Willingness and Evaluation Model of College Students' Online Learning Behavior Based on Distributed Cognition

Abstract

Guided by distributed cognition theory, we analyze the influential elements of content, tools, and contextual interactions in the online learning process through research and case studies to explore the characteristics and evaluation of college students' willingness to engage in online learning behavior under distributed cognition and provide guidance for the experience design of online education platforms. Based on distributed cognition, this paper designs a convolutional neural network model based on InceptionNet, which uses a global average pooling layer instead of a fully connected layer to reduce the number of parameters, and InceptionNet increases the depth and width of the network by branching to improve the performance of the network and avoid overfitting. Distributed cognitive theory emphasizes the distributed nature of cognition, and the intrinsic variables that influence the willingness to participate in online learning communities from a systemic viewpoint are mainly attitudes, subjective norms, expected emotions, competence, sense of relatedness, desire, and perceived behavioral control. In addition, perceived behavioral control has a direct positive effect on the willingness to participate in online learning communities.