Consideration of a Bayesian Hierarchical Model for Assessment and Adaptive Instructions

Jong W. Kim, Frank Edward Ritter

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

Abstract

People appear to practice what they do know rather than what they do not know [1], suggesting a necessity of an improved assessment of multi-level complex skill components. An understanding of the changing knowledge states is also important in that such an assessment can support instructions. The changing knowledge states can be generally visualized through learning curves. These curves would be useful to identify and predict the learner’s changing knowledge states in multi-domains, and to understand the features of task/subtask learning. Here, we provide a framework based on a Bayesian hierarchical model that can be used to investigate learning and performance in the learner and domain model context—particularly a framework to estimate learning functions separately in a psychomotor task. We also take an approach of a production rule system (e.g., ACT-R) to analyze the learner’s knowledge and skill in tasks and subtasks. We extend the current understanding of cognitive modeling to better support adaptive instructions, which helps to model the learner in multi-domains (i.e., beyond the desktop) and provide a summary of estimating a probability that the learner has learned each of a production rule. We find the framework being useful to model the learner’s changing knowledge and skill states by supporting an estimate of probability that the learner has learned from a knowledge component, and by comparing learning curves with varying slopes and intercepts.

Original languageEnglish (US)
Title of host publicationAdaptive Instructional Systems - 1st International Conference, AIS 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings
EditorsRobert A. Sottilare, Jessica Schwarz
PublisherSpringer Verlag
Pages521-531
Number of pages11
ISBN (Print)9783030223403
DOIs
StatePublished - Jan 1 2019
Event1st International Conference on Adaptive Instructional Systems, AIS 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019 - Orlando, United States
Duration: Jul 26 2019Jul 31 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11597 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on Adaptive Instructional Systems, AIS 2019, held as part of the 21st International Conference on Human-Computer Interaction, HCI International 2019
CountryUnited States
CityOrlando
Period7/26/197/31/19

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All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kim, J. W., & Ritter, F. E. (2019). Consideration of a Bayesian Hierarchical Model for Assessment and Adaptive Instructions. In R. A. Sottilare, & J. Schwarz (Eds.), Adaptive Instructional Systems - 1st International Conference, AIS 2019, Held as Part of the 21st HCI International Conference, HCII 2019, Proceedings (pp. 521-531). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11597 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-22341-0_41