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.

本研究在研究学生核心素养和创客教育本质的基础上，探索了一条提升学生核心素养的路径，构建了创客教育教学的新机制。建立了大学生创客能力评价模型，并在反向传播（BP）神经网络中引入改进的粒子群优化（IPSO）算法，提高了学生创客能力评价的准确性和速度。最后进行实验验证。结果表明，大多数学生在练习核心素养训练方法后，对知识的记忆和理解、原理探索和态度形成都有显着提高。对于创新能力评价数据集，BP神经网络模型的准确率达到76.42%。上述设计的PSO-BP网络的预测准确率为86.76%。IPSO-BP神经网络模型的准确率最高，达到4.43%。显然，创客教育的人才培养机制与信息技术的结合可以提高学生能力的评价效率。