人工智能助力学情分析的理论框架与实践路径

The Theoretical Framework and Practical Path of Learning Situation Analysis Assisted by Artificial Intelligence

学情分析是以学定教、开展教学的前提和依据。但在教学实践中,囿于教师精力、能力与技术发展等限制,学情分析面临数据难以全面获取、分析客观性不足、学情反馈滞后、结果缺乏应用等诸多现实困境。人工智能以大数据、强算法、强算力为基础,有助于全面采集学情数据、科学高效分析学情、及时反馈学情、合理应用学情分析结果,提升学情数据的采集广度、分析效度、反馈速度与应用准度。为了充分发挥人工智能技术优势,应从数据层、分析层、反馈层、应用层建构人工智能助力学情分析的理论框架,并从实践上以人工智能建立全过程学情数据采集系统、多模态学情数据分析系统、及时化学情反馈系统、多场景教学综合服务系统助力学情分析。

Learning situation analysis is the premise and basis for determining teaching and carrying out teaching based on learning.However,in teaching practice,limited by teachers'energy,ability and technological development,learning situation analysis faces many practical difficulties such as difficulty in comprehensively obtaining data,insufficient objectivity of analysis,lagging learning feedback,and lack of application of results.Artificial intelligence is based on big data,strong algorithms,and strong computing power.It helps to comprehensively collect learning data,scientifically and efficiently analyze learning data,provide timely feedback on learning status,rationally apply learning results,and improve the breadth,validity,feedback speed and application accuracy of learning situation data.In order to give full play to the advantages of artificial intelligence technology to assist student situation analysis,a theoretical framework for artificial intelligence to assist student situation analysis should be constructed from the data layer,analysis layer,feedback layer,and application layer.And in practice,a full-process academic situation data collection system should be established using artificial intelligence,a multi-modal academic situation data analysis system,a timely academic situation feedback system,and a multi-scenario teaching comprehensive service system to assist in academic situation analysis.