Abstract
Through analyzing the behavior data of MOOCs learners, a MOOCs learner's score prediction model is constructed based on clustering algorithm and neural network in this paper. By using this model, we can find out the neglected information and hidden learning rules in the MOOCs learning process. The model can provide personalized guidance for each user and improve learning efficiency. The model can provide personalized service to help learners form personalized learn-ing strategies, and it also can alert learners with low grades and risk of dropping out.
Highlights
Since 2012, Massive open online courses (MOOCs) have broken forth and spread widely among various universities in the world, it has an extremely important impact on the teaching of higher education in the world [1,2]
Canvas dataset was chosen as the source dataset for MOOCs learner behavior data analysis
It shows that the convergence accuracy of the score prediction model proposed in this paper is better than that of the RBF neural network prediction model based on random centers
Summary
Since 2012, Massive open online courses (MOOCs) have broken forth and spread widely among various universities in the world, it has an extremely important impact on the teaching of higher education in the world [1,2]. It is necessary to study and analyze the learning behavior data of MOOCs learners [4]. On the curriculum and platform analysis of the MOOCs platform, Kim used the evaluation standard of web-based teaching platform, and elaborated the usefulness of MOOCs in exploring the learner group's conscious learning [17]. After analyzing the background of MOOCs, Liu et al compared curriculum resources, learning activities and learning evaluation based on the characteristics of the curriculum design on MOOCs platform [18]. Zhuo et al take a course in UOOC as an example to collect all the data of the course, and study the user's learning behavior based on big data analysis [20]. Mou et al took six MOOC courses as an example to analyze the learners’ behavioral, they explored the learning behavior of the learner at the course level [21]
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