基于人工智能强化学习算法的素质素养预测机制研究  

Research on Quality and Literacy Prediction Mechanism Based on Artificial Intelligence Reinforcement Learning Algorithm

作　　者：靳恒清[1] JIN Hengqing(Gansu Vocational College of Agriculture,Lanzhou Gansu 730000)

机构地区：[1]甘肃农业职业技术学院,甘肃兰州730000

出　　处：《软件》2023年第9期116-119,共4页Software

摘　　要：传统的学生素质素养评估方法往往基于专家经验或标准,难以全面、客观地评估学生的素质素养。基于人工智能强化学习算法的学生素质素养预测机制是一种数据驱动的方法,它利用大量的学生数据进行模型构建和预测,经过不断地优化算法和模型,提高模型的准确性和泛化能力,同时结合学生素质素养指标实现个性化的综合性分析,从而全面地评估与预测学生的素质素养能力。此外,该预测机制能够根据学生的表现和反馈信息,不断调整和优化学生的能力模型,提高学生素质素养评价的先进性,还能够通过建模实现对学生学习行为的精细化管理。最后,将基于强化学习的学生素质素养预测机制应用到实际教学场景中,并对其效果进行评估和优化,推广其应用领域,同时将其转换为科研成果。Traditional methods for evaluating students'quality and literacy are often based on expert experience or standards,making it difficult to comprehensively and objectively evaluate students'quality and literacy.The mechanism for predicting students'quality and literacy based on artificial intelligence reinforcement learning algorithms is a data-driven approach,it utilizes a large amount of student data for model construction and prediction,continuously optimizing algorithms and models,improve the accuracy and generalization ability of the model,simultaneously combining student quality and literacy indicators to achieve personalized comprehensive analysis,to comprehensively evaluate and predict students'quality and literacy abilities.In addition,the prediction mechanism can be based on students'performance and feedback information,continuously adjusting and optimizing students'ability models,improve the progressiveness of students'quality assessment,it is also possible to achieve refined management of students'learning behavior through modeling.Finally,the prediction mechanism for students'quality and literacy based on reinforcement learning will be applied to practical teaching scenarios,and evaluate and optimize its effectiveness,promote its application areas,simultaneously convert it into scientific research achievements.

关 键 词：人工智能 强化学习算法 素质素养 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]