Cookies

We use cookies to ensure that we give you the best experience on our website. By continuing to browse this repository, you give consent for essential cookies to be used. You can read more about our Privacy and Cookie Policy.


Durham Research Online
You are in:

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

Aljohani, Tahani and Cristea, Alexandra I. and Alrajhi, Laila (2022) 'Bi-directional Mechanism for Recursion Algorithms: A Case Study on Gender Identification in MOOCs.', in Artificial Intelligence in Education. Posters and Late Breaking Results, Workshops and Tutorials, Industry and Innovation Tracks, Practitioners’ and Doctoral Consortium. , pp. 396-399. Lecture Notes in Computer Science., 13356

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.

Item Type:Book chapter
Full text:Publisher-imposed embargo until 26 July 2023.
(AM) Accepted Manuscript
File format - PDF
(733Kb)
Status:Peer-reviewed
Publisher Web site:https://doi.org/10.1007/978-3-031-11647-6_78
Publisher statement:The final authenticated version is available online at https://doi.org/10.1007/978-3-031-11647-6_78
Date accepted:No date available
Date deposited:23 September 2022
Date of first online publication:26 July 2022
Date first made open access:26 July 2023

Save or Share this output

Export:
Export
Look up in GoogleScholar