Real-time monitoring and dynamic optimization of course-level learning outcomes (CLOs) are crucial for improving the quality of higher education, yet traditional evaluation systems face challenges such as data centralization, poor real-time performance, lack of trustworthiness, and inefficient feedback loops. To address these issues, this study proposes a blockchain-enabled smart-contract ecosystem for CLOs management. First, we design a multi-layered ecosystem architecture, including the perception layer, blockchain layer, smart contract layer, application layer, and user layer, to realize full-process automation and trustworthiness of CLOs management. Second, we formalize the CLOs evaluation index system and design a dynamic weight calculation model based on analytic hierarchy process (AHP) and entropy weight method, which is encapsulated into smart contracts to realize real-time and objective evaluation of learning outcomes. Third, we propose a feedback-driven dynamic improvement mechanism, where smart contracts automatically trigger targeted teaching adjustment suggestions based on real-time evaluation results. The proposed ecosystem is implemented on the Ethereum blockchain, and experiments are conducted with 3 undergraduate courses from two universities involving 523 students. Objective experimental results show that the ecosystem achieves a data transmission delay of 0.8-1.2 seconds, a data tamper-proof rate of 100%, and an evaluation accuracy of 92.3% compared with manual evaluation. Subjective evaluation results from teachers and students indicate that the ecosystem significantly improves the timeliness of teaching feedback (89.7% positive evaluation) and the effectiveness of learning outcome improvement ( 86.3% positive evaluation). This study provides a new technical solution for real-time and trusted management of CLOs, and contributes to the digital transformation of higher education evaluation.
Xiaowei Nie (Sun,) studied this question.