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Students who take an online course, such as a MOOC, use the course's discussion forum to ask questions or reach out to instructors when encountering an issue. However, reading and responding to students' questions is difficult to scale because of the time needed to consider each message. As a result, critical issues may be left unresolved, and students may lose the motivation to continue in the course. To help address this problem, we build predictive models that automatically determine the urgency of each forum post, so that these posts can be brought to instructors' attention. This paper goes beyond previous work by predicting not just a binary decision cut-off but a post's level of urgency on a 7-point scale. First, we train and cross-validate several models on an original data set of 3,503 posts from MOOCs at University of Pennsylvania. Second, to determine the generalizability of our models, we test their performance on a separate, previously published data set of 29,604 posts from MOOCs at Stanford University. While the previous work on post urgency used only one data set, we evaluated the prediction across different data sets and courses. The best-performing model was a support vector regressor trained on the Universal Sentence Encoder embeddings of the posts, achieving an RMSE of 1.1 on the training set and 1.4 on the test set. Understanding the urgency of forum posts enables instructors to focus their time more effectively and, as a result, better support student learning.
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Age Inference Using A Hierarchical Attention Neural Network
While demographic attributes, such as age, gender, and location, have been extensively studied, most previous studies usually combine different sources of data, such as the user's biography, pictures, posts, and the user's network to obtain reasonable inference accuracies. However, it is not always practical to collect all those different forms of data. Therefore, in this paper, we consider methods for inferring age that only use Twitter posts (tweet text and emojis). We propose a hierarchical attention neural model that integrates independent linguistic knowledge gained from text and emojis when making a prediction. This hierarchical model is able to capture the intra-post relationship between these different post components, as well as the inter-post relationships of a user's posts. Our empirical evaluation using a data set generated from Wikidata demonstrates that our model achieves better performance than the state-of-the-art models, and still performs well when the number of posts per user is reduced in the training data set.
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- Award ID(s):
- 1934925
- PAR ID:
- 10351970
- Date Published:
- Journal Name:
- Proceedings of the 30th ACM International Conference on Information & Knowledge Management
- Page Range / eLocation ID:
- 3273 to 3277
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3459637.3482055
