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Development of AIS using simulated learners, Bayesian networks and knowledge elicitation methods

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/978-3-031-05887-5_11
AuthorSearch for: 1ORCID identifier: https://orcid.org/0000-0003-0901-8293; Search for: ORCID identifier: https://orcid.org/0000-0001-8423-0572; Search for: ORCID identifier: https://orcid.org/0000-0002-3478-2448; Search for: ORCID identifier: https://orcid.org/0000-0002-3872-8561; Search for: ORCID identifier: https://orcid.org/0000-0002-1029-3615; Search for: 2ORCID identifier: https://orcid.org/0000-0002-3371-1082; Search for: ORCID identifier: https://orcid.org/0000-0001-5450-4587
Affiliation
  1. National Research Council of Canada. Digital Technologies
  2. National Research Council of Canada. Ocean, Coastal and River Engineering
FormatText, Book Chapter
Conference4th International Conference, AIS 2022, Held as Part of the 24th HCI International Conference, (HCII 2022), June 26 – July 1, 2022, Virtual Event
Subjectsimulated learners; Bayesian networks; expert knowledge elicitation; tutoring strategies; marine operations
Abstract
The development of adaptive instructional systems (AIS) is an iterative process where both empirical data on human performance and learning, and experimentation using computer simulations can play a role. The paper presents our current efforts to advance adaptive instructional system technology conceived as self-improvement systems [37]. The paper describes our methodological approach for informing the design and implementation of adaptive instructional systems by conducting concurrent research activities using 1) Bayesian networks for modelling learning processes, 2) knowledge elicitation of expert instructors, and 3) simulated learners and tutors to explore AIS system design options. Each activity fulfills separate but complementary objectives. Bayesian networks modelling of learners’ performance provides the means to implement predictions of learners’ performance, and selection of adaptive learning content. Knowledge elicitation methods are fundamental in understanding human capabilities and limitations in the context of AIS systems design that support and regulate the cognitive demands of the learner and instructor. Simulated learner and tutor interactions enable the specification of detailed cognitive process models of learning and instructions.
Publication date
PublisherSpringer
In
  • Adaptive Instructional Systems. Proceedings of the 4th International Conference, AIS 2022, Held as Part of the 24th HCI International Conference (HCII 2022) (16 June 2022): 143–158.
Series
  • Lecture Notes in Computer Science 13332.
LanguageEnglish
Peer reviewedYes
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Record identifier5dce9e81-72e4-49eb-b772-8aae3294e70a
Record created2022-07-11
Record modified2022-07-12
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