Sustainable learning requires students to develop knowledge and skills for survival in increasingly complex and dynamic environments. The development of systems thinking skills for exploring complex dynamic systems is regarded as crucial to sustainable learning. To facilitate student thinking and learning about complex systems, computer simulations have been widely promoted. However, learning using computer simulations involves complex cognitive processes, which may impose a high level of cognitive demand on learners, especially on low achievers. It remains unclear whether and how high- and low-achieving students may benefit differently from learning with computer simulations. To address the gap, we conducted this study with university students who participated in simulation-assisted learning about the economy as a complex system. The results show that the students developed subject knowledge and systems thinking skills by the end of the study; high-achievers outperformed low-achievers in a subject knowledge test, but there were no significant differences between the two groups in their systems thinking skills, cognitive load, and affective experience. The findings indicate that both low- and high-achieving students can benefit from simulation-assisted learning of a complex system. In addition to developing systems thinking skills, there is a need to help students to improve the construction of their subject knowledge when learning with computer simulations.
Reinforcement learning is an important machine learning method and has become a hot popular research direction topic at present in recent years. The combination of reinforcement learning and a recommendation system, is a very important application scenario and application, and has always received close attention from researchers in all sectors of society. In this paper, we first propose a feature engineering method based on label distribution learning, which analyzes historical behavior is analyzed and constructs, whereby feature vectors are constructed for users and products via label distribution learning. Then, a recommendation algorithm based on value distribution reinforcement learning is proposed. We first designed the stochastic process of the recommendation process, described the user’s state in the interaction process (by including the information on their explicit state and implicit state), and dynamically generated product recommendations through user feedback. Next, by studying hybrid recommendation strategies, we combined the user’s dynamic and static information to fully utilize their information and achieve high-quality recommendation algorithms. Finally, the algorithm was designed and validated, and various relevant baseline models were compared to demonstrate the effectiveness of the algorithm in this study. With this study, we actually tested the remarkable advantages of relevant design models based on nonlinear expectations compared to other homogeneous individual models. The use of recommendation systems with nonlinear expectations has considerably increased the accuracy, data utilization, robustness, model convergence speed, and stability of the systems. In this study, we incorporated the idea of nonlinear expectations into the design and implementation process of recommendation systems. The main practical value of the improved recommendation model is that its performance is more accurate than that of other recommendation models at the same level of computing power level. Moreover, due to the higher amount of information that the enhanced model contains, it provides theoretical support and the basis for an algorithm that can be used to achieve high-quality recommendation services, and it has many application prospects.
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