Exploration of data mining algorithms of an online learning behaviour log based on cloud computing
Rongguo Wang
Abstract
In this research, the learning patterns and behavioural data of learners' online learning in a cloud computing environment are analysed, and the learners' learning progress, learning rules, and learning effects through data mining algorithms are studied to promote learners' self-learning consciousness and self-learning ability and achieve the learner-centred application goal. This study is aimed at an online learning course, randomly selected eight learners as research subjects, and analysed their online learning behaviour through data mining algorithms, including establishing learning behaviour models, behavioural data feature analysis and behaviour evaluation, and finally realise personalised recommendation of learning content based on data mining results. It finds that through the data mining algorithm to analyse the learning behaviour of online learners, the learner's learning state and learning effect can be intuitively understood, and the individual's learning behaviour characteristics can be roughly defined, then guiding suggestions can be given to help them complete learning goals and improve learning efficiency. Due to the small sample size of the survey, the results obtained are relatively one-sided. In future research, attention should be paid to the data collection and collation work.