Assessing Method for E-Learner Clustering
Qinghua Zheng, Jiao Ding, Du Jin, Feng Tian · 2007
Learner grouping is a key step to build both personalized e-learning system and adaptive cooperative learning environment. Clustering analysis has been widely adopted in many researches, while the validity assessments of clustering results were largely ignored. In the study, validity assessment for e-learner clustering was emphasized and a new assessing index based on label information was proposed. Experiment results on the real dataset indicated that precise and reliable learner partitions could be obtained by using clustering validation indices. In addition, by visualizing the distribution of labeled clusters, we confirmed the underlying hypothesis of learning strategies intelligent recommendation that learners with similar personality would be likely to employ similar learning strategies.