An innovative talent training mechanism for maker education in colleges and universities based on the IPSO-BP-enabled technique

Abstract

In this study, a path to improving students’ core literacy is explored, and a new mechanism is developed for maker education and teaching based on research on students’ core literacy and the essence of maker education. An evaluation model of college students’ maker ability is established, and an improved particle swarm optimisation (IPSO) algorithm is introduced into the backpropagation (BP) neural network to improve the accuracy and speed of the evaluation of students’ innovation ability. Finally, experimental verification is conducted. The results indicate that most students significantly improved their memory and understanding of knowledge, principle exploration and attitude formation after practising the core literacy training method. For an innovation ability evaluation dataset, the accuracy rate of the BP neural network model reached 76.42%. The prediction accuracy rate of the PSO-BP network designed above was 86.76%. The IPSO-BP neural network model had the highest accuracy rate, reaching 4.43%. Evidently, the combination of a talent training mechanism for maker education and information technology can improve the evaluation efficiency of students’ abilities.