Investigating online learners' knowledge structure patterns by concept maps: A clustering analysis approach.

Abstract

A deep understanding of the learning level of online learners is a critical factor in promoting the success of online learning. Using knowledge structures as a way to understand learning can help analyze online students' learning levels. The study used concept maps and clustering analysis to investigate online learners' knowledge structures in a flipped classroom's online learning environment. Concept maps (n = 359) constructed by 36 students during one semester (11 weeks) through the online learning platform were collected as analysis objects of learners' knowledge structures. Clustering analysis was used to identify online learners' knowledge structure patterns and learner types, and a non-parametric test was used to analyze the differences in learning achievement among learner types. The results showed that (1) there were three online learners' knowledge structure patterns of increasing complexity, namely, spoke, small-network, and large-network patterns. Moreover, online learners with novice status mostly had spoke patterns in the context of flipped classrooms' online learning. (2) Two types of online learners were found to have different distributions of knowledge structure patterns, and the complex knowledge structure type of learners exhibited better learning achievement. The study explored a new way for educators to analyze knowledge structures by data mining automatically. The findings provide evidence in the online learning context for the relationship between complex knowledge structures and better learning achievement while suggesting the existence of inadequate knowledge preparedness for flipped classroom learners without a special instructional design.