A Data-Driven Innovation Model of Big Data Digital Learning and Its Empirical Study
Jin He, Kuo‐Yi Lin, Ya Dai
Abstract
Digital learning is the use of telecommunication technology to deliver information for education and training. As the increased acceleration of the propagation speed of the web, a lot of data collected by automated or semi-automated way. The 4s (Volume, Velocity, Variety and Veracity) of big data increase the challenge to extract useful value via systemic framework. This study aims to construct the data model of big data digital learning. Based on the digital learning data, data-driven innovation framework was proposed to identify data form and collect data. Bayesian network was proposed to capture learning model to extract user experience of students to enhance learning efficiency. Empirical study was conducted on a university to validate the proposed approach. The results have been implemented to support the strategies to improve student learning outcomes and competitiveness.