Linking cognitive processes and learning outcomes: The influence of cognitive presence on learning performance in MOOCs. (15th February 2022)

• Record Type: Journal Article
• Authors: Liu, Bowen; Xing, Wanli; Zeng, Yifang; Wu, Yonghe
• Is Part Of: British journal of educational technology. Volume 53:Number 5(2022)
• Page Start: 1459
• Page End: 1477
• Publication Date: 2022-02-15
• Subjects: cognitive presence -- learning performance -- machine learning model -- MOOCs -- multilevel modelling; Audio-visual education -- Periodicals; 371.33
• DOI: 10.1111/bjet.13193
• Languages: English
• ISSNs: 0007-1013

Abstract

Abstract: Massively open online courses (MOOCs) offer learners opportunities for self‐improvement and knowledge development. Linking cognitive processes and learning outcomes is helpful in supporting students learning with the help of MOOCs. Based on the cognitive presence of the Community of Inquiry framework, this study quantified the effect of cognitive presence on students' learning performance by analysing more than 400, 000 posts of 13 MOOCs. First, the study built a highly predictive classification model using a machine learning algorithm to automatically identify the phases of cognitive presence in MOOC forum posts. Subsequently, multilevel modelling was used to analyse the influence of learners' cognitive presence on their learning performance. The results showed that different phases of cognitive presence influenced students' learning differently. The findings help us understand the phases and depth of students' cognitive presence and can be used as the basis for MOOCs to automatically provide appropriate cognitive feedback and support to students. Practitioner notes What is already known about this topic Massively open online courses (MOOCs) offer learners opportunities for self‐improvement and knowledge development. Linking cognitive processes and learning outcomes is helpful in supporting student learning in MOOCs. Little is known so far about the role of cognitive presence in influencing students' learning performance. What this paper adds This study built a highly predictive classification model using a machine learning algorithm to automatically identify the phases of cognitive presence in MOOC forum posts. Multilevel modelling was used to quantify the influence of learners' cognitive presence on their learning performance. The results showed that different phases of cognitive presence influenced students' learning differently. Implications for practice and/or policy This study built an effective, high‐performance machine learning model with easy feature extraction, which could be used to automatically identify students' cognitive processes in MOOC forums and beyond. The combination of cognitive presence and learning performance helps us understand the phases and depth of students' cognitive presence. The findings can be used as the basis for MOOCs to automatically provide appropriate cognitive feedback and support to students.