Title: Research on Social Support Services for Promoting Deep Learning: A Multi level Progressive Representation and Aggregation Model
Authors: Tang Xiaogan, Wang Qi, Yu Shengquan
Keywords: deep learning; Social support; Social knowledge network; Learning support services; Model construction;
Abstract: Deep learning is closely related to social learning, and the lack of social support mechanisms is an important factor restricting the development of deep learning. Effective implementation of deep learning requires a systematic review and construction of social support service models. This study focuses on the social nature of learning and elaborates on the key needs of social support from three dimensions: learners, knowledge, and learning processes. Based on this, a multi-level progressive representation and aggregation model of social support is constructed using social knowledge networks as carriers. This model covers seven core elements: "perceivable social characteristics," "accessible social knowledge," "participatory social activities," "shareable social knowledge," "developable social relationships," "joinable social groups," and "constructible social knowledge." It forms a dynamic and progressive support framework for the development process of deep learning. Under the guidance of this model, a social knowledge network tool SKN was designed and developed, which structurally aggregates and organizes multidimensional social nodes to provide adaptive social support services for the three key stages of information input, activity participation, and knowledge creation in deep learning. This provides a theoretical basis and practical reference for optimizing the social support mechanism of deep learning.