Research on the construction of outcome-based education platform based on deep learning

The traditional way of enrolling students and organizing teaching by major has been difficult to meet the needs of high-quality development. The OBE (Outcome-Based Education) model emphasizes that everyone can succeed, personalized evaluation, competency-based performance responsibility, and so on. With the continuous development of AI (artificial intelligence) technology and the sharp increase in available data in the era of big data, the application of AI is becoming increasingly important. This paper proposes an OBE platform based on deep learning, which includes personalized learning recommendations and classroom quality evaluations. This paper explores the relationship between users' learning emotions and learning efficiency and uses the content-based recommendation model of CNN (convolutional neural network) to recommend personalized learning methods. It employs the improved SSD (Single Shot MultiBox Detector) algorithm to detect classroom behavior. The research results show that the CNN recommendation model performs very well in the learning resource recommendation platform. The results indicate that the improved SSD algorithm in this paper has a good inhibitory effect on all five actions and shows a significant improvement in the detection effect of small targets, up to 14.805%. This is of great significance for promoting the deep integration of modern information technology with education and teaching and for implementing effective OBE.