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Improving Individualized Instruction...

Mostafavi, Behrooz Zakaria.

Improving Individualized Instruction in a Logic Tutor using Data-driven Methods.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Improving Individualized Instruction in a Logic Tutor using Data-driven Methods./
Author:	Mostafavi, Behrooz Zakaria.
Description:	1 online resource (117 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369621839

Improving Individualized Instruction in a Logic Tutor using Data-driven Methods.
Mostafavi, Behrooz Zakaria.

Improving Individualized Instruction in a Logic Tutor using Data-driven Methods. - 1 online resource (117 pages)

Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.

Thesis (Ph.D.)--North Carolina State University, 2016.

Includes bibliographical references

Intelligent tutoring systems (ITS) have the greatest potential to increase problem-solving performance when they adapt to individual students and domains. However, developing an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain's curriculum. Because of the large possible range of problem-solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem-solving domains.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369621839Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Improving Individualized Instruction in a Logic Tutor using Data-driven Methods.
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100 1 $a Mostafavi, Behrooz Zakaria. $3 1188638
245 1 0 $a Improving Individualized Instruction in a Logic Tutor using Data-driven Methods.
264 0 $c 2016
300 $a 1 online resource (117 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
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500 $a Source: Dissertation Abstracts International, Volume: 78-08(E), Section: B.
500 $a Adviser: Tiffany Barnes.
502 $a Thesis (Ph.D.)--North Carolina State University, 2016.
504 $a Includes bibliographical references
520 $a Intelligent tutoring systems (ITS) have the greatest potential to increase problem-solving performance when they adapt to individual students and domains. However, developing an ITS to adapt to individual students requires the involvement of experts to provide knowledge about both the academic domain and novice student behavior in that domain's curriculum. Because of the large possible range of problem-solving behavior for any individual topic, the amount of expert involvement required to create an effective, adaptable tutoring system can be high, especially in open-ended problem-solving domains.
520 $a Data-driven methods have shown much promise in increasing ITS effectiveness by analyzing previous data in order to quickly adapt to individual students. Intelligent tutoring systems that incorporate data-driven methods can present appropriate problems and challenges for each new student, provide individualized feedback by analyzing previous data for similar student behavior, and present problems and feedback accordingly while minimizing the amount of expert involvement required for development.
520 $a This work hypothesizes that applying data-driven methods to the feedback generation, problem selection, and pedagogical strategy in an intelligent tutor will increase problem-solving performance. To test this hypothesis, the Deep Thought logic proof tutor was used in three studies which test the effectiveness of applying these data-driven methods. The addition of data-driven methods to Deep Thought has been shown to reduce student dropout, allowing more students to complete the tutor, and improve student performance in constructing logic proofs.
520 $a The end goal of this work is a fully data-driven intelligent tutoring system framework that is domain agnostic. Utilizing previous student data, this framework provides interval-evaluated mastery learning, individualized problem selection, and formative feedback and pedagogy in the formof hints and worked examples.Atutor in anynewdomain can be developed fromthis framework based only on an initial expert provided description of the domain and knowledge components, a corpus of problems, and a problem solving interface.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a North Carolina State University. $b Computer Science. $3 1182345
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10583504 $z click for full text (PQDT)

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