An Experimental Study on the Optimization of Learners' Cognitive Load in Web-based Learning Environment

Abstract

With the global epidemic of COVID-19, online teaching and learning have been carried out on a large scale all over the world, and MOOC has been further developed and applied. Through experiments, this study verified the feasibility of advance organizer strategy, eliminating redundancy strategy and ARCS model design strategy. The results are as follows. (1) In the Web-based learning environment, for the more difficult materials, it is beneficial to reduce the internal cognitive load of learners to provide a certain advance organizer before the learners start formal learning; but for the easier materials, whether or not to provide advance organizer has no significant impact on the learning results of learners. (2) In the Web-based learning environment, it is better to provide advance organizer for the more difficult knowledge by implanting background music into the Web-based learning environment; it can obviously hinder the learners' learning, but it is not obvious when the e-learners learn relatively simple knowledge. (3) In the Web-based learning environment, ARCS model design can stimulate the learning motivation of e-learners and optimize the cognitive load of e-learners.