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Title: Process-BERT: A Framework for Representation Learning on Educational Process Data
Educational process data, i.e., logs of detailed student activities in computerized or online learning platforms, has the potential to offer deep insights into how students learn. One can use process data for many downstream tasks such as learning outcome prediction and automatically delivering personalized intervention. In this paper, we propose a framework for learning representations of educational process data that is applicable across different learning scenarios. Our framework consists of a pre-training step that uses BERTtype objectives to learn representations from sequential process data and a fine-tuning step that further adjusts these representations on downstream prediction tasks. We apply our framework to the 2019 nation’s report card data mining competition dataset that consists of student problem-solving process data and detail the specific models we use in this scenario. We conduct both quantitative and qualitative experiments to show that our framework results in process data representations that are both predictive and informative.  more » « less
Award ID(s):
2225091
NSF-PAR ID:
10417166
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Proceedings of the 15th International Conference on Educational Data Mining, International Educational Data Mining Society
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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