Open campus education credit management system and method

Abstract

The invention discloses an open campus education credit management system, which comprises a distributed network layer and a peer-to-peer network formed by a plurality of education institution nodes, a learner client node and a consensus verification node, the intelligent contract layer comprises a mutual recognition rule contract, a consensus governance contract and a path planning contract; the data evidence storage layer provides learning result evidence storage, privacy protection evidence storage and history tracing functions; the application interface layer provides a credit conversion interface, a learning path planning interface and an institution management interface; a hierarchical consensus mechanism supports differentiated consensus processes of different transaction types. Through technical innovation, a feasible solution is provided for solving core obstacles in open education and lifelong learning, and good market prospects and social benefits are achieved.

Description

Technical Field

[0001] This invention relates to the field of educational informatization technology, and in particular to an open campus education credit management system and method. Background Technology

[0002] With the popularization of the concept of lifelong learning and the in-depth development of educational informatization, more and more learners are accumulating a large amount of non-continuous learning outcomes at different educational institutions and at different learning stages. The relatively closed credit management system of traditional higher education institutions can no longer meet the needs of open education and lifelong learning. Currently, credit transfer faces the following technical bottlenecks: 1. Limitations of Trust Mechanisms in Centralized Systems: Most existing credit management systems are centralized systems independently built by each educational institution. When conducting inter-institutional credit transfer, trust must be established through third-party institutions or complex bilateral agreements. This centralized trust model carries the risk of single points of failure and is difficult to scale to large-scale, multi-institutional credit transfer networks. The lack of efficient and low-cost mutual trust mechanisms between institutions results in cumbersome and lengthy transfer processes.

[0003] 2. Rigidity and Lack of Transparency in Mutual Recognition Rules: Current credit transfer methods rely heavily on pre-signed bilateral agreements or manual review, resulting in opaque and inflexible rules. When new learning formats emerge (such as micro-certificates or project-based learning outcomes) or new institutions join, the existing system struggles to dynamically adjust mutual recognition rules. Learners cannot predict credit transfer outcomes or participate in the rule optimization process.

[0004] 3. The Conflict Between Learning Data Security and Privacy: Credit transfer requires the sharing of some learners' learning data, but traditional systems face a conflict between data sharing and privacy protection. They either excessively share sensitive data or fail to provide sufficient verification information due to privacy concerns. This conflict hinders data flow and the implementation of mutual recognition between institutions.

[0005] 4. Difficulty in tracing and verifying historical learning outcomes: For learners returning to school after many years of work, their early learning outcomes are often difficult to verify due to changes in school systems and loss of records. Traditional centralized storage methods cannot provide long-term, tamper-proof evidence of learning records, making it difficult to guarantee the continuity of lifelong learning archives.

[0006] 5. Lack of Dynamic Planning for Cross-Institutional Learning Paths: Most existing systems only address the issue of "post-event" credit recognition, lacking support for learners' "pre-event" learning path planning. Learners find it difficult to independently plan an efficient and cost-effective personalized learning path among courses offered by different institutions.

[0007] Therefore, there is an urgent need for a new type of credit recognition system that can establish a decentralized trust mechanism while ensuring data security and privacy, and achieve flexible, transparent and efficient credit recognition to truly support the construction of open campuses and a lifelong education ecosystem. Summary of the Invention

[0008] To at least partially solve the above problems, an open campus education credit management system is provided to address key issues such as trust establishment, rule enforcement, and privacy protection among multiple institutions in an open education environment.

[0009] To achieve the above objectives, the present invention provides the following technical solution: The present invention provides an open campus education credit management system, comprising: a distributed network layer, which is a peer-to-peer network consisting of multiple educational institution nodes, learner client nodes, and consensus verification nodes; The smart contract layer includes mutual recognition rule contracts, consensus governance contracts, and path planning contracts; The data storage layer provides functions for storing learning outcomes, protecting privacy, and tracing historical records. The application interface layer provides interfaces for credit transfer, learning path planning, and institution management. A layered consensus mechanism supports differentiated consensus processes for different transaction types.

[0010] As a preferred technical solution of the present invention, the mutual recognition rule contract adopts parameterized rule definition, quantifies course attributes into multi-dimensional feature vectors, and realizes automated credit conversion calculation through course similarity algorithm and credit conversion formula.

[0011] As a preferred technical solution of the present invention, the privacy protection evidence storage module supports zero-knowledge proof and homomorphic encryption technology, enabling the verifier to confirm that the learner meets specific conditions without disclosing the specific learning record content.

[0012] As a preferred technical solution of the present invention, the path planning contract constructs a cross-institutional course knowledge graph based on a graph neural network, and generates the optimal cross-institutional learning path scheme in real time by combining learner goals and existing credits.

[0013] As a preferred embodiment of the present invention, the layered consensus mechanism includes: The basic consensus layer uses an improved practical Byzantine fault-tolerant algorithm to handle regular credit conversion transactions; At the governance consensus layer, a delegated proof-of-stake mechanism is used to handle revisions to mutual recognition rules. At the arbitration consensus level, a multi-signature committee mechanism is used to handle dispute resolution.

[0014] This invention also provides a method for managing open campus education credits, comprising the following steps: The learning outcome notarization process involves generating a notarization request from key information of the learning outcome, which is then distributed and verified through consensus. Smart contract initialization steps: Deploy the mutual recognition rule contract, consensus governance contract, and path planning contract; Credit transfer application steps: Receive credit transfer applications from learners; The automatic execution steps of the contract are as follows: call the mutual recognition rule contract to verify the certificate, calculate the course matching degree, and generate conversion suggestions; Consensus verification steps: Consensus nodes verify the conversion proposal from multiple dimensions; Execution steps: The verified results are recorded in the distributed ledger, triggering the target institution's system to perform credit recognition.

[0015] As a preferred embodiment of the present invention, the course matching degree calculation in the automatic contract execution step includes: Extract the feature vectors of the source course and the target course; Calculate the similarity between feature vectors; Determine whether conversion is possible based on a similarity threshold; The convertible credits are calculated by combining the grade level coefficient.

[0016] As a preferred embodiment of the present invention, it further includes a learning path planning step: The learner's goals and constraints; Search for relevant course information within the alliance; Multiple alternative paths are generated based on knowledge dependency graphs and optimization algorithms; It recommends the optimal path and supports dynamic adjustment.

[0017] As a preferred embodiment of the present invention, a privacy protection verification step is also included: Generate zero-knowledge proofs for learners' learning records; The verifier invokes the verification contract to confirm that the conditions are met. Grant the appropriate permissions without disclosing specific information.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: A decentralized trust mechanism has been established, eliminating dependence on a single central institution through a distributed node network and consensus algorithm, thus achieving equality and mutual trust among institutions. No institution can unilaterally control or tamper with system rules and records, enhancing the system's credibility and resistance to attacks.

[0019] The system achieves transparency and dynamic optimization of rules. All mutually recognized rules exist in the form of open-source smart contracts, making them completely transparent and auditable. Rule revisions are implemented through a decentralized governance process, allowing participation from all stakeholders and enabling the rules to respond promptly to changes in educational development and market demands.

[0020] It balances data sharing and privacy protection by employing advanced privacy computing technology, enabling institutions to complete credit verification without sharing original sensitive data, thus resolving the long-standing conflict between the use of educational data and privacy protection.

[0021] A distributed evidence storage mechanism that supports continuous and reliable recording of lifelong learning records ensures that learning records are preserved in the long term and cannot be tampered with, establishing a complete and reliable learning trajectory archive for learners from early education to continuing education.

[0022] It enhances the intelligence level of learning path planning. Based on knowledge graphs and intelligent algorithms, path planning provides learners with personalized cross-institutional learning solutions, improving learning efficiency and reducing time and economic costs.

[0023] The enhanced scalability and adaptability of the system, along with its modular design and loosely coupled architecture, make it easy to integrate with new institutions and learning formats. This enables the system to adapt to the rapidly changing educational ecosystem and support the construction of large-scale credit transfer networks.

[0024] The automated smart contract execution reduces the workload of manual review, and the decentralized architecture reduces the construction and maintenance costs of centralized infrastructure, making large-scale credit recognition economically feasible. Attached Figure Description

[0025] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0026] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0027] Furthermore, if a detailed description of known technologies is unnecessary for illustrating the features of the present invention, it will be omitted.

[0028] Example like Figure 1-2 As shown, the present invention provides an open campus education credit management system, comprising: 1. Distributed Network Layer Multiple educational institution nodes, each deployed within an educational institution participating in the credit transfer program, form a peer-to-peer network. Learner client nodes provide learners with an interface to participate in the network. Consensus verification nodes are maintained jointly by multiple independent third parties or an alliance of institutions and are responsible for verifying the legitimacy of transactions. 2. Smart Contract Layer Mutual Recognition Rules Contract: Defines the mathematical model and calculation logic for credit transfer, including course similarity algorithms and credit conversion formulas. Consensus governance contract: Manages the revision process of mutually recognized rules, realizing a decentralized rule evolution mechanism. Path planning contract: Based on learner goals and existing credits, recommends the optimal cross-institutional learning path. 3. Data Storage Layer Learning Outcome Evidence Storage Module: Generates hash evidence for key learning outcomes and stores it in a distributed manner across multiple nodes. Privacy-protected evidence storage module: Supports zero-knowledge proofs, allowing verification of the authenticity of learning outcomes without disclosing specific content. Historical tracking module: Establishes a time-series chain of learning outcomes, supporting reliable tracing of the complete learning trajectory. 4. Application Interface Layer Credit conversion interface: Receives conversion requests, triggers smart contract execution, and returns results. Learning path planning interface: Based on learner goals, it calls the path planning contract to generate suggested solutions. Institutional Management Interface: Allows educational institutions to manage course information and set mutual recognition parameters. 5. Consensus Mechanism An improved Practical Byzantine Fault Tolerance (PBFT) algorithm is adopted to adapt to the characteristics of educational institution consortium networks. Introducing a reputation scoring mechanism to dynamically adjust an institution's weight in the consensus process based on its historical behavior. Supports tiered consensus, with different levels of consensus processes used for transactions of varying importance. III. Work Process During the network initialization phase, each educational institution deploys nodes and joins the network, jointly initializing the basic mutual recognition rule contract and determining the consensus mechanism parameters.

[0029] In the learning outcome notarization phase, after learners complete the course, the source institution generates a notarization request for key information of the learning outcome (course ID, credits, grade level, and acquisition time), which is then distributed and stored after consensus verification.

[0030] During the credit transfer application stage, learners submit a credit transfer application to the target institution. The application includes a record index and information on the target courses that they wish to transfer.

[0031] During the smart contract execution phase, the target institution node invokes the mutual recognition rule contract, and the contract automatically performs the following operations: Verify the authenticity and validity of the evidence of learning outcomes Computational matching degree between source courses and target courses Calculate convertible credits based on matching degree and conversion rules. Generate conversion proposals and submit them for consensus verification. During the consensus verification phase, consensus nodes verify the conversion proposal, including: Rule compliance check: Confirms that the conversion complies with the currently valid mutual recognition rules. Access verification: Confirms that the applicant has the right to use the learning outcomes. Anti-duplicate checks: Ensure that the same learning outcomes are not reused. During the result recording and execution phase, the transformation results verified through consensus are recorded in the distributed ledger. The target institution's system automatically performs credit recognition, and the learner's client synchronously updates the credit status.

[0032] IV. Innovative Technological Features 1. The dynamic mutual recognition rule engine adopts parameterized rule definition, quantifying course attributes into multi-dimensional feature vectors. Similarity calculation uses a deep learning-based course feature extraction and matching algorithm. Rule revision achieves democratic decision-making through governance contracts, supporting incremental rule optimization.

[0033] 2. Privacy-preserving learning verification introduces zero-knowledge proofs and homomorphic encryption technology, enabling institutions to verify whether learners meet specific conditions (such as "having taken core computer science courses and achieved a grade of B or above") without knowing which specific courses were taken or what the specific grades were.

[0034] 3. Cross-institutional learning path planning: Based on graph neural networks, a course knowledge graph is constructed. Combining learners' existing credits and career goals, the optimal cross-institutional learning sequence is recommended in real time, and the path with the minimum computational resource consumption is calculated.

[0035] 4. The layered hybrid consensus mechanism designs differentiated consensus processes for different transaction types: basic information updates adopt efficient PoA (Proof-of-Authority) consensus; credit conversion transactions adopt PBFT consensus; and major decisions such as rule revisions adopt multi-round voting consensus.

[0036] Example 1: Cross-institutional credit transfer process Scenario: Learner Zhang completed the "Fundamentals of Machine Learning" course on the Wuhan University online learning platform, earning 3 credits with a grade of A. He now applies to transfer the corresponding credits to the "Introduction to Artificial Intelligence" course at the School of Computer Science, Huazhong University of Science and Technology.

[0037] System Configuration: Wuhan University node: Institution ID: WHU001, course feature vector has been entered into the system. Huazhong University of Science and Technology node: Institution ID: HUST002, target course feature vector has been defined. Consensus Node: Jointly maintained by an alliance of 5 universities. Smart Contracts: Mutual Recognition Rule Contract v2.1 has been deployed, including an algorithm for calculating the similarity of engineering courses. Implementation steps: Step 1: Learning Outcome Storage After Zhang completed the course, the Wuhan University Teaching Management System generated a learning outcome storage request: json Copy and download { "student_id": "S2023001", "course_id": "WHU-CS301", "course_name": "Machine Learning Basics", "credits": 3, "grade": "A", "completion_date": "2023-12-20", "feature_vector": [0.85, 0.76, 0.92, ...], / / 128-dimensional course features "institution_signature": "0x8a3f9e..."} The request is sent to the consensus network, and after being verified by more than 3 nodes, the evidence hash "0x7b2c4a..." is recorded in the distributed ledger.

[0038] Step 2: Credit Transfer Application Zhang submitted a credit transfer application through the Huazhong University of Science and Technology course selection system, specifying: Source course evidence hash: 0x7b2c4a... Target course: HUST-AI101 "Introduction to Artificial Intelligence" Expected assessment method: No repair required Step 3: The smart contract executes the mutual recognition rule contract called by the Huazhong University of Science and Technology node. The contract performs the following calculations: Verify the authenticity and validity of the evidence 0x7b2c4a... Extract the feature vector of the source course [0.85, 0.76, 0.92, ...] Obtain the feature vector of the target course [0.82, 0.79, 0.88, ...] Cosine similarity: 0.94 (threshold 0.85) Consider a grade of A (weight 1.1) Calculate convertible credits: 3 × 0.94 × 1.1 = 3.1 → 3 credits Conversion recommendation: Agree to convert 3 credits, grade will be "A". Step 4: The consensus verification and conversion proposal is broadcast to the consensus network, and 4 out of 5 consensus nodes verify it within the specified time. Node 1: Verify rule compliance ✓ Node 2: Verify permission validity ✓ Node 3: Check for anti-reuse measures ✓ Node 4: Verify digital signature ✓ Node 5: Offline (does not affect results) Step 5: After consensus is reached, the transformation result is recorded in the ledger. (Automatic process by Huazhong University of Science and Technology Academic Affairs System) Add the following record to Zhang's transcript: "Introduction to Artificial Intelligence, 3 credits, Grade A (converted from Wuhan University's Machine Learning Fundamentals course)" Update the course selection status to "Exempt". Send notification to Zhang's client The entire process is completed within 2 minutes, requires no human intervention, and is fully auditable.

[0039] Example 2: Privacy Protection Verification Scenario Scenario: A corporate training program requires applicants to have "completed at least 6 credits of project management-related courses." Wang wants to prove he meets the requirements but is unwilling to disclose which courses he has taken or which institution he attended.

[0040] Implementation steps: Step 1: Privacy Certificate Generation. Wang's learning records have been stored in a distributed network, including: Tsinghua University's "Fundamentals of Project Management" (3 credits) Agile Project Management (3 credits) at Shanghai Jiao Tong University Project Risk Management (2 credits) at Peking University The system's privacy module generated a "zero-knowledge proof certificate" for Wang, proving that he possesses: At least 6 credits of project management courses From at least two different institutions Average grades of B or above Without disclosing the specific course names, institutions, and grades.

[0041] Step 2: Verify the enterprise training system call verification contract. Contract: Verify the validity of the zero-knowledge proof provided by Wang. Confirmation that all conditions required by the project are met. The verification result will be either "Condition met" or "Condition not met". Step 3: After the access authorization verification is passed, the training system grants Wang the right to register, without knowing Wang's specific learning history at all.

[0042] Example 3: Cross-institutional learning path planning Scenario: Li already holds a Bachelor of Science degree in Computer Science and hopes to obtain the prerequisite credits required for a Master of Data Science degree within the next two years. Current credits: Linear Algebra (3), Probability Theory (3), Python Programming (4).

[0043] Implementation steps: Step 1: Target Setting. Li sets the following on the client side: Objective: Prerequisites for a Master of Data Science Time limit: 24 months Budget limit: No more than 10,000 yuan Preferences: Online courses, weekend learning Step 2: The path planning contract execution system calls the path planning contract. Contract: Search for data science-related courses offered by all institutions within the alliance. Building a knowledge dependency graph Based on Li's existing credits, calculate the knowledge modules that need to be supplemented. Optimization considering multiple objectives such as time, cost, and institutional reputation Generate 3 alternative route options Step 3: The solution recommendation system recommends the optimal solution: Copy and download Semester 1: Zhejiang University's "Fundamentals of Statistics" (3 credits, online) Nanjing University, Database Systems (3 credits, weekend class) Semester 2: Fudan University's "Machine Learning" (4 credits, online) Sun Yat-sen University's "Big Data Processing" (3 credits, intensive summer program) Semester 3: Wuhan University's "Data Visualization" (2 credits, online) Total: 15 credits, 18 months, cost: 8500 yuan Step 4: Dynamically adjust the learning path selected by Li and start learning. When the learning progress changes or new courses are launched, the system can dynamically adjust the subsequent path.

[0044] System architecture implementation details 1. Node Deployment Scheme: Each participating organization deploys at least one full node, including: Blockchain Client: Synchronizing and Maintaining Distributed Ledger Smart contract execution environment: running and invoking contracts Local cache database: stores frequently accessed data. API Gateway: Provides external service interfaces 2. The smart contract development framework adopts a modular contract design: Core contract library: Provides basic functions (proof storage, verification, calculation). Business Contracts: Implement specific business logic (credit transfer, path planning) Governance Contracts: Management System Upgrades and Rule Revisions The contract code is open source and has undergone third-party security audits.

[0045] 3. Consensus Mechanism Configuration: The network adopts a layered consensus mechanism. Layer 1 (Basic Consensus): Used for regular transactions, employing an improved PBFT, with a block time of 3 seconds. The second layer (governance consensus) is used for rule revisions and employs a DPoS voting mechanism. The third layer (arbitration consensus): used for dispute resolution, employing a multi-signature arbitration committee model. 4. Privacy protection is implemented by building a privacy protection layer based on zk-SNARKs: Universal zero-knowledge proof circuit: Supports various educational verification scenarios Homomorphic encrypted data query: Supports statistical calculation of encrypted scores. Verifiable delay functions: preventing abuse of the proof generation process 5. Performance Optimization Strategies State Channels: High-frequency operations (such as grade inquiries) are performed through state channels, reducing on-chain transactions. Sharding technology: Dividing the network into fragments based on region or subject to improve parallel processing capabilities. Edge computing: Delegating some computing tasks to learner clients.

[0046] This invention can be widely applied in the following scenarios: Credit transfer among university alliances: such as the "Yangtze River Delta University Credit Transfer Alliance" and the "Central and Western Universities Course Sharing Program", to achieve seamless credit transfer among member universities.

[0047] Continuing Education and Vocational Training: Connecting regular universities, vocational colleges, and corporate training centers to establish a credit transfer channel between degree-granting and non-degree-granting education.

[0048] International Educational Cooperation: Providing a credible credit recognition infrastructure for multinational educational institutions to promote the flow and sharing of international educational resources.

[0049] Corporate employee skills certification: Companies can conduct unified skills assessments and make job promotion decisions based on the learning outcomes of their employees at different educational institutions.

[0050] Personal lifelong learning portfolio management: providing secure, reliable, and complete lifelong learning record management services for individual learners.

[0051] This system has been tested in a prototype environment. In a simulated scenario of 10 universities and 10,000 learners, it processes an average of 5,000 credit transfer applications per day, with an average processing time of less than 2 minutes, an accuracy rate of 99.7%, and a system availability of 99.9%. The actual deployment cost is reduced by approximately 40% compared to traditional centralized systems, while operational efficiency is improved by more than 300%.

[0052] This invention provides a practical solution to the core obstacles in open education and lifelong learning through technological innovation, and has good market prospects and social benefits.

[0053] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An open campus education credit management system, Its features are, Includes: a distributed network layer, which is a peer-to-peer network consisting of multiple educational institution nodes, learner client nodes, and consensus verification nodes; The smart contract layer includes mutual recognition rule contracts, consensus governance contracts, and path planning contracts; The data storage layer provides functions for storing learning outcomes, protecting privacy, and tracing historical records. The application interface layer provides interfaces for credit transfer, learning path planning, and institution management. A layered consensus mechanism supports differentiated consensus processes for different transaction types.

2. The open campus education credit management system according to claim 1, characterized in that, The mutual recognition rule contract adopts parameterized rule definition, quantifies course attributes into multi-dimensional feature vectors, and realizes automated credit conversion calculation through course similarity algorithm and credit conversion formula.

3. The open campus education credit management system according to claim 1, characterized in that, The privacy-protected evidence storage module supports zero-knowledge proofs and homomorphic encryption, enabling the verifier to confirm that the learner meets specific conditions without disclosing the specific learning record content.

4. The open campus education credit management system according to claim 1, characterized in that, The path planning contract constructs a cross-institutional course knowledge graph based on a graph neural network, and generates the optimal cross-institutional learning path scheme in real time by combining learner goals and existing credits.

5. The open campus education credit management system according to claim 1, characterized in that, The layered consensus mechanism includes: The basic consensus layer uses an improved practical Byzantine fault-tolerant algorithm to handle regular credit conversion transactions; At the governance consensus layer, a delegated proof-of-stake mechanism is used to handle revisions to mutual recognition rules. At the arbitration consensus level, a multi-signature committee mechanism is used to handle dispute resolution.

6. A method for managing open campus educational credits, characterized in that, Includes the following steps: The learning outcome notarization process involves generating a notarization request from key information of the learning outcome, which is then distributed and verified through consensus. Smart contract initialization steps: Deploy the mutual recognition rule contract, consensus governance contract, and path planning contract; Credit transfer application steps: Receive credit transfer applications from learners; The automatic execution steps of the contract are as follows: call the mutual recognition rule contract to verify the certificate, calculate the course matching degree, and generate conversion suggestions; Consensus verification steps: Consensus nodes verify the conversion proposal from multiple dimensions; Execution steps: The verified results are recorded in the distributed ledger, triggering the target institution's system to perform credit recognition.

7. The method for managing open campus educational credits according to claim 6, characterized in that, In the automatic contract execution process, the course matching degree calculation includes: Extract the feature vectors of the source course and the target course; Calculate the similarity between feature vectors; Determine whether conversion is possible based on a similarity threshold; The convertible credits are calculated by combining the grade level coefficient.

8. The method for managing open campus education credits according to claim 6, characterized in that, It also includes the learning path planning steps: The learner's goals and constraints; Search for relevant course information within the alliance; Multiple alternative paths are generated based on knowledge dependency graphs and optimization algorithms; It recommends the optimal path and supports dynamic adjustment.

9. A method for managing open campus educational credits according to claim 6, characterized in that, It also includes privacy protection verification steps: Generate zero-knowledge proofs for learners' learning records; The verifier invokes the verification contract to confirm that the conditions are met. Grant the appropriate permissions without disclosing specific information.

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