A study of instruction in college physics experiments in the context of big data

Jun Lv, Ning-sheng Ma, Kai Fang and Xian-chao Ma
Tongji University
Shanghai, China

The research on big data in the field of education is a positive response to the era of big data. Mining big data produced by students who learn through mobile learning (m-learning) and ubiquitous learning (u-learning) can promote effective instruction. In physics experimental learning, the big data about the experiment process should be recorded, mined and used. To study instruction in college physics experiments in the context of educational big data, this paper analyses the present research results on m-learning, u-learning and educational big data, and combines the characteristics of teaching and learning in physics experiments. The paper includes five parts, as follows:

1 Educational big data can promote personalized adaptive learning, which means that educational data mining and learning analytics can be used to help students find the best learning methods and resources for physics experiments, when needed.
2 Digitalizing college physics experiment courses is the foundation for us to record resource usage and the experiment operation process.
3 With physics experiment teaching reform, teachers are required to provide rich e-learning resources and useful communication platforms, which can be used to record the data which are produced by students in the physics experiment learning process. Teachers should analyse the big data to adjust their teaching methods and use different teaching strategies for different students.
4 The reform of physics experiment learning requires students to adopt the blended learning method which combines informal after-class learning and formal classroom experiment learning; and students can use the prediction function of big data to change their learning method for different experiments.
5 With reform in the physics experiment evaluation method, by analysing the whole physics experiment learning process, students’ actual level can be reflected more objectively.