Cognitive Engagement Detection of Online Learners Using GloVe Embedding and Hybrid LSTM

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

Abstract

This paper presents a method for classifying discussion posts of online courses, aiming to improve students’ cognitive engagement in online learning. This method utilizes deep learning models including a GloVe embedding and a hybrid long short-term memory (LSTM) network within an educational framework called interactive, constructive, active, and passives (ICAP), which classifies the students posts into interactive, constructive, active, and passives classes. These classes quantify the students’ level of cognitive engagement in their online course discussion posts. We used textual attributes and label-specific characteristics (e.g., text length, sentiment polarity, and subjectivity) to gain comprehensive insights into the forum posts’ emotional and cognitive depth. We further refined these features with a pre-trained GloVe embedding, which enhanced the classification accuracy. We interpreted the model’s decision-making process using local interpretable model-agnostic explanations (LIME) for added transparency and interpretability. By employing deep learning models with ICAP and LIME, this study demonstrates an effective use of the proposed system for improving student cognitive engagement in online learning.

Original languageEnglish
Title of host publicationGenerative Intelligence and Intelligent Tutoring Systems - 20th International Conference, ITS 2024, Proceedings
EditorsAngelo Sifaleras, Fuhua Lin
Pages15-26
Number of pages12
DOIs
Publication statusPublished - 2024
Event20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024 - Thessaloniki, Greece
Duration: 10 Jun. 202413 Jun. 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14799 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference20th International Conference on Generative Intelligence and Intelligent Tutoring Systems, ITS 2024
Country/TerritoryGreece
CityThessaloniki
Period10/06/2413/06/24

Keywords

  • cognitive engagement
  • deep learning
  • GloVe embedding
  • ICAP framework
  • LSTM
  • Online learning environment

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