In traditional learning contexts, teachers primarily assess students' behavior, emotional changes, and assignment completion to ensure teaching quality. Currently, there are challenges in evaluating students, such as assessments being insufficiently comprehensive and timely, a singular evaluation perspective that hinders the holistic consideration of factors affecting learning assessments, and a weak correlation among evaluation criteria, resulting in suboptimal evaluation outcomes. In recent years, with the rapid development and widespread application of artificial intelligence and information technology, the era of smart classrooms has arrived. New technologies like image processing and artificial intelligence offer opportunities for personalized support services and enhancing teaching quality. Therefore, to provide a more comprehensive and objective reflection of teaching quality, this paper proposes a multi-modal information fusion learning assessment model. This model is achieved by determining the weight values of three dimensions, cognitive attention, emotional attitude, and course acceptance along with their corresponding attributes. Subsequently, through a fusion strategy, it calculates the learning assessment score by integrating information from these three dimensions. A series of experimental data confirms the effectiveness of this approach.

在传统的学习环境中，教师主要评估学生的行为、情绪变化和作业完成情况，以保证教学质量。当前，学生评价存在评价不够全面、不够及时、评价视角单一、无法全面考虑学习评价影响因素、评价标准关联性弱等问题，导致评价结果不理想。近年来，随着人工智能和信息技术的快速发展和广泛应用，智慧教室时代已经到来。图像处理和人工智能等新技术为个性化支持服务和提高教学质量提供了机会。因此，为了更全面、客观地反映教学质量，本文提出一种多模态信息融合学习评估模型。该模型是通过确定认知注意力、情感态度和课程接受度三个维度的权重值及其相应属性来实现的。随后，通过融合策略，整合这三个维度的信息来计算学习评估分数。一系列实验数据证实了该方法的有效性。