BiLSTM-Attention-Delta: A Novel Framework for Predicting Dropout in MOOCs Within Big Data Environments

Authors

Tri Nguyen (ORCID: https://orcid.org/0000-0001-6483-0829)
Tu-Anh Nguyen-Hoang (ORCID: https://orcid.org/0000-0001-9283-769X)
Thu Nguyen (ORCID: https://orcid.org/0009-0009-6961-3976)

Institutions

Vietnam National University Ho Chi Minh City (VN)
Aalto University (FI)

Publication Details

Published: 2025-01-01
DOI: https://doi.org/10.5220/0013202900003932
Citations: 1
Primary Topic: Online Learning and Analytics
Type: article
Field-Weighted Citation Impact: 1.69

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article

BiLSTM-Attention-Delta: A Novel Framework for Predicting Dropout in MOOCs Within Big Data Environments

Tri Nguyen, Tu-Anh Nguyen-Hoang, Thu Nguyen

Jan 1, 2025

1 citations

Online Learning and Analytics

1.69

article

BiLSTM-Attention-Delta: A Novel Framework for Predicting Dropout in MOOCs Within Big Data Environments

Tri Nguyen, Tu-Anh Nguyen-Hoang, Thu Nguyen

January 1, 2025article en

1 citations

Abstract

No abstract available for this paper.

Vietnam National University Ho Chi Minh City (VN), Aalto University (FI)

Climate action

Openalex Percentile: Top 14%

Online Learning and Analytics

1.69

DOI Open Access PDF OpenAlex

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BiLSTM-Attention-Delta: A Novel Framework for Predicting Dropout in MOOCs Within Big Data Environments — Tri Nguyen, Tu-Anh Nguyen-Hoang, et al. · (2025) | TGRS Research Map | TGRS