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Learning traces, measurement and assessment templates for AIS interoperability

DOIResolve DOI: https://doi.org/10.1007/978-3-030-50788-6_6
AuthorSearch for: 1ORCID identifier: https://orcid.org/0000-0003-0901-8293
Affiliation
  1. National Research Council Canada. Digital Technologies
FormatText, Article
Conference2nd Adaptive Instructional Systems International Conference, AIS 2020, held as part of 22nd Human-Computer Interaction International Conference, HCII 2020, July 19–24, 2020, Copenhagen, Denmark
SubjectAdaptive Instructional Systems; interoperability; measurements and assessments; Generalized Intelligent Framework for Tutoring; feedback control systems
Abstract
The current paper contains elements relevant for the conceptual modelling and interoperability of measurements and assessments across adaptive instructional systems (AIS). After an introduction, the first section presents a generic use case, where knowledge acquisition is supported by a sequence of training simulators of increasing complexity, and the need to capture regularity and variations in measurements and assessments across instructional systems. The second section briefly discusses the role of measurements and assessments as a core of functions of adaptive instructional systems. The section indicates that an adaptive instructional system needs minimally to capture references to learners’ performance, knowledge components, learning tasks, and learning attempts. The third section maps the main Generalized Intelligent Framework for Tutoring (GIFT) components to a generic feedback control system model. The mapping makes explicit the dual interpretation of measurements and assessments as both results (learning traces) and functions (computation templates). The fourth section examines some non-proprietary frameworks in terms of their capability to support the interoperability of measurements and assessments across adaptive instructional systems. The section briefly discusses xAPI, the Competency And Skill System (CASS), the Evidence Trace File (ETF), the Training Objective Package (TOP), and the Human Performance Markup Language (HPML). The main contributions of the paper are: 1) an AIS conceptual model based on a feedback control system model, and 2) a brief review of learning data frameworks as they relate to measurements and assessments as both learning traces and computation templates.
Date published
PublisherSpringer, Cham
In
Series
  • Lecture Notes in Computer Science 12214.
LanguageEnglish
Peer reviewedYes
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Record identifiera7fd51e9-de16-4713-8a91-48b32f84858e
Record created2020-11-13
Record modified2020-11-30

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