Double Helix Deep Learning Model Based on Learning Cell

Abstract

The significance of deep learning lies not only in the construction of knowledge, but also in utilizing collective wisdom embedded in social networks to form a rich social knowledge network. The learning cell platform, which integrates comprehensive information such as interpersonal networks, knowledge networks and learning activities, can provide good support for online deep learning. The double helix deep learning model based on learning cells reflects a deep learning theory of social interaction, collaborative knowledge building and sharing, as well as cognitive development step by step. In this model, knowledge network and social network are two basic frameworks, developing a social knowledge network through the convergence of learning activities. The social knowledge network takes knowledge content as the core node and establishes relationships between two knowledge nodes, knowledge node and human node, as well as two human nodes. In this model, learners and teachers are equal and can exchange their roles. In the initial stage of learning, learners first construct a knowledge network and an interpersonal network through reception learning; with the gradual deepening of participatory learning, collaborative construction based on knowledge interaction promotes the development of knowledge networks; the generation of network nodes based on interpersonal interaction widens interpersonal networks, and personal learning network forms dynamically and develops spirally; in the advanced stage of learning, learners can actively connect with the networks, and construct the social knowledge space of communities through creative learning activities and activity-based knowledge contribution and creation, in order to achieve effective deep learning.