Authors:
Zhi Liu
1
;
Shiqi Liu
2
;
Cuishuang Zhang
2
;
Zhu Su
1
;
Tianhui Hu
1
and
Sannyuya Liu
3
Affiliations:
1
National Engineering Laboratory for Educational Big Data, Central China Normal University, Luoyu Road 152, 430079 Wuhan, China
;
2
National Engineering Research Center for E-Learning, Central China Normal University, Luoyu Road 152, 430079 Wuhan, China
;
3
National Engineering Laboratory for Educational Big Data, Central China Normal University, Luoyu Road 152, 430079 Wuhan, China, National Engineering Research Center for E-Learning, Central China Normal University, Luoyu Road 152, 430079 Wuhan, China
Keyword(s):
Online Discussion, Learning Analytics, LDA Topic Modeling, Regression Analysis.
Abstract:
In asynchronous forums of Blended Learning and E-learning, learners’ cognitive participation, such as knowledge construction and critical-thinking dialogues, is a crucial factor for their learning outcome, which has not yet been further exploited. This study investigated learners’ cognitive behaviors and implicit content derived from posts by using a mixed-method of text mining and statistical analysis. We adopted a content analysis approach to manual coding learners’ cognitive behaviors in a Blended Learning discussion forum. Then we proposed an improved topic model called Cognitive Behavior Topic Model (CBTM) to detect learner’s semantic content between three achievement groups (High/Medium/Low). Moreover, we performed a statistical analysis to investigate the relationship among cognitive behaviors, cognitive content, and learning outcome. The results showed that the high achievement group’s cognitive behavior frequency in all categories is higher than the other, and effective orde
r of behaviors with the learning outcome is “constructive > active > interactive”. The “application practice” related topic is more effective for learning outcome than “theoretical discussions”. Specifically, when the cognitive content changes from "theoretical discussion" to "application practice", or the number of posts on the same cognitive content-related topic is large, the high-level cognitive behaviors bound to the topic content will increase significantly. Therefore, this study could provide new insights into theoretical and practical implications.
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