Can Multi-Label Classifiers Help Identify Subjectivity? A Deep Learning Approach to Classifying Cognitive Presence in MOOCs.

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Title

Can Multi-Label Classifiers Help Identify Subjectivity? A Deep Learning Approach to Classifying Cognitive Presence in MOOCs.

Content partner

The University of Auckland Library

Collection

ResearchSpace@Auckland

Description

This paper investigates using multi-label deep learning approach to extending the understanding of cognitive presence in MOOC discussions. Previous studies demonstrate the challenges of subjectivity in manual categorisation methods. Training automatic single-label classifiers may preserve this subjectivity. Using a triangulation approach, we developed a multi-label, fine-tuning BERT classifier to analyse cognitive presence to enrich results with state-of-the-art, single-label classifiers. We ...

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Format

Research paper

Research format

Journal article

Date created

2022-09-02

Creator

Hu, Yuanyuan / Donald, Claire / Giacaman, Nasser

URL

https://hdl.handle.net/2292/61892

Related subjects

Automatic text analysis / BERT / Cognitive presence / MOOC / Multi-label classification / Online discussion / 08 Information and Computing Sciences / 13 Education

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