rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    non sort: A
    title: mixed-response intelligent tutoring system based on learning from demonstration
  name:
    name part: Álvarez Xochihua, Omar.
    role:
      role term: creator
  name:
    name part: Cifuentes, Lauren D.
    role:
      role term: thesis advisor.
  name:
    name part: Bettati, Riccardo
    role:
      role term: thesis advisor.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2012
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: Intelligent Tutoring Systems (ITS) have a significant educational impact on student's learning. However, researchers report time intensive interaction is needed between ITS developers and domain-experts to gather and represent domain knowledge. The challenge is augmented when the target domain is ill-defined. The primary problem resides in often using traditional approaches for gathering domain and tutoring experts' knowledge at design time and conventional methods for knowledge representation built for well-defined domains. Similar to evolving knowledge acquisition approaches used in other fields, we replace this restricted view of ITS knowledge learning merely at design time with an incremental approach that continues training the ITS during run time. We investigate a gradual knowledge learning approach through continuous instructor-student demonstrations. We present a Mixed-response Intelligent Tutoring System based on Learning from Demonstration that gathers and represents knowledge at run time. Furthermore, we implement two knowledge representation methods (Weighted Markov Models and Weighted Context Free Grammars) and corresponding algorithms for building domain and tutoring knowledge-bases at run time. We use students' solutions to cybersecurity exercises as the primary data source for our initial framework testing. Five experiments were conducted using various granularity levels for data representation, multiple datasets differing in content and size, and multiple experts to evaluate framework performance. Using our WCFG-based knowledge representation method in conjunction with a finer data representation granularity level, the implemented framework reached 97% effectiveness in providing correct feedback. The ITS demonstrated consistency when applied to multiple datasets and experts. Furthermore, on average, only 1.4 hours were needed by instructors to build the knowledge-base and required tutorial actions per exercise. Finally, the ITS framework showed suit…
  note: by Omar Álvarez Xochihua.
  note: "Major Subject: Computer Science"
  note: Electronic resource.
  note: Doctor of Philosophy Texas A&M University 2012 http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10793
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Computer Science
  subject:
    topic: Learning from Demonstration
  subject:
    topic: Intelligent Tutoring Systems
  subject:
    topic: Weighted Markov Models
  subject:
    topic: Weighted Context Free Grammars
  identifier: http://hdl.handle.net/1969.1/ETD-TAMU-2012-05-10793
  record info:
    record content source: TXA
    record creation date: 120803
    record change date: 20150922105011.0
    record identifier: in00002756798