Abstract
Understanding students’ learning experiences on social media is an important task in educational data mining. Since it provides more complete and in-depth insights to help educational managers get necessary information in a timely fashion and make more informed decisions. Current systems still rely on traditional machine learning methods with hand-crafted features. One more challenge is that important information can appear in any position of the posts/sentences. In this paper, we propose an attentive biLSTMs method to deal with these problems. This model utilizes neural attention mechanism with biLSTMs to automatically extract and capture the most critical semantic features in students’ posts in regard to the current learning experience. We perform experiments on a Vietnamese benchmark dataset and results indicate that our model achieves state-of-the-art performance on this task. We achieved 63.5% in the micro-average F1 score and 59.7% in the macro-average F1 score for this multi-label prediction.
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Notes
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Instead of using this softmax function, you can also use the sigmoid function as an alternative. In fact, in the binary classification both sigmoid and softmax functions are the same where as in the multi-class classification softmax function is preferred.
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Oanh, T.T. (2020). Attentive biLSTMs for Understanding Students’ Learning Experiences. In: Le Thi, H., Le, H., Pham Dinh, T., Nguyen, N. (eds) Advanced Computational Methods for Knowledge Engineering. ICCSAMA 2019. Advances in Intelligent Systems and Computing, vol 1121. Springer, Cham. https://doi.org/10.1007/978-3-030-38364-0_24
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