The Diagnosticity of Argument Diagrams

Created by W.Langdon from gp-bibliography.bib Revision:1.3989

@PhdThesis{CollinLynch-Thesis-3-11-2014,
  author =       "Collin F. Lynch",
  title =        "The Diagnosticity of Argument Diagrams",
  school =       "University of Pittsburgh",
  year =         "2014",
  address =      "USA",
  month =        jan # " 30",
  keywords =     "genetic algorithms, genetic programming,
                 Argumentation, Essay Writing, Argument Diagrams, Graph
                 Analysis, Machine Learning, Ill-Defined Domains,
                 Intelligent Tutoring Systems, Educational Datamining,
                 Multiple Representations.",
  URL =          "http://d-scholarship.pitt.edu/id/eprint/20710",
  URL =          "http://d-scholarship.pitt.edu/20710/",
  URL =          "http://d-scholarship.pitt.edu/20710/1/CollinLynch-Thesis-3-11-2014.pdf",
  size =         "291 pages",
  abstract =     "Can argument diagrams be used to diagnose and predict
                 argument performance?

                 Argumentation is a complex domain with robust and often
                 contradictory theories about the structure and scope of
                 valid arguments. Argumentation is central to advanced
                 problem solving in many domains and is a core feature
                 of day-to-day discourse. Argumentation is quite
                 literally, all around us, and yet is rarely taught
                 explicitly. Novices often have difficulty parsing and
                 constructing arguments particularly in written and
                 verbal form. Such formats obscure key argumentative
                 moves and often mask the strengths and weaknesses of
                 the argument structure with complicated phrasing or
                 simple sophistry. Argument diagrams have a long history
                 in the philosophy of argument and have been seen
                 increased application as instructional tools. Argument
                 diagrams reify important argument structures, avoid the
                 serial limitations of text, and are amenable to
                 automatic processing.

                 This thesis addresses the question posed above. In it I
                 show that diagrammatic models of argument can be used
                 to predict students essay grades and that
                 automatically-induced models can be competitive with
                 human grades. In the course of this analysis I survey
                 analytical tools such as Augmented Graph Grammars that
                 can be applied to formalize argument analysis, and
                 detail a novel Augmented Graph Grammar formalism and
                 implementation used in the study. I also introduce
                 novel machine learning algorithms for regression and
                 tolerance reduction. This work makes contributions to
                 research on Education, Intelligent Tutoring Systems,
                 Machine Learning, Educational Data-mining, Graph
                 Analysis, and online grading.",
  notes =        "Committee Chair Ashley, Kevin Committee Member Aleven,
                 Vincent Committee Member Litman, Diane Committee Member
                 Schunn, Chris",
}

Genetic Programming entries for Collin Lynch

Citations