and employment will be discussed in this section.

Figure 3 depicts the total number of awards received by our students at all levels in recent years. Prior to the implementation of the sustainable teaching model based on the OBE concept and the TSEM framework in 2020, there were only 25 national and provincial awards. Since the implementation of the sustainable teaching model, the total number of national and provincial awards has increased exponentially. Our students’ total number of awards has increased significantly over the last three years and has remained relatively stable. This demonstrates that our teaching model is effective and allows students to develop in a sustainable manner.

A sustainable teaching model based on outcome-based objectives expands students’ knowledge and increases their motivation to learn. The content and scope of competitions in computer science and technology are very broad, requiring not only the application of knowledge from a specific area of the professional curriculum but also the integration and application of all the knowledge students have learned. Students must refine what they have learned, learn by example, and broaden their knowledge. Students will experience a real working environment, practice their practical skills, and have a clear learning objective while learning, which increases their self-awareness and initiative in learning the profession. In recent years, the University and college have continued to encourage the development of disciplinary competitions for students in order to establish and improve the disciplinary competition system, strengthen the construction of professional and disciplinary laboratories, and provide more financial support to students. Students have been rewarded for winning disciplinary competitions, and teachers have been rewarded for mentoring students. It has effectively increased students’ motivation while increasing their knowledge.

Students’ self-confidence and cooperation will improve as a result of the sustainable teaching model. Students’ strong motivation to learn independently will be stimulated, while their self-confidence can be increased correspondingly by obtaining better results, which eventually translates into motivation and leads to more students participating in subject competitions. Subject competitions typically require participants to work in small groups. Regardless of the level of competition, students are typically required to collaborate on various scenarios and their own development, and after a division of labor between research, inspiration, design, and production, students work on the design around these aspects. Group cooperation is especially important in the “Internet+” Student Innovation and Entrepreneurship Competition, the Challenge Cup Student Entrepreneurship Plan Competition, and the Challenge Cup Student Extracurricular Academic Science and Technology Competition. Each member of the group can contribute their own ideas and opinions or draw inspiration from the ideas of others. The groups are constantly inspired during the discussion process to find the best presentation and production method that is consistent with the main content and promotes shared learning among the students.

Students’ innovative thinking and abilities are developed through sustainable teaching and learning models. Subject competitions not only teach students to think creatively but also help them develop their creative skills. Subject competitions can provide a platform for students to apply their fundamental knowledge to solve practical problems and develop innovative skills guided by creative thinking. Students compete in computing disciplinary competitions not only to gain knowledge, but also to gain independent access to information, analyze previous years’ competition entries, and ultimately produce independent creative work such as ideas and designs. The preceding procedure necessitates not only strong hands-on skills but also the ability to organize language in a logical way. Subject competitions can effectively improve students’ overall abilities and foster the development of innovative talents.

According to the study, students who participated in the subject competition had a better understanding of their profession and were better at practical skills. Subject competitions provide students with a wealth of new knowledge and abilities, and boost their self-esteem. Discipline competitions allow students to exercise their creative thinking skills, improve their communication skills, and develop their teamwork abilities. At the same time, many recruiting firms place a high value on disciplinary competitions and make participation in disciplinary competitions an important recruitment assessment indicator.

The School of Computer and Data Engineering adheres to the philosophy that “education increases value for students.” Guided by societal needs, it prioritizes talent training, continuously improves teaching quality and faculty level, reforms the talent training mode, and cultivates high-quality, application-oriented, complex, and outward-looking innovative talents. Students have won over 300 awards at the provincial level or higher in various subject competitions over the last three years, including the ACM Programming Competition, the National Software Design and Development Competition, the Mathematical Modeling Competition, the Student Service Outsourcing Innovation and Entrepreneurship Competition, the Student Network and Information Security Competition, and the Student Mathematics Competition. At the 37th ACM Global Finals, the students tied for 27th place worldwide. As shown in Figure 4, the employment rate of graduates has remained high and stable, with some graduates being “group-bought” by well-known companies and a large number of graduates being employed with high salaries by well-known companies such as the Alibaba Corporation, Tencent Technology Corporation, Baidu Online Network Technology Corporation, and JD. According to MyCOS’s employment survey report, in recent years the monthly incomes of the college’s graduates have been among the highest [38].

Table 4. Comparison of data from experimental and control groups.

Group	2020		2021		2022
	Provincial Awards	Employment Rates	Provincial Awards	Employment Rates	Provincial Awards	Employment Rates
Experimental Group	93	93.91%	98	97.85%	65	96.73%
Control Group	62	88.85%	61	96.92%	32	94.07%

shows that the experimental group outperformed the control group in all data aspects after being trained using the OBE concept and the TSEM framework teaching model. This demonstrates that our model improves students’ competencies in all areas and enables sustainable development.

The main contributions of the study are in the following areas:

• Concentrate on student learning outcomes. The OBE prioritizes student outcomes and places students at the center of the learning process in order to achieve specific goals. This approach to teaching and learning encourages better knowledge mastery and the development of students’ competencies.
• Develop an understanding of sustainable development. Students will gain an understanding of the significance of sustainability and sustainable development, as well as increased awareness and capacity for action on sustainable development.
• Improve the effectiveness and quality of teaching and learning. Teachers can plan lessons more effectively and use more effective teaching methods. Student outcomes are evaluated, and teaching and learning quality is improved.

The exploration of a sustainable teaching model based on a goal-oriented approach has significantly improved both the employment situation of students and their competition results. According to the graduates’ employment statistics, the employment rate, the rate of graduate studies, and the rate of going abroad have all increased since last year. According to the data presented above, the sustainable teaching model has improved the quality of teaching and learning in all areas and is superior to other classes that do not use this teaching model. This demonstrates the success of our sustainable teaching model.

5. Conclusions

This paper demonstrated how a sustainable teaching model that incorporates the OBE concept and the TSEM framework can assist students in pursuing in-depth computer science studies and developing their overall competence. Students’ abilities are improved in all areas compared to traditional methods.

With the advent of the digital education era, new expectations for talent development have emerged. The results show that the teaching model allows students to master computer science knowledge and skills at all stages, develop their ability to work in teams and innovate, and improve their long-term development, while also boosting their competitiveness in the job market.

Because this is a case study of computer science and technology students at NingboTech University’s School of Computer and Data Engineering in China, the results are limited to a modified socio-educational context. More research is needed, however, to determine the effectiveness of teaching models based on the OBE concept and the TSEM framework in empowering students in a socio-educational context.