Instruction in College Physics Experiments in the Context of Big Data

Abstract

Mining big data produced by students learning through mobile learning (m-learning) and ubiquitous learning (u-learning) can promote instructional effectiveness. So, in learning physics experiments, the big data should be recorded, mined and used. This paper analyses the research results on m-learning, u-learning and educational big data. It includes five parts. Firstly, it deals with the promotion of personalised adaptive learning, where educational data mining and learning analytics can be used to help students find the best learning methods and resources for physics experiments, when needed. Secondly, it considers the digitising of a university physics experiment course for recording resource usage and the experimental operation process. Thirdly, it examines the reform of the teaching of physics experiments, in which teachers provide rich e-learning resources and a useful communication platform for recording the data produced by students and adjust their teaching methods and strategies for different students. Fourthly, it discusses the reform of the method for learning physics experiments, in which students adopt blended learning which combines informal after-class learning and formal classroom experiment learning, and uses the prediction function of big data to change their learning method for different experiments. Finally, the paper looks at the reform of the evaluation method for physics experiments to reflect more objectively students’ actual levels of performance by analysing the whole process.