A Deterministic AI Foundation for Modeling Human Tutors: Fundamental Assumptions in Structural Learning Theory.
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| Title: | A Deterministic AI Foundation for Modeling Human Tutors: Fundamental Assumptions in Structural Learning Theory. |
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| Authors: | SCANDURA, JOSEPH M.1 scandura@scandura.com |
| Source: | Technology, Instruction, Cognition & Learning. 2018, Vol. 11 Issue 1, p73-112. 40p. |
| Subject Terms: | *Instructional systems, *Structural learning theory, *Intelligent tutoring systems, *Short-term memory, Knowledge representation (Information theory) |
| Abstract: | This paper summarizes key stages in development of the Structural Learning Theory (SLT) and explains how and why it is now possible to model human tutors in a highly efficient manner. The paper focuses on evolution of the SLT, a deterministic theory of teaching and learning, on which AuthorIT authoring and TutorIT delivery systems have been built. It explains how SLT differs fundamentally from other theories used to motivate adaptive tutor development and how AuthorIT and TutorIT technologies differ from others used in developing adaptive learning systems. Implicitly, the paper also makes clear why it has been possible using AuthorIT to develop so many TutorIT tutorials in record time at minimal cost. [ABSTRACT FROM AUTHOR] |
| Copyright of Technology, Instruction, Cognition & Learning is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 129477580 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Deterministic AI Foundation for Modeling Human Tutors: Fundamental Assumptions in Structural Learning Theory. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22SCANDURA%2C+JOSEPH+M%2E%22">SCANDURA, JOSEPH M.</searchLink><relatesTo>1</relatesTo><i> scandura@scandura.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Technology%2C+Instruction%2C+Cognition+%26+Learning%22">Technology, Instruction, Cognition & Learning</searchLink>. 2018, Vol. 11 Issue 1, p73-112. 40p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Structural+learning+theory%22">Structural learning theory</searchLink><br />*<searchLink fieldCode="DE" term="%22Intelligent+tutoring+systems%22">Intelligent tutoring systems</searchLink><br />*<searchLink fieldCode="DE" term="%22Short-term+memory%22">Short-term memory</searchLink><br /><searchLink fieldCode="DE" term="%22Knowledge+representation+%28Information+theory%29%22">Knowledge representation (Information theory)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper summarizes key stages in development of the Structural Learning Theory (SLT) and explains how and why it is now possible to model human tutors in a highly efficient manner. The paper focuses on evolution of the SLT, a deterministic theory of teaching and learning, on which AuthorIT authoring and TutorIT delivery systems have been built. It explains how SLT differs fundamentally from other theories used to motivate adaptive tutor development and how AuthorIT and TutorIT technologies differ from others used in developing adaptive learning systems. Implicitly, the paper also makes clear why it has been possible using AuthorIT to develop so many TutorIT tutorials in record time at minimal cost. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Technology, Instruction, Cognition & Learning is the property of Old City Publishing, Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=129477580 |
| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 40 StartPage: 73 Subjects: – SubjectFull: Instructional systems Type: general – SubjectFull: Structural learning theory Type: general – SubjectFull: Intelligent tutoring systems Type: general – SubjectFull: Short-term memory Type: general – SubjectFull: Knowledge representation (Information theory) Type: general Titles: – TitleFull: A Deterministic AI Foundation for Modeling Human Tutors: Fundamental Assumptions in Structural Learning Theory. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: SCANDURA, JOSEPH M. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: 2018 Type: published Y: 2018 Identifiers: – Type: issn-print Value: 15400182 Numbering: – Type: volume Value: 11 – Type: issue Value: 1 Titles: – TitleFull: Technology, Instruction, Cognition & Learning Type: main |
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