An Adaptive Content Skipping Methodology based on User Behavioral Modeling

Yuwei Tu, Elizabeth Tenorio, Christopher G. Brinton

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

Abstract

Adaptive Educational Systems (AES) have demonstrated the potential of improving learning efficacy by individualizing course delivery to particular user needs. Whereas the algorithms driving today's AES are primarily based on user responses to quiz questions, these systems are now capable of capturing fine-granular behavioral data on users, such as keystroke measurements on lecture videos and social interactions on discussion forums. In this paper, we develop a methodology that leverages behavioral data for the task of content skipping in an AES, i.e., detecting content segments that are unnecessary for a user and passing over them automatically. Our methodology contains three modules: (1) a Behavioral Data Processor, which converts user behaviors and course content into algorithm features including course topics, (2) a User State Tracer, which maintains an estimate of user knowledge state and interest on a per-topic basis, and (3) a Content Skipping Trigger, which determines the segments to be removed from the course for this user. In evaluating our approach on two real-world datasets collected from courses hosted on our existing platform, we find 80-90% accuracy in terms of identify segments that users would themselves eventually skip. In doing so, we also perform some exploratory analysis to show how the prediction results can help instructors to improve the course design quality.

Original languageEnglish (US)
Title of host publication2020 54th Annual Conference on Information Sciences and Systems, CISS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728140841
DOIs
StatePublished - Mar 2020
Externally publishedYes
Event54th Annual Conference on Information Sciences and Systems, CISS 2020 - Princeton, United States
Duration: Mar 18 2020Mar 20 2020

Publication series

Name2020 54th Annual Conference on Information Sciences and Systems, CISS 2020

Conference

Conference54th Annual Conference on Information Sciences and Systems, CISS 2020
CountryUnited States
CityPrinceton
Period3/18/203/20/20

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems
  • Signal Processing
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Artificial Intelligence

Keywords

  • Adaptive Learning
  • Topic Modeling
  • User Behavioral Modeling

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  • Cite this

    Tu, Y., Tenorio, E., & Brinton, C. G. (2020). An Adaptive Content Skipping Methodology based on User Behavioral Modeling. In 2020 54th Annual Conference on Information Sciences and Systems, CISS 2020 [9086255] (2020 54th Annual Conference on Information Sciences and Systems, CISS 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CISS48834.2020.1570629135