Towards Providing Feedback to Students in Absence of Formalized Domain Models

Gross S, Mokbel B, Hammer B, Pinkwart N. Towards Providing Feedback to Students in Absence of Formalized Domain Models. In: AIED. 2013: 644-648

The Diagnosticity of Argument Diagrams

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 Datamining, Graph Analysis, and online grading