What TutorIT Can Do Better than a Human and Why: Now and in the Future

Autor/in	Scandura, Joseph M.
Titel	What TutorIT Can Do Better than a Human and Why: Now and in the Future
Quelle	In: Technology, Instruction, Cognition and Learning, 8 (2011) 3-4, S.175-227 (53 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1540-0182
Schlagwörter	Intelligent Tutoring Systems; Automation; Tutors; Learning Theories; Educational Benefits; Mathematics Instruction; Mathematics Skills; Mastery Learning; Cost Effectiveness; Instructional Effectiveness; Knowledge Representation; Efficiency; Problem Solving; Feedback (Response) + Suchen Sie Ihr Suchwort? Intelligentes Tutorsystem; Förderlehrer; Lehrender; Tutor; Learning theory; Lerntheorie; Bildungsertrag; Mathematics lessons; Mathematikunterricht; Mathmatics achievement; Mathematics ability; Mathematische Kompetenz; Kosten-Nutzen-Analyse; Kosten-Nutzen-Denken; Unterrichtserfolg; Wissensrepräsentation; Effectiveness; Effektivität; Wirkungsgrad; Problemlösen
Abstract	More and more things that humans used to do can be automated on computer. In each case, complex tasks have been automated -- not to the extent that they can be done as well as humans, but better. I will draw and develop parallels to education -- showing how and why advances in the Structural Learning Theory (SLT) and the AuthorIT development and TutorIT delivery technologies based thereon make it possible not only to duplicate many of the things that human tutors can do but to do them better. Specifically, I will show how and why TutorIT can now do a better job than most if not all human tutors in providing more effective and efficient tutoring on essentially any well defined skill. I also will show why this approach has the potential to also match or exceed human tutoring on illdefined learning in the future. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01