可解释学习者建模:价值意蕴与应用图景

Explainable Learner Modeling: Value Implications and Application Prospects

学习者模型的“复杂性”和机器智能决策的“不透明性”,使得可解释学习者建模成为教育人工智能研究的重要议题。可解释学习者建模旨在通过对学习者多维度、多层次、多场景的精准刻画,实现学习者的可表征、可理解、可干预,进而为学习策略、教学模式、教育评价的设计和开展提供科学依据。其核心价值体现在对外在学习行为的准确表征、对学习者潜在特征的深度挖掘、对学习者模型的完整构建以及对学习机理的准确阐释,且在模型构建过程中充分体现出透明度和可解释性,进而增强教育主体对机器智能分析与决策的信任度和接受度。可解释学习者建模能够实现全景化细粒度的教育诊断,提供易于理解和接受的学习干预,推动高度适配且便于实施的教学决策,支持综合化高效能的教育管理,在“人机协同”的教育教学活动中具有广阔的应用前景。未来,还需通过加强多学科理论融合、科学智能方法运用、智能教育产品研发等途径推进可解释学习者建模研究。

The complexity of learner models and the opacity of machine intelligence decisions have made explainable learner modeling an important topic in educational artificial intelligence research.Explainable learner modeling aims to accurately depict learners in multiple dimensions,levels and scenarios,achieving representability,comprehensibility and intervention of learners,thereby providing scientific basis for the design and conduct of learning strategies,teaching modes and educational evaluation.Its core values are reflected in the accurate representation of external learning behaviors,deep exploration of learners’potential characteristics,complete construction of learner models,and accurate interpretation of learning mechanisms,in order to enhance the trust and acceptance of educational subjects in machine intelligence analysis and decision-making.Explainable learner modeling can achieve panoramic and fine-grained educational diagnosis,provide understandable and acceptable learning interventions,promote highly adaptable and implementable teaching decisions,and support comprehensive and efficient educational management.Therefore,it has broad application prospects in human-machine collaborative educational practice.In the future,it is necessary to strengthen the integration of multidisciplinary theories,the use of AI for science,and the development of intelligent educational products,in order to promote research on explainable learner modeling.