Skip to main content

Research Repository

Advanced Search

Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs

Aljohani, Tahani; Cristea, Alexandra I.; Alrajhi, Laila

Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs Thumbnail


Authors

Laila Alrajhi laila.m.alrajhi@durham.ac.uk
PGR Student Doctor of Philosophy



Contributors

Maria Mercedes Rodrigo
Editor

Noburu Matsuda
Editor

Vania Dimitrova
Editor

Abstract

Automatically identifying the learner gender, which serves as this paper’s focus, can provide valuable information to personalised learners’ experiences in MOOCs. However, extracting the gender from learner-generated data (discussion forum) is a challenging task, which is understudied in literature. Using syntactic features is still the state-of-the-art for gender identification in social media. Instead we propose here a novel approach based on Recursive Neural Networks (RecNN), to learn advanced syntactic knowledge extracted from learners’ comments, as an NLP-based predictor for their gender identity. We propose a bi-directional composition function, added to NLP state-of-the-art candidate RecNN models. We evaluate different combinations of semantic level encoding and syntactic level encoding functions, exploring their performances, with respect to the task of learner gender profiling in MOOCs.

Citation

Aljohani, T., Cristea, A. I., & Alrajhi, L. (2022). Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs. In M. Mercedes Rodrigo, N. Matsuda, A. I. Cristea, & V. Dimitrova (Eds.), Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium (396-399). Springer Verlag. https://doi.org/10.1007/978-3-031-11647-6_78

Online Publication Date Jul 26, 2022
Publication Date 2022
Deposit Date Sep 23, 2022
Publicly Available Date Mar 29, 2024
Publisher Springer Verlag
Pages 396-399
Series Title Lecture Notes in Computer Science
Series Number 13356
Book Title Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium
ISBN 978-3-031-11646-9
DOI https://doi.org/10.1007/978-3-031-11647-6_78
Public URL https://durham-repository.worktribe.com/output/1620718

Files





You might also like



Downloadable Citations