The Challenges and Adaptations of the ‘New Elderly’ in China: A Study Based on SVM Machine Learning Algorithm

The rapid development of information technology, accompanied with the enormous demand of the “new elderly” for home-acceptance and the emerging number of “new elderly” in China, the connection of mobile learning with the life of new elderly has gained rapid popularity. However, due to different from young learners with lower educational backgrounds or rather closed learning attitudes, there are rare studies applied the approach and computational tool to analyze the adaptation process and main influencing factors between new elderly learning and mobile learning. In this research, we aim to analyze challenges and learning adaptation strategy of the new elderly in China learning by mobile learning, and the influential factors in this field using the computational tool of machine learning. We carried out the qualitative data method for analyzing process, that is semi-structured interview for elderly participants, who show active interest and attitudes of mobile learning, extract the main topics with thematic analysis method and utilize Support Vector Machine (SVM) algorithm for identifying the importance and prediction of influential factors between adaptation level. The interview results shows: (1) New elderly suffered from big challenges of anxiety from new technology, uncertainties in the result of learning and emotion. (2) But simultaneously they demonstrated good adaptation ability and motivation for learning continue. The SVM-based machine learning identified the high or low influence variables contributing adaptation level successfully. The two findings provide implications for “new elderly” education development. Not only the high-level data model but also a new path to show what are the main influencing variables contribute to adaptive learning in the machine learning model.