Process-BERT: A Framework for Representation Learning on Educational Process Data

Alexander Scarlatos, Christopher Brinton, Andrew Lan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

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 BERT-type 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 15th International Conference on Educational Data Mining, EDM 2022
PublisherInternational Educational Data Mining Society
ISBN (Electronic)9781733673631
DOIs
StatePublished - 2022
Externally publishedYes
Event15th International Conference on Educational Data Mining, EDM 2022 - Hybrid, Durham, United Kingdom
Duration: Jul 24 2022Jul 27 2022

Publication series

NameProceedings of the 15th International Conference on Educational Data Mining, EDM 2022

Conference

Conference15th International Conference on Educational Data Mining, EDM 2022
Country/TerritoryUnited Kingdom
CityHybrid, Durham
Period7/24/227/27/22

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Information Systems

Keywords

  • Process data
  • representation learning
  • transfer learning

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