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

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

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: Jul 27, 2023
• 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