Aljohani, Tahani and Cristea, Alexandra I. (2019) 'Predicting Learners' Demographics Characteristics: Deep Learning Ensemble Architecture for Learners' Characteristics Prediction in MOOCs.', in Proceedings of the 2019 4th International Conference on Information and Education Innovations - ICIEI 2019. , pp. 23-27.
Author Profiling (AP), which aims to predict an author's demographics characteristics automatically by using texts written by the author, is an important mechanism for many applications, as well as highly challenging. In this research, we analyse various previous machine learning models for AP, with respect to their potential for our research problem. Based on this, we propose a Deep Learning Architecture to predict the demographics characteristics of the learners in MOOCs, incorporating multi-feature representations and ensemble learning methods. Specifically, we employ a novel pipeline, combining the most successful deep learning classifiers, Convolution Neural Networks, Recurrent Neural Networks and Recursive Neural Networks, to learn from a text. Moreover, beside the state-of-the-art training involving character and word-level input, we additionally propose phrase-level input. With this approach, we aim at deepening our understanding of the writing style of learners, and thus, predict the author profile with high accuracy. In this paper, we propose the model and architecture, and report on initial tests of our model on a large dataset from the FutureLearn platform, to predict the demographics characteristics of the learners.
|Item Type:||Book chapter|
|Full text:||Publisher-imposed embargo |
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|Publisher Web site:||https://doi.org/10.1145/3345094.3345119|
|Date accepted:||No date available|
|Date deposited:||03 November 2021|
|Date of first online publication:||10 July 2019|
|Date first made open access:||No date available|
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